ALBERT

Description: The aerosol size distribution and cloud condensation nuclei (CCN) number concentration were measured using a wide-range particle spectrometer (WPS) and a cloud condensation nuclei counter (CCNC) on Mt. Tian from 31 July to 9 September, 2019. Combined with meteorological data, distribution characteristics of aerosol size and CCN and their influencing factors were analyzed. The results indicated that the mean aerosol number concentration was 5475.6 ± 5636.5 cm−3. The mean CCN concentrations were 183.7 ± 114.5 cm−3, 729.8 ± 376.1 cm−3, 1630.5 ± 980.5 cm−3, 2162.5 ± 1345.3 cm−3, and 2575.7 ± 1632.9 cm−3 at supersaturation levels of 0.1%, 0.2%, 0.4%, 0.6%, and 0.8%, respectively. The aerosol number size distribution is unimodal, and the dominant particle size is 30–60 nm. Affected by the height of the boundary layer and the valley wind, the diurnal variation in aerosol number concentration shows a unimodal distribution with a peak at 17:00, and the CCN number concentration showed a bimodal distribution with peaks at 18:00 and 21:00. The particle size distribution and supersaturation have a major impact on the activation of the aerosol into CCN. At 0.1% supersaturation (S), the 300–500 nm particles are most likely to activate to CCN. Particles of 100–300 nm are most easily activated at 0.2% (S), while particles of 60–80 nm are most likely activated at high supersaturation (≥0.4%). The concentrations of aerosol and CCN are higher in the northerly wind. Ambient relative humidity (RH) has little relationship with the aerosol activation under high supersaturation. According to N = CSk fitting the CCN spectrum, C = 3297 and k = 0.90 on Mt. Tian, characteristic of the clean continental type.

Description: The atmospheric boundary layer height is important for constraining air pollution and meteorological models. This study attempted to validate the MODIS-estimated atmospheric boundary layer height (ABLH), and variation in the ABLH in Uganda was evaluated. The ABLH was estimated from MODIS data using the mixing ratio profile gradient method and compared to the ABLH estimated from radiosonde data using three different methods. Unlike in studies in other regions of the world, correlations between ABLH estimated using MODIS and radiosonde data were weak, implying limited usefulness of MODIS data for determining ABLH. However, the diurnal variation in MODIS-derived ABLH and particulate matter (PM10) was consistent with the expected inverse relationship between PM10 mass concentration and ABLH, and the mean MODIS-derived ABLH values were significantly lower during wet seasons than dry seasons, as expected.

Description: In our previous studies, we investigated the performance of a novel pneumatic planar magnetic separator (PMS) for the dry beneficiation of a selected magnetite ore. In the present study, we have extended the studies on the PMS with the focus on investigating how various PMS processing flowsheet configurations influence its performance. The outcomes were subsequently compared with those of a Davis tube recovery (DTR) tester. The study demonstrated that the use of PMS in the dry beneficiation of magnetite ores is feasible, and operating the PMS in different flowsheet configurations positively influences the magnetite concentrate grade and purity. Finally, the study showed that the PMS performance compares well with that of DTR and can potentially replace DTR in operations that are carried out in arid regions.

Description: Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs’ emphasis to make geospatial data available through a participatory approach.

Description: In this study, the flotation behavior of malachite was investigated using hydrophobic talc nanoparticles (TNs) as collectors. To improve the floatability of TN-deposited malachite, various experimental parameters were systematically investigated. We found that the floatability sharply increased as the size of the TNs decreased. The floatability of malachite was enhanced in the presence of smaller TNs, since higher amounts of smaller TNs were deposited on the surface of the malachite, thus rendering the surface more hydrophobic. Moreover, the floatability of the TN-deposited malachite increased as the pH decreased, likely due to the more favorable interaction between TNs and malachite by means of electrostatic attraction. Furthermore, the floatability became more enhanced as the TN concentration increased, likely associated with increases in the amount of TNs deposited on the surface of the malachite, thus enhancing the floatability by altering the hydrophobicity of the surface. Our findings suggest that the application of natural hydrophobic TNs as collectors in malachite flotation should be introduced as a new concept.

Description: East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

Description: Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. Nighttime light is an effective tool to monitor urban development from a macro perspective. However, the systematic research of nighttime light spatiotemporal variation modes and the industry-driving force of urban nighttime light are still unknown. Considering these issues, this paper analyzes the spatiotemporal variation modes of the average light index (ALI) and investigates the industry-driving force of ALI in 100 major prefecture-level cities across China mainland based on National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP VIIRS). The conclusions are as following three aspects. First, ALI is observed a funnel pattern among four regions in spatial dimension, with low in center and high in the surrounding, and it shows 5 variation modes (“W,” “√,” “Exponent,” “Logarithm,” and “N”) in temporal dimension, of which the “√” mode accounts for the highest proportion (60%). Second, the industry structure is closely related to ALI. Besides, the factor analysis result illustrates that the secondary and tertiary industry are the driving industries of ALI. Third, the classification result based on the industry contribution rate indicates that cities driven by different industries show significant spatial distribution differences. The three major industry-driving cities are mainly distributed in central and western regions, the secondary and tertiary industry-driving cities are evenly distributed, and the tertiary industry-driving cities are mainly distributed in provincial capitals. From 2013 to 2018, the fluctuation of city distribution driven by different industries changes obviously. The number of tertiary industry-driving cities increases steadily and the three major industry-driving cities are distributed wider spatially. Additionally, the impacts of location and raw coal on ALI are discussed. In general, these findings are essential to further research urban development mode and can be considered as the reference to narrow urban development gap.

Description: A low-mass and low-volume dual-polarization L-band radiometer is introduced that has applications for ground-based remote sensing or unmanned aerial vehicle (UAV)-based mapping. With prominent use aboard the ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, L-band radiometry can be used to retrieve environmental parameters, including soil moisture, sea surface salinity, snow liquid water content, snow density, vegetation optical depth, etc. The design and testing of the air-gapped patch array antenna is introduced and is shown to provide a 3-dB full power beamwidth of 37°. We present the radio-frequency (RF) front end design, which uses direct detection architecture and a square-law power detector. Calibration is performed using two internal references, including a matched resistive source (RS) at ambient temperature and an active cold source (ACS). The radio-frequency (RF) front end does not require temperature stabilization, due to characterization of the ACS noise temperature by sky measurements. The ACS characterization procedure is presented. The noise equivalent delta (Δ) temperature (NEΔT) of the radiometer is ~0.14 K at 1 s integration time. The total antenna temperature uncertainty ranges from 0.6 to 1.5 K.

Description: Avalanche disasters are extremely destructive and catastrophic, often causing serious casualties, economic losses and surface erosion. However, far too little attention has been paid to utilizing remote sensing mapping avalanches quickly and automatically to mitigate calamity. Such endeavors are limited by formidable natural conditions, human subjective judgement and insufficient understanding of avalanches, so they have been incomplete and inaccurate. This paper presents an objective and widely serviceable method for regional auto-detection using the scattering and interference characteristics of avalanches extracted from Sentinel-1 SLC images. Six indices are established to distinguish avalanches from surrounding undisturbed snow. The active avalanche belts in Kizilkeya and Aktep of the Western TianShan Mountains in China lend urgency to this research. Implementation found that smaller avalanches can be consistently identified more accurately in descending images. Specifically, 281 and 311 avalanches were detected in the ascending and descending of Kizilkeya, respectively. The corresponding numbers on Aktep are 104 and 114, respectively. The resolution area of single avalanche detection can reach 0.09 km2. The performance of the model was excellent in all cases (areas under the curve are 0.831 and 0.940 in descending and ascending of Kizilkeya, respectively; and 0.807 and 0.938 of Aktep, respectively). Overall, the evaluation of statistical indices are POD 〉 0.75, FAR 〈 0.34, FOM 〈 0.13 and TSS 〉 0.75. The results indicate that the performance of the innovation proposed in this paper, which employs multivariate comprehensive descriptions of avalanche characteristics to actualize regional automatic detection, can be more objective, accurate, applicable and robust to a certain extent. The latest and more complete avalanche inventory generated by this design can effectively assist in addressing the increasingly severe avalanche disasters and improving public awareness of avalanches in alpine areas.

Description: An extremely heavy rainfall event hit Guangdong province, China, from 27 August to 1 September 2018. There were two different extreme rain regions, respectively, at the Pearl River estuary and eastern Guangdong, and a record-breaking daily precipitation of 1056.7 mm was observed at Gaotan station on 30 August. This paper utilizes a suite of observations from soundings, a gauge network, disdrometers, and polarimetric radars to gain insights to the two rainfall centers. The large-scale meteorological forcing, rainfall patterns, and microphysical processes, as well as radar-based precipitation signatures are investigated. It is concluded that a west-moving monsoon depression played a critical role in sustaining the moisture supply to the two extreme rain regions, and the combined orographic enhancement further contributed to the torrential rainfall over Gaotan station. The raindrop size distributions (DSD) observed at Zhuhai and Huidong stations, as well as the observed polarimetric radar signatures indicate that the rainfall at Doumen region was characterized by larger raindrops but a lower number concentration compared with that at Gaotan region. In addition, the dual-polarization radars are used to quantify precipitation intensity during this extreme event, providing timely information for flood warning and emergency management decision-making.

Description: Earth remote sensing optical satellite systems are often divided into two categories—geosynchronous and sun-synchronous. Geosynchronous systems essentially rotate with the Earth and continuously observe the same region of the Earth. Sun-synchronous systems are generally in a polar orbit and view differing regions of the Earth at the same local time. Although similar in instrument design, there are enough differences in these two types of missions that often the calibration of the instruments can be substantially different. Thus, respective calibration teams develop independent methods and do not interact regularly or often. Yet, there are numerous areas of overlap and much to learn from one another. To address this issue, a panel of experts from both types of systems was convened to discover common areas of concern, areas where improvements can be made, and recommendations for the future. As a result of the panelist’s efforts, a set of eight recommendations were developed. Those that are related to improvements of current technologies include maintaining sun-synchronous orbits (not allowing orbital decay), standardization of spectral bandpasses, and expanded use of well-developed calibration techniques such as deep convective clouds, pseudo invariant calibration sites, and lunar methodologies. New techniques for expanded calibration capability include using geosynchronous instruments as transfer radiometers, continued development of ground-based prelaunch calibration technologies, expansion of RadCalNet, and development of space-based calibration radiometer systems.

Description: As satellite observation technology improves, the number of remote sensing images significantly and rapidly increases. Therefore, a growing number of studies are focusing on remote sensing image retrieval. However, having a large number of remote sensing images considerably slows the retrieval time and takes up a great deal of memory space. The hash method is being increasingly used for rapid image retrieval because of its remarkably fast performance. At the same time, selecting samples that contain more information and greater stability to train the network has gradually become the key to improving retrieval performance. Given the above considerations, we propose a deep hash remote sensing image retrieval method, called the hard probability sampling hash retrieval method (HPSH), which combines hash code learning with hard probability sampling in a deep network. Specifically, we used a probability sampling method to select training samples, and we designed one novel hash loss function to better train the network parameters and reduce the hashing accuracy loss due to quantization. Our experimental results demonstrate that HPSH could yield an excellent representation compared with other state-of-the-art hash approaches. For the university of California, merced (UCMD) dataset, HPSH+S resulted in a mean average precision (mAP) of up to 90.9% on 16 hash bits, 92.2% on 24 hash bits, and 92.8% on 32 hash bits. For the aerial image dataset (AID), HPSH+S achieved a mAP of up to 89.8% on 16 hash bits, 93.6% on 24 hash bits, and 95.5% on 32 hash bits. For the UCMD dataset, with the use of data augmentation, our proposed approach achieved a mAP of up to 99.6% on 32 hash bits and 99.7% on 64 hash bits.

Description: Protected areas are the backbone of biodiversity conservation but are fixed in space and vulnerable to anthropogenic climate change. Myanmar is exceptionally rich in biodiversity but has a small protected area system. This study aimed to assess the potential vulnerability of this system to climate change. In the absence of good biodiversity data, we used a spatial modeling approach based on a statistically derived bioclimatic stratification (the Global Environmental Stratification, GEnS) to understand the spatial implications of projected climate change for Myanmar’s protected area system by 2050 and 2070. Nine bioclimatic zones and 41 strata were recognized in Myanmar, but their representation in the protected area system varied greatly, with the driest zones especially underrepresented. Under climate change, most zones will shift upslope, with some protected areas projected to change entirely to a new bioclimate. Potential impacts on biodiversity include mountaintop extinctions of species endemic to isolated peaks, loss of climate specialists from small protected areas and those with little elevational range, and woody encroachment into savannas and open forests as a result of both climate change and rising atmospheric CO2. Myanmar needs larger, better connected, and more representative protected areas, but political, social, and economic problems make this difficult.

Description: Minerals from mantle xenoliths in the Zapolyarnaya pipe in the Upper Muna field, Russia and from mineral separates from other large diamondiferous kimberlite pipes in this field (Deimos, Novinka and Komsomolskaya-Magnitnaya) were studied with EPMA and LA-ICP-MS. All pipes contain very high proportions of sub-calcic garnets. Zapolyarnaya contains mainly dunitic xenoliths with veinlets of garnets, phlogopites and Fe-rich pyroxenes similar in composition to those from sheared peridotites. PT estimates for the clinopyroxenes trace the convective inflection of the geotherm (40–45 mW·m−2) to 8 GPa, inflected at 6 GPa and overlapping with PT estimates for ilmenites derived from protokimberlites. The Upper Muna mantle lithosphere includes dunite channels from 8 to 2 GPa, which were favorable for melt movement. The primary layering deduced from the fluctuations of CaO in garnets was smoothed by the refertilization events, which formed additional pyroxenes. Clinopyroxenes from the Novinka and Komsomolskaya-Magnitnaya pipes show a more linear geotherm and three branches in the P-Fe# plot from the lithosphere base to the Moho, suggesting several episodes of pervasive melt percolation. Clinopyroxenes from Zapolyarnaya are divided into four groups according to thermobarometry and trace element patterns, which show a stepwise increase of REE and incompatible elements. Lower pressure groups including dunitic garnets have elevated REE with peaks in Rb, Th, Nb, Sr, Zr, and U, suggesting mixing of the parental protokimberlitic melts with partially melted metasomatic veins of ancient subduction origin. At least two stages of melt percolation formed the inclined PT paths: (1) an ancient garnet semi-advective geotherm (35–45 mW·m−2) formed by volatile-rich melts during the major late Archean event of lithosphere growth; and (2) a hotter megacrystic PT path (Cpx-Ilm) formed by feeding systems for kimberlite eruptions (40–45 mW·m−2). Ilmenite PT estimates trace three separate PT trajectories, suggesting a multistage process associated with metasomatism and formation of the Cpx-Phl veinlets in dunites. Heating associated with intrusions of protokimberlite caused reactivation of the mantle metasomatites rich in H2O and alkali metals and possibly favored the growth of large megacrystalline diamonds.

Description: Upwelling and downwelling processes play a critical role in the connectivity between offshore waters and coastal ecosystems, having relevant implications in terms of intense biogeochemical activity and global fisheries production. A variety of in situ and remote-sensing networks were used in concert with the Iberia–Biscay–Ireland (IBI) circulation forecast system, in order to investigate two persistent upwelling and downwelling events that occurred in the Northwestern (NW) Iberian coastal system during summer 2014. Special emphasis was placed on quality-controlled surface currents provided by a high-frequency radar (HFR), since this land-based technology can effectively monitor the upper layer flow over broad coastal areas in near-real time. The low-frequency spatiotemporal response of the ocean was explored in terms of wind-induced currents’ structures and immediacy of reaction. Mean kinetic energy, divergence and vorticity maps were also calculated for upwelling and downwelling favorable events, in order to verify HFR and IBI capabilities, to accurately resolve the prevailing surface circulation features, such as the locus of a persistent upwelling maximum in the vicinity of Cape Finisterre. This integrated approach proved to be well-founded to efficiently portray the three-dimensional characteristics of the NW Iberian coastal upwelling system regardless of few shortcomings detected in IBI performance, such as the misrepresentation of the most energetic surface dynamics or the overestimation of the cooling and warming associated with upwelling and downwelling conditions, respectively. Finally, the variability of the NW Iberian upwelling system was characterized by means of the development of a novel ocean-based coastal upwelling index (UI), constructed from HFR-derived hourly surface current observations (UIHFR). The proposed UIHFR was validated against two traditional UIs for 2014, to assess its credibility. Results suggest that UIHFR was able to adequately categorize and characterize a wealth of summer upwelling and downwelling events of diverse length and strength, paving the way for future investigations of the subsequent biophysical implications.

Description: Shore-based phased-array HF radars have been widely used for remotely sensing ocean surface current, wave, and wind around the world. However, phase uncertainties, especially phase distortions, in receiving elements significantly degrade the performance of beam forming and direction-of-arrival (DOA) estimation for phased-array HF radar. To address this problem, the conventional array signal model is modified by adding a direction-based phase error matrix. Subsequently, an array phase manifold calibration method using antenna responses of incoming ship echoes is proposed. Later, an assessment on the proposed array calibration method is made based on the DOA estimations and current measurements that are obtained from the datasets that were collected with a multi-frequency HF (MHF) radar. MHF radar-estimated DOAs using three calibration strategies are compared with the ship directions that are provided by an Automatic Identification System (AIS). Additionally, comparisons between the MHF radar-derived currents while using three calibration strategies and Acoustic Doppler Current Profilers (ADCP)-measured currents are made. The results indicate that the proposed array calibration method is effective in DOA estimation and current measurement for phased-array HF radars, especially in the phase distortion situation.

Description: In this study we (1) mapped the areal extent of current dust sources over Northern Africa between 8°W–31°E and 22°N - Mediterranean coast; and (2) identified and characterized the geomorphic units and soil types that emit dust from these areas. We used the full resolution (3 km) data from the MSG-SEVIRI to map dust sources over a 2-year period between 2005–2006, and examined these regions with remotely sensed images and geomorphic and soil maps. A total of 〉2600 individual dust emission events were mapped; with frequency up to 34 events in the 2-year study period. The areal extent of dust emission sources exhibited a lognormal distribution with most sources ranging from 20 to 130 km2. Most dust events were singular and related to a variety of specific geomorphic units. Dust events that created hotspots were mostly located over playas and fluvial landforms, and to a lesser extent over sand dunes and anthropogenic affected regions. About 20% of dust hotspots were offset a few kilometers from clear geomorphic units. Quantitative analysis of emissions revealed that dust sourced from various geomorphic units, among them playas (12%) and fluvial systems (10%). The importance of sand dunes as dust-emission sources greatly differs between examined datasets (7% vs. 30%). Our study emphasizes the importance of scattered dust emission events that are not considered as hotspots, as these sources are usually neglected in dust emission modeling.

Description: People spend most of their time in indoors and, as a result, indoor air quality has become an issue of increasing concern. Due to the use of coal and heavy transportation in Beijing, China, concentrations of polycyclic aromatic hydrocarbons (PAHs) bound to PM2.5 have risen and caused concerns about health risk, both outdoors and indoors. This study carried out quantitative investigation of PM2.5-bound PAHs in middle school classrooms and estimated the health risk to adolescents. According to the results, indoor PM2.5 concentrations ranged from 20.9 μg/m3 to 257.6 μg/m3, indoor PAH concentrations ranged from 8.0 ng/m3 to 83.0 ng/m3, and both were statistically correlated with outdoor concentrations. Results of diagnostic ratios (DR) and the PMF (positive matrix factorization) model indicated that coal combustion was the main source of PAHs in the classroom environment. The average value of incremental lifetime cancer risk (ILCR) was estimated to be 1.49 × 10−6, which indicated a potential health risk to students according to USEPA standards. Predictions showed that by 2021–2022, the risk will be reduced to an acceptable level. Results of this study could provide useful information for air pollution control in Beijing and proposing targeted solution against indoor air pollution.

Description: Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Description: Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

Description: The shipborne high-frequency surface wave radar (HFSWR) platform produces six degrees of freedom (DOF) motion at sea, which affects the performance of radar target detection and remote sensing of ocean surface dynamics parameters. Motion compensation can mitigate the effect of six-DOF motion, but motion parameters (including amplitude and angular frequency) need to be known. Motion parameters obtained by using high precision sensors are affected by the precision error and time delay, thus affecting the effect of motion compensation. To obtain the motion parameters accurately and in real time, a method of identifying the motion parameters by using an artificially transmitted reference radio frequency (RF) signal generated at the shore is proposed. Based on the results of the parameter identification, the reference RF signal and the first-order radar cross-sections (RCSs) modulated by six-DOF motion of the shipborne HFSWR platform can be compensated. The identification of angular frequency is divided into two steps: (1) Preliminary identification results are obtained by using the reference RF signal; (2) the pattern search method is used to further improve the identification accuracy of angular frequency. The amplitude of translation (including surge and sway) can be identified accurately through the reference RF signal. Due to the small amplitude of rotation (including roll, pitch, and yaw), it needs to be identified by the reference RF signal and pattern search method. After identifying the motion parameters, division in the time domain is used for motion compensation. Through the simulation results, both translation and rotation have good motion compensation effects. In addition, the method of using high precision sensors to obtain motion parameters and compensation is compared with the method in this paper, the simulation results of motion compensation show that the latter is better.

Description: An experimental product study of the reactions of furfural with the main tropospheric oxidants (Cl, OH and NO3) has been carried out using a Fourier Transform Infrared spectrophotometer (FTIR) and a gas chromatograph–mass spectrometer with a time of flight detector (GC–TOFMS). The main gas-phase products detected were 5-chloro-2(5H)-furanone, maleic anhydride, 2-nitrofuran and CO. Molar yields were quantified for the detected products in these reactions, thus suggesting the existence of nongaseous products that could not be observed with the analytical techniques employed. The formation of Secondary Organic Aerosol (SOA) from the oxidation of furfural with Cl atoms, OH, NO3 and ozone was investigated in a smog chamber in the absence of inorganic seed aerosols. The experimental results show the formation of ultrafine particles (less than 1 µm in diameter) for all of the studied reactions except for the nitrate radical. Given their small size, these ultrafine particles (

Description: The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.

Description: Precise exposure assessment of air pollutants is crucial in epidemiologic studies to ensure valid estimates of health effects. We conducted a longitudinal study to evaluate the role of air quality monitoring (AQM) measurements and high-resolution modeling outcomes focusing on nitrogen dioxide (NO2) exposure and atopic dermatitis (AD). A total of 128 young children with AD in Seoul Metropolitan Area, Korea, were recruited as a panel. We estimated the participants’ exposure to NO2 for four months, from 1 April through 31 July 2014 based on (1) monitored levels from 60 AQM stations located at varying distances from residential areas (AQM station-based NO2, AQM-NO2) and (2) estimates from a community multi-scale air quality (CMAQ) modeling system with a high-resolution (1 × 1 km) (CMAQ-NO2). We then compared the effect of AQM-NO2 on AD symptoms with that of CMAQ-NO2. The average distance between the participants’ residences and the nearest AQM station was 2.03 ± 1.06 km, ranging from 0.28 km to 5.73 km. Based on AQM-NO2, the AD symptoms increased by 10.28% (95% confidence interval (CI): 3.24, 17.79) with an increase of 10 ppb of NO2. The effect estimates of CMAQ-NO2 were similar to those of AQM-NO2 when assessed in patients living within 3 km from the nearest AQM station. Even within 1 km, the CI estimate obtained from the CMAQ was much narrower than from AQM (44.18–49.54 vs. 7.02–64.75). However, the association of AQM-NO2 with AD symptoms of patients living beyond 3 km was not positive, whereas that of CMAQ-NO2 maintained positive. In conclusion, exposure to ambient NO2 is significantly associated with aggravation of AD symptoms in young children. In addition, our study suggests that exposure assessment of NO2 using measurement data obtained from monitoring stations far from residential locations can lead to misclassification bias.

Description: Reducing plastic pollution in rivers, lakes, and oceans is beneficial to aquatic animals and human livelihood. To achieve this, reliable observations of the abundance, spatiotemporal variation, and composition of plastics in aquatic ecosystems are crucial. Current efforts mainly focus on collecting data on the open ocean, on beaches and coastlines, and in river systems. Urban areas are the main source of plastic leakage into the natural environment, yet data on plastic pollution in urban water systems are scarce. In this paper, we present a simple method for plastic hotspot mapping in urban water systems. Through visual observations, macroplastic abundance and polymer categories are determined. Due to its simplicity, this method is suitable for citizen science data collection. A first application in the Dutch cities of Leiden and Wageningen showed similar mean plastic densities (111–133 items/km canal) and composition (75–80% soft plastics), but different spatial distributions. These observations emphasize the importance of long-term data collection to further understand and quantify spatiotemporal variations of plastics in urban water systems. In turn, this will support improved estimates of the contribution of urban areas to the plastic pollution of rivers and oceans.

Description: Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.

Description: The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.

Description: Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local position of the motifs. This makes the convolutional neural networks widely used in image processing fields such as image recognition, handwriting recognition, image super-resolution, and semantic segmentation. They also perform well in radar image enhancement. However, the local position invariant character might not be good for radar image enhancement, when features of motifs (also known as the point spread function in the radar imaging field) vary with the positions. In this paper, we proposed an SV-CNN with spatial-variant convolution kernels (SV-CK). Its function is illustrated through a special application of enhancing the radar images. After being trained using radar images with position-codings as the samples, the SV-CNN can enhance the radar images. Because the SV-CNN reads information of the local position contained in the position-coding, it performs better than the conventional CNN. The advance of the proposed SV-CNN is tested using both simulated and real radar images.

Description: Black carbon (BC), organic carbon (OC), and total carbon (TC) in snow are important for their climatic and cryospheric effects. They are also part of the global carbon cycle. Atmospheric black and organic carbon (including brown carbon) may deposit and darken snow surfaces. Currently, there are no standardized methods for sampling, filtering, and analysis protocols to detect carbon in snow. Here, we describe our current methods and protocols to detect carbon in seasonal snow using the OCEC thermal optical method, a European standard for atmospheric elemental carbon (EC). We analyzed snow collected within and around the urban background SMEARIII (Station for Measuring Ecosystem-Atmosphere Relations) at Kumpula (60° N) and the Arctic GAW (Global Atmospheric Watch) station at Sodankylä (67° N). The median BC, OC, and TC in snow samples (ntot = 30) in Kumpula were 1118, 5279, and 6396 ppb, and in Sodankylä, they were 19, 1751, and 629 ppb. Laboratory experiments showed that error due to carbon attached to a sampling bag (n = 11) was

Description: In this paper we document the design, development, results, performance and field applications of a compact directive transmit antenna for the long-range High Frequency ocean RADAR (HFR) systems operating in the International Telecommunication Union (ITU) designated 4MHz and 5MHz radiodetermination bands. The antenna design is based on the combination of the concepts of an electrically small loop with that of travelling wave antenna. This has the effect of inducing a radiated wave predominantly in a direction opposed to that of energy flow on the antenna structures. We demonstrate here that travelling wave design allows for a more compact antenna than other directive options, it has straightforward feed-point matching arrangements, and a flat frequency and phase response over an entire radiodetermination band. In situ measurements of the antenna radiation pattern, obtained with the aid of a drone, correlate well with those obtained from simulations, and show between 8dB and 30dB front-to-back suppression, with a 3dB beam width in the forward lobe of 100∘ or more. The broad-beam radiation pattern ensures proper illumination over the ocean and the significant front-to-back suppression guarantees reduced interference to terrestrial services. The proposed antenna design is compact and straight forward and can be easily deployed by minimal modifications of an existing transmission antenna. The design may be readily adapted to different environments due to the relative insensitivity of its radiation pattern and frequency response to geometric detail. The only downside to these antennas is their relatively low radiation efficiency which, however, may easily be compensated for by the available power output of a typical HFR transmitter. Antennas based on this design are currently deployed at the SeaSonde HFR sites in New South Wales, Australia, with operational ranges up to 200 km offshore despite their low radiating efficiency and the extremely low output power in use at these installations. Due to their directional pattern, it is also planned to test these antennas in phased-array Wellen RADAR (WERA) systems in both the standard receive arrays: where in-band radio frequency noise of terrestrial origin is impacting on data quality, and in the transmit array: to possibly simplify splitting, phasing and tuning requirements.

Description: Supraglacial liquid water at the margins of ice sheets has an important impact on the surface energy balance and can also influence the ice flow when supraglacial lakes drain to the bed. Optical imagery is able to monitor supraglacial lakes during the summer season. Here we developed an alternative method using polarimetric SAR from Sentinel-1 during 2017–2020 to distinguish between liquid water and other surface types at the margin of the Northeast Greenland Ice Stream. This allows the supraglacial hydrology to be monitored during the winter months too. We found that the majority of supraglacial lakes persist over winter. When comparing our results to optical data, we found significantly more water. Even during summer, many lakes are partly or fully covered by a lid of ice and snow. We used our classification results to automatically map the outlines of supraglacial lakes, create time series of water area for each lake, and hence detect drainage events. We even found several winter time drainages, which might have an important effect on ice flow. Our method has problems during the peak of the melt season, but for the rest of the year it provides crucial information for better understanding the component of supraglacial hydrology in the glaciological system.

Description: To investigate the effect of wave-induced mixing on the upper ocean structure, especially under typhoon conditions, an ocean-wave coupled model is used in this study. Two physical processes, wave-induced turbulence mixing and wave transport flux residue, are introduced. We select tropical cyclone (TC) Nepartak in the Northwest Pacific ocean as a TC example. The results show that during the TC period, the wave-induced turbulence mixing effectively increases the cooling area and cooling amplitude of the sea surface temperature (SST). The wave transport flux residue plays a positive role in reproducing the distribution of the SST cooling area. From the intercomparisons among experiments, it is also found that the wave-induced turbulence mixing has an important effect on the formation of mixed layer depth (MLD). The simulated maximum MLD is increased to 54 m and is only 1 m less than the observed value. The wave transport flux residue shows a dominant role in the mixed layer temperature (MLT) changing. The mean error of the MLT is reduced by 0.19 °C compared with the control experiment without wave mixing effects. The study shows that the effect of wave mixing should be included in the upper ocean structure modeling.

Description: Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.

Description: The article is devoted to a reconstruction of the sedimentation processes in Kamyshovoye Lake (the Kaliningrad Region, Russia) during the Late Glacial and Holocene. The results of the geochemical analysis of Kamyshovoye Lake’s bottom sediments, accompanied by statistical processing and detailed radiocarbon dating, are presented. It was established that a high proportion of mineral matter dominated in the intervals between 15,000 and 11,400 and between 1400 and 600 cal y BP; enrichment with carbonates was noted between 11,400 and 5200 cal y BP and during the past 600 years; and a high percentage of organic matter was recorded between 7800 and 600 cal y BP. We conclude that the increase in mineral matter was influenced by such factors as reduced vegetation cover due to natural and anthropogenic processes, aeolian transfer, and dead-ice melting during the Late Glacial. The increase in carbonate matter was mainly associated with humidity and the reduction conditions of the lake ecosystem. Organogenic matter content was affected by the autochthonic (biological) productivity of the lake, which directly depends on more favorable climatic conditions.

Description: The current techniques used for monitoring the blasting process in open pit mines are manual, intermittent and inefficient and can expose technical manpower to hazardous conditions. This study presents the application of unmanned aerial vehicle (UAV) systems for monitoring and improving the blasting process in open pit mines. Field experiments were conducted in different open pit mines to assess rock fragmentation, blast-induced damage on final pit walls, blast dynamics and the accuracy of blastholes including production and pre-split holes. The UAV-based monitoring was done in three different stages, including pre-blasting, blasting and post-blasting. In the pre-blasting stage, pit walls were mapped to collect structural data to predict in situ block size distribution and to develop as-built pit wall digital elevation models (DEM) to assess blast-induced damage. This was followed by mapping the production blasthole patterns implemented in the mine to investigate drillhole alignment. To monitor the blasting process, a high-speed camera was mounted on the UAV to investigate blast initiation, sequencing, misfired holes and stemming ejection. In the post-blast stage, the blasted rock pile (muck pile) was monitored to estimate fragmentation and assess muck pile configuration, heave and throw. The collected aerial data provide detailed information and high spatial and temporal resolution on the quality of the blasting process and significant opportunities for process improvement. The current challenges with regards to the application of UAVs for blasting process monitoring are discussed, and recommendations for obtaining the most value out of an UAV application are provided.

Description: Fertilized agricultural soils serve as a primary source of anthropogenic N2O emissions. In South Africa, there is a paucity of data on N2O emissions from fertilized, irrigated dairy-pasture systems and emission factors (EF) associated with the amount of N applied. A first study aiming to quantify direct N2O emissions and associated EFs of intensive pasture-based dairy systems in sub-Sahara Africa was conducted in South Africa. Field trials were conducted to evaluate fertilizer rates (0, 220, 440, 660, and 880 kg N ha−1 year−1) on N2O emissions from irrigated kikuyu–perennial ryegrass (Pennisetum clandestinum–Lolium perenne) pastures. The static chamber method was used to collect weekly N2O samples for one year. The highest daily N2O fluxes occurred in spring (0.99 kg ha−1 day−1) and summer (1.52 kg ha−1 day−1). Accumulated N2O emissions ranged between 2.45 and 15.5 kg N2O-N ha−1 year−1 and EFs for mineral fertilizers applied had an average of 0.9%. Nitrogen in yielded herbage varied between 582 and 900 kg N ha−1. There was no positive effect on growth of pasture herbage from adding N at high rates. The relationship between N balance and annual N2O emissions was exponential, which indicated that excessive fertilization of N will add directly to N2O emissions from the pastures. Results from this study could update South Africa’s greenhouse gas inventory more accurately to facilitate Tier 3 estimates.

Description: Achieving sustainable zero-waste and carbon neutral solutions that contribute to a circular economy is critically important for the long-term prosperity and continuity of traditional carbon-based energy industries. The Estonian oil shale (OS) sector is an example where such solutions are more than welcome. The combustion of OS generates a continuous flow of ashes destined to landfills. In this study, the technical feasibility of producing monolith building materials incorporating different OS ashes from Estonia was evaluated. Three binder systems were studied: self-cementation of the ashes, ceramic sintering in clay brick production and accelerated carbonation of OS ash (OSA) compacts. Results showed that most of the OSAs studied have low self-cementitious properties and these properties were affected by ash fineness and mineralogical composition. In case of clay bricks, OSA addition resulted in a higher porosity and improved insulation properties. The carbonated OSA compacts showed promising compressive strength. Accelerated carbonation of compacted samples was found to be the most promising way for the future utilization of OSAs as sustainable zero-waste and carbon neutral solution.

Description: This paper presents a review and analysis of large-scale air convection tests and the establishment of intrinsic permeability in coarse open-graded materials. Natural air convection can make a significant contribution to heat transfer during cooling periods. In seasonally freezing environments this can result in excessive frost penetration and subsequent frost-related problems. Intrinsic permeability largely defines the onset of convective heat transfer in granular materials. Conventional methods for measuring intrinsic permeability cannot be applied to very coarse materials. Large-scale laboratory experiments on natural air convection can serve as an alternative method for determining this crucial parameter. This paper gives an overview of four different experimental test setups for measuring natural air convection, all differing in physical shape, boundary conditions and heat flux/temperature measurement devices. Comparison between these is difficult because the air convection pattern can differ and in some cases the shape and number of convection cells cannot be validated. Most of the studies available in the literature use theoretical equations to approximate intrinsic permeability. A method based on the analytical Nu-Ra number relationship is employed to establish the values of intrinsic permeability. Tests that provide enough data to enable the use of the Nu-Ra relationship are very limited. The overall results show a reasonable correlation between experiment-based intrinsic permeability and theoretical approximation. However, several issues must be addressed: first, differences may exist between the intrinsic permeability of natural and of crushed materials due to the shape effect. Second, the method used is in theory valid only for two-dimensional air convection within a square enclosure heated from below. Yet the results show that this method could be extended to other conditions with a certain degree of confidence. Third, a good estimate of intrinsic permeability is possible only with accurate experimental measurement.

Description: A remarkable exposure of tubular authigenic carbonates was found on the seafloor in the Dongsha area of the South China Sea (SCS). The tubular carbonates, around 2–3 cm in diameter and usually less than 10 cm in length, represent broken fragments of once-larger pipes that now protrude from muddy sediments. The morphology, carbon and oxygen stable isotope compositions, and trace and rare earth element contents of the carbonates were analyzed to decipher the mode of carbonate formation. The tubular carbonates exhibit a dark brown coating of iron and manganese hydrous oxides, indicating prolonged exposure to oxic bottom waters. The carbonate content of the micritic pipes falls between 12.5 and 67.3 wt.% with an average of 42.0 wt.%, suggesting formation within the sediment. This inference is supported by trace and rare earth element patterns including a moderate enrichment of middle rare earth elements. Low δ13C values (as low as −50.3‰, Vienna Pee Dee Belemnite (VPDB)) suggest that carbonate precipitation was induced by the anaerobic oxidation of methane. The unusually positive δ18O values of the carbonates (as high as +5.3‰, VPDB) are believed to reflect the destabilization of locally abundant gas hydrate. Taken together, it is suggested that pipe formation was initiated by sediment-dwelling organisms, such as crustaceans or bivalves. The burrows subsequently acted as conduits for upward fluid migration. The lithification of the sediment directly surrounding the conduits and the partial filling of the conduits with carbonate cement resulted in the formation of tubular carbonates. Turbidity currents, sediment slumps, or the vigorous emission of fluids probably induced the fragmentation of tubular carbonates within the sediment. The carbonate fragments had been further subjected to winnowing by bottom currents. This study provides insight into the interaction of megafauna burrowing with fluid migration and carbonate formation at hydrocarbon seeps, highlighting the role of bottom currents and mass wasting on the formation of fragmented tubular carbonates.

Description: In November 2019, the Supreme Court of India issued a notification to all the states in the National Capital Region of Delhi to install smog towers for clean air and allocated INR 36 crores (~USD 5.2 million) for a pilot. Can we vacuum our air pollution problem using smog towers? The short answer is “no”. Atmospheric science defines the air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no boundaries and (b) a complex mixture of chemical compounds constantly forming and transforming into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same atmosphere simultaneously. In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming outdoor air pollution is unrealistic, and the long view on air quality management in Indian cities.

Description: A method for estimation of the turbulent energy dissipation rate from measurements by a conically scanning pulsed coherent Doppler lidar (PCDL), with allowance for the wind transport of turbulent velocity fluctuations, has been developed. The method has been tested in comparative atmospheric experiments with a Stream Line PCDL (Halo Photonics, Brockamin, Worcester, United Kingdom) and a sonic anemometer. It has been demonstrated that the method provides unbiased estimates of the dissipation rate at arbitrarily large ratios of the mean wind velocity to the linear scanning speed.

Description: Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts have used either component substitution (CS)-based methods or multiresolution analysis (MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose a pansharpening weight network (PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainable neural network (NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

Description: The problem of wave identification is formulated as applied to the results of measurements of the temperature and the density of the neutral atmosphere in the range height 90–120 km by the artificial periodic irregularities (APIs) technique. The technique is based on the resonant scattering of radio waves by artificial periodic irregularities of the ionospheric plasma emerging in the field of a standing wave arising from the interference of the incident and reflected waves from the ionosphere. APIs were created using SURA heating facility (named as SURA experiment). The acoustic wave theory is reformulated on the base of data which can be observed in the given experimental setup. The basic system of equations is reduced so that it accounts only upward and downward directed waves, ignoring entropy mode. The algorithm of wave identification based on usage of dynamic projection operators for such a reduced case is proposed and explicit form of projection operators is derived. Its application to finite number dataset via Discrete Fourier Transform (DFT) is described and results of its application to the DFT-transformed set of experimental observation of the temperature and density perturbations are presented. The result yields hybrid amplitudes, that allow us to calculate energy of the directed waves that enter the observed superposition. The problem of entropy mode detection is discussed, the corresponding projecting operators for the full evolution system are built and a way to apply the method to quantification of it is proposed.

Description: Digital Elevation Models (DEMs) are widely used as a proxy for bathymetric data and several studies have attempted to improve DEM accuracy for hydrodynamic (HD) modeling. Most of these studies attempted to quantitatively improve estimates of channel conveyance (assuming a non-braided morphology) rather than accounting for the actual channel planform. Accurate representation of river conveyance and planform in a DEM is critical to HD modeling and can be achieved with a combination of remote sensing (e.g., satellite image) and field data, such as water surface elevation (WSE). Therefore, the objectives of this study are (i) to develop an algorithm for predicting channel conveyance and characterizing planform via satellite images and in situ WSE and (ii) to estimate discharge using the predicted conveyance via an HD model. The algorithm is named River Bathymetry via Satellite Image Compilation (RiBaSIC) and uses Landsat satellite imagery, Shuttle Radar Topography Mission (SRTM) DEM, Multi-Error-Removed Improved-Terrain (MERIT) DEM, and observed WSE. The algorithm is tested on four study areas along the Willamette River, Kushiyara River, Jamuna River, and Solimoes River. Channel slope and predicted hydraulic radius are subsequently estimated for approximating Manning’s roughness factor. Two-dimensional HD models using DEMs modified by the RiBaSIC algorithm and corresponding Manning’s roughness factors are employed for discharge estimation. The proposed algorithm can represent river planform and conveyance in single-channeled, meandering, wandering, and braided river reaches. Additionally, the HD models estimated discharge within 14–19% relative root mean squared error (RRMSE) in simulation of five years period.

Description: Quantifying river discharge is a critical component for hydrological studies, floodplain ecological conservation research, and water resources management. In recent years, a series of remote sensing-based discharge estimation methods have been developed. An example is the use of the near infrared (NIR) band of optical satellite images, with the principle of calculating the ratio between a stable land pixel for calibration (C) and a pixel within the river for measurement (M), applying a linear regression between C/M series and observed discharge series. This study trialed the C/M method, utilizing the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance product on relatively small rivers with 30~100 m widths. Two study sites with different river characteristics and geographic settings in the Murray-Darling Basin (MDB) of Australia were selected as case studies. Two independent sets of HLS data and gauged discharge data for the 2017 and 2018 water years were acquired for modeling and validation, respectively. Results reveal high consistency between the HLS-derived discharge and gauged discharge at both sites. The Relative Root Mean Square Errors are 53% and 19%, and the Nash-Sutcliffe Efficiency coefficients are 0.24 and 0.69 for the two sites. This study supports the effectiveness of applying the fine-resolution HLS for modeling discharge on small rivers based on the C/M methodology, which also provides evidence of using multisource synthesized datasets as the input for discharge estimation.

Description: This study is focused on a barian titanian phlogopite found in an alkaline ultramafic dyke transecting Mesozoic limestones of the Gargano Promontory (Apulia, Italy). The rock containing the barian titanian phlogopite, an olivine-clinopyroxene-rich lamprophyre with nepheline and free of feldspars, has been classified as monchiquite. The present study combines chemical analyses, single crystal X-ray diffraction and Raman spectroscopy. Chemical variations suggest that the entry of Ba into the phlogopite structure can be explained by the exchange Ba + Al = K + Si. The crystal structure refinement indicates that the Ti uptake is consistent with the Ti–oxy exchange mechanism. The structural parameters associated with the oxy substitution mechanism are extremely enhanced and rarely reported in natural phlogopite: (a) displacement of M2 cation toward the O4 site (~0.7); (b) M2 octahedron bond-length distortion (~2.5); (c) very short c cell parameter (~10.14 Å). Raman analysis showed most prominent features in the 800–200 cm−1 region with the strongest peaks occurring at 773 and 735 cm−1. Only a weak, broad band was observed to occur in the OH-stretching region. As concerns the origin of the barian titanian phlogopite, the rock textural features clearly indicate that it crystallized from pockets of the interstitial melt. Here, Ba and Ti enrichment took place after major crystallization of olivine under fast-cooling conditions, close to the dyke margin.

Description: The lithobiont community encrusting an early Pleistocene palaeocliff cropping out north of Augusta (SE Sicily, Italy) was investigated based on field observations and laboratory inspection of two rocky samples. Bryozoans, serpulids, brachiopods and bivalves encrusted part of the exposed surfaces that were bored mostly by clionaid sponges. Bryozoans, with at least 25 species detected on the rocky samples, are the most diversified skeletonized lithobionts also accounting for the highest number of colonies/specimens and highest coverage. Brachiopods, with the only species Novocrania anomala and a few but large cemented valves, cover wide surfaces. Serpulids, with two species identified on the sampled rocks and further two on the outcrop, were intermediate. A multiphase colonization is present, including a final epilithobiont community locally formed on eroded surfaces exposing a network of pervasive borings. The co-occurrence of very sciaphilic species having circalittoral to bathyal distributions suggests that the studied community thrived on a rocky substratum located near or at the shelf break, probably belonging to the shelf break (or RL) biocoenosis, also in agreement with observations on the fossil content of neighboring marly sediments. The observed relationships among colonizers largely represent mere superimpositions, and real interactions are not enough to state species competitiveness.

Description: On 2 February 2018, the China Seismo-Electromagnetic Satellite (CSES) ZhangHeng 01 (ZH-01) was successfully launched, carrying on board, in addition to a suite of plasma and particle physics instruments, a high precision magnetometer package (HPM), able to observe the ultra-low frequency (ULF) waves. In this paper, a night time Pi2 pulsation observed by CSES is reported for the first time. This Pi2 event occurred on 3 September 2018, and began at 14:30 UT (02:37 magnetic local time), when the satellite was in the southern hemisphere between −49 and −13 magnetic latitude (MLAT). Kakioka (KAK) ground station in Japan detected the same Pi2 between 14:30–14:42 UT (23:30–23:42 local time). The Pi2 oscillations in the compressional, toroidal, and poloidal components at the CSES satellite and the H-component at the KAK station are investigated by estimating coherence, amplitude, and cross-phase. We noticed a high degree of similarity between the Pi2 event in the horizontal component at KAK and the ionospheric fluctuations in the compressional component at CSES. This high correlation indicated the magnetospheric source of the Pi2 event. In addition, Pi2 is exhibited clearly in the δBy component at CSES, which is highly correlated with the ground H component, so the Pi2 event could be explained by the Substorm Current Wedge (SCW). This interpretation is further confirmed by checking the compressional component of Van Allen Probe (VAP) B satellite inside the plasmasphere, which, for the first time, gives observational support for an earlier model. This ULF wave observation shows the consistency and reliability of the high precision magnetometer (HPM) equipped by two fluxgate magnetometers (FGM1 and FGM2) onboard CSES.

Description: High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the restoration of tidal marsh ecosystems in particular. However, the number of studies investigating the conditions for state shifts from bare mudflats to vegetated marshes remains relatively low. We developed a GIS approach to identify the locations of expected shifts from bare intertidal flats to vegetated marshes along a large estuary (Western Scheldt estuary, SW Netherlands), by analyzing the interactions between spatial patterns of vegetation biomass, elevation, tidal currents, and wind waves. We analyzed false-color aerial images for locating marshes, LIDAR-based digital elevation models, and spatial model simulations of tidal currents and wind waves at the whole estuary scale (~326 km²). Our results demonstrate that: (1) Bimodality in vegetation biomass and intertidal elevation co-occur; (2) the tidal currents and wind waves change abruptly at the transitions between the low-elevation bare state and high-elevation vegetated state. These findings suggest that biogeomorphic feedback between vegetation growth, currents, waves, and sediment dynamics causes the state shifts from bare mudflats to vegetated marshes. Our findings are translated into a GIS approach (logistic regression) to identify the locations of shifts from bare to vegetated states during the studied period based on spatial patterns of elevation, current, and wave orbital velocities. This GIS approach can provide a scientific basis for the management and restoration of tidal marshes.

Description: High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California.

Description: Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

Description: River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow [...]

Description: Violent tornadoes are rare in Europe but they can have devastating effects. Damage associated with individual tornadoes can reach several billion euros and they have caused hundreds of fatalities. The tornado risk varies considerably over Europe, but so far only a few national maps of tornado risk and one Europe-wide map exist. We show several different ways to create quantitative maps of tornado occurrence rates as follows: Kernel smoothing of observations, climatologies of convective parameters from reanalysis, output of a logistic regression model to link convective parameters with observed tornadoes, orography-dependent climatologies and finally the population-bias corrected tornado occurrence rates from the Risk Management Solutions (RMS) Europe Severe Convective Storm Model. We discuss advantages and disadvantages of each approach and compare the results. While the climatologies created from the individual methods show a lot of qualitative similarities, we advocate to combine the methods to achieve the most reliable quantitative climatology.

Description: Dryland ecosystems are frequently struck by droughts. Yet, woody vegetation is often able to recover from mortality events once precipitation returns to pre-drought conditions. Climate change, however, may impact woody vegetation resilience due to more extreme and frequent droughts. Thus, better understanding how woody vegetation responds to drought events is essential. We used a phenology-based remote sensing approach coupled with field data to estimate the severity and recovery rates of a large scale die-off event that occurred in 2014–2015 in Senegal. Novel low (L-band) and high-frequency (Ku-band) passive microwave vegetation optical depth (VOD), and optical MODIS data, were used to estimate woody vegetation dynamics. The relative importance of soil, human-pressure, and before-drought vegetation dynamics influencing the woody vegetation response to the drought were assessed. The die-off in 2014–2015 represented the highest dry season VOD drop for the studied period (1989–2017), even though the 2014 drought was not as severe as the droughts in the 1980s and 1990s. The spatially explicit Die-off Severity Index derived in this study, at 500 m resolution, highlights woody plants mortality in the study area. Soil physical characteristics highly affected die-off severity and post-disturbance recovery, but pre-drought biomass accumulation (i.e., in areas that benefited from above-normal rainfall conditions before the 2014 drought) was the most important variable in explaining die-off severity. This study provides new evidence supporting a better understanding of the “greening Sahel”, suggesting that a sudden increase in woody vegetation biomass does not necessarily imply a stable ecosystem recovery from the droughts in the 1980s. Instead, prolonged above-normal rainfall conditions prior to a drought may result in the accumulation of woody biomass, creating the basis for potentially large-scale woody vegetation die-off events due to even moderate dry spells.

Description: Augmented reality can improve construction and facility management by visualizing an as-planned model on its corresponding surface for fast, easy, and correct information retrieval. This requires the localization registration of an as-built model in an as-planned model. However, the localization and registration of indoor environments fail, owing to self-similarity in an indoor environment, relatively large as-planned models, and the presence of additional unplanned objects. Therefore, this paper proposes a computer vision-based method to (1) homogenize indoor as-planned and as-built models, (2) reduce the search space of model matching, and (3) localize the structure (e.g., room) for registration of the scanned area in its as-planned model. This method extracts a representative horizontal cross section from the as-built and as-planned point clouds to make these models similar, restricts unnecessary transformation to reduce the search space, and corresponds the line features for the estimation of the registration transformation matrix. The performance of this method, in terms of registration accuracy, is evaluated on as-built point clouds of rooms and a hallway on a building floor. A rotational error of 0.005 rad and a translational error of 0.088 m are observed in the experiments. Hence, the geometric feature described on a representative cross section with transformation restrictions can be a computationally cost-effective solution for indoor localization and registration.

Description: Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis of its health, as vegetation height is associated with Above Ground Biomass (AGB) and can be used to track degradation or growth over time. Traditional methods to do this, however, rely on manual measurements of stem heights that can cause harm to the marsh ecosystem. Moreover, manual measurements are limited in scale and are often time and labor intensive. Unoccupied Aircraft Systems (UAS) can provide an alternative to manual measurements and generate continuous results across a large spatial extent in a short period of time. In this study, a multirotor UAS equipped with optical Red Green Blue (RGB) and multispectral sensors was used to survey five salt marshes in Beaufort, North Carolina. Structure-from-Motion (SfM) photogrammetry of the resultant imagery allowed for continuous modeling of the entire marsh ecosystem in a three-dimensional space. From these models, vegetation height was extracted and compared to ground-based manual measurements. Vegetation heights generated from UAS data consistently under-predicted true vegetation height proportionally and a transformation was developed to predict true vegetation height. Vegetation height may be used as a proxy for Above Ground Biomass (AGB) and contribute to blue carbon estimates, which describe the carbon sequestered in marine ecosystems. Employing this transformation, our results indicate that UAS and SfM are capable of producing accurate assessments of salt marsh health via consistent and accurate vegetation height measurements.

Description: Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.

Description: Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

Description: Human activities are mainly responsible for the Aral Sea crisis, and excessive farmland expansion and unreasonable irrigation regimes are the main manifestations. The conflicting needs of agricultural water consumption and ecological water demand of the Aral Sea are increasingly prominent. However, the quantitative relationship among the water balance elements in the oasis located in the lower reaches of the Amu Darya River Basin and their impact on the retreat of the Aral Sea remain unclear. Therefore, this study focused on the water consumption of the Nukus irrigation area in the delta of the Amu Darya River and analyzed the water balance variations and their impacts on the Aral Sea. The surface energy balance algorithm for land (SEBAL) was employed to retrieve daily and seasonal evapotranspiration (ET) levels from 1992 to 2018, and a water balance equation was established based on the results of a remote sensing evapotranspiration inversion. The results indicated that the actual evapotranspiration (ETa) simulated by the SEBAL model matched the crop evapotranspiration (ETc) calculated by the Penman–Monteith method well, and the correlation coefficients between the two ETa sources were greater than 0.8. The total ETa levels in the growing seasons decreased from 1992 to 2005 and increased from 2005 to 2015, which is consistent with the changes in the cultivated land area and inflows from the Amu Darya River. In 2000, 2005 and 2010, the groundwater recharge volumes into the Aral Sea during the growing season were 6.74×109 m3, 1.56×109 m3 and 8.40×109 m3; respectively; in the dry year of 2012, regional ET exceeded the river inflow, and 2.36×109 m3 of groundwater was extracted to supplement the shortage of irrigation water. There is a significant two-year lag correlation between the groundwater level and the area of the southern Aral Sea. This study can provide useful information for water resources management in the Aral Sea region.

Description: In the literature of pan-sharpening based on neural networks, high resolution multispectral images as ground-truth labels generally are unavailable. To tackle the issue, a common method is to degrade original images into a lower resolution space for supervised training under the Wald’s protocol. In this paper, we propose an unsupervised pan-sharpening framework, referred to as “perceptual pan-sharpening”. This novel method is based on auto-encoder and perceptual loss, and it does not need the degradation step for training. For performance boosting, we also suggest a novel training paradigm, called “first supervised pre-training and then unsupervised fine-tuning”, to train the unsupervised framework. Experiments on the QuickBird dataset show that the framework with different generator architectures could get comparable results with the traditional supervised counterpart, and the novel training paradigm performs better than random initialization. When generalizing to the IKONOS dataset, the unsupervised framework could still get competitive results over the supervised ones.

Description: More people appreciate the importance of global sustainability [...]

Description: Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form m = a D b are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (Dmm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, Dmm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference χ 2 that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on Dmm are shown with both parameters significantly decreasing with Dmm, leading to smaller effective densities for larger Dmm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when Dmm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different Dmm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities.

Description: Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed.

Description: Density-dependent population growth regulates long-term urban expansion and shapes distinctive socioeconomic trends. Despite a marked heterogeneity in the spatial distribution of the resident population, Mediterranean European countries are considered more homogeneous than countries in other European regions as far as settlement structure and processes of metropolitan growth are concerned. However, rising socioeconomic inequalities among Southern European regions reflect latent demographic and territorial transformations that require further investigation. An integrated assessment of the spatio-temporal distribution of resident populations in more than 1000 municipalities (1961–2011) was carried out in this study to characterize density-dependent processes of metropolitan growth in Greece. Using geographically weighted regressions, the results of our study identified distinctive local relationships between population density and growth rates over time. Our results demonstrate that demographic growth rates were non-linearly correlated with other variables, such as population density, with positive and negative impacts during the first (1961–1971) and the last (2001–2011) observation decade, respectively. These findings outline a progressive shift over time from density-dependent processes of population growth, reflecting a rapid development of large metropolitan regions (Athens, Thessaloniki) in the 1960s, to density-dependent processes more evident in medium-sized cities and accessible rural regions in the 2000s. Density-independent processes of population growth have been detected in the intermediate study period (1971–2001). This work finally discusses how a long-term analysis of demographic growth, testing for density-dependent mechanisms, may clarify the intrinsic role of population concentration and dispersion in different phases of the metropolitan cycle in Mediterranean Europe.

Description: The complexity and high sensitivity of proteins to environmental factors give rise to a multitude of variables, which affect the stabilization mechanisms in protein foams. Interfacial and foaming properties of proteins have been widely studied, but the reported unique effect of pH, which can be of great interest to applications, has been investigated to a lesser extent. In this paper, we focus on the impact of pH on the stability of black foam films and corresponding foams obtained from solutions of a model globular protein—the whey β-lactoglobulin (BLG). Foam stability was analyzed utilizing three characteristic parameters (deviation time, transition time and half-lifetime) for monitoring the foam decay, while foam film stability was measured in terms of the critical disjoining pressure of film rupture. We attempt to explain correlations between the macroscopic properties of a foam system and those of its major building blocks (foam films and interfaces), and thus, to identify structure-property relationships in foam. Good correlations were found between the stabilities of black foam films and foams, while relations to the properties of adsorption layers appeared to be intricate. That is because pH-dependent interfacial properties of proteins usually exhibit an extremum around the isoelectric point (pI), but the stability of BLG foam films increases with increasing pH (3–7), which is well reflected in the foam stability. We discuss the possible reasons behind these intriguingly different behaviors on the basis of pH-induced changes in the molecular properties of BLG, which seem to be determining the mechanism of film rupture at the critical disjoining pressure.

Description: The oxidation state and local atomic environment of admixtures of In, Cu, and Ag in synthetic sphalerite crystals were determined by X-ray absorption spectroscopy (XAS). The sphalerite crystals doped with In, Cu, Ag, In–Cu, and In–Ag were synthesized utilizing gas transport, salt flux, and dry synthesis techniques. Oxidation states of dopants were determined using X-ray absorption near edge structure (XANES) technique. The local atomic structure was studied by X-ray absorption fine structure spectroscopy (EXAFS). The spectra were recorded at Zn, In, Ag, and Cu K-edges. In all studied samples, In was in the 3+ oxidation state and replaced Zn in the structure of sphalerite, which occurs with the expansion of the nearest coordination shells due to the large In ionic radius. In the presence of In, the oxidation state of Cu and Ag is 1+, and both metals can form an isomorphous solid solution where they substitute for Zn according to the coupled substitution scheme 2Zn2+ ↔ Me+ + In3+. Moreover, Ag K-edges EXAFS spectra fitting, combined with the results obtained for In- and Au-bearing sphalerite shows that the Me-S distances in the first coordination shell in the solid solution state are correlated with the ionic radii and increase in the order of Cu 〈 Ag 〈 Au. The distortion of the atomic structure increases in the same order. The distant (second and third) coordination shells of Cu and Ag in sphalerite are split into two subshells, and the splitting is more pronounced for Ag. Analysis of the EXAFS spectra, coupled with the results of DFT (Density Function Theory) simulations, showed that the In–In and Me+–In3+ clustering is absent when the metals are present in the sphalerite solid solution. Therefore, all studied admixtures (In, Cu, Ag), as well as Au, are randomly distributed in the matrix of sphalerite, where the concentration of the elements in the “invisible” form can reach a few tens wt.%.

Description: In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure.

Description: This study investigates the robustness of the physically-based hydrological model WaSiM (water balance and flow simulation model) for simulating hydrological processes in two data sparse small-scale inland valley catchments (Bankandi-Loffing and Mebar) in Burkina Faso. An intensive instrumentation with two weather stations, three rain recorders, 43 piezometers, and one soil moisture station was part of the general effort to reduce the scarcity of hydrological data in West Africa. The data allowed us to successfully parameterize, calibrate (2014–2015), and validate (2016) WaSiM for the Bankandi-Loffing catchment. Good model performance concerning discharge in the calibration period (R2 = 0.91, NSE = 0.88, and KGE = 0.82) and validation period (R2 = 0.82, NSE = 0.77, and KGE = 0.57) was obtained. The soil moisture (R2 = 0.7, NSE = 0.7, and KGE = 0.8) and the groundwater table (R2 = 0.3, NSE = 0.2, and KGE = 0.5) were well simulated, although not explicitly calibrated. The spatial transposability of the model parameters from the Bankandi-Loffing model was investigated by applying the best parameter-set to the Mebar catchment without any recalibration. This resulted in good model performance in 2014–2015 (R2 = 0.93, NSE = 0.92, and KGE = 0.84) and in 2016 (R2 = 0.65, NSE = 0.64, and KGE = 0.59). This suggests that the parameter-set achieved in this study can be useful for modeling ungauged inland valley catchments in the region. The water balance shows that evaporation is more important than transpiration (76% and 24%, respectively, of evapotranspiration losses) and the surface flow is very sensitive to the observed high interannual variability of rainfall. Interflow dominates the uplands, but base flow is the major component of stream flow in inland valleys. This study provides useful information for the better management of soil and scarce water resources for smallholder farming in the area.

Description: The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures; however, quantification of these quantiles in data-scarce regions has been a continuing challenge in hydrologic design. Flood frequency analysis is thus essential to reduce possible flood damage by investigating the most suitable flood prediction model. The annual maximum discharges from six representative stations in the Upper Blue Nile River Basin were fitted to the commonly used nine statistical distributions. This study also assessed the performance evolution of the probability distributions with varying spatial scales, such that three different spatial scales of small-, medium-, and large-scale basins in the Blue Nile River Basin were considered. The performances of the candidate probability distributions were assessed using three goodness-of-fit test statistics, root mean square error, and graphical interpretation approaches to investigate the robust probability distribution for flood frequency analysis over different basin spatial scales. Based on the overall analyses, the generalized extreme value distribution was proven to be a robust model for flood frequency analysis in the study region. The generalized extreme value distribution significantly improved the performance of the flood prediction over different spatial scales. The generalized extreme value flood prediction performance improvement measured in root mean square error varied between 5.84 and 67.91% over other commonly used probability distribution models. Thus, the flood frequency analysis using the generalized extreme value distribution could be essential for the efficient planning and design of hydraulic structures in the Blue Nile River Basin. Furthermore, this study suggests that, in the future, significant efforts should be put to conduct similar flood frequency analyses over the other major river basins of Ethiopia.

Description: Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

Description: Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.

Description: Transformation to Continuous Cover Forestry (CCF) is a long and difficult process in which frequent management interventions rapidly alter forest structure and dynamics with long lasting impacts. Therefore, a critical component of transformation is the acquisition of up-to-date forest inventory data to direct future management decisions. Recently, the use of single tree detection methods derived from unmanned aerial vehicle (UAV) has been identified as being a cost effective method for inventorying forests. However, the rapidly changing structure of forest stands in transformation amplifies the difficultly in transferability of current individual tree detection (ITD) methods. This study presents a novel ITD Bayesian parameter optimisation approach that uses quantile regression and external biophysical tree data sets to provide a transferable and low cost ITD approach to monitoring stands in transformation. We applied this novel method to 5 stands in a variety of transformation stages in the UK and to a independent test study site in California, USA, to assess the accuracy and transferability of this method. Requiring small amounts of training data (15 reference trees) this approach had a mean test accuracy (F-score = 0.88) and provided mean tree diameter estimates (RMSE = 5.6 cm) with differences that were not significance to the ground data (p 〈 0.05). We conclude that this method can be used to monitor forests stands in transformation and thus can also be applied to a wide range of forest structures with limited manual parameterisation between sites.

Description: Glacier surface temperature (GST) is influenced by both the energy flux from the atmosphere above and the thermal dynamics at the ice–water–debris interfaces. However, previous studies on GST are inadequate in time series research and mountain glacier surface temperature retrieval. We evaluate the GST variability at Hailuogou glacier, a temperate glacier located in Southeastern Tibetan Plateau, from 1990 to 2018. We utilized a modified mono-window algorithm to calculate the GST using the Landsat 8 thermal infrared sensor (TIRS) band 10 data and Landsat 5 thematic mapper (TM) band 6 data. Three essential parameters, including the emissivity of ice and snow, atmospheric transmittance, and effective mean atmospheric temperature, were employed in the GST algorithm. The remotely-sensed temperatures were compared with two other single-channel algorithms to validate GST algorithm’s accuracy. Results from different algorithms showed a good agreement, with a mean difference of about 0.6 ℃. Our results showed that the GST of the Hailuogou glacier, both in the upper debris-free part and the lower debris-covered tongue, has experienced a slightly increasing trend at a rate of 0.054 ℃ a−1 during the past decades. Atmospheric warming, expanding debris cover in the lower part, and a darkening debris-free accumulation area are the main causes of the warming of the glacier surface.

Description: Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others. Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment. This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects. The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail. The model is also refined by adding parametric elements for buildings and vegetation. Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data. This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing.

Description: Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 °C for the 20 years and a maximum difference of 1.7 °C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.

Description: To study the influence of complex terrain with different scales on the structure of near-surface turbulence, the turbulence observational data from Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) were analyzed. SACOL is located in typical Loess Plateau topography. The terrain around the site varies greatly with the direction. Representative data from the Northeast, Southeast, Southwest, and Northwest were selected to study the structure characteristics of the near surface turbulence. The complex topography within the flux footprint enhances the vertical scale of turbulence σw and thereby increases the vertical–longitudinal ratio of turbulence Ar. While the turbulent horizontal scale σu is also significantly affected by the distant terrain beyond the flux footprint. The upwind terrain undulation increases σu and reduces Ar. Affected by the complex terrain, the ratio of the spectrum of the vertical velocity to that of the longitudinal velocity, Sw(n)/Su(n), is far less than 4/3 in the southwest direction, and the turbulence is significantly anisotropic.

Description: Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.

Description: The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.

Description: Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.

Description: Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture and parameter value identification are critical for a physics-based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide accurate estimates of soil moisture, but such techniques are limited due to the number of physical sensors required to cover a large area effectively. Available satellite-based digital soil moisture data ranges from 9 km to 20 km in resolution which obscures the soil moisture details of a hill slope scale. This resolution limitation of available satellite-based distributed soil moisture data has impacted critical analysis of soil moisture resolution variance on physics-based distributed simulation results. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column and that would inform little about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed effective root zone soil moisture at 30 m resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with the GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 m resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 m resolution SERVES soil moisture data was resampled to 4500 m and 9000 m resolutions and was separately employed in the calibrated hydrological model to determine the soil moisture resolution effect on the model simulated outputs and the model parameter values. It was found that the simulated discharge is underestimated, infiltration rate/volume is overestimated and higher soil moisture state distribution is filtered out as the initial soil moisture resolution was coarsened. To compensate for this disparity in the simulated results, the soil saturated hydraulic conductivity value decreased with respect to the decreased resolutions.

Description: On 22 March 2020, Zagreb was struck by an M5.5 earthquake that had been expected for more than 100 years and revealed all the failures in the construction of residential buildings in the Croatian capital, especially those built in the first half of the 20th century. Because of that, extensive seismological, geological, geodetic and structural engineering surveys were conducted immediately after the main shock. This study provides descriptions of damage, specifying the building performances and their correlation with the local soil characteristics, i.e., seismic motion amplification. Co-seismic vertical ground displacement was estimated, and the most affected area is identified according to Sentinel-1 interferometric wide-swath data. Finally, preliminary 3D structural modeling of the earthquake sequence was performed, and two major faults were modeled using inverse distance weight (IDW) interpolation of the grouped hypocenters. The first-order assessment of seismic amplification (due to site conditions) in the Zagreb area for the M5.5 earthquake shows that ground motions of approximately 0.16–0.19 g were amplified at least twice. The observed co-seismic deformation (based on Sentinel-1A IW SLC images) implies an approximately 3 cm uplift of the epicentral area that covers approximately 20 km2. Based on the preliminary spatial and temporal analyses of the Zagreb 2020 earthquake sequence, the main shock and the first aftershocks evidently occurred in the subsurface of the Medvednica Mountains along a deep-seated southeast-dipping thrust fault, recognized as the primary (master) fault. The co-seismic rupture propagated along the thrust towards northwest during the first half-hour of the earthquake sequence, which can be clearly seen from the time-lapse visualization. The preliminary results strongly support one of the debated models of the active tectonic setting of the Medvednica Mountains and will contribute to a better assessment of the seismic hazard for the wider Zagreb area.

Description: Micro, small, and medium-sized enterprises (MSMEs) are an essential part of economies at the national, regional, and local levels. Understanding the determinants of the development of this sector is interesting not only for researchers but also for local governments to support the development of this sector. This paper analyses micro, small, and medium enterprises at the gmina (local) level in one region, the Kujawsko-Pomorskie voivodship (NUTS2) in Poland. The authors use multivariate linear regression, spatial econometrics, and classification trees to model the influence of different factors on the number of enterprises relative to population size. The authors found that the most crucial factor in all cases, independently of the method used, is the local government’s revenue from personal income tax per capita. This finding, together with the lack of significance of variables related to the distance to technological parks or economic zones, indicates that the enterprises in the region produce mainly for local consumption and lack innovativeness. The authors also examined the influence of spatial context on the number of enterprises. The most important factor seems to be the percentage of built-up areas, but there are also others, depending on the model type; again, this confirms the local character of the activity of micro, small, and medium enterprises in the region. Variables representing the spatial context can explain the relative number of enterprises with coefficient of determination (R2) between 0.30 and 0.45, which shows that this context played a relatively significant role in the development of the MSME sector in the region. On the other hand, the econometric models (that include the neighborhood) are only significant (improving R2) for medium enterprises, which means that medium enterprises expand their activity beyond the local range.

Description: The impact of aerosol spatio-temporal variability on the Arctic radiative budget is not fully constrained. This case study focuses on the intra-Arctic modification of long-range transported aerosol and its direct aerosol radiative effect (ARE). Different types of air-borne and ground-based remote sensing observations (from Lidar and sun-photometer) revealed a high tropospheric aerosol transport episode over two parts of the European Arctic in April 2018. By incorporating the derived aerosol optical and microphysical properties into a radiative transfer model, we assessed the ARE over the two locations. Our study displayed that even in neighboring Arctic upper tropospheric levels, aged aerosol was transformed due to the interplay of removal processes (nucleation scavenging and dry deposition) and alteration of the aerosol source regions (northeast Asia and north Europe). Along the intra-Arctic transport, the coarse aerosol mode was depleted and the visible wavelength Lidar ratio (LR) increased significantly (from 15 to 64–82 sr). However, the aerosol modifications were not reflected on the ARE. More specifically, the short-wave (SW) atmospheric column ARE amounted to +4.4 - +4.9 W m−2 over the ice-covered Fram Strait and +4.5 W m−2 over the snow-covered Ny-Ålesund. Over both locations, top-of-atmosphere (TOA) warming was accompanied by surface cooling. These similarities can be attributed to the predominant accumulation mode, which drives the SW radiative budget, as well as to the similar layer altitude, solar geometry, and surface albedo conditions over both locations. However, in the context of retreating sea ice, the ARE may change even along individual transport episodes due to the ice albedo feedback.

Description: This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R2 = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R2 = 0.59; R2 = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

Description: High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

Description: Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (〉0.85) and Yangcun (〉0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE 〉 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method.

Description: Moss biomonitoring technique was used for a heavy-metal pollution study in Macedonia in the framework of the International Cooperative Program on Effects of Air Pollution on Natural Vegetation and Crops (UNECE IPC Vegetation). Moss samples (n = 72) were collected during the summers of 2002, 2005, and 2010. The contents of 41 elements were determined by neutron activation analysis, atomic absorption spectrometry, and inductively coupled plasma atomic emission spectrometry. Using factor and cluster analyses, three geogenic factors were determined (Factor 1, including Al, As, Co, Cs, Fe, Hf, Na, Rb, Sc, Ta, Th, Ti, U, V, Zr, and rare-earth elements–RE; Factor 4 with Ba, K, and Sr; and Factor 5 with Br and I), one anthropogenic factor (Factor 2, including Cd, Pb, Sb, and Zn), and one geogenic-anthropogenic factor (Factor 3, including Cr and Ni). The highest anthropogenic impact of heavy metal to the air pollution in the country was from the ferronickel smelter near Kavadraci (Ni and Cr), the lead and zinc mines in the vicinity of Makedonska Kamenica, Probištip, and Kriva Palanka in the eastern part of the country (Cd, Pb, and Zn), and the former lead and zinc smelter plant in Veles. Beside the anthropogenic influences, the lithology and the composition of the soil also play an important role in the distribution of the elements.

Description: The Metropolitan Region of Sao Paulo (MRSP) is one of the main regions of Brazil that in recent years has shown an increase in the number of days with heat waves, mainly affecting the health of the most sensitive populations, such as the elderly. In this study, we identified the heat waves in the MRSP using three different definitions regarding the maximum daily temperature threshold. To analyze the impact of heat waves on elderly mortality, we used distributed lag nonlinear models (dlnm) and we quantified the heat wave-related excess mortality of elderly people from 1985 to 2005 and made projections for the near future (2030 to 2050) and the distant future (2079–2099) under the climate change scenarios RCP4.5 and RCP8.5 (RCP: Representative Concentration Paths). An important aspect of this research is that for the projections we take into account two assumptions: non-adaptation and adaptation to the future climate. Our projections show that the heat wave-related excess of elderly mortality will increase in the future, being highest when we consider no adaptation, mainly from cardiovascular diseases in women (up to 587 deaths per 100,000 inhabitants per year). This study can be used for public policies to implement preventive and adaptive measures in the MRSP.

Description: The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.

Description: European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently exploit multiple sources of information and leverage their complementarity, in order to obtain the most out of radar and optical data. In this work, we propose to deal with land cover mapping in an object-based image analysis (OBIA) setting via a deep learning framework designed to leverage the multi-source complementarity provided by radar and optical satellite image time series (SITS). The proposed architecture is based on an extension of Recurrent Neural Network (RNN) enriched via a modified attention mechanism capable to fit the specificity of SITS data. Our framework also integrates a pretraining strategy that allows to exploit specific domain knowledge, shaped as hierarchy over the set of land cover classes, to guide the model training. Thorough experimental evaluations, involving several competitive approaches were conducted on two study sites, namely the Reunion island and a part of the Senegalese groundnut basin. Classification results, 79% of global accuracy on the Reunion island and 90% on the Senegalese site, respectively, have demonstrated the suitability of the proposal.

Description: Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.

Description: Agricultural production in sub-Saharan Africa remains dependent on high inputs of human labor, a situation associated with direct exposure to daylight heat during critical periods of the agricultural calendar. We ask the question: how is the Wet-Bulb Globe Temperature (WBGT) going to be distributed in the future, and how will this affect the ability of smallholder farmers to perform agricultural activities? Data from general circulation models are used to estimate the distribution of WBGT in 2000, 2050 and 2100, and for high activity periods in the agricultural calendar. The distribution of WBGT is divided into recommended maximum WBGT exposure levels (°C) at different work intensities, and rest/work ratios for an average acclimatized worker wearing light clothing (ISO, 18). High WBGTs are observed during the two periods of the East African. In February to March, eastern and coastal regions of Kenya and Tanzania witness high WBGT values—some necessitating up to 75% rest/hour work intensities in 2050 and 2100. In August to September, eastern and northern Kenya and north and central Uganda are vulnerable to high WBGT values. Designing policies to address this key challenge is a critical element in adaptation methods to address the impact of climate change.

Description: Micrometeorites (MMs) are small particles that account for most of the extraterrestrial material deposited on Earth. Synchrotron X-ray fluorescence and diffraction allowed for chemical and mineral characterization to distinguish MM from atmospheric particulate. The relative components of iron, nickel, and other elements were considered in the identification of ferrous MM while high amounts of titanium were considered an indication that the particles were of atmospheric origin. Out of 100 samples collected by high school students and teachers, eight were taken to a synchrotron for analysis. Of those eight, three exhibited extraterrestrial compositions. X-ray absorption near-edge structure analysis revealed that the same three samples contained sulfide, the main sulfur form constituent in MM. X-ray microdiffraction analysis showed the presence of the minerals pentlandite and forsterite. Collectively, these results support the extraterrestrial nature of the three particles.

Description: The Yiliu tungsten polymetallic deposit, located in the south central portion of the Nanling nonferrous metal metallogenic province in South China, is an area with common Yanshanian tectonothermal events. Early Yanshanian magmatism leads to the emplacement of voluminous tungsten-bearing granite intrusions, such as the Baoshan, Benggangling and Junye plutons, which are considered temporally and spatially associated with W-polymetallic mineralization in the Yiliu region. Here, we investigate the basic geological and petrological characteristics of the Junye granites, and present major and trace element geochemical data and bulk-rock Sr-Nd-Pb-Hf isotopic data to gain insight into the petrogenesis and tectonic setting of granitic intrusions in the region. The Junye granites are high-K calc-alkaline and metaluminous to weakly peraluminous [A/CNK = molar ratios of Al2O3/(CaO + Na2O + K2O) = 0.97–1.02] with enrichment in SiO2 (75.68–76.44 wt.%), relatively high total alkalis (K2O + Na2O = 8.06–8.45 wt.%) with K2O/Na2O ratios ranging from 1.12 to 1.42, and moderate Al2O3 (12.62–13.00 wt.%), but low in P2O5 (

Description: We used observational data and the results from a high-resolution numerical simulation model to analyze the occurrence and development of an extreme precipitation event in the Ili Valley, Xinjiang, China on 26 June 2015. We analyzed the horizontal wavelength, period, speed, ducting, energy propagation and feedback mechanism of inertial gravity waves. A low-level convergence line was formed in the valley by the northerly and westerly winds as a result of Central Asian vortices and the trumpet-shaped topography of the Ili Valley. There was sufficient water vapor in the valley for the precipitation event to develop. A mesoscale vortex formed and developed on the low-level convergence line and the rainfall was distributed either near the convergence line or the mesoscale vortex. The low-level convergence line and the uplift caused by the terrain triggered convection, and then the convection triggered waves at lower levels. The combination of ascending motion induced by the lower level waves and the mesoscale vortex led to the development of convection, causing the precipitation to intensify. When the convection moved eastward to Gongliu County, it was coupled with the ascending phase of upper level waves, causing both the convection and precipitation to intensify again. We applied spectral analysis methods to verify that the waves were inertial gravity waves. The upper level inertial gravity waves propagated westward at a mean speed of −12 m s−1 with periods of 73–179 min and horizontal wavelengths of 50–55 km. The lower level inertial gravity waves propagated eastward at a mean speed of 8 m s−1 with periods of 73–200 min and a horizontal wavelength of 85 km. The more (less) favorable waveguide conditions determined whether the gravity waves persisted for a long (short) time and propagated for a longer (shorter) distance. Based on the mesoscale Eliassen–Palm flux theory, the wave energy of inertial gravity waves had an important effect on the maintenance and development of convection and precipitation by affecting wind strength and wind divergence. Feedback was mainly through the meridional and vertical transport of zonal momentum and the meridional transport of heat.

Description: In convergent zones, several parts of the geodynamic system (e.g., continental margins, back-arc regions) can be deformed, uplifted, and eroded through time, each of them potentially delivering clastic sediments to neighboring basins. Tectonically driven events are mostly recorded in syntectonic clastic systems accumulated into different kinds of basins: trench, fore-arc, and back-arc basins in subduction zones and foredeep, thrust-top, and episutural basins in collisional settings. The most widely used tools for provenance analysis of synorogenic sediments and for unraveling the tectonic evolution of convergent zones are sandstone petrography and U–Pb dating of detrital zircon. In this paper, we present a comparison of previously published data discussing how these techniques are used to constrain provenance reconstructions and contribute to a better understanding of the tectonic evolution of (i) the Cretaceous transition from extensional to compressional regimes in the back-arc region of the southern Andean system; and (ii) the involvement of the passive European continental margin in the Western Alps subduction system during impending Alpine collision. In both cases, sediments delivered from the down-bending continental block are significantly involved. Our findings highlight its role as a detrital source, which is generally underestimated or even ignored in current tectonic models.