ALBERT

Description: Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May to September growing season (July or August median temperature, diurnal rate of change, and magnitude and timing of growing season maximum) chosen through principal component analysis of 78 candidate metrics. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance between points. Calculation of spatial autocorrelation based on travel or retention time in place of network distance yielded tighter-fitting Torgegrams with less scatter but did not improve overall model prediction accuracy. We predicted monthly median July or August stream temperatures as a function of median air temperature, estimated urban heat island effect, shaded solar radiation, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, and presence or maximum depth of a lake immediately upstream, with an overall root-mean-square prediction error of 1.4 and 1.5 ○ C, respectively. Growing season maximum water temperature varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for July or August daily range, maximum daily rate of change, and timing of growing season maximum were statistically significant but explained a much lower proportion of variance than the above models (5-14% of total) . This article is protected by copyright. All rights reserved.

Description: Spontaneous counter-current imbibition into a finite porous medium is an important physical mechanism for many applications, included but not limited to irrigation, CO 2 storage and oil recovery. Symmetry considerations that are often valid in fractured porous media allow us to study the process in a one-dimensional domain. In 1D, the onset of imbibition can be captured by self-similar solutions and the imbibed volume scales with . At later times, the imbibition rate decreases and the finite size of the medium has to be taken into account. This requires numerical solutions. Here, we present a new approach to approximate the whole imbibition process semi-analytically. While the onset is captured by a semi-analytical solution. We also provide an a priori estimate of the time until which the imbibed volume scales with . This time is significantly longer than the time it takes until the imbibition front reaches the model boundary. The remainder of the imbibition process is obtained from a self-similarity solution. We test our approach against numerical solutions that employ parametrizations relevant for oil recovery and CO 2 sequestration. We show that this concept improves common first order approaches that heavily underestimate early-time behaviour and note that it can be readily included into dual porosity models. This article is protected by copyright. All rights reserved.

Description: Water extraction for anthropogenic use has become a major flux in the hydrological cycle. With increasing demand for water and challenges supplying it in the face of climate change, there is a pressing need to better understand connections between human populations, climate, water extraction, water use, and its impacts. To understand these connections, we collected and analyzed stable isotopic ratios of more than 800 urban tap water samples in a series of semiannual water surveys (spring and fall, 2013 to 2015) across the Salt Lake Valley (SLV) of northern Utah. Consistent with previous work, we found that mean tap water had a lower 2 H and 18 O concentration than local precipitation, highlighting the importance of nearby montane winter precipitation as source water for the region. However, we observed strong and structured spatiotemporal variation in tap water isotopic compositions across the region which we attribute to complex distribution systems, varying water management practices and multiple sources used across the valley. Water from different sources was not used uniformly throughout the area and we identified significant correlation between water source and demographic parameters including population and income. Isotopic mass balance indicated significant inter- and intra-annual variability in water losses within the distribution network due to evaporation from surface water resources supplying the SLV. Our results demonstrate the effectiveness of isotopes as an indicator of water management strategies and climate impacts within regional urban water systems, with potential utility for monitoring, regulation, forensic and a range of water resource research. This article is protected by copyright. All rights reserved.

Description: As the prospect for more frequent and severe extreme weather events gains scientific support, many nations are evaluating mitigation and adaptation options. Insurance and home retrofits could reduce household welfare losses due to flood events. Yet, even after disasters, households often fail to take risk mitigation actions. This paper presents the first randomized field experiment that tests the effect of information provision on household uptake of flood insurance and home retrofits. A sample of 364 flood-prone households in Bangkok was randomly split into treatment and control groups. The treatment group received practical details on home retrofits and flood insurance as well as social information regarding the insurance purchase decisions of peers. Results indicate that the information intervention increased insurance purchases by about five percentage points, while no effect was detected for home retrofits. This effect is nearly equal to the increase in uptake that the national insurance program in Thailand has achieved through all other means since its establishment in 2012. If scaled up to include all uninsured, flood-prone households in Bangkok, nearly 70,000 additional households could be insured. The results suggest that well-designed information interventions could increase uptake of flood insurance, without additional premium subsidies or mandates. This article is protected by copyright. All rights reserved.

Description: The need to understand potential climate impacts and feedbacks in Arctic regions has prompted recent interest in modeling of permafrost dynamics in a warming climate. A new fine-scale integrated surface/subsurface thermal hydrology modeling capability is described and demonstrated in proof-of-concept simulations. The new modeling capability combines a surface energy balance model with recently developed three-dimensional subsurface thermal hydrology models and new models for nonisothermal surface water flows and snow distribution in the microtopography. Surface water flows are modeled using the diffusion wave equation extended to include energy transport and phase change of ponded water. Variation of snow depth in the microtopography, physically the result of wind scour, is modeled phenomenologically with a diffusion wave equation. The multiple surface and subsurface processes are implemented by leveraging highly parallel community software. Fully integrated thermal hydrology simulations on the tilted open book catchment, an important test case for integrated surface/subsurface flow modeling, are presented. Fine-scale 100-year projections of the integrated permafrost thermal hydrological system on an ice wedge polygon at Barrow Alaska in a warming climate are also presented. These simulations demonstrate the feasibility of microtopography-resolving, process-rich simulations as a tool to help understand possible future evolution of the carbon-rich Arctic tundra in a warming climate. This article is protected by copyright. All rights reserved.

Description: Over the last 15 years, Azerbaijan has doubled its protected areas territory to almost 10 % of the country’s surface area. Although the daily management of the existing strictly protected areas continues to pose a challenge, the establishment of nine national parks has been a remarkable achievement. At present, the designation of UNESCO biosphere reserves (BR) is the expressed political will of the government. By reviewing the last 25 years of nature conservation, and, in particular, the national park programme, we present an institutional analysis for integrative nature conservation in Azerbaijan. Against the cultural, social and political background, in particular the transition of the state, the challenges for cross-hierarchical governance of protected areas are outlined at the example of a BR. We find that the implementation of the UNESCO MAB philosophy, which is based on inter alia participation by administration, local communities and rural stakeholder, is hampered by this present-day political reality. The political situation and an autocratic governance scheme put successful implementation of a “model region of sustainable development” at risk. Education and empowerment are a prerequisite for eventual success; this is true for Azerbaijan as well as for other Post-Soviet countries with strong top-down regimes.

Description: A general probabilistic prediction network is proposed for hydrological drought examination and environmental flow assessment. This network consists of three major components. First, we present the joint streamflow drought indicator (JSDI) to describe the hydrological dryness/wetness conditions. The JSDI is established based on a high-dimensional multivariate probabilistic model. In the second part, a drought-based environmental flow assessment method is introduced, which provides dynamic risk-based information about how much flow (the environmental flow target) is required for drought recovery and its likelihood under different hydrological drought initial situations. The final part involves estimating the conditional probability of achieving the required environmental flow under different precipitation scenarios according to the joint dependence structure between streamflow and precipitation. Three watersheds from different countries (Germany, China, and United States) with varying sizes from small to large were used to examine the usefulness of this network. The results show that the JSDI can provide an assessment of overall hydrological dryness/wetness conditions and performs well in identifying both drought onset and persistence. This network also allows quantitative prediction of targeted environmental flow required for hydrological drought recovery and estimation of the corresponding likelihood. Moreover, the results confirm that the general network can estimate the conditional probability associated with the required flow under different precipitation scenarios. The presented methodology offers a promising tool for water supply planning and management and for drought-based environmental flow assessment. The network has no restrictions that would prevent it from being applied to other basins worldwide. This article is protected by copyright. All rights reserved.

Description: The repository concept for geological disposal of spent nuclear fuel in Sweden and Finland is planned to be constructed in sparsely fractured crystalline bedrock and with an engineered bentonite buffer to embed the waste canisters. An important stage in such a deep repository is the post-closure phase following the deposition and the backfilling operations when the initially unsaturated buffer material gets hydrated by the groundwater delivered by the natural bedrock. We use numerical simulations to interpret observations on buffer wetting gathered during an in situ campaign, the Bentonite Rock Interaction Experiment, in which unsaturated bentonite columns were introduced into deposition holes in the floor of a 417 m deep tunnel at the Äspö Hard Rock Laboratory in Sweden. Our objectives are to assess the performance of state-of-the-art flow models in reproducing the buffer wetting process and to investigate to which extent dependable predictions of buffer wetting times and saturation patterns can be made based on information collected prior to buffer insertion. This would be important for preventing insertion into unsuitable bedrock environments. Field data and modeling results indicate the development of a de-saturated zone in the rock and show that in most cases, the presence or absence of fractures and flow heterogeneity are more important factors for correct wetting predictions than the total inflow. For instance, for an equal open-hole inflow value, homogeneous inflow yields much more rapid buffer wetting than cases where fractures are represented explicitly thus creating heterogeneous inflow distributions. This article is protected by copyright. All rights reserved.

Description: Many aquifers that are highly contaminated by arsenic in South and Southeast Asia are in the floodplains of large river networks. Under natural conditions, these aquifers would discharge into nearby rivers; however large-scale groundwater pumping has reversed the flow in some areas so that rivers now recharge aquifers. At a field site near Hanoi Vietnam, we find river water recharging the aquifer becomes high in arsenic, reaching concentrations above 1000 μg/L, within the upper meter of recently (〈 ∼10 yrs ) deposited riverbed sediments as it is drawn into a heavily pumped aquifer along the Red River. Groundwater arsenic concentrations in aquifers adjacent to the river are largely controlled by river geomorphology. High (〉 50 μg/L) aqueous arsenic concentrations are found in aquifer regions adjacent to zones where the river has recently deposited sediment and low arsenic concentrations are found in aquifer regions adjacent to erosional zones. High arsenic concentrations are even found adjacent to a depositional river reach in a Pleistocene aquifer, a type of aquifer sediment which generally hosts low arsenic water. Using geochemical and isotopic data we estimate the in-situ rate of arsenic release from riverbed sediments to be up to 1000 times the rates calculated on inland aquifer sediments in Vietnam. Geochemical data for riverbed porewater conditions indicate that the reduction of reactive, poorly crystalline iron oxides controls arsenic release. We suggest that aquifers in these regions may be susceptible to further arsenic contamination where riverine recharge drawn into aquifers by extensive groundwater pumping flows through recently deposited river sediments before entering the aquifer. This article is protected by copyright. All rights reserved.

Description: Training image-based geostatistical methods are increasingly popular in groundwater hydrology even if existing algorithms present limitations that often make real-world applications difficult. These limitations include a computational cost that can be prohibitive for high-resolution 3D applications, the presence of visual artifacts in the model realizations, and a low variability between model realizations due to the limited pool of patterns available in a finite-size training image. In this paper, we address these issues by proposing an iterative patch-based algorithm which adapts a graph cuts methodology that is widely used in computer graphics. Our adapted graph cuts method optimally cuts patches of pixel values borrowed from the training image and assembles them successively, each time accounting for the information of previously stitched patches. The initial simulation result might display artifacts, which are identified as regions of high cost. These artifacts are reduced by iteratively placing new patches in high-cost regions. In contrast to most patch-based algorithms, the proposed scheme can also efficiently address point conditioning. An advantage of the method is that the cut process results in the creation of new patterns that are not present in the training image, thereby increasing pattern variability. To quantify this effect, a new measure of variability is developed, the merging index, quantifies the pattern variability in the realizations with respect to the training image. A series of sensitivity analyses demonstrates the stability of the proposed graph cuts approach, which produces satisfying simulations for a wide range of parameters values. Applications to 2D and 3D cases are compared to state-of-the-art multiple-point methods. The results show that the proposed approach obtains significant speedups and increases variability between realizations. Connectivity functions applied to 2D models transport simulations in 3D models are used to demonstrate that pattern continuity is preserved. This article is protected by copyright. All rights reserved.

Description: ABSTRACT We investigate the potential of integrating desalination to existing reservoir systems to mitigate supply uncertainty. Desalinated seawater and wastewater are relatively reliable but expensive. Water from natural resources like reservoirs is generally cheaper but climate sensitive. We propose combining the operation of a reservoir, and seawater and wastewater desalination plants for an overall system that is less vulnerable to scarcity and uncertainty, while constraining total cost. The joint system is modeled as a multi-objective optimization problem with the double objectives of minimizing risk and vulnerability, subject to a minimum limit on resilience. The joint model is applied to two cases, one based on the climate and demands of a location in India and the other of a location in California. The results for the Indian case indicate it possible for the joint system to reduce risk and vulnerability to zero given a budget increase of 20-120% under current climate conditions and 30-150% under projected future conditions. For the Californian case, this would require budget increases of 20-80% and 30-140% under current and future conditions respectively. Further, our analysis shows a two-way interaction between the reservoir and desalination plants where the optimal operation of the former is just as much affected by the latter as the latter by the former. This highlights the importance of an integrated management approach. This study contributes to a greater quantitative understanding of desalination as a redundancy measure for adapting water supply infrastructures for a future of greater scarcity and uncertainty. This article is protected by copyright. All rights reserved.

Description: The cosmic-ray neutron method was developed for intermediate-scale soil moisture detection, but may potentially be used for other hydrological applications. The neutron signal of different hydrogen pools is poorly understood and separating them is difficult based on neutron measurements alone. Including neutron transport modeling may accommodate this shortcoming. However, measured and modeled neutrons are not directly comparable. Neither the scale nor energy ranges are equivalent, and the exact neutron energy sensitivity of the detectors is unknown. Here, a methodology to enable comparability of the measured and modeled neutrons is presented. The usual cosmic-ray soil moisture detector measures moderated neutrons by means of a proportional counter surrounded by plastic, making it sensitive to epithermal neutrons. However, that configuration allows for some thermal neutrons to be measured. The thermal contribution can be removed by surrounding the plastic with a layer of cadmium, which absorbs neutrons with energies below 0.5 eV. Likewise, cadmium-shielding of a bare detector allows for estimating the epithermal contribution. First, the cadmium difference method is used to determine the fraction of thermal and epithermal neutrons measured by the bare and plastic-shielded detectors, respectively. The cadmium difference method results in linear correction models for measurements by the two detectors, and has the greatest impact on the neutron intensity measured by the moderated detector at the ground surface. Next, conversion factors are obtained relating measured and modeled neutron intensities. Finally, the methodology is tested by modeling the neutron profiles at an agricultural field site and satisfactory agreement to measurements is found. This article is protected by copyright. All rights reserved.

Description: Increasing environmental issues, economic and cultural differences, the regulatory framework and the great attention to social responsibility, have forced companies to face the challenge of sustainability. Thus, the adaption of a proper marketing strategy in order to achieve a sustainable competitive advantage in the market (specially export markets) has become an important principle in accepting companies. In this regard, this study is a systematic literature review, which investigated 102 published articles in international journals between 1964 and 2015. In the literature review, the main focus was on key issues such as sustainability, marketing strategies, financial and market export performance. In this article, at first, the concept of sustainability from different aspects was collected and described. Then, external factors, such as competitive intensity, public concern, regulations, technology, cultural/social, and economic, as well as internal factors, such as managers, employees, stakeholders, affecting sustainability, were studied. Finally, adaption of export marketing strategies in order to achieve sustainability export performance is discussed.

Description: This paper describes how energy policy can be evaluated in practice. The goal is to make policy makers aware of how an evaluation can be based on scientific principles and to help economists appreciate how an evaluation can be performed if strong empirical evidence is lacking. We have built a basic evaluation framework and have applied this to a selection of Dutch energy policy instruments, namely production subsidies and their complementary non-financial instrument, DEN-B. Our evaluation criterion was effectiveness, defined as the extent to which policy instruments contribute to achieving policy goals. The evaluation was based on existing ex post evaluations complemented by interviews and other available data. We conclude that production subsidies and DEN-B were (partly) effective. Furthermore, the Dutch government increased effectiveness by reconsidering the design of production subsidies. We also formulate lessons for future policy evaluations and energy markets policies.

Description: Many developing countries like Nigeria face serious challenges with regards to the provision of public housings for their citizenry. The purpose of this research article is to establish critical success factors (CSFs) for public housing projects (PHPs) in Nigeria. The data collections were performed in Nigeria using interviews, pilot survey and a questionnaire survey. The interviews were conducted with experts in housing so as to determine the sufficiency and appropriateness of the success attributes gathered from the literature review with respect to Nigerian situation. After analysing the results of the interviews, a draft questionnaire was prepared and pretested. Following a slight revision, a final version of the questionnaire was designed. A questionnaire survey was performed in which five hundred and fifty (550) questionnaires were distributed by means of stratified sampling techniques. The respondents were construction professionals with experience in PHPs implementation in Nigeria who work in developers’ companies, consultancy firms, contracting companies or public housing agencies. Two hundred and eighty-one copies of the administered questionnaires were completed and brought back, equivalent to 51 % response rate. Structural equation modelling technique was employed in the data analyses process. The study establishes seven CSFs for PHPs in Nigeria. These factors are: (1) institutional framework for public housing, (2) availability of competent personnel, (3) effective project management, (4) good maintenance management practice, (5) appropriate design and good location, (6) effective housing finance system and (7) adequate political support. The CSFs established in this study can serve as a guide to housing policy makers, public housing developers and project managers towards successful accomplishments of PHPs in Nigeria.

Description: Along-track multichannel synthetic aperture radar is usually used to achieve ground moving target detection and imaging. Nevertheless, there is a design dilemma between azimuth high resolution and wide swath (HRWS). To solve this problem in HRWS mode, we introduce a virtual multichannel (VMC) scheme. For each virtual channel, the low real pulse repetition frequency (PRF) improves the ability of resolving range ambiguity for wide-swath, and the high virtual PRF improves the capability of resolving Doppler ambiguity for azimuth high resolution. For multiple virtual channels, strong ground clutter is eliminated by the joint VMC processing. Furthermore, a detailed signal model of a moving target in the virtual channel is given, and the special false-peak effect in the azimuthal image is analyzed. Moreover, we propose a novel ground moving target processing method based on the VMC scheme and the clutter suppression interferometry (CSI) technique, which is called VMC-CSI. The integration of detection, location, velocity estimation, and imaging for ground moving targets can be achieved. Accounting for the unresolved main peak and false peak for a moving target, in the VMC-CSI method, we adopt a two-step scheme to estimate the radial velocity and along-track velocity, namely, rough estimation and precise estimation. Meanwhile, considering the same interferometric phases of the main peak and the false peak, we use false peaks first for the robustness of initial azimuth location estimation and remove false peaks afterward. Numerical simulations are provided for testing the effect of the false peak and the effectiveness of VMC-CSI.

Description: This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations' canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumping can be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.

Description: Multifrequency and multioffset ground-penetrating radar data acquisition modes are used to maximize the information content and parameter retrieval capabilities. However, they also increase the computational cost dedicated to the inversion procedure. In this paper, the impact of the number of frequencies and the multistatic configurations on the information retrieval capabilities is investigated through the response surface topographies of the objective functions. We resort to a full-wave-inversion procedure and a recently developed electromagnetic model which takes advantage of a closed-form solution of Maxwell's equations to describe the antenna–medium system. We show with numerical and laboratory experiments the possibility of reducing the number of frequencies from several hundreds to one or several tens of components without affecting the information retrieval capabilities. We also show through several scenarios that the presence of a perfect electrical conductor increases the number of frequencies required to ensure an acceptable retrieval of the subsurface properties whereas the conductivity of the first layer and the relative permittivity of the second layer do not affect it. The results highlight that information content analyses are important in order to study and optimize data acquisition and inversion procedures, and thereby the computation time.

Description: This paper addresses some important aspects for the spaceborne/stationary bistatic synthetic aperture radar (SAR) (SS-BiSAR) imaging with the transmitter, TerraSAR-X, operated in staring spotlight (ST) mode. With the large integration time reaching 7.5 s and the azimuth steering span reaching $pm 2.2^{0}$ , several significant effects occur, including troposphere delay, precision phase and time synchronization, the curved orbit effect, azimuth spectrum aliasing problem, and efficient frequency domain focusing algorithm. To circumvent the main effects, corresponding solutions are proposed, including a precise synchronization strategy with troposphere delay correction based on the direct signal from the transmitter and a modified and integrative bistatic polar format algorithm (PFA). This paper covers the theoretical development, implementation, and analysis of the SS-BiSAR PFA based on 2-D fast Gaussian gridding nonuniform fast Fourier transform with wavefront curvature correction. Furthermore, the high-resolution ST-mode SS-BiSAR image processed by the proposed algorithm is acquired, and the differences of scattering behaviors between monostatic and bistatic SAR images are analyzed in detail.

Description: The ground penetrating radar (GPR) is a popular and successful remote sensing modality that has been investigated for landmine detection. GPR offers excellent detection performance, but it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR, but they do not yet achieve comparable detection performance. This paper proposes a new sensor management approach, called multistate management (MSM), which combines large and short standoff sensors on the same platform in a way that leverages their respective advantages, yielding a system with better ROA and detection performance. MSM is more difficult to analyze than traditional systems because it allows sensor activity and system velocity to change over time. Therefore, a new probabilistic model based on queuing theory, called Q-MSM, is also proposed for analyzing and designing detection systems operating with MSM. Simulations were conducted using real field-collected data for a system with a large standoff forward-looking infrared camera and a GPR. The system is operated with MSM, and the results show that this leads to better ROA and detection performance than can be attained otherwise. Furthermore, the results show that Q-MSM can accurately predict the behavior of the MSM system, validating its utility for analyzing and designing such systems.

Description: The concept of simultaneous source has recently become of interest in seismic exploration, due to its efficient or economic acquisition or both. The blended data overlapped between shot records are acquired in simultaneous source acquisition. Separating the blended data and recovering the single-shot seismic signals (the recovery) are of great importance in the scenario of current workflows, which can be called seismic simultaneous source separation. In the context of general random time-dithering firing, we propose an alternative method to separate the blended data by combining patchwise dictionary learning with sparse inversion, in which the dictionary is directly learned from the measured blended data. Apart from the sparse coding used for the coefficients, an additional regularization term on the dictionary is particularly designed to remove the severe interference noise. The efficient and flexible alternating direction method of multipliers (ADMM) is used to update the dictionary in the used alternating optimization scheme. The results obtained from the synthetic and real examples reasonably suggest that the separated seismic signals by using dictionary learning are more accurate and robust compared with that using the fixed transform basis, such as the local discrete cosine transform. The learned dictionary tailors for the recovery and is similar to the local seismic waveform, which improves the sparsity of the recovery substantially and is highly advantageous for producing the promised results.

Description: This paper investigates the impact of the soil moisture distribution in the top layer on the accuracy of soil moisture retrieval by microwave remote sensing methods. We modeled soil emission at L-band by coherent and noncoherent models for the different moisture distributions in the top layer. As a result, it is found that, at high moisture gradients, the difference between average moisture within the sensing depth at L-band and the moisture retrievable from remote sensing data can be more than 20% in absolute terms. In addition, high differences between Soil Moisture and Ocean Salinity (SMOS) Level 2 data and the in situ measurements were revealed in cases of high gradients. Such high gradients may be observed during some time in the top layer of the drying soil after rainfall. These differences are significantly more than the accuracy declared by SMOS development team. We proposed a simple method that allows the assessment of the type of soil moisture profile by SMOS and Global Change Observation Mission‐Water “SHIZUKU” (GCOM-W1) satellites data. The procedure for simple processing of data of the two satellites is described. In addition, we compared the type of soil moisture profile retrieved from satellite data and the soil moisture profile found by in situ measurements.

Description: An iterative alternating direction method of multipliers (ADMM) is proposed for inverse finite-element–boundary-integral (FE–BI) problem with total variation (TV) regularization. The inverse FE–BI fits to a wide class of penetrable sensing applications, where this study specifically targets the problem of radio tomography of asteroid interior structure using orbiting spacecraft. The TV regularizer enforces sparsity on the gradient of reconstructed permittivity, which agrees well with the “piecewise constant” reality of “rocks embedded in soil” scenario and, meanwhile, addresses the inherent ill-posedness. For large-scale asteroid problems, the distributed ADMM algorithm is adapted to solve the linear TV inversion at each iteration. The 2-D inversion is validated with the Fresnel Institute measurement data. Simulated cases of asteroid internal imaging are also presented. The proposed iterative ADMM can be also applied to similar penetrable imaging applications.

Description: GNSS Reflectometry, Scatterometry and Radio Occultation aboard ISS is the mission concept under study within the European Space Agency. Its core payload consists of an interferometric GNSS-Reflectometry ocean altimeter/scatterometer which does not need to generate any clean replicas of the GNSS codes for its operation. This paper describes a new interferometric technique by which such payload could also perform radio occultation as an add-on, without requiring any additional hardware resources, like the generation of clean code replicas or a storage of them. Two possibilities are studied. The first one consists of performing the complex autocorrelation function of the received signal transmitted by a rising or setting GNSS satellite. The autocorrelation function is evaluated around time epochs that are multiples of the period of suitable codes found in the modulation of the navigation signals. Satellite discrimination has to be performed spatially, through the antenna pattern. The second possibility consists in acquiring the reference signal separately from the occultation event which, in turn, has two options depending upon the geometry at which the reference is recorded: zenithal and top of the atmosphere. The signal-to-noise ratio, the satellite discrimination, and the impact of clock errors are assessed.

Description: Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high - ccuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

Description: Classification of hyperspectral images usually suffers from high dimensionality and few reference data, which limits the performance of the pixelwise classifiers. The spectral–spatial classifiers, which integrate the spectral data and the spatial information during the classification, perform impressively in terms of the high classification accuracy and the homogeneous appearance of the classification map. In this paper, we propose a new probabilistic framework for spectral–spatial classification (PFSSC), which integrates the spectral data and the spatial information from the probabilistic point of view. Both the spectral data and the spatial information are used to estimate the per-pixel probability, which gives the likelihood that one pixel belongs to one class, respectively. The classification map can then be directly derived from the joint probability. In the proposed framework, a pixelwise probabilistic classifier can be extended as a spectral–spatial one since it can integrate spatial information easily. Furthermore, these spectral–spatial classifiers in the proposed framework are realized in an iterative way to avoid the problem caused by the limited reference data to some extent. In each iterative step, some unassigned pixels are classified by considering the pixels assigned in previous iterative steps. In this iterative process, pixels are assigned to specific labels step by step gradually. In the proposed framework, the probabilistic support vector machine (SVM) and random forest (RF) are extended to be two spectral–spatial classifiers. In short, we denote them as SVM-PFSSC and RF-PFSSC, respectively. The experimental results show that SVM-PFSSC and RF-PFSSC outperform some pixelwise and spectral–spatial classifiers.

Description: Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.

Description: The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

Description: Target detection in hyperspectral images (HSIs) is an active area of research; it seeks to detect objects that are small in both number and size within a scene. The proposed work presents a new methodology for target detection in HSIs by combining kurtosis, level sets, and a size-based thresholding strategy. Kurtosis is used as a preprocessing step to initially enhance the targets in an image. Then, level sets identify and mark associations of pixels with similar spectral information as candidate targets. Finally, the size-based thresholding strategy detects true targets and discards false alarms that do not fit with target dimensions set as input parameter. In addition, we propose a novel version of level sets, which is suitable for target detection tasks in HSIs. Results show that the proposed algorithm could successfully detect targets in HSIs, and it gave better performance in terms of the receiver operating characteristic curve than other techniques widely used in target detection such as orthogonal subspace projection, constrained signal detector, constrained energy minimization, adaptive cosine/coherent estimator algorithm, and generalized-likelihood ratio test.

Description: A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Description: Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

Description: Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981–1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000–2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps ( $text{RMSE}=0.7848$ and $R^{2}=0.8095$ ) than the GEOV1 LAI values ( $text{RMSE}=0.9084$ and $R^{2}=0.7939$ ) and the MOD15 LAI values ( $text{RMSE}=1.1173$ and $R^{2}=0.6705$ ).

Description: A novel approach called Spectral–Spatial 1-D Manifold Embedding (SS1DME) is proposed in this paper for remotely sensed hyperspectral image (HSI) classification. This novel approach is based on a generalization of the recently developed smooth ordering model, which has gathered a great interest in the image processing area. In the proposed approach, first, we employ the spectral–spatial information-based affinity metric to learn the similarity of HSI pixels, where the contextual information is encoded into the affinity metric using spatial information. In our derived model, based on the obtained affinity metric, the created multiple 1-D manifold embeddings (1DMEs) consist of several different versions of 1DME of the same set of all HSI points. Since each 1DME of the data is a 1-D sequence, a label function on the data can be obtained by applying the simple 1-D signal processing tools (such as interpolation/regression). By collecting the predicted labels from these label functions, we build a subset of the current unlabeled points, on which the labels are correctly labeled with high confidence. Next, we add a proportion of the elements from this subset to the original labeled set to get the updated labeled set, which is used for the next running instance. Repeating this process for several loops, we get an extended labeled set, where the new members are correctly labeled by the label functions with much high confidence. Finally, we utilize the extended labeled set to build the target classifier for the whole HSI pixels. In the whole process, 1DME plays the role of learning data features from the given affinity metric. With the incrementation of learning features during iteration, the proposed scheme will gradually approximate the exact labels of all sample points. The proposed scheme is experimentally demonstrated using four real HSI data sets, exhibiting promising classification performance when compared with other recently introduced - patial analysis alternatives.

Description: We evaluate the potential of troposphere models derived from ground meteorological data (pressure, temperature, and relative humidity) and Global Positioning System (GPS) data to improve InSAR measurements and models derived from them. We test this approach on an ERS-2/Envisat data set collected during a transient surface deformation episode that occurred from January to July 2005 in the San Gabriel Valley, southern California, USA. We find that the interferometric phase change observed over the corresponding period cannot be solely attributed to hydrological uplift associated with rising groundwater levels but also includes a significant contribution from differential tropospheric delay due to differing quantities of water vapor in the troposphere on the two SAR observation dates. We show that, if the tropospheric phase contribution is mistakenly interpreted as the range change associated with changes in groundwater storage, both the surface displacement and the groundwater storage coefficient may be overestimated by up to 30%. This method could be applied in real time where meteorological measurements are available near one or more GPS permanent site(s).

Description: In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

Description: This paper presents a method for estimating the solar transmittance of urban trees using airborne light detection and ranging (LiDAR), and the radiative transfer simulation of vegetation. The leaf area density (LAD) distribution of trees with voxel size $1 text{m}times 1 text{m}times 0.5 text{m}$ is estimated using high-resolution and multireturn airborne LiDAR data. The LAD of voxels having few incident laser beams is corrected from the surrounding voxels. The LAD of the periphery of the crown is discretized into $0.5 text{m}times 0.5 text{m}times 0.5 text{m}$ voxels to accurately calculate the shaded area. The resulting LAD distribution is used in a radiative transfer simulation to calculate the solar transmittance of the trees. We verified the accuracy of the calculated transmittance by comparing it with empirical data for a Zelkova serrata . The comparisons were conducted under different angles of incidence of laser beams and solar radiation. When the angle between the incident laser beams and solar radiation was small, the transmittance could be accurately estimated. The LAD correction enabled the method to be applied to a broader range of the angle between beams and solar radiation. When the zenith angle of the incident laser beams was small $(< 10^{circ})$ and the LAD correction was carried out, the errors in transmittance were within 0.06 for solar altitudes greater than 40°. Next, we examined the difference in solar transmittance among streets caused by the layout of trees and buildings and the growth condition of the trees. It was shown that the present method is able to quantify the solar shading provided by urban trees and take into account LAD, tree layout, and the spatial geometry of the surr- unding buildings.

Description: This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.

Description: Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day–night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection” approach from FILDA that lowers the 4- $mutext{m}$ brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4- $mutext{m}$ BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night.

Description: Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral–spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral–spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods.

Description: Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

Description: This paper analyzes the availability and accuracy of coastal altimetry sea level products in the Strait of Gibraltar. All possible repeats of two sections of the Envisat and AltiKa ground-tracks were used in the eastern and western portions of the strait. For Envisat, along-track sea level anomalies (SLAs) at 18-Hz posting rate were computed using ranges from two sources, namely, the official Sensor Geophysical Data Records (SGDRs) and the outputs of a coastal waveform retracker, the Adaptive Leading Edge Subwaveform (ALES) retracker; in addition, SLAs at 1 Hz were obtained from the Centre for Topographic studies of the Ocean and Hydrosphere (CTOH). For AltiKa, along-track SLA at 40 Hz was also computed both from SGDR and ALES ranges. The sea state bias correction was recomputed for the ALES-retracked Envisat SLA. The quality of these altimeter products was validated using two tide gauges located on the southern coast of Spain. For Envisat, the availability of data close to the coast depends crucially on the strategy followed for data screening. Most of the rejected data were due to the radar instrument operating in a low-precision nonocean mode. We observed an improvement of about 20% in the accuracy of the Envisat SLAs from ALES compared to the standard (SGDR) and the reprocessed CTOH data sets. AltiKa shows higher accuracy, with no significant differences between SGDR and ALES. The use of products from both missions allows longer times series, leading to a better understanding of the hydrodynamic processes in the study area.

Description: This paper describes the analysis performed on coherent simultaneously recorded monostatic and bistatic sea clutter data. The data were generated using a networked pulsed radar system, namely, NetRAD. This analysis is completed in both the temporal and Doppler domains, and the parameters characterized are compared between multiple bistatic angles and different polarizations. The K-distribution model is used to assess the variation in the clutter amplitude statistics between multiple bistatic data and the corresponding monostatic data. Key characteristics of the Doppler data, such as the spectrum width, center of gravity (CoG), and variance of the spectral width, are evaluated as a function of bistatic angle allowing novel relationships to be defined. The results conclude that the bistatic Doppler data have a lower K-distribution shape parameter in the majority of bistatic angles compared with the simultaneous monostatic data. In addition, novel trends in the relationship between the clutter spectrum CoG and the clutter intensity are presented.

Description: Icebergs represent hazards to maritime traffic and offshore operations. Satellite synthetic aperture radar (SAR) is very valuable for the observation of polar regions, and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this paper, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross-polarization and higher cross- over copolarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. First, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high-resolution data. Subsequently, a more exhaustive analysis is carried out using dual-polarization ground-detected Sentinel-1a extra wide swath images acquired over the time span of two months. The test areas are in the east coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross-polarization channel are carried out, exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection.

Description: A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

Description: Debris flows are a typical hazard on steep slopes after wildfire, but unlike debris flows that mobilize from landslides, most post-wildfire debris flows are generated from water runoff. The majority of existing debris-flow modeling has focused on landslide-triggered debris flows. In this study we explore the potential for using process-based rainfall-runoff models to simulate the timing of water flow and runoff-generated debris flows in recently burned areas. Two different spatially distributed hydrologic models with differing levels of complexity were used: the full shallow water equations and the kinematic wave approximation. Model parameter values were calibrated in two different watersheds, spanning two orders of magnitude in drainage area. These watersheds were affected by the 2009 Station Fire in the San Gabriel Mountains, CA, USA. Input data for the numerical models were constrained by time series of soil moisture, flow stage, and rainfall collected at field sites, as well as high-resolution lidar-derived digital elevation models. The calibrated parameters were used to model a third watershed in the burn area, and the results show a good match with observed timing of flow peaks. The calibrated roughness parameter (Manning's $n$) was generally higher when using the kinematic wave approximation relative to the shallow water equations, and decreased with increasing spatial scale. The calibrated effective watershed hydraulic conductivity was low for both models, even for storms occurring several months after the fire, suggesting that wildfire-induced changes to soil-water infiltration were retained throughout that time. Overall the two model simulations were quite similar suggesting that a kinematic wave model, which is simpler and more computationally efficient, is a suitable approach for predicting flood and debris flow timing in steep, burned watersheds. This article is protected by copyright. All rights reserved.

Description: We analyze the probability distribution of the hazard attenuation factor for a non-carcinogenic reactive compound captured by a well in heterogeneous porous formations. The hazard attenuation factor is defined as the ratio between the hazard index HI at a detection well and at the source. Heterogeneity of the aquifer is represented through the Multi-Indicator Model (a collection of blocks of independent permeability) while flow and transport are solved by the means of the Self-Consistent Approach, that is able to deal with any degree of heterogeneity. Due to formation heterogeneity, HI is a random variable and similar for hazard attenuation index. The latter can be fully characterized by its cumulative distribution function (CDF), which in turn can be related to the statistics of the travel time of solute particles, from the source to the detection well. The approach is applied to the case of a solute which undergoes decay and a well with a screen much smaller than the correlation scale of hydraulic conductivity. The results show that the probability of exceeding a given acceptable threshold of the hazard index is significantly affected by the level of heterogeneity comparable to the one observed for the MADE site, and the distance between the source and the well. This article is protected by copyright. All rights reserved.

Description: Transit times through hydrologic systems vary in time, but the nature of that variability is not well understood. Transit times variability was investigated in a 1 m 3 sloping lysimeter, representing a simplified model of a hillslope receiving periodic rainfall events for 28 days. Tracer tests were conducted using an experimental protocol that allows time-variable transit time distributions (TTDs) to be calculated from data. Observed TTDs varied with the storage state of the system, and the history of inflows and outflows. We propose that the observed time variability of the TTDs can be decomposed into two parts: ‘internal' variability associated with changes in the arrangement of, and partitioning between, flow pathways; and ‘external' variability driven by fluctuations in the flow rate along all flow pathways. These concepts can be defined quantitatively in terms of rank StorAge Selection (rSAS) functions, which is a theory describing lumped transport dynamics. Internal variability is associated with temporal variability in the rSAS function, while external is not. The rSAS function variability was characterized by an ‘inverse storage effect', whereby younger water is released in greater proportion under wetter conditions than drier. We hypothesize that this effect is caused by the rapid mobilization of water in the unsaturated zone by the rising water table. Common approximations used to model transport dynamics that neglect internal variability were unable to reproduce the observed breakthrough curves accurately. This suggests that internal variability can play an important role in hydrologic transport dynamics, with implications for field data interpretation and modeling. This article is protected by copyright. All rights reserved.

Description: In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g. in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multi-objective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design , i.e. the optimal location of pressure meters , using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multi-objective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks. This article is protected by copyright. All rights reserved.

Description: In this paper, a methodology is developed to identify consistency of rating curve data based on a quality analysis of model results. This methodology, called Bidirectional Reach (BReach), evaluates results of a rating curve model with randomly sampled parameter sets in each observation. The combination of a parameter set and an observation is classified as non-acceptable if the deviation between the accompanying model result and the measurement exceeds observational uncertainty. Based on this classification, conditions for satisfactory behavior of a model in a sequence of observations are defined. Subsequently, a parameter set is evaluated in a data point by assessing the span for which it behaves satisfactory in the direction of the previous (or following) chronologically sorted observations. This is repeated for all sampled parameter sets and results are aggregated by indicating the endpoint of the largest span, called the maximum left (right) reach. This temporal reach should not be confused with a spatial reach (indicating a part of a river). The same procedure is followed for each data point and for different definitions of satisfactory behavior. Results of this analysis enable the detection of changes in data consistency. The methodology is validated with observed data and various synthetic stage-discharge data sets and proves to be a robust technique to investigate temporal consistency of rating curve data. It provides satisfying results despite of low data availability, errors in the estimated observational uncertainty and a rating curve model that is known to cover only a limited part of the observations. This article is protected by copyright. All rights reserved.

Description: With population growth, increasing water demands and climate change the need to understand the current and future pathways to water security is becoming more pressing. To contribute to addressing this challenge, we examine the link between water stress and society through socio-hydrological modeling. We conceptualize the interactions between an agricultural society with its environment in a stylized way. We apply the model to the case of the ancient Maya, a population that experienced a peak during the Classic Period (AD 600-830) and then declined during the ninth century. The hypothesis that modest drought periods played a major role in the society's collapse is explored. Simulating plausible feedbacks between water and society we show that a modest reduction in rainfall may lead to an 80% population collapse.Population density and crop sensitivity to droughts, however, may play an equally important role. The simulations indicate that construction of reservoirs results in less frequent drought impacts, but if the reservoirs run dry, drought impact may be more severe and the population drop may be larger. Index terms: 1812 Drought (4303) 1834 Human impacts (4323) 4330 Vulnerability. Keywords: socio-hydrology, Ancient Maya, drought, vulnerability. This article is protected by copyright. All rights reserved.

Description: Reliable characterization of hydraulic parameters is important for the understanding of groundwater flow and solute transport. The normal-score ensemble Kalman filter (NS-EnKF) has proven to be an effective inverse method for the characterization of non-Gaussian hydraulic conductivities by assimilating transient piezometric head data, or solute concentration data. Groundwater temperature, an easily captured state variable, has not drawn much attention as an additional state variable useful for the characterization of aquifer parameters. In this work, we jointly estimate non-Gaussian aquifer parameters (hydraulic conductivities and porosities) by assimilating three kinds of state variables (piezometric head, solute concentration, and groundwater temperature) using the NS-EnKF. A synthetic example including seven tests is designed, and used to evaluate the ability to characterize hydraulic conductivity and porosity in a non-Gaussian setting by assimilating different numbers and types of state variables. The results show that characterization of aquifer parameters can be improved by assimilating groundwater temperature data and that the main patters of the non-Gaussian reference fields can be retrieved with more accuracy and higher precision if multiple state variables are assimilated. This article is protected by copyright. All rights reserved.

Description: Since the 1970s, debates on “population growth” and “sustainability” have fluctuated markedly between pessimistic forecasts and optimistic certainties. Nowadays, they have reached a strange, comfortable illusion according to which the demographic future of our planet does not seem to be a problem at all. Nevertheless, median population growth projections by 2100 indicate that an arrest of the increase by the end of the century is rather unlikely. The future prospect is made even more critical by the fact that the increase will occur in what can be called demographic “Growing spots,” strongly opposed to just as many “Aging spots.” In these terms, therefore, the future demographic dynamics will certainly pose a challenge to the “population growth-sustainability” combination, for which it will be necessary to develop a new paradigm capable of operating in a “strong transcalar perspective,” within a global space that will increasingly acquire characteristics of fluidity and changeability.

Description: Automatic target generation process (ATGP) has been widely used for unsupervised target detection. However, as designed, it detects targets using full-band information. Unfortunately, on many occasions, various targets can be detected using varying bands, and ATGP can only provide one-shot target detection with all bands being used. This paper develops a new approach which can implement ATGP bandwise in a progressive manner, called progressive band processing of ATGP (PBP-ATGP) so that ATGP can be carried out band by band. Since PBP-ATGP must repeatedly implement orthogonal projections, recursive equations are further derived for PBP-ATGP, to be called recursive band processing of ATGP (RBP-ATGP) which can implement PBP-ATGP recursively. As a result, many advantages can be benefited from RBP-ATGP. Most importantly, RBP-ATGP can generate 3-D interband progressive profiles from band to band that can be used for progressive target detection, a task for which no target detection techniques using full-band information can provide.

Description: With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.

Description: The Clouds and the Earth's Radiant Energy System (CERES) instrument requires in-flight calibration and validation to maintain its accuracy during orbit operations over an extended period. An internal calibration system provides calibration for the three channels; however, there is no device for calibration of the shortwave response of the total channel. A three-channel comparison technique has been developed to calibrate the shortwave response of the total channel using the tropical oceans as a vicarious calibration target. The difference between day and night outgoing longwave radiances (OLR) averaged over the tropical oceans is used to validate the day OLR. This paper evaluates the efficacy of the technique. A relation is computed at night between the window channel radiance and the OLR retrieved from the total channel for each month for each instrument. The relation has a standard deviation of 0.28 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ . Given 120 months of data, the precision of the curved line faired through these data is better than 0.05 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ . A bias is found between FM-1 and FM-3 of 0.3 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ , which is taken to be the accuracy with which the total channels can be calibrated with the internal blackbodies. This result includes the differences of longwave spectral responses of the instruments. The tropical mean OLR is between 87.4 and 90.2 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ at night, with a standard deviation of 0.44 for FM-1 and 0.47 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ for FM-3. The avera- e difference between day and night tropical mean from the four instruments is $0.6pm 0.09 text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ over their data periods.

Description: The problem of supervised classification of multiresolution images, which are composed of a higher resolution panchromatic channel and of several coarser resolution multispectral channels, is addressed in this paper by proposing a novel contextual method based on Markov random fields. The method iteratively exploits a linear mixture model for the relationships between data at different resolutions and a graph cut approach to Markovian energy minimization to generate a contextual classification map at the highest resolution available in the input data set. The estimation of the parameters of the method is performed by extending recently proposed techniques based on the expectation-maximization and Ho–Kashyap's algorithms. The method is experimentally validated with semisimulated and real data involving both IKONOS and Landsat-7 ETM+ images, and the results are compared with those generated by previous approaches to the classification of multiresolution imagery.

Description: Phased array weather radars, particularly with high temporal resolution, essentially need a robust and fast beamformer to accurately estimate precipitation profiles such as reflectivity and Doppler velocity. In this paper, we introduce a neural-network-based beamformer to address this problem. In particular, the optimum weight vector is computed by modeling the problem as a three-layer radial basis function neural network (RBFNN), which is trained with I/O pairs obtained from the optimum Wiener solution. The RBFNN was chosen because of its characteristic of accurate approximation and good generalization, and its robustness against interference and noise. The proposed RBFNN beamforming method is compared with traditional beamforming methods, namely, Fourier beamforming (FR), Capon beamforming, and the flower pollination algorithm (FPA), which is a recently proposed nature-inspired optimization algorithm. It is shown that the RBFNN approach has nearly optimal performance in various precipitation radar signal simulations relative to the rival methods. The validity of the RBFNN beamformer is demonstrated by using real weather data collected by the phased array radar (PAR) at Osaka University, and compared with, in addition to the FR and FPA methods, the minimum mean square error beamforming method. It is shown that the RBFNN method estimates the reflectivity of the PAR at Osaka University with less clutter level than those of the other three methods.

Description: The detection of ground moving targets with arbitrary linear motion from an airborne multichannel radar via a long coherent processing interval is considered. The technique maximizes the target signal energy in synthetic aperture radar (SAR) images by matching to the target's linear motion profile. A reparameterization of the target's linear motion is developed that decouples the array processing from the image formation process. An explicit approach to forming SAR images focused with respect to a particular along-track velocity is presented. The ground clutter is canceled via an adaptive array technique, which also yields an estimate of the target radial velocity. Because the algorithm generates estimates of the target's along-track and cross-track velocity components, a unique determination of the target's motion parameters is possible. The approach is derived and investigated via simulated point spread functions. Algorithm performance is demonstrated on the GOTCHA SAR ground moving target indication data set.

Description: Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.

Description: The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary-correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary-correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects.

Description: The Global Navigation Satellite System Occultation Sounder (GNOS) has been planned for the five Feng-Yun 3 series (FY3) weather satellites since 2013, the first of which, the FY3C satellite, was launched successfully at 03:07 UTC on September 23, 2013 from the Taiyuan Satellite Base, Shanxi province, China, into the orbit of 836-km altitude and 98.75° inclination. In addition to the Global Positioning System (GPS), the FY3C/GNOS is capable of tracking the occultation signal of the BeiDou Navigation Satellite System (BDS) (also called COMPASS) from space for the first time. The quality of BDS radio occultation (RO) has been verified in terms of signal-to-noise ratio. In this paper, the electron density profiles (EDPs) observed by FY3C/GNOS from both GPS RO and BDS RO, which were processed and archived in the National Satellite Meteorological Center of China Meteorological Administration, are compared with 32 globally distributed ionosonde observations, and then, we compare GPS RO EDPs with ionosonde observations at Mohe (52.0° N, 122.5° E), Beijing (40.3° N, 116.2° E), Wuhan (31.0° N, 114.5° E), and Sanya (18.3° N, 109.6° E). FY3C/GNOS EDPs show good agreement with ionosonde measurements, with larger discrepancies near the equatorial ionization anomaly region at Wuhan and Sanya. The ionospheric peak density (NmF2) and peak height (hmF2) derived from FY3C/GNOS EDPs are also compared with those obtained from the globally distributed ionosondes for the day of year 274–365 in 2013. In general, NmF2 and hmF2 have a higher correlation coefficient in the middle–high latitude than in the lower latitude region, due to the difference of ionospheric horizontal inhomogeneity. We also compared the NmF2 and hmF2 maps between FY3C/GNOS and the International Reference Ionosphere 2012 (IRI-2012) model. However, the wavenumber-4 structure, which can be indicated clearly from FY3C/GNOS observations, could not- be reproduced well by IRI-2012. Further investigations show that the nighttime EDPs have obvious ionization enhancement around the ionospheric E layer over the Aurora and the South Atlantic Anomaly regions due to the energetic particle precipitation indicated by the Space Environment Monitor observations onboard FY3C.

Description: Clouds often limit the ability of optical satellite sensors (such as the newly launched Gaofen-1 (GF-1) satellite in China) to observe regional soil moisture at high spatial resolutions, especially under full-cloud-contamination condition. Thus, accurate reconstruction of regional soil moisture in this case became a great methodological challenge because of the complexity and ill-posed nature of the problem. In this paper, we present a Satellite and In situ sensor Collaborated Reconstruction (SICR) method. In this method, four reconstruction rules were proposed to rebuild four kinds of corresponding missing pixels, defined as follows: C1 pixel (including one in situ sensor in its area), C2 pixel (physically similar to C1), C3 pixel (with a regular soil moisture observation sequence), and C4 pixel (remaining). By analyzing soil moisture observation relationships between these four types of pixels with in situ measurements and within these pixels, four numerical reconstruction rules were established. Linear regression, similar pixel determination, least square method, and geostatistical interpolation algorithms were used in these four rules. At last, all blank soil moisture pixels in the target soil moisture image can be filled by the SICR method. The experiment conducted in the central south of U.S. integrated 11 in situ soil moisture sensors from the United States Department of Agriculture with 11 GF-1 satellite soil moisture images. It was demonstrated that GF-1 soil moisture observations on October 17, 2014, were successfully reconstructed by the SICR method, based on the evaluations of visual appearance comparison, error distribution analysis, subimage comparison, average relative error, and universal image quality index. SICR also performed better than the reconstruction results only based on in situ or satellite sensor data. Moreover, the comparison with the soil moisture obs- rvation from the microwave sensor demonstrated the value of SICR in regional high-resolution soil moisture reconstruction. It was suggested that the SICR method provided an effective reconstruction method under full cloud contamination and showed great potential for collaborating satellite and in situ sensors.

Description: Multibaseline 2-D phase unwrapping (PU) is a critical step for the multibaseline synthetic aperture radar interferometry. Compared with the single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, i.e., it is applicable to the terrain with the violent change. However, the performance of the multibaseline PU is directly related to noise level. In order to improve the noise robustness of the multibaseline PU, in this paper, we transplant the framework of the single-baseline PU into the multibaseline PU and propose a two-stage programming approach, referred to as TSPA, which makes use of the gradient information of the interferogram similar to how the conventional single-baseline PU method does. Fortunately, although the proposed method belongs to the integer programming (usually, the integer programming is an NP-hard problem which is hard to solve), the constraint of the optimization model of the TSPA method is unimodular, so it can be efficiently solved. Furthermore, interestingly, some useful and important concepts of the single-baseline PU, for example, residue and branch cut, are also transplanted into the multibaseline PU in this paper, and we discuss the potential of extending most of the representative single-baseline PU methods into the multibaseline domain as well. Finally, the experiment results show the effectiveness and noise robustness of the TSPA multibaseline PU method.

Description: The high-spatial-resolution aerosol retrieval algorithm using Chinese High-Resolution Earth Observation Satellite I (GF-1) wide-field images is developed, which retrieves the aerosol optical depth (AOD) over China for studying the impact of aerosol on climatic and environmental change. The algorithm is based on the red/blue surface reflectance correlations and the lookup table method. To reduce the enormous relative error caused by the constant surface reflectance relationship in the retrieval algorithm, the correlation is parameterized as a function of low, medium, and high values of normalized difference vegetation index (NDVI). Three linear relationships are simulated using MODIS BRDF-adjusted reflectance products (MCD43A4), and MODIS NDVI products are used to ascertain the value of NDVI. By applying the present algorithm to GF-1 images, two different aerosol cases of clear and turbid are analyzed to test the algorithm. Compared with the 10-km MODIS aerosol properties productions, the GF-1 retrieved AOD by our algorithm revealed a significant correlation coefficient with MODIS Dark Target AOD $(R=0.912)$ and Deep Blue AOD $(R=0.895)$ . Otherwise, the retrieved AOD results are found to be highly correlated with Aerosol Robotic Network (AERONET) sunphotometer observations $(R=0.931)$ . Compared with the results relying on the MODIS surface reflectance model, preliminary validation is encouraging that the method based on our updated surface reflectance assumptions successfully improved the accuracy, particularly under the clear sky background and over bright surface.

Description: Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.

Description: In this paper, for a multichannel synthetic aperture radar–ground moving target indication (SAR--GMTI) system, a new high-accuracy focusing and relocating method using instantaneous interferometry, i.e., carrying out interferometry operation in the azimuth time domain before focusing, is proposed. One of the key steps of this method is to perform instantaneous interferometry to get accurate equivalent cross-track velocity (ECV) estimation for cross-track motion compensation. After that, the signal from a moving target is concentrated in range, and along-track motion compensation becomes convenient. Motion compensation transforms a moving target into a stationary one; thus, the conventional SAR imaging algorithm can be applied to focus the moving target. Finally, a strategy for accurately relocating a moving target is presented. The processing results of simulated and measured data illustrate the effectiveness of the proposed method.

Description: Aircraft detection from very high resolution (VHR) remote sensing images has been drawing increasing interest in recent years due to the successful civil and military applications. However, several challenges still exist: 1) extracting the high-level features and the hierarchical feature representations of the objects is difficult; 2) manual annotation of the objects in large image sets is generally expensive and sometimes unreliable; and 3) locating objects within such a large image is difficult and time consuming. In this paper, we propose a weakly supervised learning framework based on coupled convolutional neural networks (CNNs) for aircraft detection, which can simultaneously solve these problems. We first develop a CNN-based method to extract the high-level features and the hierarchical feature representations of the objects. We then employ an iterative weakly supervised learning framework to automatically mine and augment the training data set from the original image. We propose a coupled CNN method, which combines a candidate region proposal network and a localization network to extract the proposals and simultaneously locate the aircraft, which is more efficient and accurate, even in large-scale VHR images. In the experiments, the proposed method was applied to three challenging high-resolution data sets: the Sydney International Airport data set, the Tokyo Haneda Airport data set, and the Berlin Tegel Airport data set. The extensive experimental results confirm that the proposed method can achieve a higher detection accuracy than the other methods.

Description: Previous studies have shown that thermal infrared anomalies can be detected in the crust rocks in satellite infrared images before an earthquake. However, thermal infrared remote sensing is easily affected by weather conditions because of the short wavelength of infrared radiation. In this paper, instead of infrared radiation, we focus on the microwave radiation characteristics of loaded rock. First, a microwave observation system was built to observe the loading process of rocks in an outdoor environment with a cold sky background. Then, the microwave radiation changes in the loaded granite samples during elastic deformation and fracturing stages were analyzed. The experiments yielded the following results. First, the microwave brightness temperature has a linear positive correlation with the load in the elastic deformation stage of the granite samples, and lateral pressure accelerates the changes in the microwave radiation. Second, the microwave brightness temperature usually decreases as the rock develops layered fractures but increases as the rock develops surface fractures, which significantly alter the surface morphology and roughness. The mechanisms responsible for the changes in microwave radiation during the rock deformation and fracturing processes are discussed. This study demonstrates the potential ability to use microwave-sensing satellites to observe seismogenic processes and earthquakes.

Description: Displacements of the Earth's surface can be estimated using differential interferometric synthetic aperture radar. The estimates are derived from the phase difference between two radar acquisitions. When at least three such acquisitions are available, one can compute the displacement between the first and the third acquisition and compare it with the sum of the two intermediate displacements. These two are expected to be equal for a piston-like spatially uniform deformation. However, this is not necessarily the case in measured data. Such lack of phase closure can be due to decorrelation noise alone. It has also been attributed to complex scattering processes such as soil moisture changes or multiple scattering sources. However, the nature of these nonrandom effects is only poorly understood in cold regions, as the role of snow and freeze/thaw processes has not been studied to date. To distinguish the noise-like and the systematic effects, an asymptotic Wald significance test is proposed. It detects situations when the observed closure error cannot solely be explained by noise. Such situations with $p < 0.05$ are observed at the Ku-band during snow metamorphism and melt and following a summer precipitation event in Sodankylä, Finland. They can also be prevalent (> 25%) in the X-band observations of ice-rich permafrost regions in the Lena Delta, Russia, indicating the presence of processes that can have systematic and deleterious impacts on the estimation of surface movements. Satellite-based monitoring of these displacements is thus possibly subject to complex error sources in high-latitude regions.

Description: China is facing the problem of climate change, environment protection and energy security. Therefore, China has to create a low-carbon society to address them. The purpose of this paper is to make a comprehensive and in-depth analysis of China’s reality and transition to a low-carbon society. The research indicates that China’s transition to low-carbon society will inevitably meet many difficulties under the relatively backward situation of China’s socio-economic structure and technologies at the current stage. Therefore, China has to take concrete policies and countermeasures to promote its development gradually. In particular, China has to vigorously promote the innovation of low carbon system, technologies, subsidy and tax, financing and investment to lay groundwork for comprehensive development of low-carbon society.

Description: Numerical morphological modelling of braided rivers, using a physics-based approach, is increasingly used as a technique to explore controls on river pattern and, from an applied perspective, to simulate the impact of channel modifications. This paper assesses a depth averaged non-uniform sediment model (Delft3D) to predict the morphodynamics of a 2.5 km long reach of the braided Rees River, New Zealand, during a single high-flow event. Evaluation of model performance primarily focused upon using high-resolution Digital Elevation Models (DEMs) of Difference, derived from a fusion of terrestrial laser scanning and optical empirical bathymetric mapping, to compare observed and predicted patterns of erosion and deposition, and reach scale sediment budgets. For the calibrated model, this was supplemented with planform metrics (e.g. braiding intensity). Extensive sensitivity analysis of model functions and parameters was executed, including consideration of numerical scheme for bedload component calculations, hydraulics, bed composition, bedload transport and bed slope effects, bank erosion and frequency of calculations. Total predicted volumes of erosion and deposition corresponded well to those observed. The difference between predicted and observed volumes of erosion was less than the factor of two that characterises the accuracy of the Gaeuman et al. bedload transport formula. Grain size distributions were best represented using two-phi intervals. For unsteady flows, results were sensitive to the morphological time scale factor. The approach of comparing observed and predicted morphological sediment budgets shows the value of using natural experiment datasets for model testing. Sensitivity results are transferable to guide Delft3D applications to other rivers. This article is protected by copyright. All rights reserved.

Description: Three-dimensional numerical simulations are used to provide insight into the behavior of methane as it migrates from a leaky decommissioned hydrocarbon well into a shallow aquifer. The conceptual model includes gas-phase migration from a leaky well, dissolution into groundwater, advective-dispersive transport and biodegradation of the dissolved methane plume. Gas-phase migration is simulated using the DuMu x multi-phase simulator, while transport and fate of the dissolved phase is simulated using the BIONAPL/3D reactive transport model. Methane behavior is simulated for two conceptual models: first in a shallow confined aquifer containing a decommissioned leaky well based on a monitored field site near Lindbergh, Alberta, Canada, and secondly on a representative unconfined aquifer based loosely on the Borden, Ontario, field site. The simulations show that the Lindbergh site confined aquifer data are generally consistent with a 2-year methane leak of 2 to 20 m 3 /d, assuming anaerobic (sulfate-reducing) methane oxidation and with maximum oxidation rates of 1 × 10 − 5 to 1 × 10 − 3 kg/m 3 /d. Under the highest oxidation rate, dissolved methane decreased from solubility (110 mg/L) to the threshold concentration of 10 mg/L within 5 years. In the unconfined case with the same leakage rate, including both aerobic and anaerobic methane oxidation, the methane plume was less extensive compared to the confined aquifer scenarios. Unconfined aquifers may therefore be less vulnerable to impacts from methane leaks along decommissioned wells. At other potential leakage sites, site-specific data on the natural background geochemistry would be necessary to make reliable predictions on the fate of methane in groundwater. This article is protected by copyright. All rights reserved.

Description: ABSTRACT Two different methods are currently used for measuring interfacial areas between immiscible fluids within 3-D porous media, high-resolution microtomographic imaging and interfacial partitioning tracer tests (IPTT). Both methods were used in this study to measure non-wetting/wetting interfacial areas for a natural sand. The microtomographic imaging was conducted on the same packed columns that were used for the IPTTs. This is in contrast to prior studies comparing the two methods, for which in all cases different samples were used for the two methods. In addition, the columns were imaged before and after the IPTTs to evaluate the potential impacts of the tracer solution on fluid configuration and attendant interfacial area. The interfacial areas measured using IPTT are ∼5 times larger than the microtomographic-measured values, which is consistent with previous work. Analysis of the image data revealed no significant impact of the tracer solution on NAPL configuration or interfacial area. Other potential sources of error were evaluated, and all were demonstrated to be insignificant. The disparity in measured interfacial areas between the two methods is attributed to the limitation of the microtomography method to characterize interfacial area associated with microscopic surface roughness due to resolution constraints. This article is protected by copyright. All rights reserved.

Description: Honami and monami waves are caused by large-scale coherent vortex structures which form in shear layers generated by canopies. In order to reach new insights on the onset of such waves, the instability of these shear layers is studied. Two different approach are used. In the first approach the presence of the canopy is modeled via a drag coefficient, taken to vary along the canopy as by experimental indications. The second approach considers the canopy as a porous medium and different governing equations for the fluid flow are deduced. In this second case the anisotropy of the canopy, composed by rigid cylindrical elements, is accounted for via an apparent permeability tensor. The results obtained with the latter approach approximate better experimental correlations for the synchronous oscillations of the canopy. This article is protected by copyright. All rights reserved.

Description: This paper presents the fundamental theory and laboratory test results on a new device that is deployed in boreholes in fractured rock aquifers to characterize vertical distributions of water and contaminant fluxes, aquifer hydraulic properties, and fracture network properties (e.g., active fracture density and orientation). The device, a fractured rock passive flux meter (FRPFM), consists of an inflatable core assembled with upper and lower packers that isolate the zone of interest from vertical gradients within the borehole. The outer layer of the core consists of an elastic fabric mesh equilibrated with a visible dye which is used to provide visual indications of active fractures and measures of fracture location, orientation, groundwater flux, and the direction of that flux. Beneath the outer layer is a permeable sorbent that is preloaded with known amounts of water soluble tracers which are eluted at rates proportional to groundwater flow. This sorbent also captures target contaminants present in intercepted groundwater. The mass of contaminant sorbed is used to quantify cumulative contaminant flux; whereas, the mass fractions of resident tracers lost are used to provide measures of water flux. In this paper, the FRPFM is bench tested over a range of fracture velocities (2-20 m/day) using a single fracture flow apparatus (fracture aperture = 0.5 mm). Test results show a discoloration in visible dye corresponding to the location of the active fracture. The geometry of the discoloration can be used to discern fracture orientation as well as direction and magnitude of flow in the fracture. Average contaminant fluxes were measured within 16% and water fluxes within 25% of known imposed fluxes. This article is protected by copyright. All rights reserved.

Description: Groundwater quality is a concern in alluvial aquifers that underlie agricultural areas, such as in the San Joaquin Valley of California. Shallow domestic wells (less than 150 m deep) in agricultural areas are often contaminated by nitrate. Agricultural and rural nitrate sources include dairy manure, synthetic fertilizers, and septic waste. Knowledge of the relative proportion that each of these sources contributes to nitrate concentration in individual wells can aid future regulatory and land management decisions. We show that nitrogen and oxygen isotopes of nitrate, boron isotopes, and iodine concentrations are a useful, novel combination of groundwater tracers to differentiate between manure, fertilizers, septic waste, and natural sources of nitrate. Furthermore, in this work, we develop a new Bayesian mixing model in which these isotopic and elemental tracers were used to estimate the probability distribution of the fractional contributions of manure, fertilizers, septic waste, and natural sources to the nitrate concentration found in an individual well. The approach was applied to 56 nitrate-impacted private domestic wells located in the San Joaquin Valley. Model analysis found that some domestic wells were clearly dominated by the manure source and suggests evidence for majority contributions from either the septic or fertilizer source for other wells. But, predictions of fractional contributions for septic and fertilizer sources were often of similar magnitude, perhaps because modeled uncertainty about the fraction of each was large. For validation of the Bayesian model, fractional estimates were compared to surrounding landuse and estimated source contributions were broadly consistent with nearby landuse types. This article is protected by copyright. All rights reserved.

Description: We use a multiphase level set approach to simulate capillary-controlled motions of isolated fluid ganglia surrounded by two other continuous fluids (i.e., double displacements) during three-phase flow on 3-D porous rock geometries. Double displacements and three-phase snap-off mechanisms are closely related. Water snap-off on gas/oil interfaces can initiate double displacements that mobilize isolated oil ganglia in water-wet rock, but it can also terminate ongoing double displacements and trap oil in water. The multiphase level set approach allows for calculating the evolution of disconnected-phase pressure during the motion. In the events of pore filling by double displacement of oil ganglia, and water snap-off on gas/oil interfaces, we find that the local gas/oil capillary pressure drops, while local oil/water capillary pressure increases, by a similar magnitude as observed for the capillary pressure drops during single-pore filling events in dynamic pore-scale experiments of two-phase drainage. We also find that oil ganglia decrease their surface area, and achieve a more compact shape, when the gas/oil interfacial area decreases at the expense of increased oil/water interfacial area during double displacement. By comparison with similar two-phase gas/water simulations, we find that the level of the gas/water capillary pressure curves, including hysteresis loops, are smaller when a mobile, disconnected oil is present, which suggests double displacement of oil is more favorable than direct gas/water displacement. We also present cases in which phase trapping occurred in the three-phase simulations, but not in the corresponding two-phase simulations, supporting the view that more trapping is possible in three-phase flow. This article is protected by copyright. All rights reserved.

Description: The hunting of wild animals remains a common activity in various parts of the world, especially in rural communities with poor socioeconomic conditions. To investigate patterns of mammal hunting, this study adopted a rural community located in semiarid Brazil as a model to test whether variables such as perceived abundance, activity period (day or night) and animal biomass could influence the consumption potential of mammal taxa. For this study, a checklist/interview technique using 32 photographs of mammals recorded in the region and two photographs of species that did not occur in the region, which acted as “control” species, was used, in addition to other visual stimuli and free lists. All species presented in the photographs, with the exception of the “control” species, were recognized as occurring in the region. Fourteen species were cited as being those most hunted locally. The species cited as most hunted had an average perceived abundance higher than the group of the least-hunted species. However, there was no significant relationship between hunting of a species and its locally perceived abundance. No significant difference in hunting pressure between diurnal and nocturnal species was found, nor was a relationship between animal biomass and hunting pressure observed. Our findings suggest that perceived abundance is an important factor for choosing a resource fauna, but other factors such as intended use, meat flavor and vulnerability to hunting, among others, may influence the potential use of a species.

Description: We address the problem of stochastic simulation of soil particle-size curves (PSCs) in heterogeneous aquifer systems. Unlike traditional approaches that focus solely on a few selected features of PSCs (e.g., selected quantiles), our approach considers the entire particle size curves and can optionally include conditioning on available data. We rely on our prior work [Menafoglio et al, 2014,2015] to model PSCs as cumulative distribution functions, and interpret their density functions as functional compositions. We thus approximate the latter through an expansion over an appropriate basis of functions. This enables us to (a) effectively deal with the data dimensionality and constraints, and (b) to develop a simulation method for PSCs based upon a suitable and well defined projection procedure. The new theoretical framework allows representing and reproducing the complete information content embedded in PSC data. As a first field application, we demonstrate the quality of unconditional and conditional simulations obtained with our methodology by considering a set of particle-size curves collected within a shallow alluvial aquifer in the Neckar river valley, Germany. This article is protected by copyright. All rights reserved.

Description: The assumptions and empirical evidence linking climate change adaptation to development cooperation have led to the emergence of a range of climate change adaptation tools aiming at improving the quality of development cooperation initiatives in times of global change. In order to assess the quality of these tools, we develop an analytic framework to critically assess the social learning and sustainable development outcomes of climate change adaptation tools. The analytic framework defines project objectives, participation typology, participation tools, participation stages, scenarios development, modelling exercises, stakeholder analysis and risk communication strategies that support quality participation outcomes.

Description: The narrow region of soil around roots, the so-called rhizosphere, defers in its hydraulic properties from the bulk soil. The rhizosphere hydraulic properties primarily depend on the drying and wetting rate of mucilage, a polymeric gel exuded by plant roots. Under equilibrium conditions mucilage increases the water holding capacity. Upon drying mucilage turns hydrophobic and makes the rhizosphere temporarily water repellent. There are several models of root water uptake, from analytical models of water flow to a single root to complex numerical models that consider the root architecture. Most of these models, however, do not account for the specific hydraulic properties of the rhizosphere. Here we describe a single-root model that includes the altered hydraulic properties of the rhizosphere due to mucilage exudation. We use the model to reproduce existing experiments reporting unexpected and puzzling hysteresis in the rhizosphere, which could not be explained under the assumption of homogeneous hydraulic properties. In our model the hydraulic properties depend on the concentration of mucilage. This enables a continuous transition from the bulk soil to the root surface. We assumed that: (a) mucilage increases the water holding capacity in equilibrium conditions, (b) hydrophobicity, swelling and shrinking of mucilage cause a non-equilibrium relation between water content and water potential and (c) mucilage reduces the mobility of water molecules in the liquid phase resulting in a lower hydraulic conductivity at a given water content. Our model reproduces well the experiments and suggests that mucilage softens drought stress in plants during severe drying events. This article is protected by copyright. All rights reserved.

Description: This paper addresses the requirements of electrical energy for an isolated island of Masirah in Oman. The paper studied the possibility of using sources of renewable energy in combination with current diesel power plant on the island to meet the electrical load demand. There are two renewable energy sources used in this study, solar and wind energy. This study aimed to design and evaluate hybrid solar/wind/diesel/battery system in terms of cost and pollution. By using HOMER software, many simulation analyses have been proposed to find and optimize different technologies that contain wind turbine, solar photovoltaic, and diesel in combination with storage batteries for electrical generation. Four different hybrid power systems were proposed, diesel generators only, wind/diesel/battery, PV/diesel/battery, and PV/wind/diesel/battery. The analysis of the results shows that around 75 % could reduce the cost of energy by using PV/wind/diesel hybrid power system. Also, the greenhouse emission could be reduced by around 25 % compared with these by using diesel generators system that currently utilize in the Masirah Island. The solar/wind/diesel hybrid system is techno-economically viable for Masirah Island.

Description: Interfacial areas between nonwetting-wetting (NW-W) liquids in natural porous media were measured using a modified version of the interfacial partitioning tracer test (IPTT) method that employed simultaneous two-phase flow conditions, which allowed measurement at NW saturations higher than trapped residual saturation. Measurements were conducted over a range of saturations for a well-sorted quartz sand under three wetting scenarios of primary drainage (PD), secondary imbibition (SI), and secondary drainage (SD). Limited sets of experiments were also conducted for a model glass-bead medium and for a soil. The measured interfacial areas were compared to interfacial areas measured using the standard IPTT method for liquid-liquid systems, which employs residual NW saturations. In addition, the theoretical maximum interfacial areas estimated from the measured data are compared to specific solid surface areas measured with the N 2 /BET method and estimated based on geometrical calculations for smooth spheres. Interfacial areas increase linearly with decreasing water saturation over the range of saturations employed. The maximum interfacial areas determined for the glass beads, which have no surface roughness, are 32±4 and 36±5 cm −1 for PD and SI cycles, respectively. The values are similar to the geometric specific solid surface area (31±2 cm −1 ) and the N 2 /BET solid surface area (28±2 cm −1 ). The maximum interfacial areas are 274±38, 235±27, and 581±160 cm −1 for the sand for PD, SI, and SD cycles, respectively, and ∼7625 cm −1 for the soil for PD and SI. The maximum interfacial areas for the sand and soil are significantly larger than the estimated smooth-sphere specific solid surface areas (107±8 cm −1 and 152±8 cm −1 , respectively), but much smaller than the N 2 /BET solid surface area (1387±92 cm −1 and 55224 cm −1 , respectively). The NW-W interfacial areas measured with the two-phase flow method compare well to values measured using the standard IPTT method. This article is protected by copyright. All rights reserved.

Description: Understanding the influence of attached microbial biomass on water flow in variably saturated soils is crucial for many engineered flow systems. So far, the investigation of the effects of microbial biomass has been mainly limited to water-saturated systems. We have assessed the influence of biofilms on the soil hydraulic properties under variably-saturated conditions. A sandy soil was incubated with Pseudomonas Putida and the hydraulic properties of the incubated soil were determined by a combination of methods. Our results show a stronger soil water retention in the inoculated soil as compared to the control. The increase in volumetric water content reaches approximately 0.015 cm 3 cm −3 but is only moderately correlated with the carbon deficit, a proxy for biofilm quantity, and less with the cell viable counts. The presence of biofilm reduced the saturated hydraulic conductivity of the soil by up to one order of magnitude. Under unsaturated conditions, the hydraulic conductivity was only reduced by a factor of four. This means that relative water conductance in biofilm-affected soils is higher compared to the clean soil at low water contents, and that the unsaturated hydraulic conductivity curve of biofilm-affected soil cannot be predicted by simply scaling the saturated hydraulic conductivity. A flexible parameterization of the soil hydraulic functions accounting for capillary and non-capillary flow was needed to adequately describe the observed properties over the entire wetness range. More research is needed to address the exact flow mechanisms in biofilm-affected, unsaturated soil and how they are related to effective system properties. This article is protected by copyright. All rights reserved.

Description: A framework for interpreting transient pumping tests in heterogeneous transmissivity fields is developed to infer the overall geostatistical parameters of the medium without reconstructing the specific heterogeneous structure point wise. The methodology of Radial Coarse Graining is applied to deduce an effective radial description of multi-Gaussian transmissivity. It was used to derive an Effective Well Flow Solution for transient flow conditions including not only the storativity, but also the geometric mean, the variance, and the correlation length of log-transmissivity. This solution is shown to be appropriate to characterize the pumping test drawdown behavior in heterogeneous transmissivity fields making use of ensembles of simulated pumping tests with multiple combinations of statistical parameters. Based on the Effective Well Flow Solution , a method is developed for inferring heterogeneity parameters from transient pumping test drawdown data by inverse estimation. Thereby, the impact of statistical parameters on the drawdown is analyzed, allowing to determine the dependence of reliability of parameter estimates on location and number of measurements. It is shown, that the number of measurements can be reduced compared to steady state pumping tests. Finally, a sampling strategy for single aquifer analysis is developed, which allows to estimate the statistical parameters, in particular variance and correlation length for individual heterogeneous transmissivity fields making use of transient pumping test measurements at multiple locations. This article is protected by copyright. All rights reserved.

Description: The role of groundwater in sustaining plant transpiration constitutes an important but not well understood aspect of the interactions between groundwater, the land surface, vegetation and the atmosphere. The effect of the hydraulic redistribution (HR) process by plant roots on the interplay between plant transpiration and groundwater dynamics under water-limited climates is investigated by using the Variable Infiltration Capacity Plus (VIC+) land surface model. Numerical experiments, with or without explicitly considering HR, are conducted on soil columns over a range of groundwater table depths (GWTDs) under different vegetative land covers, soil types and precipitation conditions. When HR is not included, this study obtains transpiration – GWTD relationships consistent with those from watershed studies that do not include HR. When HR is included, the transpiration – GWTD relationships are modified. The modification introduced by HR is manifested in the soil moisture of the root zone. The mechanism of HR is explained by detailing the roles of the hydraulically redistributed water, the upward diffusion of soil water and the daytime root uptake. We have found that HR is particularly important in water-limited climates under which plants have high transpiration demand. At the beginning stage of a dry period, HR modulates the severe impacts that climate has on plant transpiration. Only after a prolonged dry period, impacts of HR are lessened when the groundwater table drops below the depth of water uptake by roots and are diminished when plant transpiration is decoupled from groundwater dynamics. This article is protected by copyright. All rights reserved.

Description: Laboratory measurements of the permeability and spectral induced polarization (SIP) response of samples consisting of unconsolidated sands typical of those found in New Zealand aquifers have been made. After correction of measured formation factors to allow for the fact that some were measured at only one fluid conductivity, predictions of permeability from the grain size ( d ) of the samples are found to agree well with measured values of permeability. The Cole-Cole time constant (derived from the SIP measurements) is found, as expected, to depend upon d 2 , but can be affected by the inclusion of smaller grains in the sample. Measurements made on samples comprising of mixtures of grain sizes show that inclusion in a sample of even 10% of smaller grains can significantly reduce both the Cole-Cole time constant ( τ CC ) and the permeability, and support theoretical derivation of how the permeability of a mixture of grain sizes varies with the content of the mixture. Proposed relationships for using τ CC as a predictor for permeability are tested and found to be crucially dependent on the assumed relationship between the dynamic pore radius and grain size. The inclusion of a multiplicative constant to take account of numerical approximations results in good predictions for the permeability of the samples in this study. It seems unlikely, however, that there is a single global expression for predicting permeability from SIP data for all samples. This article is protected by copyright. All rights reserved.

Description: Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 km 2 at mid-latitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This work proposes a variant of existing ensemble-based GRACE-TWS data assimilation schemes. The new algorithm differs in how the analysis increments are computed and applied. Existing schemes correlate the uncertainty in the modeled monthly TWS estimates with errors in the soil moisture profile state variables at a single instant in the month and then apply the increment either at the end of the month or gradually throughout the month. The proposed new scheme first computes increments for each day of the month and then applies the average of those increments at the beginning of the month. The new scheme therefore better reflects sub-monthly variations in TWS errors. The new and existing schemes are investigated here using gridded GRACE-TWS observations. The assimilation results are validated at the monthly time-scale, using in situ measurements of groundwater depth and soil moisture across the US. The new assimilation scheme yields improved (although not in a statistically significant sense) skill metrics for groundwater compared to the open-loop (no assimilation) simulations and compared to the existing assimilation schemes. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Finally, we demonstrate that the scaling parameters that are applied to the GRACE observations prior to assimilation should be consistent with the land surface model that is used within the assimilation system. This article is protected by copyright. All rights reserved.

Description: Knowledge of sediment dynamics in rivers is of great importance for various practical purposes. Despite its high relevance in riverine environment processes, the monitoring of sediment rates remains a major and challenging task for both suspended and bedload estimation. While the measurement of suspended load is currently an active area of testing with non-intrusive technologies (optical and acoustic), bedload measurement does not mark a similar progress. This paper describes an innovative combination of measurement techniques and analysis protocols that establishes the proof-of-concept for a promising technique, labeled herein Acoustic Mapping Velocimetry (AMV). The technique estimates bedload rates in rivers developing bedforms using a non-intrusive measurements approach. The raw information for AMV is collected with acoustic multi-beam technology that in turn provides maps of the bathymetry over longitudinal swaths. As long as the acoustic maps can be acquired relatively quickly and the repetition rate for the mapping is commensurate with the movement of the bedforms, successive acoustic maps capture the progression of the bedform movement. Two-dimensional velocity maps associated with the bedform migration are obtained by implementing algorithms typically used in particle image velocimetry to acoustic maps converted in gray-level images. Furthermore, use of the obtained acoustic and velocity maps in conjunction with analytical formulations (e.g., Exner equation) enables estimation of multi-directional bedload rates over the whole imaged area. This paper presents a validation study of the AMV technique using a set of laboratory experiments. This article is protected by copyright. All rights reserved.

Description: In this paper, a new approach based on two fusion schemes is proposed to overcome the uncertainties in land surface emissivity (LSE) estimation and, consequently, land surface temperature (LST) retrieval. The fusion schemes are called image-based weighted methods and knowledge-based weighted methods, in which each of them includes two LSE estimation methods. The effectiveness of the two proposed fusion schemes is empirically tested over two scenes of Landsat-8 (known as Landsat Data Continuity Mission) data sets, and the obtained LSEs by individual and proposed methods were compared to the LSE product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) by image-based and class-based cross-comparison. In both scenes, the adjusted normalized emissivity method (ANEM) and NDVI-based emissivity method (NBEM) provide appropriate results among five individual methods. In contrast, weighted to median (WMED) achieves superior results among the proposed fusion methods for both scenes. In addition, the root-mean-square error (rmse) values of LSE obtained by ANEM and WMED are 1.48% and 0.87%, which lead to 1.25 K and 0.73 K errors in the LST retrieval by the single-channel algorithm in the first scene, respectively. For the second scene, the error values of NBEM and WMED are 1.10% and 0.52%, which lead to 0.93 K and 0.44 K errors in the LST, respectively. Moreover, the error ranges and rmse of cross-comparison for the obtained LSE in the proposed methods were remarkably decreased. Also, in this research, for LST cross-comparison, an alternative scaling method based on LST products of the Moderate Resolution Imaging Spectroradiometer was proposed. The LST validation results demonstrated that the proposed methods provide better estimates in terms of three accuracy measures in both examined data sets.

Description: Television (TV) radio frequency interference (TFI) signals are found in the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations of those channels with their frequencies centered at 18.7- or 10.65-GHz frequencies over coastal regions near the U.S. and Europe, respectively. When TV signals are reflected off the ocean surface and get into AMSR2 field of views, the AMSR2-measured radiance contains not only information of natural emission from Earth's surface but also the reflected TV signals. If not detected and corrected, TFI introduces errors into the geophysical retrieval products. The occurrence and intensity of TFI are determined by the angle between the observation beam vector and the reflected TV signal vector (i.e., TFI glint angle) and the background TV signal intensity. In this paper, an empirical model is developed to quantitatively calculate the contribution of TFI signals to AMSR2 observations based on TFI glint angle and TV signal intensity. This empirical model is then applied to AMSR2 K-band channels over North America and X-band channels over Europe. It is shown that the annual mean bias for the TFI-affected observations of the 18.7-GHz channel at horizontal (vertical) polarization reduces from a value of more than 5 K (2 K) to about −0.5 K (0.5) after TFI correction over the coastal ocean near North America. The annual mean bias for the TFI-affected observations of the 10.65-GHz channel at horizontal (vertical) polarization reduces from a value of about 2.5 K to about −0.7 K (0.5 K) after TFI correction over the coastal ocean near Europe. False maxima in AMSR2-retrieved cloud liquid water path and dry anomalies in AMSR2-retrieved total precipitable water near the coastal regions are also eliminated after incorporating the TFI correction.

Description: The simplex growing algorithm (SGA) has been widely used for finding endmembers. It can be considered as a sequential version of the well-known endmember finding algorithm, N-finder algorithm (N-FINDR), which finds endmembers one at a time by growing simplexes. However, one of the major hurdles for N-FINDR and SGA is the calculation of simplex volume (SV) which poses a great challenge in designing any algorithm using SV as a criterion for finding endmembers. This paper develops an orthogonal projection (OP)-based SGA (OP-SGA) which essentially resolves this computational issue. It converts the issue of calculating SV to calculating the OP on previously found simplexes without computing matrix determinants. Most importantly, a recursive Kalman filter-like OP-SGA, to be called recursive OP-SGA (ROP-SGA), can be also derived to ease computation. By virtue of ROP-SGA, several advantages and benefits in computational savings and hardware implementation can be gained for which N-FINDR and SGA do not have.

Description: In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling–Lawley trace (HLT) statistic for measuring the similarity of two covariance matrices. The distribution of the HLT statistic is approximated by a Fisher–Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detection rates and error rates that are comparable with the generalized likelihood ratio test.

Description: This paper deals with the exploitation of Doppler centroid measurements for ship velocity estimation from focused single-look complex synthetic aperture radar (SAR) images. An algorithm is presented, which can be used as a discrimination tool to reduce the false alarm rate of standard adaptive threshold detectors and to complement the ship detection task with velocity estimation. The outputs are the indication of the presence of a moving target and the estimate of its slant range velocity. After a review of its theoretical background, algorithm features and performance are verified by application to TerraSAR-X data. The proposed method performs robust Doppler spectrum derivation for candidate ship targets and background pixels. The presented results show that the estimated radial velocity is in very good agreement (5% root-mean-square deviation) with that resulting from the azimuth offset method. Rejection of bright sea features and azimuth ambiguities is also demonstrated, taking advantage of the performed velocity analysis.

Description: Global Positioning System (GPS) radio-occultation (RO) is an atmospheric sounding technique utilizing the received GPS signal through the stratified atmosphere to measure refractivity, which provides information on temperature and humidity. The GPS-RO technique is now operational on several Low Earth Orbiting (LEO) satellites, which cannot provide high temporal and spatial resolution soundings necessary to observe localized transient events, such as tropical storms. An airborne RO (ARO) system has thus been developed for localized GPS-RO campaigns. RO signals in the lower troposphere are adversely affected by rapid phase accelerations and severe signal power fading. These signal dynamics often cause the phase-locked loop in conventional GPS survey receivers to lose lock in the lower troposphere, and the open-loop (OL) tracking in postprocessing is used to overcome this problem. OL tracking also allows robust processing of rising GPS signals, approximately doubling the number of observed occultations. An approach for “backward” OL tracking was developed, in which the correlations are computed sequentially in reverse time so that the signal can be acquired and tracked at high elevations for rising occultations. Ultimately, the signal-to-noise ratio (SNR) limits the depth of tracking in the atmosphere. We have developed a model relating the SNR to the variance in the residual phase of the observed signal produced from OL tracking. In this paper, we demonstrate the applicability of the phase variance model to airborne data. We then apply this model to set a threshold on refractivity retrieval based upon the cumulative unwrapping error bias to determine the altitude limit for reliable signal tracking. We also show consistency between the ARO SNR and collocated COSMIC satellite observations and use these results to evaluate the antenna requirements for an improved ARO system.

Description: The imagery of highly squinted synthetic aperture radar mounted on maneuvering platforms with nonlinear trajectory is a challenging task due to the existence of acceleration and the cross-range-dependent range migration and Doppler parameters. In order to accommodate these issues, a frequency-domain imaging algorithm based on tandem two-step nonlinear chirp scaling (TNCS) with small aperture is proposed. For the cross-range-dependent range cell migration (RCM) caused by the linear range walk correction and acceleration, the first-step NCS is introduced to suppress this dependence and realize the unified RCM correction. Based on the differences between full-aperture and small-aperture data in the cross-range processing, the second-step NCS is introduced in frequency domain to equalize the cross-range-dependent Doppler parameters, for cross-range processing is more sensitive to the cross-range dependence than range processing. Furthermore, a novel geometric correction method based on inverse projection is utilized to eliminate the negative effects caused by the imaging processing. Simulation results and real data processing are presented to validate the proposed approach.

Description: We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by a modified Fmask code. Surface reflectance is inferred from Tanré's formulation of the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) is estimated over dark objects (DOs) that are identified in a combined database and image-based approach, where information on their temporal persistency is utilized. AOD is inferred with consideration of the actual target reflectance and background contamination effect. In case of absent DOs in bright scenes, a fallback approach with a modeled AOD climatology is used instead. Topographic normalization is performed by a modified C-correction. The data are projected into a single coordinate system and are organized in a gridded data structure for simplified pixel-based access. We based the assessment of the produced data set on an exhaustive analysis of overlapping pixels: 98.8% of the redundant overlaps are in the range of the expected ±2.5% overall radiometric algorithm accuracy. AOD is in very good agreement with Aerosol Robotic Network sunphotometer data ( $R^{2}$ : 0.72 to 0.79, low intercepts, and slopes near unity). The uncertainty in using the water vapor fallback climatology is approximately ±2.8% for the TM SWIR1 band in the wet season. The topographic correction was considered successful by an investigation of the nonrelationship between the illumination angle and the corrected radiance.

Description: In this paper, a transform-domain filtering method is proposed for polarimetric synthetic aperture radar (POLSAR) images via patch ordering and simultaneous sparse coding (SSC). First of all, we establish a signal-dependent additive noise model for the POLSAR covariance matrix and derive the noise variance for each element of the matrix based on the complex Wishart distribution. Next, we propose an extended patch ordering algorithm for POLSAR images by extracting sliding patches and organizing them in a regular way. Then, the ordered patches are filtered by SSC, for the purpose of which we develop a new weighted simultaneous orthogonal matching pursuit algorithm by embedding the signal-dependent noise model of the POLSAR data. Finally, the filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. Experimental results with both simulated and real POLSAR images demonstrate that the proposed method can achieve state-of-the-art filtering performance.