ALBERT

Description: The most popular practice for analyzing nonstationarity of flood series is to use a fixed single-type probability distribution incorporated with the time-varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time-varying under changing environments. To allow the investigation of this complex nature, the time-varying two-component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series (AMFS) of two stations in the Weihe River basin (WRB), with the model parameters calibrated by the meta-heuristic maximum likelihood (MHML) method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single-type distributions, stationary mixture distributions and time-varying single-type distributions. The results highlighted the advantages of TTMD with physically-based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. This article is protected by copyright. All rights reserved.

Description: While the effects of land use change in urban areas have been widely examined, the combined effects of climate and land use change on the quality of urban and urbanizing streams have received much less attention. We describe a modeling framework that is applicable to the evaluation of potential changes in urban water quality and associated hydrologic changes in response to ongoing climate and landscape alteration. The grid-based spatially distributed model, DHSVM-WQ, is an outgrowth of the Distributed Hydrology-Soil-Vegetation Model (DHSVM) that incorporates modules for assessing hydrology and water quality in urbanized watersheds at a high spatial and temporal resolution. DHSVM-WQ simulates surface runoff quality and in-stream processes that control the transport of nonpoint-source (NPS) pollutants into urban streams. We configure DHSVM-WQ for three partially urbanized catchments in the Puget Sound region to evaluate the water quality responses to current conditions and projected changes in climate and/or land use over the next century. Here we focus on total suspended solids (TSS) and total phosphorus (TP) from nonpoint sources (runoff), as well as stream temperature. The projection of future land use is characterized by a combination of densification in existing urban or partially urban areas, and expansion of the urban footprint. The climate change scenarios consist of individual and concurrent changes in temperature and precipitation. Future precipitation is projected to increase in winter and decrease in summer, while future temperature is projected to increase throughout the year. Our results show that urbanization has a much greater effect than climate change on both the magnitude and seasonal variability of streamflow, TSS and TP loads largely due to substantially increased streamflow, and particularly winter flow peaks. Water temperature is more sensitive to climate warming scenarios than to urbanization and precipitation changes. Future urbanization and climate change together are predicted to significantly increase annual mean streamflow (up to 55%), water temperature (up to 1.9 °C), TSS load (up to 182%), and TP load (up to 74%). This article is protected by copyright. All rights reserved.

Description: The resolution of a digital elevation model (DEM) is a crucial factor in watershed hydrologic and environmental modeling. DEM resolution can cause significant variability in the representation of surface topography, which further affects quantification of hydrologic connectivity and simulation of hydrologic processes. The objective of this study is to examine the effects of DEM resolution on (1) surface microtopographic characteristics, (2) hydrologic connectivity, and (3) the spatial and temporal variations of hydrologic processes. A puddle-to-puddle (P2P) modeling system was utilized for surface delineation and modeling of the P2P overland flow dynamics, surface runoff, infiltration, and unsaturated flow for nine DEM resolution scenarios of a field plot surface. Comparisons of the nine modeling scenarios demonstrated that coarser DEM resolutions tended to eliminate topographic features, reduce surface depression storage, and strengthen hydrologic connectivity and surface runoff. We found that reduction in maximum depression storage and maximum ponding area was as high as 97.56% and 76.36%, respectively, as the DEM grid size increased from 2 cm to 80 cm. The paired t-test and fractal analysis demonstrated the existence of a threshold DEM resolution (10 cm for the field plot), within which the DEM-based hydrologic modeling was effective and acceptable. The effects of DEM resolution were further evaluated for a larger surface in the Prairie Pothole Region (PPR) subjected to observed rainfall events. It was found that simulations based on coarser resolution DEMs (〉10 m) tended to overestimate ponded areas and underestimate runoff discharge peaks. The simulated peak discharge from the PPR surface reduced by approximately 50% as the DEM resolution changed from 2 m to 90 m. Fractal analysis results elucidated scale dependency of hydrologic and topographic processes. In particular, scale analysis highlighted a unique constant-threshold-power relationship between DEM scale and topographic and hydrologic parameters/variables. Not only does this finding allow one to identify threshold DEM, but also further develop functional relationships for scaling to achieve valid topographic characterization as well as effective and efficient hydrologic modeling. This article is protected by copyright. All rights reserved.

Description: This paper presents a methodology for hydrograph separation in mountain watersheds, which aims at identifying flow sources among ungauged headwater sub-catchments through a combination of observed streamflow and data on natural tracers including isotope and dissolved solids. Daily summer and bi-daily spring season water samples obtained at the outlet of the Juncal River Basin in the Andes of Central Chile were analyzed for all major ions as well as stable water isotopes, δ 18 O and δD. Additionally, various samples from rain, snow, surface streams and exfiltrating subsurface water (springs), were sampled throughout the catchment. A principal component analysis (PCA) was performed in order to address cross-correlation in the tracer dataset, reduce the dimensionality of the problem and to uncover patterns of variability. Potential sources were identified in a two-component U-space that explains 94% of the observed tracer variability at the catchment outlet. Hydrograph separation was performed through an Informative - Bayesian model. Our results indicate that the Juncal Norte Glacier headwater sub-catchment contributed at least 50% of summer flows at the Juncal River Basin outlet during the 2011–12 water year (a hydrologically dry period in the Region), even though it accounts for only 27% of the basin area. Our study confirms the value of combining solute and isotope information for estimating source contributions in complex hydrologic systems, and provides insights regarding experimental design in high-elevation semi-arid catchments. The findings of this study can be useful for evaluating modeling studies of the hydrological consequences of the rapid decrease in glacier cover observed in this region, by providing insights into the origin of river water in basins with little hydrometeorological information.

Description: The average flow of Silver Springs, one of the largest magnitude springs in central Florida, declined 32% from 2000 to 2012. The average groundwater head in the springshed declined 0.14 m, and the spring pool altitude increased 0.24 m during the same period. This paper presents a novel explanation of the spring flow recession curve for Silver Springs using Torricelli model, which uses the groundwater head at a sentinel well, the spring pool altitude, and the net recharge to groundwater. The effective springshed area and net recharge (defined as recharge minus groundwater pumping and evapotranspiration) were estimated based on the observed recession slopes for spring flow, groundwater head, and spring pool altitude. The results show that the effective springshed area continuously declined since 1989, and the net recharge declined since the 1970s with a significant drop in 2002. Subsequent to 2002, the net recharge increased modestly but not to the levels prior to the 1990s. The reduction in net recharge was caused by changes in hydroclimatic conditions including precipitation and temperature, along with groundwater withdrawals, which contributed to the declined spring flow.

Description: Discharge simulation from snow-dominated catchments seems to be an easy task. Any spatially-explicit precipitation-runoff model coupled to a temperature-index snow model generally yields simulations that mimic well the observed daily discharges. The robustness of such models is, however, questionable: in presence of strong annual discharge cycles, small model residuals do not guarantee high explanatory power of the underlying model. This paper proposes a methodology for snow hydrological model identification within a limits-of-acceptability framework, where acceptable model simulations are the ones that reproduce a set of signatures within an a priori specified range. The signatures proposed here namely include the relationship between the air temperature regime and the discharge regime, a new snow hydrology signature that can be readily transferred to other Alpine settings. The discriminatory power of all analyzed signatures is assessed with a new measure of their discriminatory power in the model prediction domain. The value of the proposed snow hydrology signatures and of the limits-of-acceptability approach is demonstrated for the Dischma river in Switzerland, whose discharge shows a strong temporal variability of hydrologic forcing conditions over the last 30 years. The signature-based model identification for this case study leads to the surprising conclusion that the observed discharge data contains a multi-year period that cannot be reproduced with the model at hand. This model-data mismatch might well result from a yet to be identified problem with the discharge observations, which would have been difficult to detect in a classical residual-based model identification approach. Overall, the detailed results for this case study underline the robustness of the limits-of-acceptability approach in the presence of error-prone observations if it is applied in combination with relatively robust signatures. Future work will show whether snow hydrology signatures and their limits-of-acceptability can be regionalized to ungauged catchments, which would make this model selection approach particularly powerful for Alpine environments. This article is protected by copyright. All rights reserved.

Description: Changes in land use and land cover are major drivers of hydrological alteration in the tropical Andes. However, quantifying their impacts is fraught with difficulties because of the extreme diversity in meteorological boundary conditions, which contrasts strongly with the lack of knowledge about local hydrological processes. Although local studies have reduced data scarcity in certain regions, the complexity of the tropical Andes poses a big challenge to regional hydrological prediction. This study analyses data generated from a participatory monitoring network of 25 headwater catchments covering three of the major Andean biomes ( páramo , jalca , and puna ), and link their hydrological responses to main types of human interventions (cultivation, afforestation and grazing). A paired catchment setup was implemented to evaluate the impacts of change using a “trading space-for-time” approach. Catchments were selected based on regional representativeness and contrasting land use types. Precipitation and discharge have been monitored and analysed at high temporal resolution for a time period between 1 and 5 years. The observed catchment responses clearly reflect the extraordinarily wide spectrum of hydrological processes of the tropical Andes. They range from perennially humid páramos in Ecuador and northern Peru with extremely large specific discharge and baseflows, to highly seasonal, flashy catchments in the drier punas of southern Peru and Bolivia. The impacts of land use are similarly diverse and their magnitudes are a function of catchment properties, original and replacement vegetation, and management type. Cultivation and afforestation consistently affect the entire range of discharges, particularly low flows. The impacts of grazing are more variable, but have the largest effect on the catchment hydrological regulation. Overall, anthropogenic interventions result in increased streamflow variability and significant reductions in catchment regulation capacity and water yield, irrespective of the hydrological properties of the original biome. This article is protected by copyright. All rights reserved.

Description: Temporal streamflow variability in an inland hydrologic station and temporal trends and frequency changes at three weather stations in a semiarid river basin located in Loess Plateau, China were detected using linear regression, Mann-Kendall (MK) analysis, and wavelet transform methods. Double Cumulative Curve and Ordered Clustering were used to identify the hydrological periods of upper Sang-kan (USK) basin between 1957 and 2012. The results indicate that: (1) precipitation in the USK basin over the study period did not show any trend while the temperature showed a significant increase; (2) streamflow flowing out of the USK basin indicated a significant decrease; (3) two distinct hydrological periods; the “natural period” from 1957 to 1984 and the “human impact period” from 1985 to 2012 were present; and (4) the contribution of climate change and human activities to reduce the streamflow was 36.9% and 63.1%, respectively. The results indicate that human activities may be contributing to a decrease in streamflow in the USK basin. This article is protected by copyright. All rights reserved.

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: Hydrochemistry methods were used to decipher the weathering and geochemical processes controlling solute acquisition of river waters in the dry season in the middle Loess Plateau (MLP), one of the most severely eroded areas and turbid riverine systems in the world. River waters were neutral to slightly alkaline with pH varying from 7.6 to 9.6. The total dissolved solids (TDS) decreased from northwest to southeast with a mean value of 804 mg/L, much higher than the global average and other large rivers in China. Ternary diagram showed that river waters were dominated by Na + , HCO 3 - , and Cl - with the main water-type of HCO 3 - -Cl - -Na + . Saturation index (SI) values, Mg 2+ , Ca 2+ , and HCO 3 - analyses indicated the preferential Ca 2+ removal by calcite precipitation. Gibbs plots and stoichiometry plots indicated that the dissolved solutes were mainly derived from rock weathering with minor anthropogenic and atmospheric inputs. Samples in the northwestern basin are also influenced by evaporation. A forward model of mass budget calculation showed that, owing to high soluble characteristics, evaporite dissolution was a major feature of river waters and contributed 41% to the total dissolved cations on average, while carbonate and silicate weathering contributed 28%, and 25% on average, respectively. Beside evaporite dissolution, cation exchange is also responsible for the high concentrations of Na + in river water. Spatial variations showed that evaporite dissolution and silicate weathering were higher in the northern basin, whereas carbonate weathering was higher in the southern basin. Different from most rivers in the world, the physical erosion rates (varying from 117.7 to 4116.6 t/km 2 y) are much higher than the chemical weathering rates (varying from 3.54 to 6.76 t/km 2 y) in the MLP due to the loose structure of loess and poor vegetation in the basin. In the future, studies on comparison of water geochemistry in different seasons and on influence of different types of land use and soil salinization on water geochemistry, denudation rates, and water quality should be strengthened in the MLP. These results shed some lights on processes responsible for modern loess weathering and also indicated the importance of time-series sampling strategy for river water chemistry.

Description: The Tagus River Basin is an ultimately important water source for hydropower production, urban and agricultural water supply in Spain and Portugal. Growing electricity and water supply demands, over-regulation of the river and construction of new dams, as well as large inter-basin and intra-basin water transfers aggravated by strong natural variability of climate in the catchment have already imposed significant pressures on the river. The substantial reduction of discharge is observed already now, and projected climatic change is expected to alter the water budget of the catchment further. In this study we address the effects of projected climate change on the water resources availability in the Tagus River Basin, and influence of potential changes on hydropower generation of the three important reservoirs in the basin. The catchment scale, process-based eco-hydrological model Soil and Water Integrated Model (SWIM) was set up, calibrated and validated for the entire Tagus River Basin, taking into account fifteen large reservoirs in the catchment. The future climate projections were selected from those generated within the Inter-Sectoral Impact Model Intercomparison Project. They include five bias-corrected climatic datasets for the region, obtained from Global Circulation Models runs under two emissions scenario – moderate and extreme ones, and covered the whole century. The results show a strong agreement among model runs in projecting substantial decrease of discharge of the Tagus River discharge and, consequently, a strong decrease in hydropower production under both future climate scenarios.

Description: Simulations from hydrological models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of such sources of uncertainty is essential to support model calibration, validation and diagnostic evaluation, and to prioritise efforts for uncertainty reduction. It can also support the identification of ‘disinformative data’ whose values are the consequence of measurement errors or inadequate observations. Sensitivity Analysis (SA) provides the theoretical framework and the numerical tools to quantify the relative contribution of different sources of uncertainty to the variability of the model outputs. In traditional applications of GSA, model outputs are aggregations of the full set of a simulated variable. For example, many GSA applications use a performance metric (e.g. the root mean squared error) as model output that aggregates the distances of a simulated time series to available observations. This aggregation of propagated uncertainties prior to GSA may lead to a significant loss of information and may cover up local behaviour that could be of great interest. Time-varying sensitivity analysis (TVSA), where the aggregation and SA are repeated at different time-steps, is a viable option to reduce this loss of information. In this work, we use TVSA to address two questions: [1] Can we distinguish between the relative importance of parameter uncertainty versus data uncertainty in time? [2] Do these influences change in catchments with different characteristics? To our knowledge, the results present one of the first quantitative investigation on the relative importance of parameter and data uncertainty across time. We find that the approach is capable of separating influential periods across data and parameter uncertainties, while also highlighting significant differences between the catchments analysed.

Description: In this paper we assess the performance of the catchment model SIMCAT, to predict nitrate and soluble reactive phosphorus (SRP) concentrations against four monitoring regimes with different spatial and temporal sampling frequencies. The GLUE uncertainty framework is used, along with a general sensitivity analysis to understand relative parameter sensitivity. Improvements to model calibration are explored by introducing more detailed process representation using the INCA water quality model, driven by the E-HYPE hydrological model. The results show how targeted sampling of headwater watercourses upstream of point discharges is essential for calibrating diffuse loads, and can exert a strong influence on the whole-catchment model performance. Further downstream, if the point discharges and loads are accurately represented, then the improvement in the catchment scale model performance is relatively small as more calibration points are added or frequency is increased. The higher order, dynamic model INCA-P, which incorporates sediment and biotic interaction, resulted in improved whole-catchment performance over SIMCAT, although there are still large epistemic uncertainties from land phase export coefficients and runoff. However, the very large sampling errors in routine monitoring make it difficult to invest confidence in the modelling, especially since we know phosphorous transport to be very episodic and driven by high flow conditions for which there are few samples. The environmental modelling community seems to have been stuck in this position for some time, and whilst it is useful to use an uncertainty framework to highlight these issues, it has not widely been adopted, perhaps because there is no clear mechanism to allow uncertainties to influence investment decisions. This raises the question as to whether it might better place a cost on uncertainty, and use this to drive more data collection or improved models, before making investment decisions concerning for example mitigation strategies. This article is protected by copyright. All rights reserved.

Description: This study was undertaken to evaluate the effects of climatic variability on inter-annual variations in each component of evapotranspiration ( ET ) and the total ET in a temperate coniferous forest in Japan. We conducted eddy covariance flux and meteorological measurements for 7 years and parameterized a one-dimensional multi-layer biosphere-atmosphere model (Kosugi et al., 2006) that partitions ET to transpiration ( Tr ), wet-canopy evaporation ( E wet ), and soil evaporation ( E soil ). The model was validated with the observed flux data. Using the model, the components of ET were estimated for the 7 years. Annual precipitation, ET , Tr , E wet , and E soil over the 7 years were 1536 ± 334 mm, 752 ± 29 mm, 425 ± 37 mm, 219 ± 34 mm, and 108 ± 10 mm, respectively. The maximum inter-annual fluctuation of observed ET was 64 mm with a coefficient of variance (CV) of 2.7%, in contrast to relatively large year-to-year variations in annual rainfall (CV = 20.1%). Tr was related to the vapor pressure deficit, incoming radiation, and air temperature with relatively small inter-annual variations (CV = 8.2%). E soil (CV = 8.6%) was related mainly to the vapor pressure deficit. E wet was related to precipitation with large inter-annual variations (CV = 14.3%) because of the variability in precipitation. The variations in E wet were counterbalanced by the variations in Tr and E soil , producing the small inter-annual variations in total ET . This article is protected by copyright. All rights reserved.

Description: Backflow, the temporary reversal of discharge at the outlet of a lake, is an important mechanism controlling flow and transport in many connected river-lake systems. This study used statistical methods to examine long-term variations and primary causal factors of backflow from the Yangtze River to a laterally connected, large floodplain lake (Poyang Lake, China). Additionally, the effects of backflow on the lake hydrology were explored using a physically based hydrodynamic model and a particle-tracking model. Although backflow into Poyang Lake occurs frequently, with an average of 16 backflow events per year, and varies greatly in magnitude between years, statistical analysis indicates that both the frequency and magnitude of backflow reduced significantly during 2001-2010 relative to the previous period of 1960-2000. The ratio of Poyang Lake catchment inflows to Yangtze River discharge can be used as an indication of the daily occurrence of backflow, which is most likely to occur during periods when this ratio is lower than 5%. Statistical analysis also indicates that the Yangtze River discharge is the main controlling factor of backflow during July-October, rather than catchment inflows to the lake. Hydrodynamic modeling reveals that, in general, backflow disturbs the normal northward water flow direction in Poyang Lake, and transports mass ~20 km southward into the lake. The effects of backflow on flow direction, water velocities and water levels propagate to virtually its upstream extremity. The current study represents a first attempt to explore backflow and causal factors for a highly dynamic lake-floodplain system. An improved understanding of Poyang Lake backflow is critical for guiding future strategies to manage the lake, its water quality and ecosystem value, given proposals to modify the lake-river connectivity. This article is protected by copyright. All rights reserved.

Description: This paper reports the first results on δ 18 O and δ 2 H analysis of precipitations, cave drip waters, and groundwaters from sites in Mallorca (Balearic Islands, western Mediterranean), a key region for paleoclimate studies. Understanding the isotopic variability and the sources of moisture in modern climate systems is required to develop speleothem isotope-based climate reconstructions. The stable isotopic composition of precipitation was analyzed in samples collected between March 2012 and March 2013. The values are in the range reported by GNIP Palma station. Based on these results, the local meteoric water line δ 2 H = 7.9 (±0.3) δ 18 O + 10.8 (±2.5) was derived, with slightly lower slope than GMWL. The results help tracking two main sources of air masses affecting the study sites: rain events with the highest δ 18 O values (〉 –5 ‰) originate over the Mediterranean Sea, whereas the more depleted samples (〈 –8 ‰) are sourced in the North Atlantic region. The back trajectory analysis and deuterium excess values, ranging from 0.4 to 18.4 ‰, further support our findings. To assess the isotopic variation across the island, water samples from eight caves were collected. The δ 18 O values range between –6.9 and –1.6 ‰. With one exception (Artà), the isotopic composition of waters in caves located along the coast (Drac, Vallgornera, Cala Varques, Tancada, and Son Sant Martí) indicates Mediterranean-sourced moisture masses. By contrast, the drip water δ 18 O values for inland caves (Campanet, ses Rates Pinyades) or developed under a thick (〉50 m) limestone cap (Artà) exhibit more negative values. A well-homogenized aquifer supplied by rainwaters of both origins is clearly indicated by groundwater δ 18 O values, which show to be within 2.4 ‰ of the unweighted arithmetic mean of –7.4 ‰. Although limited, the isotopic data presented here constitute the baseline for future studies using speleothem δ 18 O records for western Mediterranean paleoclimate reconstructions. This article is protected by copyright. All rights reserved.

Description: Extremely high precipitation occurs in the Southern Alps of New Zealand, associated with both orographic enhancement and synoptic-scale weather processes. In this study, we test the hypothesis that atmospheric rivers (ARs) are a key driver of floods in the Southern Alps of New Zealand. Vertically-integrated water vapour and horizontal water vapour transport, and atmospheric circulation, are investigated concurrently with major floods on the Waitaki River (a major South Island river). Analysis of the largest eight annual maximum floods between 1979 and 2012 indicates that all are associated with ARs. Geopotential height fields reveal that these ARs are located in slow eastward moving extra-tropical cyclones, with high pressure to the northeast of New Zealand. The confirmation of ARs as a contributor to Waitaki flooding indicates the need for their further exploration to better understand South Island hydrometeorological extremes. This article is protected by copyright. All rights reserved.

Description: Land cover has been increasingly recognized as an important factor affecting hydrologic processes at the basin and regional level. Therefore, improved understanding of how land cover change affects hydrologic systems is needed for better management of water resources. The objective of this study is to investigate the effects of land cover change on the duration and severity of high and low flows by using the Soil Water Assessment Tool (SWAT) model, Bayesian Model Averaging (BMA) and copulas. Two basins dominated by different land cover in the Ohio River basin are used as study area in this study. Two historic land covers from the 1950s and 1990s are considered as input to the SWAT model, thereby investigating the hydrologic high and low flow response of different land cover conditions of these two basins. The relationships between the duration and severity of both low and high flow are defined by applying the copula method; changes in the frequency of the duration and severity are investigated. The results show that land cover changes affect both the duration and severity of both high and low flows. An increase in forest area leads to a decrease in the duration and severity during both high and low flows, but its impact is highest during extreme flows. The results also show that the land cover changes have had significant influences on changes in the joint return periods of duration and severity of low and high flows. While this study sheds light on the role of land cover change on severity and duration of high and low flow conditions, more studies using various land cover conditions and climate types are required in order to draw more reliable conclusions in future. This article is protected by copyright. All rights reserved.

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: The mountain headwater Bow River at Banff, Alberta, Canada was subject to a large flood in June 2013, over which considerable debate has ensued regarding its probability of occurrence. It is therefore instructive to consider what information long term streamflow discharge records provide about environmental change in the Upper Bow River basin above Banff. Though protected as part of Banff National Park, since 1885, the basin has experienced considerable climate and land cover changes, each of which has the potential to impact observations, and hence the interpretations of flood probability. The Bow River at Banff hydrometric station is one of Canada's longest operating reference hydrological basin network stations and so has great value for assessing changes in flow regime over time. Furthermore, the station measures a river that provides an extremely important water supply for Calgary and irrigation district downstream and so is of great interest for assessing regional water security. These records were examined for changes in several flood attributes and to determine whether flow changes may have been related to landscape change within the basin as caused by forest fires, conversion from grasslands to forest with fire suppression, and regional climate variations and/or trends. Floods in the Upper Bow River are generated by both snowmelt and rain-on-snow (ROS) events, the latter type which include floods events generated by spatially and temporally large storms such as occurred in 2013. The two types of floods also have different frequency characteristics. Snowmelt and ROS flood attributes were not correlated significantly with any climate index or with burned area except that snowmelt event duration correlated negatively to the Pacific Decadal Oscillation. While there is a significant negative trend in all floods over the past 100 years, when separated based on generating process, neither snowmelt floods nor large ROS floods associated with mesoscale storms show any trends over time. Despite extensive changes to the landscape of the basin and in within the climate system, the flood regime remains unchanged, something identified at smaller scales in the region but never at larger scales.

Description: A theoretical, dimensionless rainfall-runoff model was used to simulate the discharge of Wulongdong spring in western Hubei Province, South China. The single parameter (time constant τ) in the model is easy to obtain by fitting the recession rate of the observed hydrographs. The model was scaled by simply matching the total annual flow volume of the model to the observed value. Annual distribution of actual evapotranspiration was embedded in the model input to calculate the accumulated deficit of soil moisture before each rain event. Hourly precipitation input data performed better than daily data, defining τ of 0.85 days, and returning a Nash-Sutcliffe efficiency of 0.89 and the RMSE of 0.07. This model offers an effective way to simulate the discharge of karst springs that respond sensitively to rainfall events. The model parameters of a successful simulation can be used to estimate the recharge area and indicate the intrinsic response time of the basin.

Description: The change of hydrological regimes may cause impacts on human and natural system. Therefore, investigation of hydrologic alteration induced by climate change is essential for preparing timely proper adaptation to the changes. This study employed 24 climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 scenario. The climate projections were downscaled at a station-spacing for seven Korean catchments by a statistical downscaling method that preserves a long-term trend in climate projections. Using an ensemble of future hydrologic projections simulated by three conceptual rainfall-runoff models (GR4J, IHACRES, and Sacramento models), we calculated Hydrologic Alteration Factors (HAFs) to investigate degrees of variations in Indicators of Hydrologic Alteration (IHAs) derived from the hydrologic projections. The results showed that the seven catchments had similar trend in terms of the HAFs for the 24 IHAs. Given that more frequent severe floods and droughts were projected over Korean catchments, sound water supply strategies are definitely required to adapt to the alteration of streamflow. A wide range of HAFs between rainfall-runoff models for each catchment was detected by large variations in the magnitude of HAFs with the hydrologic models and the difference could be the hydrologic prediction uncertainty. There were no-consistent tendency in the order of HAFs between the hydrologic models. In addition, we found that the alterations of hydrologic regimes by climate change are smaller as the size of catchment is larger.

Description: The lower stretch of the Vistula is the most ice-jammed river section on the North European Plain. Since 1982, the structure of hanging dams has been studied by means of a mechanical non-core sampler. In this article, a selected of field research results of the hanging dams structure and the degree of filling of the cross-section with ice obtained during surface ice jam events in the years 1995-2014 are presented, along with an explanation of their causes. Surface ice jams occurred during spring snowmelt surges and ice breakups and also during freeze-up and ice covered periods. Their main cause was changes in the river flow and were also affected by anthropogenic sources. A characteristic feature of the analysed cross-sections was the considerable share of the underhanging ice dam's firm accumulation with ice floes, when the cross-section would be filled with ice in excess of 70%. In most cases, due to low river discharge, there was no substantial flooding damage. This article is protected by copyright. All rights reserved.

Description: Uruguay has stimulated the development of its forest sector since the promulgation of Forest Law N° 15 939 in December of 1987. Nevertheless, the substitution of natural grasslands with forest plantations for industrial use has raised concerns regarding hydrological processes of groundwater recharge and water consumption involving evapotranspiration. The purpose of this study is to assess the effects of this substitution approach on water resources. Input data were collected from two small experimental watersheds of roughly 100-200 hectares located in western Uruguay. The watersheds are characterized by Eucalyptus Globulus ssp. Maidenni and natural grasslands for cattle use. Total rainfall, stream discharge, rainfall redistribution, soil water content and groundwater level data were collected. Groundwater recharge was estimated from water table fluctuations and from groundwater contributions to base flows. Seasonal and annual water budgets were computed from October of 2006 to September of 2014 to evaluate changes in the hydrological processes. The data show a decrease in annual specific discharge of roughly 17% for mean hydrological years and no conclusive effects on annual groundwater recharge in the forested watershed relative to the reference pasture watershed. Reduced annual specific discharge is equivalent to the mean annual interception. The computed actual annual evapotranspiration is consistent with international catchment measurements. Reduction rates vary seasonally and according to accumulated rainfall and its temporary distribution. The degree of specific discharge decline is particularly high for drier autumns and winters (32 to 28%) when the corresponding rainfall varies from 275 to 400 mm. These results are of relevance for water resources management efforts, as water uses downstream can be affected. These findings, based on a study period dominated by anomalous wet springs and summers and by dry autumns and winters, oppose earlier results based on 34 years of rainfall and discharge data drawn from Uruguayan large basins. Copyright © 2016 John Wiley & Sons, Ltd.

Description: River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data-demanding deterministic models, and modeling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family (Toffolon and Piccolroaz, 2015), which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low-land natural rivers, lake outlets, snow-fed rivers, and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross-validation conducted over a 30-year period indicates that they can be suitably adopted for long-term analyses.

Description: As large, high-severity forest fires increase and snowpacks become more vulnerable to climate change across the western US, it is important to understand post-fire disturbance impacts on snow hydrology. Here, we examine, quantify, parameterize, model, and assess the post-fire radiative forcing effects on snow to improve hydrologic modeling of snow-dominated watersheds having experienced severe forest fires. Following a 2011 high-severity forest fire in the Oregon Cascades, we measured snow albedo, monitored snow and micrometeorological conditions, sampled snow surface debris, and modeled snowpack energy and mass balance in adjacent burned and unburned forest sites. For three winters following the fire, charred debris in the burned forest reduced snow albedo, accelerated snow albedo decay, and increased snowmelt rates thereby advancing the date of snow disappearance compared to the unburned forest. We demonstrate a new parameterization of post-fire snow albedo as a function of days-since-snowfall and net snowpack energy balance using an empirically-based exponential decay function. Incorporating our new post-fire snow albedo decay parameterization in a spatially-distributed energy and mass balance snow model, we show significantly improved predictions of snow cover duration and spatial variability of snow water equivalent across the burned forest, particularly during the late snowmelt period. Field measurements, snow model results, and remote sensing data demonstrate that charred forests increase the radiative forcing to snow and advance the timing of snow disappearance for several years following fire. This article is protected by copyright. All rights reserved.

Description: A spatially distributed representation of basin hydrology and transport processes in hydrologic models facilitates the identification of critical source areas and the placement of management and conservation measures. Floodplains are critical landscape features that differ from neighboring uplands in terms of their hydrological processes and functions. Accordingly, an important step in watershed modeling is the representation of floodplain and upland areas within a watershed. The aim of this study is (1) to evaluate four floodplain-upland delineation methods that use readily available topographic data (topographic wetness index, slope position, uniform flood stage, and variable flood stage) with regard to their suitability for hydrological models and (2) to introduce an evaluation scheme for the delineated landscape units. The methods are tested in three U.S. watersheds ranging in size from 334 to 629 km 2 with different climatic, hydrological and geomorphological characteristics. Evaluation of the landscape delineation methods includes visual comparisons, error matrices (i.e. cross-tabulations of delineated versus reference data), and geometric accuracy metrics. Reference data was obtained from SSURGO (Soil Survey Geographic database) and FEMA (Federal Emergency Management Agency) flood maps. Results suggest that the slope position and the variable flood stage method work very well in all three watersheds. Overall percentages of floodplain and upland areas allocated correctly were obtained by comparing delineated and reference data. Values range from 83 to 93 % for the slope position and from 80 to 95 % for the variable flood stage method. Future studies will incorporate these two floodplain-upland delineation methods into the subwatershed-based hydrologic model SWAT to improve the representation of hydrological processes within floodplain and upland areas. 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.

Description: Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of $min$ , $max$ , $text{direct}$ , and $text{subtractive}$ filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.

Description: In synthetic aperture radar (SAR) images, moving targets are usually smeared and/or imaged at incorrect positions due to the target motions during the SAR integration time. Moreover, since a high-resolution wide-swath SAR system is operated with a rather low pulse repetition frequency, a moving target will cause multiple ghost targets in the reconstructed SAR image. A new space–time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images. In this new framework, the dynamic steering vector concept is proposed. In addition, this paper develops a moving target processing scheme for clutter suppression and moving target imaging and location for a high-resolution wide-swath SAR system. Finally, we locate the well-focused moving targets at the stationary scene image without any disturbing artifacts. The simulated and real data are used to validate the effectiveness of our proposed method.

Description: The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most appropriate and disseminated models used to describe high-resolution polarimetric synthetic aperture radar (PolSAR) clutter. Generally, the texture is assumed polarization independent, which causes PolSAR data to present a spherical symmetry property, allowing for the usage of most of the algorithms present in the literature. Nevertheless, the existence of polarization-dependent clutter has also been reported, for which specific algorithms need to be derived. Therefore, it becomes clear that the first step in SAR data analysis should be the validation of the model employed. Within this context, this paper presents a new methodological framework to assess the conformity of multivariate high-resolution SAR data with respect to the product model in terms of asymptotic statistics. More precisely, spherical symmetry is investigated by applying statistical hypothesis testing on the structure of the quadricovariance matrix. Simulated data, data from the P-band airborne data set acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the performance of the derived test. The detection results are qualitatively and quantitatively analyzed, and some important conclusions are drawn regarding the methodology employed in analyzing SAR data.

Description: A synthetic aperture radar (SAR) imaging system usually produces pairs of bright area and dark area when depicting the ground objects, such as a building or tree and its shadow. Many buildings (trees) are aggregated together to form urban areas (forests). It means that the pairs of bright and dark areas often exist in the aggregated scenes. Conventional unsupervised segmentation approaches usually segment the scenes (e.g., urban areas and forests) into different regions simply according to the gray values of the image. However, a more convincing way is to regard them as the consistent regions. In this paper, we aim at addressing this issue and propose a new SAR image segmentation approach via a hierarchical visual semantic and adaptive neighborhood multinomial latent model. In this approach, the hierarchical visual semantic of SAR images is proposed, which divides SAR images into aggregated, structural, and homogeneous regions. Based on the division, different segmentation methods are chosen for these regions with different characteristics. For the aggregated region, locality-constrained linear coding-based hierarchical clustering is used for segmentation. For the structural region, visual semantic rules are designed for line object location, and a geometric structure window-based multinomial latent model is proposed for segmentation. For the homogeneous region, a multinomial latent model with adaptive window selection is proposed for segmentation. Finally, these results are integrated together to obtain the final segmentation. Experiments on both synthetic and real SAR images indicate that the proposed method achieves promising performances in terms of the consistencies of the regions and the preservations of the edges and line objects.

Description: Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output $text{RMSE}=16.3 text{cm}$ , improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. While the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined her- could potentially be extended to built environments.

Description: The product of multilook amplitudes (PMA) detector has been used to detect ships in high-resolution dual-polarization synthetic-aperture-radar images. However, the adaptive constant false-alarm rate (CFAR) technique of the PMA detector is desirable for practical applications, wherein a crucial problem is to find an appropriate model to describe the PMA statistics for varied sea surfaces. First, we consider a new probability density function to characterize the PMA statistics of homogeneous sea surfaces. Second, by using the new density and multiplicative model, the PMA detector's statistical model for nonhomogeneous sea surfaces is specified and demonstrated to be the $mathcal{G}^{0}$ distribution. Then, a theoretical analysis of the relationship between the performance of the standard CFAR detection and the parameters in the $mathcal{G}^{0}$ distribution is conducted. Experiments performed on the measured RADARSAT-2 and NASA/JPL AIRSAR images verify the effectiveness and appropriateness of the $mathcal{G}^{0}$ model for describing the statistical behavior of the PMA of sea clutter, as well as the usefulness of the model for practical ship-detection applications.

Description: Extraction of residential areas plays an important role in remote sensing image processing. Extracted results can be applied to various scenarios, including disaster assessment, urban expansion, and environmental change research. Quality residential areas extracted from a remote sensing image must meet three requirements: well-defined boundaries, uniformly highlighted residential area, and no background redundancy in residential areas. Driven by these requirements, this study proposes a global and local saliency analysis model (GLSA) for the extraction of residential areas in high-spatial-resolution remote sensing images. In the proposed model, a global saliency map based on quaternion Fourier transform (QFT) and a global saliency map based on adaptive directional enhancement lifting wavelet transform (ADE-LWT) are generated along with a local saliency map, all of which are fused into a main saliency map based on complementarities. In order to analyze the correlation among spectrums in the remote sensing image, the phase spectrum information of QFT is used on the multispectral images for producing a global saliency map. To acquire the texture and edge features of different scales and orientations, the coefficients acquired by ADE-LWT are used to construct another global saliency map. To discard redundant backgrounds, the amplitude spectrum of the Fourier transform and the spatial relations among patches are introduced into the panchromatic image to generate the local saliency map. Experimental results indicate that the GLSA model can better define the boundaries of residential areas and achieve complete residential areas than current methods. Furthermore, the GLSA model can prevent redundant backgrounds in residential areas and thus acquire more accurate residential areas.

Description: Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.

Description: Inspired by a cognitive radar in exploiting its environment to update current operation parameters and frequency diverse array (FDA) in offering a range-dependent beampattern due to the employment of frequency increments across the elements, this paper proposes a moving-target tracking approach to achieve cognitive radio frequency stealth using an FDA antenna for surveillance applications. Since surveillance systems are highly visible to intercept receivers, a traditional high-gain phased-array antenna beam is replaced by a series of low-gain FDA beams with spoiled frequency increments to reduce the system visibility, without degrading the surveillance system performance. Moreover, a cognitive closed-loop update scheme is presented to update the operation parameters in real time for improved moving-target tracking performance. All of the proposed methods are verified by simulation results.

Description: The replacement of the rational function model (RFM) by the rigorous sensor model (RSM) has been studied extensively and verified with many types of sensors and remote-sensing applications. However, relatively less research has been conducted on recovering RSM from RFM, and the few relating techniques can only be applied in specific circumstances. This paper proposes a novel linear method to obtain the position, attitude, and interior orientation (IO) elements of satellites based on the orientation information of the rays implied by the RFM. Instead of resection, forward intersection is used to solve for position, and an equivalent body coordinate system is introduced to overcome the strong correlation between the attitude and IO. The orientation information of the rays implied by the RFM is used to calculate the IO pixel by pixel. Experiments using the Ziyuan 3 panchromatic nadir sensor show that this method can recover the exterior orientation and IO elements effectively.

Description: The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected.

Description: Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, particularly in signal and image processing. Current NMF techniques have been limited to a single-objective optimization problem, in either its linear or nonlinear kernel-based formulation. In this paper, we propose to revisit the NMF as a multiobjective problem, particularly a biobjective one, where the objective functions defined in both input and feature spaces are taken into account. By taking the advantage of the sum-weighted method from the literature of multiobjective optimization, the proposed biobjective NMF determines a set of nondominated, Pareto optimal, solutions. Moreover, the corresponding Pareto front is approximated and studied. Experimental results on unmixing synthetic and real hyperspectral images confirm the efficiency of the proposed biobjective NMF compared with the state-of-the-art methods.

Description: We propose a lightweight sparsity-based algorithm, namely, the basic thresholding classifier (BTC), for hyperspectral image (HSI) classification. BTC is a pixelwise classifier which uses only the spectral features of a given test pixel. It performs the classification using a predetermined dictionary consisting of labeled training pixels. It then produces the class label and residual vector of the test pixel. Since incorporating spatial and spectral information in HSI classification is quite an effective way of improving classification accuracy, we extend our proposal to a three-step spatial–spectral framework. First, every pixel of a given HSI is classified using BTC. The resulting residual vectors form a cube which could be interpreted as a stack of images representing residual maps. Second, each residual map is filtered using an averaging filter. Finally, the class label of each test pixel is determined based on minimal residual. Numerical results on public data sets show that our proposal outperforms well-known support vector machine-based techniques and sparsity-based greedy approaches like simultaneous orthogonal matching pursuit in terms of both classification accuracy and computational cost.

Description: The intrinsic dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the spectral signatures of macroscopic materials) in the image, for dimensionality reduction or for subspace learning, among others. Recently, some approaches to perform spectral unmixing and superresolution locally have been proposed, which require a local estimation of the number of endmembers to use. However, the role of ID in local regions of hyperspectral images has not been properly addressed. Some important issues when dealing with small regions of hyperspectral data can seriously affect the performance of conventional hyperspectral ID estimators. We show that three factors mainly affect local ID estimation: the number of pixels in the local regions, which has to be high enough for the estimations to be relevant, the number of hyperspectral bands which complicates the estimations if the ambient space has a high dimensionality, and the noise, which can be misinterpreted as a signal when its power is important. Here, we review the hyperspectral ID estimators on the literature for local ID estimation, we show how they behave in a local setting on synthetic and real data sets, and we provide some guidelines to make proper use of these estimators in local approaches.

Description: This paper presents an algorithm for the retrieval of ozone profiles from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership satellite using a nonlinear optimal estimation method. The issue of channel selection is discussed. Based on a sensitivity analysis, we selected a spectral range of 990–1070 cm −1 for the ozone profile retrievals. Compared with ERA-Interim ozone profiles and eigenvector regression method profiles, the ozone climatology profile is better able to construct the a priori state. The retrieved CrIS profiles are in good agreement with smoothed high-vertical-resolution ozonesonde profiles. An analysis of the information content of the CrIS retrievals demonstrates that the CrIS measurements can provide useful information for capturing the spatial and temporal variations in ozone and are insensitive to ozone below 400 hPa. An error analysis revealed that smoothing error represents the main error source for the retrieved CrIS ozone profiles.

Description: This paper investigates the bistatic scattering from randomly corrugated surfaces with irregular grooves. For a given incident wave, the surface fields and the scattered field were computed by the method of moments in which the rooftop basis function was used to account for fast phase changes due to steep surface slopes. The total scattered field is decomposed into coherent and incoherent components to analyze their respective contributions. We found that, for randomly corrugated surfaces with irregular ridges or grooves, the coherent scattering is profound at several scattering angles with strong main lobes, whose beamwidths are strongly correlated with the ridge density. The numerical simulation has shown that the lobe angular shift away from the specular direction is quasi-linearly dependent on the ridge density, suggesting that the coherent scattering pattern substantially contains the surface geometric information. We expect this paper to offer deeper understanding of coherent imaging of rough surface and to help in designing a novel imaging system.

Description: There has been a great deal of research interest regarding changes in flow path/runoff source with increases in catchment area. However, there have been very few quantitative studies taking subscale variability and convergence of flow path/runoff source into account, especially in relation to headwater catchments. This study was performed to elucidate how the contributions and discharge rates of subsurface water (water in the soil layer) and groundwater (water in fractured bedrock) aggregate and change with catchment area increase, and to elucidate whether the spatial variability of the discharge rate of groundwater determines the spatial variability of stream discharge or groundwater contribution. The study area was a 5-km 2 forested headwater catchment in Japan. We measured stream discharge at 113 points and water chemistry at 159 points under base flow conditions. End-member mixing analysis (EMMA) was used to separate stream water into subsurface water and groundwater. The contributions of both subsurface water and groundwater had large variability below 1 km 2 . The contribution of subsurface water decreased markedly, while that of groundwater increased markedly, with increases in catchment area. The specific discharge of subsurface water showed a large degree of variability and decreased with catchment area below 0.1 km 2 , becoming almost constant above 0.1 km 2 . The specific discharge of groundwater showed large variability below 1 km 2 and increased with catchment area. These results indicated that the variabilities of stream discharge and groundwater contribution corresponded well with the variability of the discharge rate of groundwater. However, below 0.1 km 2 , it was necessary to consider variations in the discharge rates of both subsurface water and groundwater.

Description: In arctic and sub-arctic environments, mercury (Hg), and more specifically toxic methylmercury (MeHg) is of growing concern to local communities because of its accumulation in fish. In these regions, there is particular interest in the potential mobilisation of atmospherically-deposited Hg sequestered in permafrost that is thawing at unprecedented rates. Permafrost thaw and the resulting ground surface subsidence transforms forested peat plateaus into treeless and permafrost-free thermokarst wetlands where inorganic Hg released from the thawed permafrost and draining from the surrounding peat plateaus may be transformed to MeHg. This study begins to characterise the spatial distribution of MeHg in a peat plateau-thermokarst wetland complex, a feature that prevails throughout the wetland-dominated southern margin of thawing discontinuous permafrost in Canada's Northwest Territories. We measured pore water total Hg, MeHg, dissolved organic matter (DOM) characteristics and general water chemistry parameters to evaluate the role of permafrost thaw on the pattern of water chemistry. A gradient in vegetation composition, water chemistry and DOM characteristics followed a toposequence from the ombrotrophic bogs near the crest of the complex to the poor fens at its downslope margins. We found that pore waters in poor fens contained elevated levels of MeHg and the water draining from these features had dissolved MeHg concentrations 4.5 to 14.5 times higher than the water draining from the bogs. It was determined through analysis of historical aerial images that the poor fens in the toposequence had formed relatively recently (early 1970s) as a result of permafrost thaw. Differences between the fens and bogs are likely to be due to their differences in groundwater function and this suggests that permafrost thaw in this landscape can result in hotspots for Hg methylation that are hydrologically connected to downstream ecosystems.

Description: The objective of this work was to build a prognostic water flow model and potentially toxic elements (lead, cadmium, zinc) transport model in the unsaturated zone. Research was conducted in the catchment area of Kosnica regional wellfield, where the unsaturated zone is characterised by Fluvisol. Lower sorption capacities were determined in the first horizons for all three potentially toxic elements. Correlation coefficient of the measured and simulated values of tracer concentration is 0.58 for the AC horizon and 0.84 for the 2C/C1 horizon. Based on calibrated water flow and transport parameters, a prognostic water flow model and potentially toxic elements (lead, cadmium, zinc) transport model in the unsaturated zone was built. In case of an accidental spill of potentially toxic elements with concentrations of 1000 mg/L, the risk of contamination of the aquifer is present.