ALBERT

Description: Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.

Description: Land–sea contamination observed in Soil Moisture and Ocean Salinity (SMOS) brightness temperature images is found to have two main contributions: the floor error inherent of image reconstruction and a multiplicative error either in the antenna temperature or in the visibility samples measured by the correlator. The origin of this last one is traced down to SMOS calibration parameters to yield a simple correction scheme, which is validated against several geophysical scenarios. Autoconsistency rules in interferometric synthesis together with redundant and complementary calibration procedures provide a robust SMOS calibration scheme.

Description: This letter proposes a novel algorithm, which is based on the generalized method of moments (GMM), for the estimation and correction of phase errors induced in synthetic aperture radar (SAR) imagery. The GMM algorithm is used to replace the original phase-estimation kernel in the basic structure of the phase-gradient-autofocus algorithm. Since this novel algorithm does not require the observed signal to be a certain distribution model, it is able to estimate arbitrary phase errors. The GMM algorithm has the ability of estimating range-dependent phase errors, which makes it an efficient estimator. As a result, higher accuracy of the estimated phase errors and a better focused image can be achieved. Excellent results have been obtained in autofocusing and imaging experiments on real SAR data.

Description: Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.

Description: A concern in hyperspectral image classification is the high number of required training samples. When traditional classifiers are applied, feature reduction (FR) techniques are the most common approaches to deal with this problem. Subspace-based classifiers, which are developed based on high-dimensional space characteristics, are another way to handle the high dimension of hyperspectral images. In this letter, a novel subspace-based classification approach is proposed and compared with basic and improved subspace-based classifiers. The proposed classifier is also compared with traditional classifiers that are accompanied by an FR technique and the well-known support vector machine classifier. Experimental results prove the efficiency of the proposed method, especially when a limited number of training samples are available. Furthermore, the proposed method has a very high level of automation and simplicity, as it has no parameters to be set.

Description: To improve the spatial density of measurement points of persistent-scatterer interferometry, distributed scatterer (DS) should be considered and processed. An important procedure in DS interferometry is the phase triangulation (PT). This letter introduces two modified PT algorithms (i.e., equal-weighted PT and coherence-weighted PT) and analyzes the mathematical relations between different published PT methods (i.e., the maximum-likelihood phase estimator, least squares estimator, and eigendecomposition-based phase estimators). The analysis shows that the above five PT methods share very similar mathematical forms with different weight values in the estimation procedure.

Description: Sparse representation-based classifier and its variants have been widely adopted for hyperspectral image (HSI) classification recently. However, sparse representation is unstable so that similar features might obtain significantly different sparse codes. Despite the instability, we find that the sparse codes follow a class-dependent distribution under the structured dictionary consisting of training samples from all classes. Based on this observation, a novel discriminative feature, sparse code histogram (SCH), is developed for HSI classification. By counting the SCH of each sample from the sparse codes of its spatial neighbors, we can statistically obtain the distribution pattern of sparse codes of the class to which the sample belongs, and then treat the SCH as a new feature for classification. To reduce the possible outliers among the neighbors, a shape-adaptive neighborhood extractor is also employed to enhance the stability of the histogram feature. Experimental results demonstrate that SCH enjoys a strong discriminative power, which can achieve notably better performance than several state-of-the-art methods for HSI classification with limited training samples.

Description: Several detection statistics have been proposed for detecting fine ground disturbances between two synthetic aperture radar (SAR) images, such as vehicle tracks. The standard method involves estimating a local correlation coefficient between images. Other methods have been proposed using various statistical hypothesis tests. One of these alternative methods is a generalized likelihood ratio test (GLRT), which compares a full-correlation image model to a no-correlation image model. In this letter, we expand the GLRT to polarimetric SAR data and derive the appropriate GLRT detection statistics. Additionally, we explore relaxing the equal variance/equal polarimetric covariance assumptions used in previous results and find improved performance on macroscopic scene changes.

Description: For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.

Description: This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.

Description: In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input–multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.

Description: In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.

Description: In this letter, new models for the spatial correlation of sea clutter texture and intensity are proposed as improved versions of current power law models or exponential decay model. The models for texture have three unknown parameters, and thus can be called triparametric models. The structure of the models is a weighted sum of two components, which can describe the decaying process of the correlation coefficient with spatial lags, as well as the periodic behavior due to the existence of transient coherent structures in sea clutter. Unknown parameters are optimized by the nonlinear least square fit method. Models for sea clutter intensity can be obtained through a linear transform for uncorrelated speckle based on the compound-Gaussian representation of sea clutter. The proposed models are validated and compared with current models using S- and C-band measured sea clutter data. Analysis results indicate the effectiveness of the proposed models in that they can describe the behavior of spatial correlation coefficients with higher accuracy.

Description: Terrestrial laser scanning (TLS) has become a popular tool for acquiring source data points which can be used to construct digital elevation models (DEMs) for a wide number of applications. A TLS point cloud often has a very fine spatial resolution, which can represent well the spatial variation of a terrain surface. However, the uncertainty in DEMs created from this relatively new type of source data is not well understood, which forms the focus of this letter. TLS survey data representing four terrain surfaces of different characteristics were used to explore the effects of surface complexity and typical TLS data density (in terms of data point spacing) on DEM accuracy. The spatial variation in TLS data can be decomposed into parts corresponding to the signal of spatial variation (of terrain surfaces) and noise due to measurement error. We found a linear relation between the DEM error and the typical TLS data spacings considered (30–100 mm) which arises as a function of the interpolation error, and a constant contribution from the propagated data noise. This letter quantifies these components for each of the four surfaces considered and shows that, for the interpolation method considered here, higher density sampling would not be beneficial.

Description: A novel way to estimate the live fuel moisture content (LFMC) was explored from the ratio of canopy water content (CWC) and foliage dry biomass (FDB). The CWC was estimated using the PROSAIL (PROSPECT + SAIL) radiative transfer model from the Landsat 8 product. A weak constraint 4-D variational data assimilation method was employed to assimilate the temporally estimated leaf area index into a soil-water-atmosphere-plant (SWAP) model for optimizing the model control variables. Then, the SWAP model was reinitialized with this optimum set of control variables, and better prediction of FDB was obtained. Results showed that a high accuracy level was achieved for the estimated CWC ( $R^{2}=0.91$ , $mbox{RMSE}=84.74 mbox{g/m}^2$ ) and FDB ( $R^2=0.88$ , $mbox{RMSE}=48.54 mbox{g/m} ^2$ ) when compared with in situ measured values. However, the accuracy level of estimated LFMC was poor ( $R^2=0.59$ , $mbox{RMSE} =30.85%$ ) . Further analyses find that the estimated LFMC is reliable for low LFMC but challenged for high LFMC, which indicates that the presented method still makes sense to the assessment of wildfire risk since the wildfire generally occurs when the vegetation is in low LFMC condition.

Description: In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.

Description: Automatic urban area detection in remote sensing images is an important application in the field of earth observation. Most of the existing methods employ feature classifiers and thereby contain a data training process. Moreover, some methods cannot detect urban areas in complex scenes accurately. This letter proposes an automatic urban area detection method that uses multiple features that have different resolutions. First, a downsampled low-resolution image is used to segment the candidate area. After the corner points of the urban area are extracted, a weighted Gaussian voting matrix technique is employed to integrate the corner points into the candidate area. Then, the edge features and homogeneous region are extracted by using the original high-resolution image. Using these results as the input, the processes of guided filtering and contrast enhancement can finally detect accurately the urban areas. This method combines multiple features, such as corner, edge, and regional characteristics, to detect the urban areas. The experimental results show that the proposed method has better detection accuracy for urban areas than the existing algorithms.

Description: In marine sciences, time series are often nonlinear and nonstationary. Adequate and specific methods are needed to analyze such series. In this letter, an application of the empirical mode decomposition method (EMD) associated to the Hilbert spectral analysis (HSA) is presented. Furthermore, EMD-based time-dependent intrinsic correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series. Four temperature time series obtained from automatic measurements in nearshore waters of the Réunion island are considered, recorded every 10 min from July 2011 to January 2012. The application of the EMD on these series and the estimation of their power spectra using the HSA are illustrated. The authors identify low-frequency tidal waves and display the pattern of correlations at different scales and different locations. By TDIC analysis, it was concluded that the high-frequency modes have small correlation, whereas the trends are perfectly correlated.

Description: Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV).

Description: We compare five slope correction methods developed by Walter et al. , Montes et al. , Schleppi et al. , España et al. , and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

Description: In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Description: Accurately mapping forest carbon density by combining sample plots and remotely sensed images has become popular because this method provides spatially explicit estimates. However, mixed pixels often impede the improvement of the estimation. In this letter, regression modeling and spectral unmixing analysis were integrated to improve the estimation of forest carbon density for the You County of Hunan, China, using Landsat Thematic Mapper images. Linear spectral unmixing with and without a constraint (LSUWC and LSUWOC) and nonlinear spectral unmixing (NSU) were compared to derive the fractions of five endmembers, particularly forests. Stepwise regression, logistic regression, and polynomial regression (PR) with and without the forest fraction used as an independent variable and the product of the forest fraction image and the map from the best model without the forest fraction were compared. The models were developed using 56 sample plots, and their results were validated using 26 test plots. The decomposition of mixed pixels was assessed using higher spatial resolution SPOT images and a corresponding land cover map. The results showed that 1) LSUWC more accurately estimated the endmember fractions than LSUWOC and NSU, 2) PR had the greatest estimation accuracy of forest carbon, and 3) combining regression modeling and spectral unmixing increased the estimation accuracy by 31%–39%, and introducing the forest fraction into the regressions performed better than the product of forest fraction image and the results from PR without the fraction. This implied that the integrations provided great potential in reducing the impacts of mixed pixels in mapping forest carbon.

Description: This letter proposes a multiresolution technique to address the high computational cost in remote sensing image registration. The scale-invariant feature transform is applied to detect keypoints and descriptors, and then, global information combined with descriptors is utilized to establish keypoint mappings. Keypoints are first classified according to their octaves. Then, in the lowest resolution, the keypoints of the largest octave are mapped with descriptors and the global information, giving an initial affine transformation $T_0$ . In the next octave, the keypoints of the second largest octave are mapped by employing $T_0$ to narrow the space of matching keypoints. By this means, the process of establishing keypoint correspondences is conducted from one resolution (octave) to the next as the obtained transformation gets finer until we get to the highest resolution. Due to the high computational expense of computing global information, the proposed technique is important for aligning large-size remote sensing imagery. Experimental results show that the proposed method can achieve a comparable registration accuracy but with a less computational cost than directly building keypoint mappings on images of large size.

Description: Presents corrections to the paper, "Estimation of forest biomass from two-level model inversion of single-pass InSAR data" (Soja, M.J., et al.,Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 5083???5099, Sep. 2015).

Description: In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial–spectral HSI classification for the first time in the literature.

Description: Compared with airborne laser scanning, terrestrial laser scanning (TLS) offers ground-based point cloud data of trees and provides greater potential to accurately estimate tree and stand parameters. However, there is a lack of effective methods to accurately identify locations of individual trees from TLS point cloud data. It is also unknown whether the estimation accuracy of the parameters, including tree height (H), diameter at breast height (DBH), and so on, using TLS can meet the requirement of forest management and planning. In this letter, a novel method to effectively process point cloud data and further determine the locations of individual trees in a stand based on the central coordinates of point cloud data on a defined grid according to the largest DBH was developed. Moreover, a point-cloud-data-based convex hull algorithm and the cylinder method were, respectively, used to estimate DBH and H of individual trees. This study was conducted in a pure Chinese fir plantation of 45 trees located in Huang-Feng-Qiao forest farm, You County of Hunan, China. The comparison of the estimated and observed values showed that the obtained tree locations had errors of less than 20 cm, and the relative root mean square errors for the estimates of both DBH and H were less than 5%. This implies that TLS is very promising for the retrieval of tree and stand parameters in forest stands. For the applications of these methods to mixed forests with a structure of multilayer canopies, further examination is needed.

Description: In this letter, a novel algorithm for attitude measurement based on a 3-D electromagnetic model (3-D em-model) is proposed. The 3-D em-model is established offline based on the geometric structure of the target, and it can be used to predict the scattering features at different target attitudes. In order to measure the attitude of the air target, we design a bistatic step frequency radar system. The directions of the two radars' lines of sight (LOSs) relative to the target are acquired by matching the high-resolution range profiles (HRRPs) from the target echoes to the HRRPs generated from the 3-D em-model. Since the directions of two radars' LOSs relative to the Earth are already known, the absolute attitude of the target can be acquired. The innovative contributions of this letter are as follows: 1) A comprehensive theoretical analysis of air target attitude measurement based on its own 3-D em-model is proposed; 2) the method can be applied to different kinds of air targets such as aircraft, satellite, missile, etc.; 3) the proposed attitude measurement method does not require target motion model in advance; and 4) the proposed algorithm can be applied to any kind of step frequency waveforms. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in the chamber verify the validity of the method.

Description: The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.

Description: We investigate how the choice of injection mode impacts transport properties in kilometer-scale three-dimensional discrete fracture networks (DFN). The choice of injection mode, resident or flux-weighted, is designed to mimic different physical phenomena. It has been hypothesized that solute plumes injected under resident conditions evolve to behave similarly to solutes injected under flux-weighted conditions. Previously, computational limitations have prohibited the large scale simulations required to investigate this hypothesis. We investigate this hypothesis by using a high performance DFN suite, dfnWorks , to simulate flow in kilometer-scale three-dimensional DFNs based on fractured granite at the Forsmark site in Sweden, and adopt a Lagrangian approach to simulate transport therein. Results show that after traveling through a pre-equilibrium region both injection methods exhibit linear scaling of the first moment of travel time and power law scaling of the breakthrough curve with similar exponents, slightly larger than two. The physical mechanisms behind this evolution appear to be the combination of in-network channeling of mass into larger fractures, which offer reduced resistance to flow, and in-fracture channeling, which results from the topology of the DFN. This article is protected by copyright. All rights reserved.

Description: Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical post-processing in order to account for systematic errors in terms of both location and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate post-processing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS) post-processing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers three sub-catchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. In this study the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modelling the temporal dependencies of probabilistic hydrologic forecasts. This article is protected by copyright. All rights reserved.

Description: A three-dimensional mathematical model that describes transport of contaminant in a horizontal aquifer with simultaneous diffusion into a fractured clay formation is proposed. A group of semi-analytical solutions is derived based on specific initial and boundary conditions as well as various source functions. The analytical model solutions are evaluated by numerical Laplace inverse transformation and analytical Fourier inverse transformation. The model solutions can be used to study the fate and transport in a three-dimensional spatial domain in which a non-aqueous phase liquid exists as a pool atop a fractured low permeability clay layer. The non-aqueous phase liquid gradually dissolves into the groundwater flowing past the pool, while simultaneously diffusing into the fractured clay formation below the aquifer. Mass transfer of the contaminant into the clay formation is demonstrated to be significantly enhanced by the existence of the fractures, even though the volume of fractures is relatively small compared to the volume of the clay matrix. The model solution is a useful tool in assessing contaminant attenuation processes in a confined aquifer underlain by a fractured clay formation. This article is protected by copyright. All rights reserved.

Description: The objective of this study is to incorporate a time-dependent SCS CN method (SMA_CN) in Soil and Water Assessment Tool (SWAT) and compare its performance with the existing CN method in SWAT by simulating the hydrology of two agricultural watersheds in Indiana, United States. Results show that fusion of the SMA_CN method causes decrease in runoff volume and increase in profile soil moisture content, associated with larger groundwater contribution to the streamflow. In addition, the higher amount of moisture in the soil profile slightly elevates the actual evapotranspiration. The SMA-based SWAT configuration consistently produces improved goodness of fit scores and less uncertain outputs with respect to streamflow during both calibration and validation. The SMA_CN method exhibits better match with the observed data for all flow regimes, thereby addressing issues related to peak and low flow predictions by SWAT in many past studies. Comparison of the calibrated model outputs with field-scale soil moisture observations reveal that the SMA overhauling enables SWAT to represent soil moisture condition more accurately, with better response to the incident rainfall dynamics. While the results from the modification of the SCS method in SWAT are promising, more studies including watersheds with various physical and climatic settings are needed to validate the proposed approach. This article is protected by copyright. All rights reserved.

Description: We adapted Newton's Law of Cooling to model downstream water temperature change in response to stream-adjacent forest harvest on small and medium streams (average 327 ha in size) throughout the Oregon Coast Range, USA. The model requires measured stream gradient, width, depth and upstream control reach temperatures as inputs and contains two free parameters which were determined by fitting the model to measured stream temperature data. This model reproduces the measured downstream temperature responses to within 0.4 C ° for 15 of the 16 streams studied and provides insight into the physical sources of site-to-site variation among those responses. We also use the model to examine how the pre-to-post harvest change in daily maximum stream temperature depends on distance from the harvest reach. The model suggests that the pre-to-post harvest temperature change approximately 300  m downstream of the harvest will range from roughly 82% to less than 1% of that temperature change which occurred within the harvest reach, depending primarily on the downstream width, depth, and gradient. Using study-averaged values for these channel characteristics the model suggests that for a stream representative of those in the study, the temperature change approximately 300  m downstream of the harvest will be 56% of the temperature change which occurred within the harvest reach. This adapted Newton's Law of Cooling procedure represents a highly practical means for predicting stream temperature behavior downstream of timber harvests relative to conventional heat budget approaches, and is informative of the dominant processes affecting stream temperature. This article is protected by copyright. All rights reserved.

Description: The temporal variations (diurnal and annual) in arboreal $(varepsilon_mathrm{Tree})$ and bare soil $(varepsilon_mathrm{Soil})$ dielectric constants and their correlation with precipitation were examined for several trees in Japan. A significant (1 $sigma$ (standard deviation) and 2 $sigma$ ) $varepsilon_mathrm{Tree}$ increase is observed after rainfall at 89.8% and 90.5% probability. However, rainfall does not always induce significant $varepsilon_mathrm{Tree}$ increases. Rainfall of more than 5 mm/day can induce 1 $sigma$ $varepsilon_mathrm{Tree}$ Tree increase at a 59.6% probability. In order to examine whether the increase in $varepsilon_mathrm{Tree}$ affects the L-band $sigma^{0}$ variation in a forest, the four-year temporal variation of the L-band backscattering coefficient $(sigma^{0})$ was estimated from observations by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar. Observed maximum absolute deviations from the mean over the forest area were 1.0 and 1.2 dB for $sigma_{mathrm{HH}}^{0}$ and $sigma_mathrm{HV}^{0}$ , respectively, and 4.0 and 3.0 dB over open land. $sigma^{0}$ and rainfall correlations show that $varepsilon_mathrm{Tree}$ and $sigma_mathrm{Forest}^{0}$ are proportional to precipitation integrated over seven or eight days; $varepsilon_mathrm{Soil}$ and $sigma_mathrm{Open land}^{0}$ are proportional to precipitation integrated over three days. This finding indicates that $varepsilon_mathrm{Tree}$ variations influence $sigma_{mathrm{Forest areas}}^{0}$ . A stronger correlation between $sigma_mathrm{HV}^{0}$ and precipitation is observed in several sites with low $sigma_mathrm{HV}^{0}$ , where less biomass is expected, and several sites with high $sigma_mathrm{HV}^{0}$ , where more biomass is expected. A weaker correlation between $sigma_mathrm{HV}^{0}$ and precipitation is observed for several sites with high $sigma_mathrm{HV}^{0}$ . These differences may be explained by the different contributions of double bounce scattering and potential transpiration, which is a measure of the ability of the atmosphere to remove water from the surface th

Description: The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle-dependent patterns of surface reflected radiation propagate into products, hinder quantitative assessment of biophysical/biochemical parameters, and decrease the comparability of data from multiple flight lines. The Ross–Li model, originally developed for multiangular observations, can be inverted to estimate and correct for surface anisotropy effects. This requires land cover be stratified into distinct types of scattering behavior. When the observations subsumed in these classes cover a range of view angles, a pseudo multiangular view on the surface can be employed to invert the Ross–Li model. A discrete land cover classification, however, bears the risk of inappropriate scattering correction resulting in spatial artifacts in the corrected data, predominantly in transition regions of two land cover types (e.g., soil and sparse vegetation with varying fractions). We invert the Ross–Li model on continuous land cover fraction layers. We decompose land cover in dominating structural types using linear spectral unmixing. Ross–Li kernel weights and formulations are estimated for each type independently; the correction is then applied pixel-wise according to the fractional distribution. The corrected Airborne Prism EXperiment imaging spectrometer data show significant reduction of anisotropic reflectance effects of up to 90% (average 60% to 75%, $p=0.05$ ), measured in the overlapping regions of adjacent flight lines. No spatial artifacts or spectral irregularities are observed after correction.

Description: Most spaceborne sensors have a tradeoff between high spatial and high temporal resolutions. This tradeoff limits the use of remote sensing data in various applications that require images in both the high spatial and high temporal resolutions. In this paper, we propose a novel technique to create a fine spatial and high temporal resolution images at a ground-based data processing system. Resourcesat-2 is one of the Indian Space Research Organization missions, and it carries the Linear Imaging Self-Scanning sensors (LISS III and LISS IV) and an Advanced Wide-Field Sensor (AWiFS) onboard. The spatial resolution of LISS III is 23.5 m, and that of AWiFS is 56 m. The temporal resolution of LISS III is 24 days, and that of AWiFS is five days. The proposed method creates a synthetic LISS-III image at 23.5-m spatial and five-day temporal resolutions. It is based on the subpixel relationship between a single AWiFS–LISS-III image pair, which is acquired before or after the prediction date. In temporal data composition, spurious spatial discontinuities are inevitable for land-cover type changes. These discontinuities were identified with temporal edge primitives and were smoothed with a spatial-profile-averaging method. A synthetic LISS-III image for time $t_{k}$ is predicted from an AWiFS image at time $t_{k}$ and a single AWiFS–LISS-III image pair at time $t_{0}$ , where $t_{0}ne t_{k}$ . Experimental results demonstrated that the proposed method is superior in terms of the computational efficiency and prediction accuracy with the other existing methods.

Description: Glacier avalanches are natural hazards that could damage infrastructures and threaten lives in high-altitude mountainous terrains. On April 7, 2012, a massive ice avalanche struck a Pakistani base at Gayari sector, Saltoro Valley, and buried/killed 148 soldiers and civilians. Keeping in view the catastrophe, a study was designed with the objectives to: 1) model and simulate the Gayari sector glacier avalanche incident in terms of height, extent, velocity, pressure, and momentum; 2) generate hazard risk assessment of possible other glacier avalanches in the Saltoro Valley through modeling and simulation; and 3) suitability analysis of current camp sites and recommendation of new safe camps sites locations in the Saltoro valley. To simulate the Gayari sector glacier event and other Glacier possible avalanches, a physical process-based rapid mass movements (RAMMS) was used. The RAMMS has two main components, i.e., Voellmy–Salm (VS) model and random kinetic energy, which deals with variables such as avalanche height and the mean avalanche velocity during the course of simulation. The suitability analysis of current camp sites were achieved using weighted overlay analysis with different constraints in ArcGIS Spatial Analyst. The RAMMS model simulation of the Gayari avalanche event predicted a maximum velocity of 74 ms -1 , generating a pressure of 5074 kPa and attaining a height of 45 m, whereas the predicted debris volume on the ground was 3.8145 million m 3 . A good agreement was found between actual debris height and extent, as compared with the RAMMS model output. The RAMMS model simulated all the potential tributary glacier avalanches of Saltoro valley very well. The weighted overlay analyses in ArcGIS Spatial Analyst revealed that the existing camp sites are safe and were not threatened by the glacier avalanche hazard. However, it was recommended that the Gayari camp should not be constructed at the same location and should be relocated- to the proposed safe camp sites identified in this research study. The proposed methodology developed in the current study could be applied in the Siachen conflict zone for avalanche hazard/risk analysis of all the camp sites located in the valley.

Description: Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested.

Description: We introduce a practical and accurate model, referred to as “GO4,” to describe near-nadir microwave scattering from the sea surface, and at the same time, we address the issue of the filtered mean square slope (mss) conventionally used in the geometrical optics model. GO4 is a simple correction of this last model, taking into account the diffraction correction induced by the rough surface through what we call an effective mean square curvature (msc). We evaluate the effective msc as a function of the surface wavenumber spectrum and the radar frequency and show that GO4 reaches the same accuracy as the physical optics model in a wide range of incidence and frequency bands with the sole knowledge of the mss and msc parameters. The key point is that the mss entering in GO4 is not the filtered but the total slope. We provide estimation of the effective msc on the basis of classical sea spectrum models. We also evaluate the effective msc from near-nadir satellite data in various bands and show that it is consistent with model predictions. Non-Gaussian effects are discussed and shown to be incorporated in the effective msc. We give some applications of the method, namely, the estimation of the total sea surface mss and the recalibration of relative radar cross sections.

Description: This paper evaluates the radiometric accuracy of observations from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership and Sondeur Atmospherique du Profil d' Humidité Intropicale par Radiométrie (SAPHIR) onboard Megha-Tropiques through intercalibration and validation versus in situ radiosonde and Global Positioning System Radio Occultation (GPS-RO) observations. SAPHIR and ATMS water vapor channels operate at slightly different frequencies. We calculated the bias due to radiometric errors as the difference between the observed and simulated differences between the two instruments. This difference, which is often referred to as double difference, ranges between 0.3 and 0.7 K, which shows good consistency between the instruments. We used a radiative transfer model to simulate the satellite brightness temperatures (Tbs) using radiosonde and GPS-RO profiles and then compared simulated and observed Tbs. The difference between radiosonde and ATMS Tbs for the middle and upper tropospheric temperature sounding channels was less than 0.5 K at most stations, but the difference between radiosonde and ATMS/SAPHIR Tbs for water vapor channels was between 0.5 and 2.0 K. The larger bias for the water vapor channels is mainly due to several errors in radiosonde humidity observations. The mean differences between the ATMS observations and the Tbs simulated using GPS-RO profiles were 0.2, 0.3, 0.4, 0.2, and −0.2 K for channels 10–14, respectively; and the uncertainty increases from 0.02 K for channel 10 to 0.07 K for channel 14.

Description: This paper presents a completely automatic processing chain for orthorectification of optical pushbroom sensors. The procedure is robust and works without manual intervention from raw satellite image to orthoimage. It is modularly divided in four main steps: metadata extraction, automatic ground control point (GCP) extraction, geometric modeling, and orthorectification. The GCP extraction step uses georeferenced vector roads as a reference and produces a file with a list of points and their accuracy estimation. The physical geometric model is based on collinearity equations and works with sensor-corrected (level 1) optical satellite images. It models the sensor position and attitude with second-order piecewise polynomials depending on the acquisition time. The exterior orientation parameters are estimated in a least squares adjustment, employing random sample consensus and robust estimation algorithms for the removal of erroneous points and fine-tuning of the results. The images are finally orthorectified using a digital elevation model and positioned in a national coordinate system. The usability of the method is presented by testing three RapidEye images of regions with different terrain configurations. Several tests were carried out to verify the efficiency of the procedure and to make it more robust. Using the geometric model, subpixel accuracy on independent check points was achieved, and positional accuracy of orthoimages was around one pixel. The proposed procedure is general and can be easily adapted to various sensors.

Description: This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequency-modulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.

Description: Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

Description: We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.

Description: Radar-based remote sensing for measurement of ocean surface waves presents advantages over conventional point sensors such as wave buoys. As its use becomes more widespread, it is important to understand the sensitivity of the extracted wave parameters to the characteristics of the radar and the scatterers. To examine such issues, experiments were performed offshore of the Scripps Institution of Oceanography pier in July 2010. Radar measurements in low wind speeds were performed with a dual-polarized high-resolution X-band pulse-Doppler radar at low grazing angles along with two independent measurements of the surface waves using conventional sensors, a GPS-based buoy, and an ultrasonic array. Comparison between radar cross section (RCS) and Doppler modulations shows peak values occurring nearly in-phase, in contrast with tilt modulation theory. Spectral comparisons between Doppler-based and RCS-based spectra show that Doppler-based spectra demonstrate greater sensitivity to swell-induced modulations, whereas RCS-based spectra show greater sensitivity to small-scale modulations (or generally have more noise at high frequency), and they equally capture energy at the wind wave peak. Doppler estimates of peak period were consistent with the conventional sensors, whereas the RCS differed in assignment of peak period to wind seas rather than swell in a couple of cases. Higher order period statistics of both RCS and Doppler were consistent with the conventional sensors. Radar-based significant wave heights are lower than buoy-based values and contain nontrivial variability of ∼33%. Comparisons between HH and VV polarization data show that VV data more accurately represent the wave field, particularly as the wind speeds decrease.

Description: Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model.

Description: The on-orbit radiometric calibration of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite is carried out primarily through observations of a fully illuminated solar diffuser (SD) panel. Accurate knowledge of the solar spectral radiance scattered from the SD is available. The sensor aperture spectral radiance is assumed to be a quadratic polynomial function of a VIIRS detector's background-subtracted response in digital number. The coefficients of the polynomial were initially determined prelaunch. Once on orbit, we assume that these coefficients change uniformly by a common calibration factor, which is referred to as the $F$ -factor. The known solar spectral radiance scattered from a fully illuminated SD allows for the determination of these $F$ -factors. We describe the methodology and the associated algorithms used in the calculation of the RSB $F$ -factors. Our results show that the $F$ -factors change over time, with the largest change occurring at a wavelength of 862 nm (with a value of about 1.55 on day 950 after the satellite launch, relative to its value at the beginning of the launch) . In addition, we estimate the relative error standard deviations of the computed top-of-the-atmosphere reflectance at the detector pixel level. On day 950 of the mission, the relative error standard deviations are all less or equal to 0.016, except for the M11 band (band central wavelength of 2257 nm) , which has a relative error standard deviation of about 0.049 due to a very low signal-to-noise ratio.

Description: Digital elevation models (DEMs) that are used in hydrological applications must be processed to remove sinks, mainly topographic depressions. Flow enforcement techniques include filling methods, which raise elevations within depressions, breaching, which carves channels through blockages, and hybrid methods. Despite previous research demonstrating the large impact to DEMs and subsequent analyses of depression filling, it is common practice apply this technique to flow enforcement. This is partly due to the greater efficiency of depression filling tools compared to breaching counterparts, which often limits breaching to applications of small- to moderate-sized DEMs. A new hybrid flow enforcement algorithm is presented in this study. The method can be run in complete breaching, selective breaching (either breached or filled), or constrained breaching (partial breaching) modes, allowing for greater flexibility in how practitioners enforce continuous flow paths. Algorithm performance was tested with DEMs of varying topography, spatial extents, and resolution. The sites included three moderate sized DEMs (52,000,000 to 190,000,000 cells) and three massive DEMs of the Iberian Peninsula, and the Amazon and Nile River basins, the largest containing nearly one billion cells. In complete breaching mode, the new algorithm required 87% of the time needed by a filling method to process the test DEMs, while the selective breaching and constrained breaching modes, operating with maximum breach depth constraints, increased run times by 8% and 27% respectively. Therefore, the new algorithm offers comparable performance to filling and the ability to process massive topographic data sets, while giving practitioners greater flexibility and lowering DEM impact. This article is protected by copyright. All rights reserved.

Description: Mountain snowpacks provide most of the annual discharge of western U.S. rivers, but the future of water resources in the western U.S. is tenuous, as climatic changes have resulted in earlier spring melts that have exacerbated summer droughts. Compounding changes to the physical environment are biotic disturbances including the mountain pine beetle (MPB), which has decimated millions of acres of western North American forests. At the watershed scale, MPB disturbance increases the peak hydrograph, and at the stand scale the ‘gray’ phase of MPB canopy disturbance decreases canopy snow interception, increases snow albedo, increases net shortwave radiation and decreases net longwave radiation versus the ‘red’ phase. Fewer studies have been conducted on the red phase of MPB disturbance, and in the mixed coniferous stands that may follow MPB-damaged forests. We measured the energy balance of four snowpacks representing different stages of MPB damage, management, and recovery: a lodgepole pine stand, a MPB-infested stand in the red phase, a mixed coniferous stand (representing one successional trajectory), and a clearcut (representing reactive management) in the Tenderfoot Creek Experimental Forest in Montana, USA. Net longwave radiation was lower in the MPB-infested stand despite higher basal area and plant area index of the other forests, suggesting that the dessicated needles serve as a less effective thermal buffer against longwave radiative losses. Eddy covariance observations of sensible and latent heat flux indicate that they are of similar but opposite magnitude, on the order of 20 MJ m −2 during the melt period. Further analyses reveal that net turbulent energy fluxes were near zero due to the temperature and atmospheric vapor pressure encountered during the melt period. Future research should place snow science in the context of forest succession and management, and address important uncertainties regarding the timing and magnitude of needlefall events. This article is protected by copyright. All rights reserved.

Description: This work focuses on the implementation of a Shallow Water-Exner model for compound natural channels with complex geometry and movable bed within the finite volume framework. The model is devised for compound channels modeling: cross-section overbanks are treated with fixed bed conditions, while the main channel is left free to modify its morphology. A capacitive approach is used for bedload transport modeling, in which the solid flow rates are estimated with bedload transport formulas. The model equations pose some numerical issues in the case of natural channels, where bedload transport may occur for both subcritical and supercritical flows and geometry varies in space. An explicit path-conservative scheme, designed to overcome all these issues, is presented in the paper. The scheme solves liquid and solid phases dynamics in a coupled manner, in order to correctly model near critical currents/channel interactions and is well-balanced, that is able to properly reproduce steady states. The Roe and Osher Riemann solvers are implemented, so as to take into account the spatial geometry variations of natural channels. The scheme reaches up to 2 nd order accuracy. Validation is performed with fixed and movable bed test cases whose analytical solution is known, and with flume experimental data. An application of the model to a real case study is also shown. This article is protected by copyright. All rights reserved.

Description: In this paper we use a physical modelling approach to explore the effect of lateral confinement on gravel bed river planform style, bed morphology, and sediment transport processes. A set of 27 runs was performed in a large flume (25 m long, 2.9 m wide), with constant longitudinal slope (0.01) and uniform grain size (1 mm), changing the water discharge (1.5 to 2.5 l/s) and the channel width (0.15 m to 1.5 m) to model a wide range of channel configurations, from narrow, straight, embanked channels to wide braided networks. The outcomes of each run were characterized by a detailed digital elevation model describing channel morphology, a map of dry areas and areas actively transporting sediment within the channel, and continuous monitoring of the amount of sediment transported through the flume outlet. Analysis reveals strong relationships between unit stream power and parameters describing the channel morphology. In particular, a smooth transition is observed between narrow channels with an almost rectangular cross section profile (with sediment transport occurring across the entire channel width) and complex braided networks where only a limited proportion (30%) of the bed is active. This transition is captured by descriptors of the bed elevation frequency distribution, e.g. standard deviation, skewness and kurtosis. These summary statistics represent potentially useful indicators of bed morphology that are compared with other commonly used summary indicators such as the braiding index and the type and number of bars. This article is protected by copyright. All rights reserved.

Description: We believe that there are too many models in hydrology and we should ask ourselves the question, if we are currently wasting time and effort in developing another model again instead of focusing on the development of a community hydrological model. In other fields this kind of models have been quite successful, but due to several reasons, no single community model has been developed in the field of hydrology yet. The concept, strength and weakness of a community model was discussed at the Chapman Conference on Catchment Spatial Behaviour and Complex Organisation held in Luxembourg in September 2014. This discussion as well as out own opinions about the potential of a community models, or at least the necessary discussion to establish one are debated in this commentary. This article is protected by copyright. All rights reserved.

Description: We present simplified expressions for the cross-polarized backscatter of a randomly rough surface predicted by the second-order small-slope approximation (SSA2). The simplification is based on appropriate polynomial approximations of the SSA2 kernel function. We obtain numerically efficient expressions for the cross-polarized backscattering amplitude of a deterministic surface in the form of a single space integral involving only the surface elevation and the second (mixed) derivative of the surface elevation. The ensemble average normalized radar cross section is then derived under a Gaussian random process assumption for the surface. The resulting expression has the form of a Kirchhoff integral involving the roughness correlation function and its second- and fourth-order cross-derivatives. Further simplification is achieved for off-nadir observations using a high-frequency approximation; the result is an analytical formula involving only the resonant curvature and the radar-filtered mean square slope in the out-of-plane direction. A numerical validation of the simplified expressions is provided by comparison with exact SSA2 predictions in representative test cases. The dependence of cross-polarized backscattering on the incidence angle as well as wind speed and direction is then investigated for the case of a directional sea surface model. At near nadir incidence, a clear maximum in azimuth of the cross-polarized backscatter is observed for radar look directions 45 $^{circ}$ from the wind direction.

Description: Soil surface sealing is a widespread natural process occurring frequently in bare soil areas between vegetation patches. The low hydraulic conductivity that characterizes the seal layer reduces both infiltration and evaporation fluxes from the soil, and thus has the potential to affect local vegetation water uptake (VWU). This effect is investigated here using experimental data, 2D physically based modelling and a long-term climatic dataset from three dry sites presenting a climatic gradient in the Negev Desert, Israel. The Feddes VWU parameters for the dominant shrub at the study site ( Sarcopoterium spinosum ) were acquired using lysimeter experiments. The results indicate that during the season surface sealing could either increase or decrease VWU depending on initial soil water content, rainfall intensity, and the duration of the subsequent drying intervals. These factors have a marked effect on inter-annual variability of the seal layer effect on VWU, which on average was found to be 26% higher under sealed conditions than in the case of unsealed soil surfaces. The seal layer was found to reduce the period where the vegetation was under water stress by 31% compared with unsealed conditions. This effect was more pronounced for seasons with total rainfall depth higher than 10 cm/y, and was affected by interseasonal climatic variability. These results shed light on the importance of surface sealing in dry environments and its contribution to the resilience of woody vegetation. This article is protected by copyright. All rights reserved.

Description: As a result of climate change/variation and its aggravation by human activities over the past several decades, the hydrological conditions in the middle Yellow River in China have dramatically changed, which has led to a sharp decrease of streamflow and the drying up of certain tributaries. This paper simulated and analysed the impact of sediment-trapping dams (STDs, a type of large-sized check dam used to prevent sediment from entering the Yellow River main stem) on hydrological processes, and the study area was located in the 3,246 km 2 Huangfuchuan (HFC) River basin. Changes in the hydrological processes were analysed, and periods of natural and disturbed states were defined. Subsequently, the number and distribution of the STDs were determined based on data collected from statistical reports and identified from remote sensing images, and the topological relationships between the STDs and high-resolution river reaches were established. A hydrological model, the Digital Yellow River Integrated Model, was used to simulate the STD impact on the hydrological processes, and the maximum STD impact was evaluated through a comparison between the simulation results with and without the STDs, which revealed that the interception effect of the STDs contributed to the decrease of the streamflow by approximately 39%. This paper also analysed the relationship between the spatial distribution of the STDs and rainfall in the HFC River basin and revealed that future soil and water conservation measures should focus on areas with a higher average annual rainfall and higher number of rainstorm hours. This article is protected by copyright. All rights reserved.

Description: Female salmonids bury and lay their eggs in streambeds by digging a pit, which is then covered with sediment from a second pit. The spawning process alters streambed topography, winnows fine sediment, and mixes sediment in the active layer. The resulting egg nests (redds) contain coarser and looser sediments than those of unspawned streambed areas, and display a dune-like shape with an amplitude and length that vary with fish size, substrate conditions, and flow conditions. Redds increase local bed surface roughness (〈10 −1 channel width, W ), but may reduce the size of macro-bedforms by eroding reach scale topography (10 ° -10 1 W ). Research has suggested that spawning may increase flow resistance due to redd form drag, resulting in lower grain shear stress and less particle mobility. Spawning however also prevents streambed armoring through surface and subsurface material mixing, potentially increasing particle mobility. Here, we use 2-dimensional hydraulic modeling with detailed pre- and post-spawning bathymetries and field observations to test the effect of small spawning salmonids on sediment transport. Our results show that topographical roughness added by small-bodied salmon redds has negligible effects on shear stress at the reach-unit scale, and limited effects at the local scale. Conversely, our results indicate sediment mixing reduces armoring and enhances sediment mobility, which increases potential bed load transport by subsequent floods. River restoration in fish-bearing streams should take into consideration the effects of redd excavation on channel stability. This is particularly important for streams that historically supported salmonids, and at present are the focus of habitat restoration actions. This article is protected by copyright. All rights reserved.

Description: Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses and NGOs for modelled flood hazard data in these data-scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally, and present a framework methodology that leverages recent cross-disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56˚S and 60˚N, and results are validated against high resolution government flood hazard datasets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterised subgrid channel network, and comparison to both a simplified 2D only variant and an independently developed pan-European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. Whilst careful processing of existing global terrain datasets enables reasonable model performance in urban areas, adoption of forthcoming next-generation global terrain datasets will offer the best prospect for a step-change improvement in model performance. This article is protected by copyright. All rights reserved.

Description: Understanding how channel bed morphology affects flow conditions (and vice versa) is important for a wide range of fluvial processes and practical applications. We investigated interactions between bed roughness and flow velocity in a steep, glacier-fed mountain stream (Riedbach, Ct. Valais, Switzerland) with almost flume-like boundary conditions. Bed gradient increases along the 1-km study reach by roughly one order of magnitude ( S =3-41%), with a corresponding increase in streambed roughness, while flow discharge and width remain approximately constant due to the glacial runoff regime. Streambed roughness was characterized by semi-variograms and standard deviations of point clouds derived from terrestrial laser scanning. Reach-averaged flow velocity was derived from dye tracer breakthrough curves measured by 10 fluorometers installed along the channel. Commonly used flow resistance approaches (Darcy-Weisbach equation and dimensionless hydraulic geometry) were used to relate the measured bulk velocity to bed characteristics. As a roughness measure, D 84 yielded comparable results to more laborious measures derived from point clouds. Flow resistance behavior across this large range of steep slopes agreed with patterns established in previous studies for both lower-gradient and steep reaches, regardless of which roughness measures were used. We linked empirical critical shear stress approaches to the variable power equation for flow resistance to investigate the change of bed roughness with channel slope. The predicted increase in D 84 with increasing channel slope was in good agreement with field observations. This article is protected by copyright. All rights reserved.

Description: Large wood governs channel morphology, as well as the availability of in-stream habitat, in many forested streams. In this paper we use a stochastic, physically based model to simulate wood recruitment and in-stream geomorphic processes, in order to explore the influence of disturbance history on the availability of aquatic habitat. Specifically, we consider the effects of fire on a range of stream sizes by varying the rate of tree toppling over time in a simulated forest characterized by a tree height of 30 m. We also consider the effects of forest harvesting with various riparian buffer sizes, by limiting the lateral extent of the riparian stand. Our results show that pulsed inputs of wood increase the availability and variability of physical habitat in the post-fire period; reach-averaged pool area and deposit area double in small streams, while side-channels increase by over 50% in intermediate-sized channels. By contrast, forest harvesting reduces the availability of habitat within the reach, though the effects diminish with increasing buffer size or stream width; in laterally stable streams the effects are minimal so long as buffer width is large enough for key pieces to be recruited to the reach. This research emphasizes the importance of natural disturbance in creating and maintaining habitat heterogeneity and shows that scenario-based numerical modeling provides a useful tool for assessing the historical range of variability associated with natural disturbance, as well as changes in habitat relevant to fish. It can be also used to inform forest harvesting and management. This article is protected by copyright. All rights reserved.

Description: Spreading of conservative solutes in groundwater due to aquifer heterogeneity is quantified by the macrodispersivity, which was found to be scale dependent. It increases with travel distance, stabilizing eventually at a constant value. However, the question of its asymptotic behaviour at very large scale is still a matter of debate. It was surmised in the literature that macrodispersivity scales up following a unique scaling law. Attempts to define such a law were made by fitting a regression line in the log-log representation of an ensemble of macrodispersivities from multiple experiments. The functional relationships differ among the authors, based on the choice of data. Our study revisits the data basis, used for inferring unique scaling, through a detailed analysis of literature marcodispersivities. In addition, values were collected from the most recent tracer tests reported in the literature. We specified a system of criteria for reliability and re-evaluated the reliability of the reported values. The final collection of reliable estimates of macrodispersivity does not support a unique scaling law relationship. On the contrary, our results indicate, that the field data can be explained as a collection of macrodispersivities of aquifers with varying degree of heterogeneity where each exhibits its own constant asymptotic value. Our investigation concludes that transport, and particularly the macrodispersivity, is formation-specific, and that modeling of transport cannot be relegated to a unique scaling law. Instead, transport requires characterization of aquifer properties, e.g. spatial distribution of hydraulic conductivity, and the use of adequate models. This article is protected by copyright. All rights reserved.

Description: For the past few decades, heat has been used to estimate river-aquifer exchange flux at discrete locations by comparison of river and groundwater temperature. In recent years, heat has also been employed to estimate reach-scale river-aquifer exchange flux based only on river temperature. However, there are many more parameters that govern heat exchange and transport in surface water than in groundwater. In this study, we analyzed the sensitivities of surface water temperature to various parameters and assessed the accuracy of temperature-based estimates of exchange flux in two synthetic rivers and in a field setting. For the large synthetic river with a flow rate of 63 m 3 s −1 (i.e., 5.44 × 10 6 m 3 d −1 ), the upper and lower bounds of the groundwater inflow rate can be determined when the actual groundwater inflow is around 100 m 2 d −1 . For higher and lower fluxes, only minimum and maximum bounds respectively can be determined. For the small synthetic river with the flow rate of 0.63 m 3 s −1 (i.e., 5.44 × 10 4 m 3 d −1 ), the bounds of the groundwater inflow rate can only be estimated when the actual groundwater inflow rate is near 10 m 2 d −1 . In the field setting, results show that the inflow rate must be less than 100 m 2 d −1 , but a lower bound for groundwater inflow cannot be determined. The large ranges of estimated groundwater inflow rates in both theoretical and field settings indicate the need to reduce parameter errors and combine heat measurements with other isotopic and/or chemical methods. This article is protected by copyright. All rights reserved.

Description: Climate state can be an important predictor of future hydrologic conditions. In ensemble streamflow forecasting, where historical weather inputs or streamflow observations are used to generate the ensemble, climate index weighting is one way to represent the influence of climate state. Using a climate index, each forecast variable member of the ensemble is selectively weighted to reflect the climate state at the time of the forecast. A new approach to climate index weighting of ensemble forecasts is presented. The method is based on a sampling-resampling approach for Bayesian updating. The original hydrologic ensemble members define a sample drawn from the prior distribution; the relationship between the climate index and the ensemble member forecast variable is used to estimate a likelihood function. Given an observation of the climate index at the time of the forecast, the estimated likelihood function is then used to assign weights to each ensemble member. The weights define the probability of each ensemble member outcome given the observed climate index. The weighted ensemble forecast is then used to estimate the posterior distribution of the forecast variable conditioned on the climate index. The Bayesian climate index weighting approach is easy to apply to hydrologic ensemble forecasts; its parameters do not require calibration with hindcasts, and it adapts to the strength of the relation between climate and the forecast variable, defaulting to equal weighting of ensemble members when no relationship exists. A hydrologic forecasting application illustrates the approach and contrasts it with traditional climate index weighting approaches. This article is protected by copyright. All rights reserved.

Description: How to produce the difference data of the two temporal images is a crucial factor in image change detection. In this letter, we propose multicontextual mutual information data (MMID) based on the bivariate Gaussian distribution (BGD) for synthetic aperture radar (SAR) image change detection and illustrate their superiorities over the classical difference data. MMID, which are an improved form of image spatial mutual information, are constructed based on the quadrilateral Markov random field (QMRF) and can be factored into the linear combination of the entropies. Then to adapt MMID to the change detection, we construct the 2-D entropies based on the BGD. In this way, MMID are able to capture the intertemporal statistical dependence of the two temporal images and thus can be taken as the feature-level difference data rather than the pixel-level data. The maximum-likelihood method, the automatic threshold method, and the Markov random field method are performed on the MMID of the real two temporal SAR images for the change detection. Experimental results demonstrate the superiorities of MMID over the traditional difference data.

Description: Spectral unmixing has been a popular technique for analyzing remotely sensed hyperspectral images. The goal of unmixing is to find a collection of pure spectral constituents (called endmembers ) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances . Over the last years, many algorithms have been presented to address the three main parts of the spectral unmixing chain: 1) estimation of the number of endmembers; 2) identification of the endmember signatures; and 3) estimation of the per-pixel fractional abundances. However, to date, there is no standardized tool that integrates these algorithms in a unified framework. In this letter, we present HyperMix, an open-source tool for spectral unmixing that integrates different approaches for spectral unmixing and allows building unmixing chains in graphical fashion, so that the end-user can define one or several spectral unmixing chains in fully configurable mode. HyperMix provides efficient implementations of most of the algorithms used for spectral unmixing, so that the tool automatically recognizes if the computer has a graphics processing unit (GPU) available and optimizes the execution of these algorithms in the GPU. This allows for the execution of spectral unmixing chains on large hyperspectral scenes in computationally efficient fashion. The tool is available online from http://hypercomphypermix.blogspot.com.es and has been validated with real hyperspectral scenes, providing state-of-the-art unmixing results.

Description: Seismic signals are nonlinear, and the seismic state-space model can be described as a nonlinear system. The particle filter (PF) method, as an effective method for estimating the state of a nonlinear system, can be applied to deal with seismic random noise attenuation. However, PF suffers from sample impoverishment caused by resampling, which results in serious loss of valid seismic information and leads to inaccurate representation of the reflected signal. To address the impoverishment issue and to further improve the particle quality, we propose a novel method to suppress seismic random noise—the adaptive fission particle filter (AFPF). In AFPF, all the particles undergo a fission process and produce “offspring” particles to maintain particle diversity. To implement the adaptation and to monitor the degree of fission, we apply a fission factor, which takes into account weights that indicate the quality of the particles. This leads to significant improvements in the particle quality, i.e., the proportion of highly weighted particles is increased. The effective seismic information provided by the resulting particles reproduces the true signal more reliably, reducing the bias of PF. In addition, we establish a dynamic state-space model suitable for seismic signals. Experimental results on synthetic records and field data illustrate the superior performance of AFPF in noise attenuation and reflected signal preservation compared with the PF.

Description: Distributed, continuous hydrologic models promote better understanding of hydrology and enable integrated hydrologic analyses by providing a more detailed picture of water transport processes across the varying landscape. However, such models are not widely used in routine modeling practices, due in part to the extensive data input requirements, computational demands, and complexity of routing algorithms. We developed a two-dimensional continuous hydrologic model, HYSTAR, using a time-area method within a grid-based spatial data model with the goal of providing an alternative way to simulate spatiotemporally varied watershed-scale hydrologic processes. The model calculates the direct runoff hydrograph by coupling a time-area routing scheme with a dynamic rainfall excess sub-model implemented here using a modified curve number method with an hourly time step, explicitly considering downstream ‘reinfiltration’ of routed surface runoff. Soil moisture content is determined at each time interval based on a water balance equation, and overland and channel runoff is routed on time-area maps, representing spatial variation in hydraulic characteristics for each time interval in a storm event. Simulating runoff hydrographs does not depend on unit hydrograph theory or on solution of the Saint Venant equation, yet retains the simplicity of a unit hydrograph approach and the capability of explicitly simulating two-dimensional flow routing. The model provided acceptable performance in predicting daily and monthly runoff for a 6-year period for a watershed in Virginia (USA) using readily available geographic information about the watershed landscape. Spatial and temporal variability in simulated effective runoff depth and time area maps dynamically show the areas of the watershed contributing to the direct runoff hydrograph at the outlet over time, consistent with the variable source area overland flow generation mechanism. The model offers a way to simulate watershed processes and runoff hydrographs using the time-area method, providing a simple, efficient, and sound framework that explicitly represents mechanisms of spatially and temporally varied hydrologic processes. This article is protected by copyright. All rights reserved.

Description: Given the importance of groundwater temperature to the biogeochemical health of aquatic ecosystems, a floodplain study was implemented to improve understanding of rural land use impacts on shallow groundwater (SGW) temperature. Study sites included a historic agricultural field (Ag) and bottomland hardwood forest (BHF), each with nine piezometers in an 80 × 80 m grid. Piezometers were equipped with pressure transducers to monitor SGW temperature and level at 30 minute intervals during the 2011, 2012, 2013, and 2014 water years. The study is one of the first to utilize long-term, continuous, automated, in situ monitoring to investigate rural land use impacts on shallow groundwater temperatures. Average SGW temperature during the study period was 11.1 and 11.2 °C at the Ag and BHF sites, respectively. However, temperature range at the Ag site was 72% greater than at the BHF site. Results indicate a greater responsiveness to seasonal climate fluctuations in Ag site SGW temperature related to absence of forest canopy. Patterns of intra-site groundwater temperature differences at both study sites illustrate the influence of stream-aquifer thermal conduction and occasional baseflow reversals. Considering similar surface soil temperature amplitudes and low average groundwater flow values at both sites, results suggest that contrasting rates of plant water use, groundwater recharge, and subsurface hydraulic conductivity are likely mechanistic causes for the observed SGW temperature differences. Results highlight the long-term impact of forest removal on subsurface hydrology and groundwater temperature regime. This article is protected by copyright. All rights reserved.

Description: The synoptic determination of ocean circulation using the data acquired from space, with a coherent depiction of its turbulent characteristics, remains a fundamental challenge in oceanography. This determination has the potential of revealing all aspects of the ocean dynamic variability on a wide range of spatiotemporal scales and will enhance our understanding of ocean–atmosphere exchanges at superresolution, as required in the present context of climate change. Here, we show a four-year time series of spatial superresolution (4 km) turbulent ocean dynamics generated from satellite data using emerging ideas in signal processing coming from nonlinear physics, low-resolution dynamics, and superresolution oceanic sea surface temperature data acquired from optical sensors. The method at its core consists in propagating across the scales the low-resolution dynamics in a multiresolution analysis computed on adimensional critical transition information.

Description: This paper presents the theory, algorithm, and results of a new bistatic interferometry synthetic aperture radar (InSAR) method. It employs the data acquired in an innovative bistatic configuration, which uses the orbital sensors as transmitters of opportunity and the stationary receivers on the ground, to generate a digital elevation model (DEM). In the bistatic spaceborne/stationary InSAR configuration, the interferometric phase only depends on the target-receiver range, which could not be obtained directly from the measured bistatic range. Therefore, the conventional transforming relationship between the interferometric phase and the topographic height is no longer practical. In order to solve the problem, we introduce a new conversion relationship between the interferometric phase and the topographic height, which is derived by the model of the ellipsoidal projection in the bistatic configuration. Meanwhile, the error analysis of the new conversion is carried out through a simulation. Both the simulated and measured data are used to test and verify the feasibility of the new bistatic InSAR method. In the spaceborne/stationary InSAR experiment, YaoGan-3 (an L-band spaceborne SAR system launched by China) was selected as the transmitter and two stationary receivers were mounted on the top of a tall building. The generated DEM of high quality shows that the presented method performs very well in the bistatic InSAR data process.

Description: We present a nonparametric Bayesian hierarchical model (HDP_IBPs) to classify very high resolution panchromatic satellite images in an unsupervised way, in which the hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) are combined on multiple scenes. The main contribution of this paper is a novel application framework to solve the problems of traditional probabilistic topic models and achieve the effective unsupervised classification of very high resolution (VHR) panchromatic satellite images. In this framework, a VHR satellite image is first oversegmented into basic processing units and divided into a set of subimages. We use the Chinese restaurant franchise process as a construct method of the HDP to capture the latent semantic structures (i.e., classes) and the class proportion (i.e., co-occurrence of topics) for each subimage. Meanwhile, the subimages are grouped into different scenes based on the class proportion. Finally, the IBP is employed to select the most appropriate classes for each subimage from all of the classes based on different scenes in turn. The hierarchical structure of our model transmits the spatial information from the original image to the scene layer implicitly and provides useful cues of classification. The experimental results show that HDP_IBPs outperforms state-of-the-art models in terms of both qualitative and quantitative evaluations.

Description: This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Götz, Druckmüller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10–28% for two sets of invariant terrain features.

Description: Sparse graph embedding (SGE) is a promising technique useful for the nonlinear feature extraction (FE) of hyperspectral images (HSIs). However, such images exhibit spatial variability and spectral multimodality, presenting challenges to existing FE methods, including SGE. To address this issue, this paper presents two novel SGE methods for HSI classification. One method, which is termed simultaneous SGE (SSGE), is designed to consider the spatial variability of spectral signatures by using a simultaneous sparse representation (SSR) model integrated with a shape-adaptive neighborhood building approach. In addition, a sparse graph is constructed via matrix computation based on sparse codes. Then, low-dimensional features are produced by employing linear graph embedding (LGE) based on the constructed sparse graph. The other method, which is termed simultaneous sparse multimanifold learning (SSMML), is proposed to handle the multimodality of an HSI. In SSMML, multiple views are generated to represent different modalities. Then, multiview-oriented submanifolds are produced by adopting SSGE, and they are further integrated via coregularization. SSGE is capable of modeling both local and global data structures. Furthermore, SSMML serves as a prototype that can model multimodal data structures. The proposed methods are evaluated by using sparse multinomial logistic regression for HSI classification. Experimental results with two popular hyperspectral data sets validate the good performance of the two methods in producing more representative low-dimensional features and yielding superior classification results compared with other related approaches.

Description: We present a new algorithm that simultaneously retrieves aerosol properties and land surface bidirectional reflectance distribution function (BRDF) over Australian from Advanced Along-Track Scanning Radiometer images. Three key components are addressed: 1) an analytical radiative transfer formulation, based on Green's function, linking top-of-atmosphere (TOA) reflectance to the surface BRDF; 2) a novel approach to modeling BRDF using an extended compositing period; and 3) a set of representative aerosol models based on a published typology of Australian aerosols. Due to the generally low aerosol loadings and widespread bright surfaces over Australia, BRDF modeling is crucial. By using a 9-month compositing period, 90% of the Australian continent can be modeled with an error in the forward-to-nadir reflectance ratio of 2.5% or less. Comparison with suitably processed Moderate Resolution Imaging Spectroradiometer BRDF/albedo products demonstrates satisfactory agreement. For the studying period from 2002 to 2008, validation of aerosol optical depth (AOD) against eight sun photometers across Australia encompassing widely different atmospheric and surface regimes shows high accuracy, with a mean absolute error in AOD at 550 nm of 0.03 and a bias of 0.007. About 60% of the matchup points are within an absolute error of 0.03, 80% are within 0.05, and 96% are within 0.1. The algorithm selects for each cell an optimal aerosol type from a set of four predefined candidate models. Continental aerosol maps derived from the new method indicate broadscale agreement with known seasonal aerosol sources, while providing new insights into the spatial and temporal distributions of aerosol over the Australian continent.

Description: This investigation examines the phenomenology effects of the squint angle on the morphology of moving target smears in spotlight synthetic aperture radar (SAR). This analysis includes both the smears resulting from standard image formation applied to simulated radar measurements as well as the theoretical predictions for the central contours of the signatures. In particular, this paper generates the down-range and cross-range components of the predicted central 2-D contours of mover signatures, including the locations of the cross-range offsets. The analytics for squinted geometry include additional contributions in the signature contour equations that do not arise for the case of broadside imaging. These terms can affect the overall contour morphology, particularly in terms of shape and extent. Numerous examples are presented to demonstrate that the signature prediction equations yield excellent agreement with standard image formation with simulated radar data. Therefore, this analysis can provide an effective tool in predicting the shape, extent, and location of smears due to arbitrarily moving surface targets for squinted spotlight SAR.

Description: In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature.

Description: The conventional spaced-receiver approach uses amplitude scintillations to estimate equatorial ionospheric irregularity drift velocities. This approach is less applicable at high latitudes where there is a lack of substantial amplitude scintillations. This paper presents a method to estimate ionosphere irregularity horizontal drift velocities based on GPS signal carrier phase measurements. Joint time–frequency analysis of the carrier phase measurements using an adaptive periodogram technique generates time-varying spectrograms of ionospheric irregularity-induced phase fluctuations. Cross correlation of the spectrograms between antenna pairs provides time lag information on propagating radio signals through the same ionospheric structure. The time lag information is combined with known positions of the receiver array, satellite orbits, and assumed irregularity altitude to infer ionospheric irregularity horizontal drift velocity. This paper presents the methodology and demonstrates its feasibility using data collected by a GPS receiver array at Gakona, Alaska. The potential error sources of this method are also analyzed.

Description: Field hydrology is on the decline. Meanwhile, the need for new field-derived insight into the age, origin and pathway of water in the headwaters, where most runoff is generated, is more needed than ever. Water Resources Research (WRR) has included some of the most influential papers in field-based runoff process understanding, particularly in the formative years when the knowledge base was developing rapidly. Here, we take advantage of this 50 th anniversary of the journal to highlight a few of these important field-based papers and show how field scientists have posed strong and sometimes outrageous hypotheses—approaches so needed in an era of largely model-only research. We chronicle the decline in field work and note that it is not only the quantity of field work that is diminishing but its character is changing too: from discovery science to data collection for model parameterisation. While the latter is a necessary activity, the loss of the former is a major concern if we are to advance the science of watershed hydrology. We outline a vision for field research to seek new fundamental understanding, new mechanistic explanations of how watershed systems work, particularly outside the regions of traditional focus. This article is protected by copyright. All rights reserved.

Description: Growing demand on groundwater resources and the semi-arid climate in the North China Plain (NCP) highlight the need for improved understanding of connections between regional climate change and groundwater recharge. Hydrologic time series of precipitation and groundwater levels were analyzed in three representative geographical zones throughout the NCP for the period of 1960-2008 using trend analysis and spectral analysis methods. A significant change point around 1975 is followed by a long term decline trend in precipitation time series, which coincides with the Pacific Decadal Oscillation (PDO) positive phase. However, the magnitudes of groundwater levels variability due to heavily pumping overwhelm the low-frequency signal of groundwater levels. Nonlinear trends that related to long-term climatic variability and anthropogenic activities are removed by using the Singular Spectrum Analysis (SSA) method. Spectral analyses of the detrended residuals demonstrate significant short-term oscillations at the frequencies of 2–7 years, which have strong correlations with the El Niño-Southern Oscillation (ENSO) modes. This study contributes to improved understanding of dynamic relationship between groundwater and climate variability modes in the NCP, and demonstrates the importance of reliable detrending methods for groundwater levels that are affected greatly by pumping. This article is protected by copyright. All rights reserved.

Description: On September 3, 1998, a glacial lake outburst flood (GLOF) that originated from Tam Pokhari occurred in the Hinku valley of the eastern Nepal Himalaya. This study analyzes the lake's geomorphic and hydrologic conditions prior to the outburst, and evaluates the conditions that could contribute to a future flood through photogrammetric techniques. We processed high-resolution Corona KH-4A (2.7 m) and ALOS PRISM (2.5 m) stereo-images taken before and after the GLOF event, and produced detailed topographic maps (2-m contour interval) and DEMs (5 m × 5 m). We (re-) constructed lake water surfaces before (4410 ± 5 m) and after (4356 ± 5 m) the outburst, and reliably estimated the lake water surface lowering (54 ± 5 m) and the water volume released (19.5 ± 2.2 × 10 6  m 3 ) from the lake, showing good agreement with the results obtained from ground-based measurements. The most relevant conditions that may have influenced the catastrophic drainage of Tam Pokhari in 1998 include the presence of: i) a narrow (75 ± 6 m), steep (up to 50°) and high (120 ± 5 m) moraine dam; ii) high lake level (8 ± 5 m of freeboard); and iii) a steep overhanging glacier (〉40°). The lake outburst substantially altered the immediate area, creating a low and wide (〉500 m) outwash plain below the lake, a wide lake outlet channel (~50 m) and a gentle channel slope (~3–5°). Our new data suggest that the likelihood of a future lake outburst is low. Our results demonstrate that the datasets produced by photogrammetric techniques provide an excellent representation of micro-landform features on moraine dams, lake water surfaces and the changes in both over time, thereby allowing highly accurate pre- and post-GLOF (volumetric) change analysis of glacial lakes. Furthermore, it enables precise measurement of several predictive variables of GLOFs that can be useful for identifying potentially dangerous glacial lakes or prioritizing them for detailed field investigations. This article is protected by copyright. All rights reserved.

Description: Although the importance to account for microrelief in the calculation of specific yields for shallow groundwater systems is well recognized, the microrelief influence is often treated very simplified, which can cause considerable errors. We provide a general one-dimensional expression that correctly represents the effect of a microrelief on the total specific yield that is composed of the soil and surface specific yield. The one-dimensional expression can be applied for different soil hydraulic parameterizations and soil surface elevation frequency distributions. Applying different van Genuchten parameters and a simple linear microrelief model, we demonstrate that the specific yield is influenced by the microrelief not only when surface storage directly contributes to specific yield by (partial) inundation but also when water levels are lower than the minimum surface elevation. Compared to a simplified representation of the soil specific yield, in which a mean soil surface is assumed for the calculation of soil specific yield, the correct representation can lead to lower as well as higher soil specific yields depending on the specific interaction of the soil water retention characteristics and the microrelief. The new equation can be used to obtain more accurate evapotranspiration estimates from water level fluctuations and to account for the effect of microtopographic subgrid variability on simulated water levels of spatially-distributed hydrological models. This article is protected by copyright. All rights reserved.

Description: Globally, Dissolved Inorganic Carbon (DIC) accounts for more than half the annual flux of carbon exported from terrestrial ecosystems via rivers. Here we assess the relative influences of biogeochemical and hydrological processes on DIC fluxes exported from a tropical river catchment characterized by distinct land cover, climate and geology transition from the wet tropical mountains to the low lying savanna plains. Processes controlling changes in river DIC were investigated using dissolved organic carbon (DOC), particulate organic carbon (POC) and DIC concentrations and stable isotope ratios of DIC (δ 13 C DIC ) at two time scales; seasonal and diel. The recently developed Isotopic Continuous Dissolved Inorganic Carbon Analyser (ISO-CADICA) was used to measure diel DIC concentration and δ 13 C DIC changes at a 15 minute temporal resolution. Results highlight the predominance of biologically mediated processes (photosynthesis and respiration) controlling diel changes in DIC. These resulted in DIC concentrations varying between 3.55-3.82 mg/L, and δ 13 C DIC values ranging from -19.7 ± 0.31 to -17.1 ± 0.08 ‰. In contrast, at the seasonal scale we observe wet season DIC variations predominantly from mixing processes, and dry season DIC variations due to both mixing processes and biological processes. The observed wet season increases in DIC concentrations (by 6.81 mg/L) and δ 13 C DIC values of river water (by 5.4 ‰) largely result from proportional increases in subsurface inflows from the savanna plains (C 4 vegetation) region relative to inflows from the rainforest (C 3 vegetation) highlands. The high DIC river load during the wet season results in the transfer of 97% of the annual river carbon load. Therefore, in this gaining river there are significant seasonal variations in both the hydrological and carbon cycles, and there is evidence of substantial coupling between the carbon cycles of the terrestrial and the fluvial environments. Recent identification of a substantial savanna carbon sink in wetter years in the recent past does not take into account the possibility of a substantial, rapid, lateral flux of carbon to rivers and back to the atmosphere. This article is protected by copyright. All rights reserved.

Description: Various remote-sensing methods are available to estimate soil moisture, but few address the fine spatial resolutions (e.g., 30 m grid cells) and root-zone depth requirements of agricultural and other similar applications. One approach that has been previously proposed to estimate fine-resolution soil moisture is to first estimate the evaporative fraction from an energy balance that is inferred from optical and thermal remote-sensing images (e.g., using the ReSET algorithm) and then estimate soil moisture through an empirical relationship to evaporative fraction. A similar approach has also been proposed to estimate the degree of saturation. The primary objective of this study is to evaluate these methods for estimating soil moisture and degree of saturation, particularly for a semiarid grassland with relatively dry conditions. Soil moisture was monitored at twenty-eight field locations in southeastern Colorado with herbaceous vegetation during the summer months of three years. In-situ soil moisture and degree of saturation observations are compared with estimates calculated from Landsat imagery using the ReSET algorithm. The in-situ observations suggest that the empirical relationships with evaporative fraction that have been proposed in previous studies typically provide overestimates of soil moisture and degree of saturation in this region. However, calibrated functions produce estimates with an accuracy that may be adequate for various applications. The estimates produced by this approach are more reliable for degree of saturation than for soil moisture, and the method is more successful at identifying temporal variability than spatial variability in degree of saturation for this region. This article is protected by copyright. All rights reserved.

Description: Groundwater movements in volcanic mountains and their effects on streamflow discharge and representative elementary area (REA) have remained largely unclear. We surveyed the discharge and chemical composition of spring and stream water in two catchments: the Hontani river (NR) catchment (6.6 km 2 ) and the Hosotani river (SR) catchment (4.0 km 2 ) at the southern part of Daisen volcano, Japan. Daisen volcano is a young volcano (17 × 10 3  years) at an early stage of erosion. Our study indicated that deep groundwater that moved through thick lava and pyroclastic flows and that could not be explained by shallow movements controlled by surface topography contributed dominantly to streamflow at larger catchment areas. At the NR catchment, the deep groundwater contribution clearly increased at a catchment boundary defined by an area of 3.0 km 2 and an elevation of 800 m. At the SR catchment, the contribution deep groundwater to the stream also increased suddenly at a boundary threshold of 2.0 km 2 and 700 m. Beyond these thresholds, the contributions of deep bedrock groundwater remained constant, indicating that the REA is between 2 and 3 km 2 at the observed area. These results indicate that the hydrological conditions of base flow were controlled mainly by the deep bedrock groundwater that moved through thick lava and pyroclastic flows in the undissected volcanic body of the upper part of the catchment. Our study demonstrates that deep and long groundwater movements via a deep bedrock layer including thick deposits of volcanic materials at the two catchments on Daisen volcano strongly determined streamflow discharge instead of the mixing of small-scale hydrological conditions. This article is protected by copyright. All rights reserved.

Description: We propose a novel technique for improving a long-term multi-step-ahead streamflow forecasts. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow three, six, nine and twelve months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared to the wavelet and artificial neural networks based model. The robustness of the models is evaluated. This article is protected by copyright. All rights reserved.

Description: The combined use of water erosion models and geographic information systems (GIS) has facilitated soil loss estimation at the watershed scale. Tools such as the Geo-spatial interface for the Water Erosion Prediction Project (GeoWEPP) model provide a convenient spatially-distributed soil loss estimate but require discretization to identify hillslopes and channels. In GeoWEPP, the TOpographic PArameteriZation (TOPAZ) model is used as an automated procedure to extract a watershed boundary, hillslopes and channels from a digital elevation model (DEM). Previous studies in small watersheds have shown that the size of the hillslopes and the channel distribution affect the model estimates, but in large watersheds the effects on the soil loss estimates have yet to be tested. Therefore, the objective of this study was to evaluate the effect of discretization on the hillslope sediment yield estimates using GeoWEPP in two large watersheds (〉10 km 2 ). The watersheds were selected and discretized varying the TOPAZ parameters (CSA – Critical Source Area, and MSCL – Minimum Source Channel Length) in a 30-m resolution DEM. The drainage networks built with TOPAZ were compared among each other using the drainage density index. The results showed that the discretization affected hillslope sediment yield estimates and their spatial distribution more than total runoff. The drainage density index and the hillslope sediment yield were proportional but inversely related, thus soil loss estimates were highly affected by the spatial discretization. As a result of this analysis, a method to choose the CSA and MSCL values that generates the greatest fraction of hillslopes having profile lengths less than 200 m was developed. This slope length condition is particularly crucial when using the WEPP and GeoWEPP models, in order for them to produce realistic estimates of sheet and rill erosion. Finally, and as a result of this analysis, a more reliable method was developed for selecting the TOPAZ channel network parameters (CSA and MSCL).

Description: Linkages between the controls on surface storage and catchment streamflow response were examined in a wetland dominated basin in the Canadian Prairie Pothole region. Snowmelt, surface storage, water table elevation, atmospheric fluxes, and streamflow were monitored during spring snowmelt and summer in a 1 km 2 sub-catchment containing a semi-permanent pond complex connected via an intermittent stream. Snow accumulation in the basin in spring of the 2013 study year was the largest in the 24-year record. Rainfall totals in 2013 were close to the long term average, though June was an anomalously wet month. The water budget of the pond complex indicates that there was a significant subsurface contribution to surface storage. Activation of an effective transmission zone occurred between uplands and the stream network where the water table was located near the ground surface, which allowed significant lateral movement of subsurface water into the stream network. This was also important for maintaining and re-establishing surface connectivity and streamflow during rainfall events. The observed period of surface-water connectivity was one of the longest on record in the catchment due to unusually wet conditions; nevertheless, the results of this study have implications for how contributing area and runoff should be considered in monitoring and modelling studies in the region, as inclusion of more frequent and varied runoff processes will be essential to understanding changing streamflow regimes. This article is protected by copyright. All rights reserved.

Description: Stream-subsurface exchange plays a significant role in the fate and transport of contaminants in streams. It has been modeled explicitly by considering fundamental processes such as hydraulic exchange, colloid filtration, and contaminant interactions with streambed sediments and colloids. The models have been successfully applied to simulate the transport of inorganic metals and nutrients. In this study, laboratory experiments were conducted in a recirculating flume to investigate the exchange of a hydrophobic organic contaminant (HOC), p,p′ -DDE, between a stream and a quartz sand bed. A previously developed process-based multiphase exchange model was modified by accounting for the p,p′ -DDE kinetic adsorption to and desorption from the bed sediments/colloids and was applied to interpret the experimental results. Model input parameters were obtained by conducting independent small-scale batch experiments. Results indicate that the immobilization of p,p′ -DDE in the quartz sand bed can occur under representative natural stream conditions. The observed p,p′ -DDE exchange was successfully simulated by the process-based model. The model sensitivity analysis results show that the exchange of p,p′ -DDE can be sensitive to either the sediment sorption/desorption parameters or colloidal parameters depending on the experimental conditions tested. For the experimental conditions employed here, the effect of colloids on contaminant transport is expected to be minimal and the stream-subsurface exchange of p,p′ -DDE is dominated by the interaction of p,p′ -DDE with bed sediment. The work presented here contributes to a better mechanistic understanding of the complex transport process that HOCs undergo in natural streams, and to the development of reliable, predictive models for the assessment of impacted streams. This article is protected by copyright. All rights reserved.

Description: The physical and hydrological conditions in extracted peatlands often act as barriers to the regeneration of the keystone peat-forming genus Sphagnum . Although previous work has suggested that Sphagnum mosses regenerating on cutover peat surfaces quickly become vulnerable to water stress as the thickness of the regenerated layer increases, uncertainties regarding the storage and transmission properties of this layer and how these might evolve over time have made this assertion difficult to evaluate. This study investigates the hydrophysical properties and hydrological behaviour of regenerating Sphagnum layers ranging from 3-43 years in age using both field and laboratory methods. The 〉40 year old regenerated layers had significantly (p 〈 0.001) higher bulk density and retention capacity in the 5 cm thick basal layer directly overlying the cutover peat than the newer (〈10 year old) regenerated layers. Capillarity was a much stronger control on surficial water content (θ) than precipitation, which was poorly retained in the Sphagnum canopy, suggesting that regulation of water table position is an effective method of controlling θ as a means of optimizing productivity. In general, the θ sustained at a given water table position decreased as regenerated layer thickness increased. Analysis of water table position relative to the former cutover peat surface in different areas of the site suggests that the soil water dynamics of the 〉40 year old regenerated layers may be becoming increasingly similar to those of a natural bog peatland. This article is protected by copyright. All rights reserved.

Description: We present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatio-temporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a non-quadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulic head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation and extent of the intrusion from the steady-state data only. Addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations. This article is protected by copyright. All rights reserved.

Description: Human societies are increasingly altering the water and biogeochemical cycles to both improve ecosystem productivity and reduce risks associated with the unpredictable variability of climatic drivers. These alterations, however, often cause large negative environmental consequences, raising the question as to how societies can ensure a sustainable use of natural resources for the future. Here we discuss how ecohydrological modeling may address these broad questions with special attention to agroecosystems. The challenges related to modeling the two-way interaction between society and environment are illustrated by means of a dynamical model in which soil and water quality supports the growth of human society but is also degraded by excessive pressure, leading to critical transitions and sustained societal growth-collapse cycles. We then focus on the coupled dynamics of soil water and solutes (nutrients or contaminants), emphasizing the modeling challenges, presented by the strong nonlinearities in the soil and plant system and the unpredictable hydro-climatic forcing, that need to be overcome to quantitatively analyze problems of soil water sustainability in both natural and agricultural ecosystems. We discuss applications of this framework to problems of irrigation, soil salinization, and fertilization and emphasize how optimal solutions for large-scale, long-term planning of soil and water resources in agroecosystems under uncertainty could be provided by methods from stochastic control, informed by physically and mathematically sound descriptions of ecohydrological and biogeochemical interactions. This article is protected by copyright. All rights reserved.

Description: Water resource management (WRM) through dams or reservoirs is worldwide necessary to support key human-related activities, ranging from hydropower production to water allocation and flood risk mitigation. Designing of reservoir operations aims primarily to fulfil the main purpose (or purposes) for which the structure has been built. However, it is well known that reservoirs strongly influence river geomorphic processes, causing sediment deficits downstream, altering water and sediment fluxes, leading to river bed incision and causing infrastructure instability and ecological degradation. We propose a framework that, by combining physically based modelling, surrogate modelling techniques and Multi-Objective (MO) optimization, allows to include fluvial geomorphology into MO optimization whose main objectives is the maximization of hydropower revenue and the minimization of river bed degradation. The case study is a run-of-the-river power plant on the River Po (Italy). A 1D mobile-bed hydro-morphological model simulated the river bed evolution over a ten year horizon for alternatives operation rules of the power plant. The knowledge provided by such a physically based model is integrated into a MO optimization routine via surrogate modelling using the response surface methodology. Hence, this framework overcomes the high computational costs that so far hindered the integration of river geomorphology into WRM. We provided numerical proof that river morphologic processes and hydropower production are indeed in conflict, but that the conflict may be mitigated with appropriate control strategies. This article is protected by copyright. All rights reserved.

Description: This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the tradeoff between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real-world reservoir in northeastern China. This article is protected by copyright. All rights reserved.

Description: Nutrient concentrations in stream water, rainfall, throughfall, stem flow, surface flow and ground water were compared before, during, and after strip thinning (intensive 50%) in plantation forested watersheds in Tochigi, Japan. Influences were evaluated comparing four thinning-applied and two reference basins for one year before, six months during and one year after the thinning. Results show that this strip thinning significantly increased DTP, TP and DOC (DTP: 0.01 mg l -1 , TP: 0.04 mg l -1 , DOC: 0.53 mg l -1 ) during the thinning period and DTN and TN (DTN: 0.34 mg l -1 , TN: 0.46 mg l -1 ) after the thinning in stream waters relative to the unthinned basins. The increased phosphorus during thinning indicated ground disturbances by the strip thinning, with a concomitant increase in DOC. Changes in biotic and abiotic conditions resulted in increased nitrogen after the thinning, particularly in the dissolved pool. Changes in hydrological processes due to thinning, e.g. a change in flow distributions (less high nutrient stem flow and more low nutrient throughfall) and an increase in water discharge in stream water, possibly weakened the direct influences of thinning on nutrient concentrations. This article is protected by copyright. All rights reserved.

Description: Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method ( $mathbf{R}^{2}=0.809$ , $mathbf{RMSE} =0.157$ ) was comparable with that of the GEOV1 FVC product ( $mathbf{R}^{2}=0.775$ , $mathbf{RMSE} =0.166$ ) , which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Description: This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial–spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods.

Description: Atmospheric refraction in the troposphere causes the propagation speed of electromagnetic signals to be less than the light speed. This creates a difference between the actual propagation path delay and the distance of the geometrical straight-line path, i.e., a quantity known as the tropospheric delay. As classical imaging algorithms for spaceborne synthetic aperture radar (SAR) do not take the tropospheric delay into account, imaging filters are designed based on the assumption of rectilinear propagation with the light speed. Therefore, a residual phase exists in imaging results, which affects focusing quality under the condition of high resolution. In order to compensate for the impact of tropospheric delay on focusing performance, this paper modifies the spaceborne SAR echo model and then proposes an imaging compensation algorithm. The key to this algorithm is to fit a range delay coefficient based on the European Geostationary Navigation Overlay Service model of zenith delay and Niell mapping function, which projects the zenith delay onto the looking direction. After range compensation, classical imaging, and azimuth compensation, which compose the proposed algorithm, the processed results are well focused.

Description: Accurate correction of atmospheric effects on data captured by an infrared (IR) camera is crucial for several applications such as vegetation monitoring, temperature monitoring, satellite images, hyperspectral imaging, numerical model simulations, surface properties characterization, and IR measurement interpretation. Atmospheric effects depend on the temporal changes, i.e., year, season, day, hour, etc., and on the geometry between the camera and the measured scene, i.e., line of sight. The orientation and the optical depth of the camera significantly affect the variation of the geometry across the pixels. In this paper, we propose a method to estimate the range and zenith angle of each pixel using only the Global Positioning System (GPS) coordinates of the camera and a point of interest in the scene. The estimated geometry and measured meteorological data are used to obtain the spectral atmospheric transmittance and path radiance. Furthermore, we propose an atmospheric effects removal, i.e., atmospheric correction, method that considers the spectral characteristics of the detector, lens, and filter. The proposed atmospheric correction process is analyzed in detail with the simultaneous measurements of two IR cameras. In this process, an enhanced temperature calibration method is developed and it is shown that the temperature accuracy for the dynamic range of the IR camera is very close to the noise equivalent temperature difference (NETD) value of the camera.

Description: Multiple scattering within a mixed pixel results in a nonlinear effect on the measured spectra in remotely sensed imagery. This study provides a quantitative assessment of multiple scattering in the reflectance of semiarid shrublands and explores its relationship to the characteristics of shrubs (density and height) and imaging parameters (wavelength and viewing angles). Field measurements were conducted at the southern fringe of the Otindag Sandy Land in China. A Monte Carlo ray tracing model, the Forest LIGHT interaction model (FLIGHT), was applied to simulate the multiple scattering results. FLIGHT simulation results were first evaluated against field measurements and then compared with a Landsat-8 OLI image. Results show that: 1) the contribution of multiple scattering to the spectra of a scene increases linearly with the fractional cover of vegetation and crown height; 2) in general, multiple scattering has a stronger effect on the near-infrared (NIR) domain than on the visible bands; 3) shadows significantly strengthen the multiple scattering effect, specifically within the visible bands; and 4) 80 to 100% of the total multiple scattering is caused by the second-order scattering within the visible bands and 60% to 90% within the NIR band. This study helps to improve our understanding of the multiple scattering effect and to select between linear and nonlinear spectral unmixing models to solve the abundances of shrubs and soil in mixed pixels.

Description: The main objective of the Chinese Chang'E-3 (CE-3) lunar satellite is to achieve soft-landing and roving exploration on the lunar surface. A multibeam radar in the lunar lander is implemented to measure the echoes from the lunar rough surface during its descending and to derive the speed of the lander. In this paper, numerical simulation of multiangular radar echoes and speed inversions from Doppler frequency are presented. An area of the Lunar Sinus Iridum bay, as landing site, is specifically selected. The rough surface described with the real DTM data is first divided into triangular patches for numerical Kirchhoff approximation calculation. The radar echoes of multiangular radar beams of CE-3 during the landing are numerically simulated. The echo phase and the Doppler frequency are then derived to obtain the vertical speed.