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Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

Eddies in the Marginal Ice Zones (MIZ) contribute to an enhanced melting of sea ice by forcing contact between the sea ice and the warmer water off the ice edge. The horizontal current field can be retrieved by tracking the movement of ice floes between sequential satellite images. However, the traditional method of current retrieval imposes stringent requirements on the time intervals between sequential images, and the performance of the traditional method diminishes rapidly with increasing time intervals. In this paper, an improved method of retrieving currents from multi-sensor remote sensing images is proposed, which can significantly improve the accuracy of current retrieval. The method first extracts feature from remote sensing images based on texture features and a support vector machine model and then extracts prior information about the ice-edge eddy from the first two remote sensing images. Under the guidance of prior information, horizontal currents of the eddy are retrieved by tracking the movement of sea ice from sequential remote sensing images. The proposed method was used to retrieve currents from Sentinel-1 and Sentinel-2 sequential images over the MIZ of the Fram Strait. Comparisons with the traditional method show encouraging results that the proposed method can significantly reduce the number of mismatches and improve the reliability of current vectors. [ABSTRACT FROM AUTHOR]

In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas. [ABSTRACT FROM AUTHOR]

To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. [ABSTRACT FROM AUTHOR]

Inundation mapping is an essential part of environmental monitoring, flood disaster management and risk mitigation. There are many earth observation sensors that can provide spatial information about inundation extent at different times. However, these extents need to be comparable to provide an accurate and consistent estimate of a flood's progression. Monitoring inundation extent around the peak of a flood event is important because it captures the hazardous period during a large flood event, and it identifies connections between off-stream waterholes and flood waters for environmental monitoring. This paper presents results from a study comparing near-coincident flood inundation maps derived from optical (Landsat, MODIS, Sentinel-2 and VIIRS) and Synthetic Aperture Radar (SAR) (Sentinel-1 and NovaSAR-1) remote sensing imagery. We also compare all inundation maps derived from remote sensing data with near-coincident hydrodynamic (HD) modelling. The study was conducted for the Fitzroy floodplain in Western Australia, a large, complex and remotely located river basin. The results show that optical remote sensing data have average F1 scores ranging from 0.57 to 0.65 when compared to the HD model results, with Landsat and MODIS performing best. Sentinel-1 and NovaSAR-1 SAR have a poor agreement (average F1 score of 0.31 and 0.35 respectively) when compared to the HD model results, particularly within scattered vegetation adjacent to the river channel, with better results in open water on the floodplain and in the river. If comparisons are made only during the peak flood stage, the F1 scores improve for the optical data (0.61 up to 0.8). Comparisons of the remote sensing inundation maps show that the optical data are suitable for interchangeable mapping during large flood events. [ABSTRACT FROM AUTHOR]

SAR parameters have been used many times to characterize crops. However, it was found that the scattering mechanisms derived from quad-pol SAR were different from that of the dual-pol SAR data. The objective of this study is to better understand the scattering mechanism of the maize field to dual-pol (VV, VH) SAR data and propose a new radar vegetation index to improve the inversion accuracy of the crop height. We first analyzed the temporal variation of SAR parameters before the maize height stopped increasing. It showed that the sensitivity of SAR parameters to the plant height was strong in the early stage, but the correlation became weak later. Then, according to the scattering mechanism of the maize field to the dual pol (VV, VH) SAR data, we combined use of the backscattering coefficient of σ0VH and the depolarization parameter proposed the mPRVI index. In this paper, the simple linear regression model was used to fit the six parameters separately to estimation the plant height, including σ0VH (R = 0.84), VIRatio (R = 0.82), RVI (R = 0.83), DOD+RVI (R = 0.85), PRVI (R = 0.86) and mPRVI (R = 0.88), which showed a relatively high Pearson correlation coefficient (R) with the plant height. According to adjusted coefficient of determination R2 (0.70, 0.70, 0.71, 0.74, 0.76, 0.80) and RMSE (62.8 cm, 62.7 cm, 61.8 cm, 57.5 cm, 56.3 cm, 51.2 cm) for σ0VH, VIRatio, RVI, DOD+RVI, PRVI, mPRVI, respectively, the mPRVI index outperforms other parameters throughout the maize growing season. And this outperformance is not only manifested in the early growth stage of the maize, but also in the later stage before the plant height stopped increasing. [ABSTRACT FROM AUTHOR]

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product. [ABSTRACT FROM AUTHOR]

Google Earth Engine (GEE) provides a convenient platform that enables the use of a diverse array of applications based on optical satellite images covering long time periods and large areas. However, cloud contamination often causes optical satellite images to lose land surface information, which can greatly affect their spatial and temporal availability in areas with frequent cloud cover. Reconstructing the missing values is very important when researchers attempt to improve the potential for using optical satellite images. Recently, convolutional neural network (CNN)-based methods have shown great potential in cloud removal. Unfortunately, current CNN-based cloud removal methods can only be applied locally, which would result in inefficient data downloading and storage problems. To address these problems, this paper proposes a method that integrates GEE data and a multi-level feature connected CNN (DeepGEE-S2CR) to remove clouds in Sentinel-2 imagery using Sentinel-1 Synthetic Aperture Radar imagery as auxiliary data. Specifically, the training process of the multi-level feature connected CNN was conducted locally, so that cloud removal could then be performed on the client of GEE using online data with the trained CNN model. An experiment using a set of globally distributed Sentinel-2 images was conducted to validate the proposed method; the results showed the process could generate results comparable to a classic deep Sentinel-2 cloud removal method (DSen2-CR) that should be run locally (the average root mean square errors of DeepGEE-S2CR and DSen2-CR were 0.045 and 0.042, respectively). The proposed method is especially convenient for removing clouds in Sentinel-2 imagery covering a large area. [ABSTRACT FROM AUTHOR]

An approach to monitor vegetation without the influence of cloud cover and employing Synthetic Aperture Radar (SAR) data was developed by the DPSVI (Dual Polarization SAR Vegetation Index), based on Sentinel-1 mission data. However, DPSVI performance in areas of dense vegetation and variation in vegetation classes is unknown. Thus, this paper aimed to investigate the performance of DPSVI in the monitoring of vegetation in the Brazilian Atlantic Forest biome, as well as to propose modifications to improve its capacity to monitor the vegetation and investigate seasonal and spatial influences on the performance of the proposed index (DPSVIm). Three approaches were adopted: 1) use Sentinel-1 and Landsat 8 scenes obtained from four hydrological years (2015–2016 to 2018–2019), to compute monthly DPSVI, DPSVIm, NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) indices, to investigate seasonal influences; 2) perform topographic correction in Landsat 8 and Sentinel-1 data to verify the influence of relief on DPSVIm performance; and 3) confronting DPSVIm with an attribute of Atlantic Forest fragments: the above ground biomass (AGB). The scenes were processed on the Google Earth Engine platform. The DPSVIm was able to better distinguish the vegetation, compared to the DPSVI, mainly in forest planting areas and Atlantic Forest, which evidences the proposed improvements. The rainfall regime made the DPSVIm performance seasonal in comparison with the NDVI and EVI (Normalized Difference Vegetation Index and Enhanced Vegetation Index), presenting better agreements in the dry season and worse in the rainy season. There was a greater agreement between DPSVIm and EVI in the results to monitor dense vegetation since EVI does not saturate as NDVI does. Finally, the DPSVIm has a good fit to AGB, being able to distinguish different levels of AGB from 50 to 200 Mg ha, approximately. The study allowed improving the DPSVI making the DPSVIm adequate to quantify vegetation in the study area, even considering the effects of topography and rainfall regime. The latter environmental factor should be addressed in future studies, searching to mitigate this source of temporal uncertainty in DPSVIm. [ABSTRACT FROM AUTHOR]

Persistent scatterer interferometry (PSI) has been proved to be an advanced Interferometric Synthetic Aperture Radar (InSAR) technique used to measure and monitor terrain deformation. Two of the critical problems with InSAR have been the effect of the refractive atmosphere and decorrelation on the interferometric phases due to long spatial-temporal baseline. The low density of persistent scatterers (PS) in non-urban areas affected by spatial-temporal decoherence more seriously has inspired the development of alternative approaches. Sentinel-1 (S1) has improved the data acquisition throughout, and compared to previous sensors, increased considerably the differential interferometric SAR (DInSAR) and PSI deformation monitoring potential. This paper describes an innovative methodology to process S1 SAR data. Different with PSI, its most original part includes two key processing stages: high and low frequency splitting from wrapped phases, prior to atmospheric filtering, and final direct integration to generate the complete deformation with time series containing linear and nonlinear components. The proposed method has two fundamental advantages compared with traditional PSI approach: the final monitoring results with excellent coverage of coherent points and the generation of active maps even for the areas with serious deformation in short term to break through the inherent limitation of PSI. The effectiveness of the proposed tools is illustrated using a case study located in Catalonia (Spain). This methodology has supposed a definitive step towards the implementation of DInSAR based techniques to support decision makers against geohazards. In this work, the deformation procedures happened in three different areas of the Catalonia (Spain) are presented and analysed. The maximum accumulated subsidence of over – 60 cm induced by mining activity can be detected by proposed methodology with nice coverage from January 2017 to January 2019. These reported cases illustrate how DInSAR based techniques can provide detailed terrain deformation for geohazard activity with complex topographical conditions. The active deformation areas map can be generated in fast aimed at geohazard risk early warning and management. [ABSTRACT FROM AUTHOR]

Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) data have been widely and primarily used for soil-moisture estimation. The original radar backscattering coefficients show great noise and temporal-spatial decorrelation due to complex surface features and seasonal vegetation changes, which increase the difficulty of soil-moisture estimation over the Qinghai-Tibet Plateau (QTP). To overcome these limitations, a soil-moisture retrieval method based on the Distributed Scatterers Adaptive Filter (DSAF) is proposed and applied in this paper. First, the statistically homogeneous pixels (SHP) with similar scatter behaviour are identified using the Anderson-Darling (AD) test method. Then, the Distributed Scatterers (DSs) are selected according to the SHPs threshold, and the backscattering coefficients of DSs are filtered using the SHPs distribution and coherences. In order to eliminate the negative influence of vegetation and roughness, the filtered backscattering coefficients are further processed by coupling the Water Cloud Model (WCM) and the images acquired in the winter season. The response of soil moisture to backscattering coefficients is greatly improved. Finally, the backscatter coefficients ( σ 0 ) are transformed into soil-moisture units by using the Least Square Regression (LSR) model and multi-temporal Change Detection (CD) method, respectively. The 15 scenes of TerraSAR-X images and 18 scenes of Sentinel-1 images in Northern Piedmont river, Northern Tibet, were used for experimental study. The results reveal that the response of the filtered backscattering coefficients to in-situ soil moisture is greatly improved. The soil-moisture content estimated by LSR and CD model using TerraSAR-X data is consistent with in-situ measurements, with coefficient of determination (R2) of 0.52 and 0.56, Root Mean Square Error (RMSE) of 0.08 and 0.06, respectively. The results show that the proposed method can improve the accuracy of soil-moisture estimation, especially over the complex surface features of the QTP permafrost region. [ABSTRACT FROM AUTHOR]

Long-term dynamic monitoring and sustainable prediction of rock salt mines is extremely important in preventing potential geological damages. However, the future prediction of the mining-induced surface displacement after the underground mining extraction cannot be achieved with single Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-InSAR) technique, which can only reveal the deformation history during the period of Synthetic Aperture Radar (SAR) acquisition dates. In this paper, a novel dynamic model, namely Dynamic Probability Integral Method (DPIM), based on the integration of Probability Integral Method (PIM) and Weibull temporal function, is proposed in order to predict the future dynamic subsidence for the period which can not covered by the SAR acquisitions. Firstly, the time series deformation over the test salt mine are generated by Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with use of 43 Sentinel-1 images, and then the functional relationship between the derived deformation and DPIM is established; Subsequently, the unknown parameters, including the PIM parameters and the Weibull temporal parameters are estimated following the genetic algorithm; Finally, the dynamic surface deformation occurring during the entire period of the underground extraction of the test rock salt mine is predicted. Both simulation and real deformation data over Xinhua (a typical rock salt mine in Hunan province, China) have been investigated. The scenario using the simulated data shows that the overall root-mean-square error of DPIM-predicted subsidence is ±3 mm. The real data experimental results show that the maximum predicted vertical subsidence over the rock salt mine was up to 246 mm. The root-mean-square error with comparison to the levelling measurements is estimated as ±7 mm. [ABSTRACT FROM AUTHOR]

The use of freely accessible Sentinel-1 synthetic aperture radar (S-1 SAR) and Sentinel-2 multispectral instrument (S-2 MSI) data are currently a feasible way of mapping forest aboveground biomass (AGB) over large areas. Despite the extensive mapping of forest AGB by remote sensing, how to effectively combine different sensors data, selecting the proper statistical modelling method, and variable screening are still poorly understood. This paper presents a framework for Sentinel-based AGB estimation through the use of four variable screening techniques, namely, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), boruta, and removal-based; and three statistical modelling methods including multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF). These methods were examined across two forested sites with different levels of human activity (highly-degraded: HD and minorly-degraded: MD) in Zagros coppice oak forests. We used backscatter coefficients and texture metrics of S-1 SAR, in addition to raw bands, simple band ratios, vegetation indices and texture metrics of S-2 MSI to extract 53 explanatory variables. Our findings showed that GA and removal-based variable screening techniques outperformed the others and RF and MLR statistical modelling methods obtained the best results. The use of different variable screening techniques and modelling methods demonstrated higher accuracies of AGB estimates for the MD site compared to the HD site. S-2 MSI (Coefficient of determination (R2) = 0.76 and Root Mean Square Error (RMSE) = 35.08% of the mean) provided a higher accuracy than S-1 SAR (R2 = 0.42 and RMSE = 52.94% of the mean). Overall, the study demonstrates encouraging results in the retrieval and predictive mapping of the AGB of Zagros forests using the freely accessible Sentinel imagery. [ABSTRACT FROM AUTHOR]

The Beijing–Tianjin Intercity Railway (BTR) passes through the subsidence centre of the Chaoyang Wangsiying-Heizhuanghu subsidence funnel edge, which is the most serious subsidence areas in the Beijing Plain. In this paper, we performed Persistent Scatterers Interferometry (PSI) time-series analyses technique to investigate land subsidence along the BTR with ENVISAT-ASAR, TerraSAR-X, and Sentinel-1 data. We found the serious subsidence area occupies 14.6% of the total area and reaches a maximum rate of 70 mm yr−1 in Dougezhuang. Sequential subsidence aggregation obtained by global Moran's I shows that the subsidence heterogeneity is the greatest in 2013. Using the Getis-Ord statistics, the uneven sections of spatial subsidence along the railway are DK7–11 and DK21–23. Combining the compressible layer thickness with groundwater data reveals that the greater the compressible layer thickness, the more serious the subsidence is and the correlation is highest between the extraction of groundwater from the second and third layers and the ground subsidence. With the start of the South-to-North Water Diversion Project, the extraction of groundwater began to slow, but the slowing trend of subsidence was not obvious because of the lag between groundwater and land subsidence. [ABSTRACT FROM AUTHOR]

The studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method. [ABSTRACT FROM AUTHOR]

In Bucharest, new subway tunnels are under construction since 2011. The whole project of extending Bucharest underground infrastructure is due to be finished by 2030. The M-5 artery has been under construction since 2011 and ongoing. In December 2015, dewatering procedures in one of the metro stations caused serious damage to the surface infrastructure above, which subsided and partially collapsed in some locations. This paper relies on multi-temporal interferometry techniques to characterize displacement patterns of the ground surface above M-5 beltway between 2014 and 2018, and presents the capacity of Sentinel-1 satellite imagery to detect potential hazards in urban environments. Two stacks of Synthetic Aperture Radar (SAR) images acquired between October 2014 and October 2018 by the Sentinel-1 A and B satellites on ascending and descending orbits have been processed using the Delft Persistent Scatterer Interferometry algorithm (DePSI). The results indicate atypical acceleration in velocities starting in November 2015, prior to the described events, and manifesting until April 2016, when the ground was completely stabilized. The displacement trends and velocity values in the area of the Eroilor underground station were successfully validated using the ground-based Global Navigation Satellite Systems (GNSS) technique. [ABSTRACT FROM AUTHOR]

The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band ~ 3 cm, C-band ~ 6 cm, and L-band ~ 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34°) and TerraSAR-X (HH, 22° and 53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration. [ABSTRACT FROM AUTHOR]

In this paper, an approach for estimating the soil moisture content (SMC) in arid environment in Tunisia is presented. In countries characterized by arid and semi-arid climate, it is very important to obtain reliable estimates of soil moisture evolution for water management purposes, in order to reduce water wastes and properly schedule agricultural practices. On the other hand, the retrieval of SMC is often hampered by the small humidity range (below 10%). A retrieval algorithm aiming at estimating the soil moisture and based on artificial neural networks (ANN) has therefore been implemented, using the data collected by the Synthetic Aperture Radar (SAR) sensor of Sentinel-1. By taking advantage of the fast computation and high retrieval accuracy, ANN are able to generate reliable output maps of SMC starting from the complex SAR images and using little auxiliary information, as Digital Elevation Models, Local Incidence angle, Normalized Difference Vegetation Index (NDVI), and so on. The peculiar strategy adopted for the training, which has been obtained by combining satellite measurements with data simulated by electromagnetic model (based on the Integral Equation Model, IEM), made this algorithm robust and almost site independent. The obtained results demonstrated that ANN represent a powerful tool for estimating SMC, provided that they have been trained with consistent datasets, made up by both experimental and theoretical data. The relationship of the algorithm validation between the estimated and measured SMC showed Pearson's correlation coefficient, r = 0.77, and RMSE = 1.84%, in spite of the very low SMC values found on the area. [ABSTRACT FROM AUTHOR]

Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015–2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level. [ABSTRACT FROM AUTHOR]

A procedure for soil moisture (SM) estimation over flat lands in the Argentinian Pampas region, using the water balance equation that considers SM to be the result of water inflows and outflows to the soil system, is presented. In recent years, remotely sensed data with Synthetic Aperture Radar (SAR) and radiometer sensors have been used to develop different methodologies to obtain SM maps. Thus, a variety of methodologies with different levels of complexity are available nowadays. These models require soil information such as soil physical properties and mineral composition, not readily available in Argentina and many other remote areas of the world. The procedure presented in this paper takes into account water input and output processes of the soil system and represents them with different hydro-environmental variables and SAR data. The water balance equation was solved with Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) statistical models, fed with readily available data over Comisión Nacional de Actividades Espaciales (CONAE) core site located in Cordoba province, Argentina. The resulting models were obtained with precipitation (PP), air temperature () and relative humidity (RH) observations and with SAR data from the Sentinel-1A satellite mission. The accuracy of the model estimates represents 10 of the observed measured values of SM and is in line with state of the art algorithms. Results suggest that any model can be used with similar precision, since they show similar errors, although the MLR method allows analyzing and quantifying the errors introduced by the variables. [ABSTRACT FROM AUTHOR]

Soil salinity is a major environmental threat, which has a negative impact on soil productivity and agricultural fields. One of the most promising methods for monitoring affected areas, which has special importance in land management studies, is through remote sensing technologies. While the potential of optical imagery in detecting saline soils is widely investigated, limited studies have been dedicated to assessing the potential of Synthetic Aperture Radar (SAR) imagery in monitoring soil salinity. Accordingly, this paper deals with soil salinity estimation using Sentinel-1 SAR imagery in an area which is highly affected by salinity hazard. Due to lack of a suitable theoretical model for simulating radar backscatter of soil based on salt contents, we investigated a new method to relate radar intensity to measured in-situ salinity directly. In the first step, Sentinel-1 dual polarized VV (Vertical transmit Vertical receive) and VH (Vertical transmit Horizontal receive) data were acquired from the study site. A field study was also conducted, simultaneously, and the Electrical Conductivity (EC) of several soil samples was measured. We then extracted some features based on the intensity images of both VV and VH polarization. Based on the fact that the target texture affects the radar response, an analysis of the texture was also performed by calculating the first and second order statistics, extracted from the histogram and Gray Level Co-occurrence Matrix (GLCM), respectively. The Support Vector Regression (SVR) technique, with different kernel functions, was used to relate explanatory variables to ground measured salinity. We also applied Feature Selection (FS) algorithms of the Genetic Algorithm (GA) and Sequential Feature Selection (SFS) for optimizing the model and selecting the best explanatory features. The results showed that ε-SVR with Radial Basis Function (RBF) kernel had the most accuracy with the Coefficient of Determination (R2) = 0.9783 and Root Mean Square Error (RMSE) = 0.3561 when the GA FS was applied. Also and had the best performance in salinity detection. It can be concluded that the intensity images of VV and VH polarization of SAR imagery have the potential to discriminate saline surface soils, regardless of the failure of common backscattering models. [ABSTRACT FROM AUTHOR]

In tropical and subtropical countries, the awareness on the importance of bamboos to the environment and economy is increasing and so is the demand for spatial bamboo information. However, mapping bamboos especially those naturally grown has been challenging, as these grasses are often mixed with other land-use and land-cover (LULC). In this study, we used Sentinel 1 and Sentinel 2 remote sensing (RS) images, and their vegetation indices to accurately map the bamboos of Iloilo province in the Philippines using: (1) pixel-based method that mapped bamboos and other LULC at 10 m resolution, and (2) fractional cover method that mapped bamboo cover at 100 m resolution (% ha−1). A random forest model was trained for each method and then validated per hectare basis using a 50:50 training-validation ratio of a stratified random sample. The fractional cover method showed 0.34 higher Nash-Sutcliffe Efficiency (NSE) and 5.10% lower Root Mean Square Error (RMSE) than the pixel-based method. Further validation within upland and lowland sites also favoured the fractional cover method, but the results of the two methods were closer in the upland site (bamboo plantation). Errors at 10 m resolution especially in the lowlands were mostly commission errors, likely because of the spectral similarity and proximity between bamboos and > 14 vegetations. Averaging the RS inputs into 100 m resulted in at most 12% separation of reflectance values among bamboos, forests, and other vegetations. Using the bamboo cover map, a total of 14,795 (± 1,283) ha bamboos and 7.45 (± 4.20) million harvestable culms (poles) were estimated for the whole province, where 54% come from the lowland. We suggest using the fractional cover method for nationwide baselining of bamboo resources. [ABSTRACT FROM AUTHOR]