Search

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest–grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar–optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region. [ABSTRACT FROM AUTHOR]

Ensuring the safety and operational efficiency of hydraulic infrastructures is paramount, considering the widespread consequences that damages can inflict on people, communities, and the environment. To mitigate risks and prevent significant losses, continuous surveillance is vital. While some damages might appear minor, they can jeopardize the complete operational reliability of dams, leading to substantial economic losses, especially in energy production and related activities. The rapid growth in 20th-century infrastructure development globally has made security monitoring a necessity for numerous civil structures. Rigorous inspection programs, particularly for reservoir dams, are essential for safeguarding citizens and their properties. However, individually monitoring each dam is often impractical due to the associated costs and time constraints, potentially posing safety risks. Fortunately, satellite-based differential radar interferometry (DInSAR) offers an effective and cost-efficient remote sensing solution. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques, particularly utilizing Persistent Scatterers, have proven successful in monitoring various infrastructures, natural phenomena, and geological activities. MT-InSAR provides precise measurements without the need for fieldwork, utilizing historical SAR image archives dating back to the 1990s. Technological advancements, such as the Sentinel-1 C-band with a six-day revisit time until the end of 2021, have enhanced monitoring capabilities. Additionally, commercial radar images in the X-band and the development of multi-interferometric InSAR techniques have opened new avenues for monitoring. This study showcases the adaptation and application of MT-InSAR for monitoring dams and large ponds constructed with loose materials. By assessing vertical displacements and consolidation rates, the technique identifies potential issues, aiding in further field investigations. Case studies involving dams and large reservoirs in Andalusia illustrate the effectiveness of satellite radar interferometry in monitoring their structural stability from space as a routine practice. [ABSTRACT FROM AUTHOR]

In this study, a machine learning approach is applied to wind speed retrieval from vertical-vertical (VV) polarized synthetic aperture radar (SAR) during tropical cyclones (TC). More than 2400 dual-polarized (VV and vertical-horizontal (VH)) Sentinel-1 (S-1) images acquired in interferometric wide (IW) and extra wide (EW) mode are collected in 2016–2022. Along-track observations from stepped-frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) are available for several images. The azimuthal cut-off wavelength (ACW) is derived from VV-polarized image and the Doppler centroid anomaly (DCA) is provided from Ocean (OCN) product. It is found that SFMR wind speed linearly relates with ACW and DCA. Following this rationale, a machine learning method, called eXtreme Gradient Boosting (XGBoost), is trained for constructing SAR wind speed retrieval geophysical model function (GMF) through abundant matchups from 2000 images collocated with wind speeds from soil moisture active passive (SMAP) microwave radiometer and SFMR, denoted as TCWIND2-S1. Then GMF TCWIND2-S1 is applied for more than 400 images and the validation against SMAP and SFMR products up to 70 m s−1 shows a 3.01 m s−1 root mean squared error (RMSE) with a 0.16 scatter index (SI) and a 0.92 correlation (COR). Retrieval is also compared with CyclObs wind products derived from more than 400 dual-polarized images, yielding a 2.66 m s−1 RMSE with a 0.19 SI and a 0.93 COR. Our work provides an approach for VV-polarized SAR wind retrieval at wind speed ranged from 0 to 70 m s−1 during TCs. This work is conveniently adopted for TerraSAR-X without operating at cross polarization channel. [ABSTRACT FROM AUTHOR]

Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R2 of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values. [ABSTRACT FROM AUTHOR]

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions. [ABSTRACT FROM AUTHOR]

The M w 6.8 Murghob earthquake is the third earthquake in an M w 6.4+ sequence occurring in the Pamir initiated by the 2015 Sarez M w 7.2 earthquake. It is of great significance to investigate their interactions and to assess future seismic hazards in the region. In this paper, we use Sentinel-1 radar interferometric data to retrieve coseismic deformation, invert for the slip distributions of the four events, and then investigate their interactions. The cumulative Coulomb failure stress changes (ΔCFS) suggest that the 2023 Murghob earthquake was promoted by the three prior earthquakes in the sequence. Pre-stress from historical earthquakes is a key factor in explaining the triggering mechanism of the two 2016 M w 6.4+ earthquakes. Stress loading and unloading effects on major faults in the region indicate that future attention should be paid in (1) the segment of the Sarez-Karakul fault north of the Kokuibel Valley, (2) the segment of the Sarez-Murghab thrust fault west of the Sarez-Karakul fault and (3) the east segments of the Pamir thrust fault system, all with a large positive ΔCFS. [ABSTRACT FROM AUTHOR]

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]

Satellite data have been widely utilized for flood detection and mapping tasks, and in recent years, there has been a growing interest in using Synthetic Aperture Radar (SAR) data due to the increased availability of recent missions with enhanced temporal resolution. This capability, when combined with the inherent advantages of SAR technology over optical sensors, such as spatial resolution and independence from weather conditions, allows for timely and accurate information on flood event dynamics. In this study, we present an innovative automated approach, SAR-RST-FLOOD, for mapping flooded areas using SAR data. Based on a multi-temporal analysis of Sentinel 1 data, such an approach would allow for robust and automatic identification of flooded areas. To assess its reliability and accuracy, we analyzed five case studies in areas where floods caused significant damage. Performance metrics, such as overall (OA), user (UA), and producer (PA) accuracy, as well as the Kappa index (K), were used to evaluate the methodology by considering several reference flood maps. The results demonstrate a user accuracy exceeding 0.78 for each test map when compared to the observed flood data. Additionally, the overall accuracy values surpassed 0.96, and the kappa index values exceeded 0.78 when compared to the mapping processes from observed data or other reference datasets from the Copernicus Emergency Management System. Considering these results and the fact that the proposed approach has been implemented within the Google Earth Engine framework, its potential for global-scale applications is evident. [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]

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories. [ABSTRACT FROM AUTHOR]

Along the western coastal plain of the Red Sea, the discovery of lithic tools proved that the occurrence of archaeological remains belonged to the Stone Age era. Unfortunately, until recently, most of the Stone Age site plans in the western Red Sea periphery have been unknown because of change in climate in addition to covering these sites with a large layer of sand. Recently, remote sensing (RS) data have proven to be effective at identifying buried archaeological sites using radar satellite imagery due to their ability to penetrate the land surface. This paper aims to identify and reconstruct several potential Stone Age sites using radar satellite images in the Tokar region, North-Eastern Sudan, on the western plain of the Red Sea. Radar (Sentinel-1 SAR GRD: C-band) data were processed utilizing the advantages of the Google Earth Engine platform and Snap software. The results of this research showed that some potential settlements belonging to the Stone Age era (expectedly dating back to the end of the Pleistocene and/or the initial phase of the Holocene) have subcircular shapes with some interior subcircles. These results shed new light on the potential construction technique, size, and function of the settlement in the Stone Age era and can provide some initial insights into the ancient lifestyle in this region. Additionally, this study can aid in future archaeological surveys and excavation missions in this region. [ABSTRACT FROM AUTHOR]

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. [ABSTRACT FROM AUTHOR]

The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model's high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts' by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to detect the coastal erosion and progradation effects in the 'Atrato River' delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu's method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons. [ABSTRACT FROM AUTHOR]

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σ V V 0 , σ V H 0 ), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σ V H 0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σ V H 0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σ V V 0 , σ V H 0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture. [ABSTRACT FROM AUTHOR]

Floods are among the most dangerous natural disasters, and their frequency and severity are rapidly increasing due to climate change. Flood inundation mapping is critical for better understanding which areas are prone to flooding and identifying emerging floods. Recently, deep learning algorithms have been developed to improve the accuracy and timeliness of the production of these maps. In this study a new architecture, named Explainable Flood Inundation Mapping Network (XFIMNet) is presented, which is an explainable deep learning segmentation model for producing flood inundation maps from both synthetic aperture radar (SAR) and multi-spectral optical images. Comparative analysis with state-of-the-art (SOTA) models is performed, where the versatility of the model is demonstrated, in that it achieves superior performance across both image types, while the SOTA models are less consistent. The models are further evaluated with only the red, green, and blue (RGB) bands of the Sentinel-2 images, demonstrating that the full 13-band image is the superior choice for training a deep learning model. XFIMNet achieves an Intersection Over Union (IOU) of 0.59 with SAR images, 0.7 with optical, and 0.47 with the RGB bands, outperforming each of the SOTA models, as well as providing the most detailed and accurate visual segmentation mask, with minimal increase in the computation time for training or inference. The findings of this study highlight that XFIMNet is not only able to provide the most accurate segmentation mask but also prioritizes the most appropriate features when making decisions across both image types. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on urban built-up areas is significant for managing urban growth and development. Earth Observation (EO) data are valuable sources for meeting this demand. However, the extraction of urban built-up areas from EO data is challenging due to the limitations of EO data sources. To overcome this challenge, this study follows an approach that assesses the performance of optical (Sentinel-2), radar (Sentinel-1) and fused (Sentinel-1 and Sentinel-2) data to extract urban built-up areas using machine learning algorithms including Random Forest (RF), K-Nearest Neighbors (KNN) and KDTree KNN. The results were statistically analyzed by considering the Overall Accuracy (OA) and kappa coefficient. In addition, 15 cm GSD (Ground Sample Distance) aerial photography of the study area was used to validate the results. According to the results, Sentinel-2 produced better representation and accuracy of urban built-up areas than Sentinel-1 and even the fused image. Regarding to machine learning algorithms classification performance, RF performed better in both OA and Kappa coefficient along all datasets. The research findings can have significant implications for various domains, such as urban planning, land use management and open avenues for further comparisons of different EO data sources and machine learning algorithms for built-up areas extraction. [ABSTRACT FROM AUTHOR]

In dry southwestern South Tyrol, Italy, rock glaciers are dominant landforms of the high-mountain cryosphere. Their spatial distribution and degree of activity hold critical information on the current state of discontinuous permafrost and consequently on the response potential to climate warming. Traditional geomorphologic mapping, however, owing to the qualitative expert-based nature, typically displays a high degree of uncertainty and variability among operators with respect to the dynamic classification of intact (permafrost-bearing) and relict (permafrost-devoid) rock glaciers. This limits the reliability of geomorphologic rock glacier inventories for basic and applied purposes. To address this limitation, (i) we conduct a systematic evaluation of the improvements that synthetic aperture radar interferometry (InSAR) can afford to the detection and dynamic classification of rock glaciers and (ii) build an integrated inventory that combines the strengths of geomorphologic- and InSAR-based approaches. To exploit fully InSAR-based information towards a better understanding of the topo-climatic conditions that sustain creeping permafrost, we further explore how velocity and the spatial distribution of moving areas (MAs) within rock glaciers may vary as a function of simple topographic variables known to exert first-order controls on incoming solar radiation, such as elevation and aspect. Starting from a geomorphologic inventory (n=789), we characterize the kinematics of InSAR-based MAs and the relevant hosting rock glaciers on 36 Sentinel-1 interferograms in the 2018–2019 period. With respect to the original inventory, InSAR analysis allowed us to identify 14 previously undetected rock glaciers. Further, it confirmed that 246 (76 %) landforms, originally interpreted as intact, do exhibit detectable movement (i.e., ≥1 cm yr -1) and that 270 (60 %) of the relict labeled counterparts do not, whereas 144 (18 %) were kinematically undefined due to decorrelation. Most importantly, InSAR proved critical for reclassifying 121 (15 %) rock glaciers, clarifying that 41 (13 %) of those interpreted as being intact do not exhibit detectable movement and that 80 (17 %) of the original relict ones do move. Reclassification (i) allowed us to identify a cluster of intact rock glaciers below 2000 m a.s.l. associated with positive mean annual air temperature (MAAT), and (ii) by increasing the altitudinal overlap between intact and relict rock glaciers, it depicts a broad transition belt in the aspect–elevation space, which varies from 50 m on west-facing slopes to 500 m on easterly ones. This finding deteriorates the significance of elevation and aspect as topographic proxies for modeling permafrost occurrence and highlights the importance of using InSAR to inform such models. From a process-oriented standpoint, InSAR information proves fundamental for imaging how this altitudinal transition manifests through changing rates and styles of rock glacier surface deformation. Specifically, we find that, as rock glaciers move faster, an increasingly larger proportion of their surface becomes kinematically involved (i.e., percent MA cover) and that this proportion increases with elevation up to 2600–2800 m, beyond which an inflection occurs and consistent average values are attained. Considering that the inflection falls between the -1 and -2 °C MAAT – the lower boundary for discontinuous permafrost – and is independent of slope gradient, we conclude that this altitudinal pattern represents a geomorphic signature: the dynamic expression of increasing permafrost distribution, from sporadic to discontinuous. [ABSTRACT FROM AUTHOR]

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations. [ABSTRACT FROM AUTHOR]

Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management. [ABSTRACT FROM AUTHOR]

Observation frequency is analyzed over four areas of the Mediterranean basis where poaching, illegal fishing, and illegal trafficking of goods and people are active. To this end, a geometrical observation and dynamical model is utilized which accounts for multiple satellites and multiple orbital planes and is applied to SIASGE and Sentinel-1 missions. Statistics show that a few hours are needed in the mean to reobserve the same area. [ABSTRACT FROM AUTHOR]

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area. [ABSTRACT FROM AUTHOR]

Soil moisture plays an important role in crop yield estimation, irrigation management, etc. Remote sensing technology has potential for large-scale and high spatial soil moisture mapping. However, offline remote sensing data processing is time-consuming and resource-intensive, and significantly hampers the efficiency and timeliness of soil moisture mapping. Due to the high-speed computing capabilities of remote sensing cloud platforms, a High Spatial Resolution Soil Moisture Estimation Framework (HSRSMEF) based on the Google Earth Engine (GEE) platform was developed in this study. The functions of the HSRSMEF include research area and input datasets customization, radar speckle noise filtering, optical-radar image spatio-temporal matching, soil moisture retrieving, soil moisture visualization and exporting. This paper tested the performance of HSRSMEF by combining Sentinel-1, Sentinel-2 images and in-situ soil moisture data in the central farmland area of Jilin Province, China. Reconstructed Normalized Difference Vegetation Index (NDVI) based on the Savitzky-Golay algorithm conforms to the crop growth cycle, and its correlation with the original NDVI is about 0.99 (P < 0.001). The soil moisture accuracy of the random forest model (R2 = 0.942, RMSE = 0.013 m3/m3) is better than that of the water cloud model (R2 = 0.334, RMSE = 0.091 m3/m3). HSRSMEF transfers time -consuming offline operations to cloud computing platforms, achieving rapid and simplified high spatial resolution soil moisture mapping. [ABSTRACT FROM AUTHOR]

Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning‐based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high‐image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non‐local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi‐scale feature information from the echo and image data. PixelShuffle is utilised for up‐sampling to generate high‐quality echo data. The experimental results on MSTAR and Sentinel‐1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Wideband interference (WBI) can significantly reduce the image quality and interpretation accuracy of synthetic aperture radar (SAR). To eliminate the negative effects of WBI on SAR, we propose a novel end-to-end data-driven approach to mitigate WBI. Specifically, the WBI is mitigated by an explicit function called WBI mitigation–generative adversarial network (WBIM-GAN), mapping from an input WBI-corrupted echo to its properly WBI-free echo. WBIM-GAN comprises a WBI mitigation network and a target echo discriminative network. The WBI mitigation network incorporates a deep residual network to enhance the performance of WBI mitigation while addressing the issue of gradient saturation in the deeper layers. Simultaneously, the class activation mapping technique fully demonstrates that the WBI mitigation network can localize the WBI region rather than the target echo. By utilizing the PatchGAN architecture, the target echo discriminative network can capture the local texture and statistical features of target echoes, thus improving the effectiveness of WBI mitigation. Before applying the WBIM-GAN, the short-time Fourier transform (STFT) converts SAR echoes into a time–frequency domain (TFD) to better characterize WBI features. Finally, by comparing different WBI mitigation methods applied to several real measured SAR data collected by the Sentinel-1 system, the efficiency and superiority of WBIM-GAN are proved sufficiently. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm to differentiate leads from sea ice using the dual polarization synthetic aperture radar (SAR) data from the Sentinel-1 satellite in an extrawide swath mode. The algorithm uses the polarimetric features of the sea surface signal obtained in the SAR images: the ratio between co- and cross-polarization. A technique is proposed for classifying the SAR images to identify discontinuities (cracks, leads) in drifting sea ice using the ratio and difference of polarizations together with texture features and the neural network implementation. The method was tested using the satellite data obtained over the Arctic seas in the Russian Federation. [ABSTRACT FROM AUTHOR]

Monitoring the subsidence at different periods in high underground water level coal mine areas can provide a data basis for land reclamation. For the purpose of monitoring subsidence in I working face located in Guotun Coal Mine, 22 Sentinel-1 images were processed by using differential interferometric synthetic aperture radar (D-InSAR). After dividing subsidence periods, the characteristics and monitoring accuracy at different subsidence periods were explored. The results showed that: (1) The formation of single-gradient subsidence funnel is an important sign to identify active period. (2) The root mean square error (RMSE) and mean absolute error (MAE) in central area of subsidence are reduced by 50% and 49%, respectively, compared with those in edge area. (3) The monitoring accuracy is high at different subsidence periods. The coefficient of determination (R2) can reach more than 0.89. Therefore, D-InSAR technique can accurately monitor the subsidence at different periods based on Sentinel-1 data. [ABSTRACT FROM AUTHOR]

The snowpack evolution during the melt season on an Arctic glacier is assessed using ground-based oblique-view cameras, spaceborne imaging and spaceborne RADAR. The repeated and systematic Synthetic Aperture RADAR (SAR) imaging by the European Space Agency's Sentinel-1 spaceborne RADARs allows for all-weather, all-illumination condition monitoring of the snow-covered fraction of the glacier and hence assessing its water production potential. A comparison of the RADAR reflectivity with optical and multispectral imaging highlights the difference between the observed quantities—water content in the former, albedo in the latter—and the complementarity for understanding the snow melt processes. This work highlights the temporal inertia between the visible spring melting of the snowpack and the snow metamorphism. It was found that the snowpack exhibits that approximately 30 days before it starts to fade. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm's performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors. [ABSTRACT FROM AUTHOR]

The results of analysis of spatiotemporal variability of submesoscale surface eddy structures from May to October of 2019–2022 in Lake Ladoga are summarized using more than 1000 Sentinel–1A/B high-resolution images as the original data. The wide occurrence of small eddy structures on the lake surface is revealed during the warm period of the year. In total, 496 surface eddies with an average diameter of 2 km are registered. More than 90% of the recorded eddies belong to the submesoscale variability range. Cyclonic rotation is typical of 84% of the structures. A seasonal maximum of eddy activity is registered during the stable stratification over most of Lake Ladoga. The eddies occur most frequently to the north and northeast of the Valaam Island in areas ~100 m deep but close to the seabed unevenness. During the spring thermocline, the eddies are often registered from the cold side of the frontal boundary indicating their important contribution to the water exchange and mixing processes. [ABSTRACT FROM AUTHOR]

In this study, we investigated the quality of Interferometric Synthetic Aperture Radar (InSAR) data to measure surface displacements in upstate New York, an area with dense vegetation, snowy winters, and strong seasonal signals. We used data from the German Space Agency's TerraSAR-X and TanDEM-X satellites (X-band, 3.1 cm radar wavelength) as well as the European Space Agency's Sentinel-1 satellite (C-band, 5.6 cm radar wavelength); both datasets covered a ~3-year time period from 2018 to 2021. Using persistent scatterer interferometry (PSI), we were able to observe several deforming features in the region with sub-centimeter/year deformation rates. We also examined a version of the X-band data that we spatially averaged to the same pixel size as the Sentinel-1 imagery in order to separate out the effects of wavelength and pixel size on PSI accuracy and coverage. Overall, the largest number of stable PS points was found in the full-resolution X-band data, which was followed by the C-band data and then by the downsampled X-band data. Our analysis also included a subset of snow-free imagery so that we could assess the effect that snow-covered images had on the distribution and accuracy of PS points and the resulting time series. This analysis revealed that PS populations increased by 50–60% for the snow-free data when compared with analyses using the full datasets. The average deformation rates inferred from the time series generated using only snow-free images were nearly identical to those estimated from the full time series. We assessed the accuracy of the inferred rates through comparisons between the results of different datasets and with limited ground survey data. We found that all of the inferred deformation rates from each of the datasets agreed with in situ measurements in an area of known ground subsidence above an underground salt mine in Lansing, NY. The S1 datasets, however, had higher levels of noise. [ABSTRACT FROM AUTHOR]

This paper considers the use of radar and optical data from the Sentinel 1 and Sentinel 2 satellites for 2019–2020 to assess the humus content and the percentage of clay and moisture in the topsoil using the example of chernozems and gray forest soils of the Novosibirsk oblast. Particular attention is paid to the selection of satellite images, because in order to quantify the humus content and clay in the soil, it is necessary to fulfill the conditions for the soil to be dry and bare. The humus content is estimated for five test sites based on a regression model (Karavanova and Orlov, 1996), which includes surface reflection coefficients at the wavelength of the B6 spectral channel of the Sentinel 2 satellite. The model parameters are adjusted for the conditions of the study area separately for chernozems and gray forest soils. The percentage of physical clay in the soil is estimated using Sentinel 2 optical data, ground measurements, and regression models with an exponential dependence of clay content on soil reflectances at the SWIR wavelengths of the Sentinel 2 spectral channels (Bousbih et al., 2019; Shabou et al., 2015). The change in the percentage of humus and clay content in the test sites of soils over the year is shown according to Sentinel 2 data. The topsoil moisture content is estimated based on radar, optical, and the combined use of radar and optical data. [ABSTRACT FROM AUTHOR]

In the past decades, important research has been carried out to map the natural disturbances in the Paraná River Delta. The benefits of the combined use optical and radar data are also known. The main objective of this paper is to assess the wetland fire cartography through a synergetic use of radar and optical data. We focus on integrating radar (SAOCOM) and Sentinel 1, as well as Sentinel 2 optical data, concerning the fires impact analyses in the wetland areas. The generation of water masks through the radar images can contribute to improve the burned wetland area estimations. The relationship between landforms, vegetation cover, and the spatial/temporal resolution imposed by the flood pulse, play a vital role in the results. Burnt areas represent a total of 2439.57 sq km, which is more than 85% of the wetland, during the winter and spring (Q3 and Q4) periods. Understanding the wetland heterogeneity and its recovery pattern after a fire, is crucial to improve the cartography of the burned areas; for this, biweekly or monthly image compositions periodicity are of crucial importance. The inclusion of different indexes, for optical and radar images, improve the precision for the final classification. The results obtained here are promising for post-flood and post-fire evaluation, even applying radar and optical data integration into the evaluation and the monitoring of wetland fires is far from being a uniform standardized process. [ABSTRACT FROM AUTHOR]

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam's substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage. [ABSTRACT FROM AUTHOR]

This manuscript presents the results of the study of snow covers' influence on the interferometric measurements of the stability of industrial infrastructure in the vicinity of Norilsk city, Russia. Fuel tanks of the Norilsk thermal power plant (TPP) were selected as an object of study due to a well-known accident when about 20,000 tons of diesel fuel spilled from one of the tanks. Sentinel-1 synthetic aperture radar data acquired over the territory of Norilsk TPP were used in the DInSAR study of the possible displacements of the tanks that could be the cause of the tank's damage. For twelve days, radar interferograms that were generated in the study covered the cold and warm seasons of 2018–2020, including the catastrophic event—the rupture of the tank with diesel fuel—in order to shed light on the possible impact of the area subsidence because of permafrost thaw under the tanks. As the tank walls and adjacent concrete base constituted the virtual dihedral corner reflector, the accumulation of snow on the surface near the tanks created a distorting effect on the results of monitoring the stability of the tank's location. Three models of snow layer within the dihedral proposed could help explain the deviations in the signal amplitude and phase in the case of snowfalls occurring between radar observations. We propose three ways to minimize the influence of snow on interferometric measurements. One of them, the selection of the radar data acquired in proper observation conditions, made it possible to assess the stability of the mutual location of the tanks. Among the most important processing and analysis results in the paper is a conclusion about the high stability of the fuel tank's location on the yearly time interval, including the troubleshooting tank. [ABSTRACT FROM AUTHOR]

The timing of snowmelt runoff is critical for water resource applications, but its spatiotemporal evolution remains poorly understood. We present a scalable approach to map snowmelt runoff onset using Sentinel‐1 synthetic aperture radar data for the past 8 years with 10 m spatial resolution and a median temporal resolution of 3.9 days. A systematic analysis of stratovolcanoes in the Western United States showed that snowmelt runoff onset is strongly dependent on elevation (r = 0.81), with a median runoff onset lapse rate of 4.9 days per 100 m of elevation gain. During the 2015 snow drought, we observed snowmelt runoff onset 25 days early relative to the 2015–2022 median. We document a median shift in snowmelt runoff onset of +2.0 days later in the year per year between 2016 and 2022. Our open‐source tools can be used to create snowmelt runoff onset maps anywhere on Earth. Plain Language Summary: Snowmelt timing is important–knowing when water leaves mountain snowpack is critical for downstream water resource applications like irrigation and hydropower. Snowmelt timing is also affected by regional climate change. However, it is hard to make detailed measurements of when and where snow melts across mountainous regions. We developed an improved method to map snowmelt using satellite based radar data, and we applied this method to study snow on mountains in the Western United States. We documented the detailed relationship between elevation and snowmelt, and how this relationship changed over the past 8 years. In general, at higher elevations and latitudes, snow melts later in the year. We also observed snow melting much earlier than it usually does during the 2015 snow drought, which helps us prepare for future years with low snow accumulation. Finally, from 2016 to 2022, we documented a shift toward snowmelt happening earlier in the year, which means earlier spring flow in rivers. We publicly released interactive, user‐friendly software, so anyone can use our method to study snowmelt timing anywhere on Earth. Collectively, our work will help scientists better understand regional climate change and allow water managers to better manage water resources today and in the future. Key Points: We present a scalable method and open toolbox to map snowmelt runoff onset timing with high spatial and temporal resolutionWe quantify the topographic and geographic controls on snowmelt runoff onset for stratovolcanoes in the Western United StatesWe document early snowmelt runoff onset during the 2015 snow drought and intra/interannual variability from 2016 to 2022 [ABSTRACT FROM AUTHOR]

We employ a time series of Sentinel‐1 differential radar interferometry data from 2018 to detect the variability in grounding line position of the Fisher, Mellor, and Lambert glaciers, which drain about 47 billion tons of ice per year from East Antarctica. We observe kilometer‐scale tidal migration, two orders of magnitude larger than expected for ice flowing over a hard bed. The migration is not in phase with changes in oceanic tide. In two estuaries underlaid by subglacial channels, we observe two states of migration that switch on and off over time scales of several weeks. The range of vertical motion reveals a water column thickness of 2–20 cm. Such intrusions of seawater over wide grounding zones are not accounted for in physical models. Including them will add vigorous melting of grounded ice that will enhance the sensitivity of glaciers to ocean warming and increase projections of mass loss. Plain Language Summary: Grounding lines (GLs) are assumed to migrate back and forth with changes in oceanic tides based on hydrostatic equilibrium, which induces horizontal migration in the range of a few 100 m, similar to the spatial resolution of ice sheet models. Differential radar interferometry data from the Sentinel‐1 mission, however, reveals that for the Fisher, Mellor, and Lambert glaciers, the migration is two orders of magnitude larger. Such widespread intrusions of seawater will melt grounded ice and affect the regime of basal friction of glaciers and their stability. Physical models in charge of projecting sea level rise assume a fixed GL, with zero melt. We report regions of enhanced migration (10–15 km) along estuaries underlaid by subglacial channels where water gets trapped for several weeks before draining out. These irregular, widespread interactions between ice and seawater will have a strong impact on glacier evolution and projections if implemented in physical models. Key Points: The zone of tidal variability of Amery Ice Shelf grounding line (GL) is two orders of magnitude wider than expected from hydrostatic equilibriumWe detect GL migration uncorrelated with tide up to 15 km upstream, along subglacial troughs, with 2–20 cm thick water intrusionsSeawater intrusion in estuaries has high potential for melting ice in grounding zone and enhance sensitivity of glaciers to ocean warming [ABSTRACT FROM AUTHOR]

Mapping agricultural land cover data is important as an effort to support national food security, especially in Boyolali, Central Java Province, Indonesia which is one of the national rice granaries. However, mapping in the mountain slope area using optical data only is challenging due to cloud cover. The development of remote sensing technology has encouraged the possibility to integrate data with different sensors. This data integration is needed to optimize the ability to detect and map cropland that has a variety of characteristics. Therefore, this study aims to identify the cropland through the integration of time-series optical and Synthetic Aperture Radar (SAR) data. Detection of cropland was carried out using 2021 data. Polarisation of VV, VH, and ratio of VV/VH data was derived from the Sentinel-1, whereas image indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil Adjusted Vegetation Index (SAVI) data were obtained from Sentinel-2. Data of Sentinel-1 and Sentinel-2 was combined and several features were selected based on their importance score. Random Forest (RF) classification was then performed. The result show that the mapping using integrated data could improve the accuracy. This indicates the possibility of data to be implemented in further studies such as the cropland type mapping and the estimation of food productivity. [ABSTRACT FROM AUTHOR]

Translation abstract Deformation monitoring of bridges applying remote sensing satellite radar measurementsDeformations of the Earth's surface can be measured from space using remote sensing radar interferometry (InSAR). This paper demonstrates the use of these data for deformation monitoring on the Schottwienbrücke‐S6 Semmering motorway. The European Space Agency (ESA) recently began providing free research satellite data from the Sentinel‐1 mission. The approach uses historically recorded natural reflection points as well as corner reflectors artificially attached to the structure. These are measured coordinately and analyzed using remote sensing methods. Thermal displacements of the structure are compensated to increase accuracy. A new model predicts the temperature of the structure based on network weather data. This means that the deformation measurements from the satellites can be analyzed and thermally compensated without sensors on the building, and the deviations can be more than halved. The recorded and processed annual measurements from 2022 onwards show a standard deviation of 1.7 to 3.0 mm compared to the reference system (Liquid Leveling System). Good agreement with the long‐term settlement behavior of the bridge was also found for natural reflection points. Satellites can also be used to simultaneously record different structures at 6‐day intervals up to 2016. Due to the time‐delayed processing, they are currently well suited for long‐term deformation monitoring of long‐span bridges. [ABSTRACT FROM AUTHOR]

• Glacier radar zones identification and mapping of glacier facies using multi-temporal SAR images and polarimetric decompositions. • Interferometric glacial velocity using ERS 1/2 tandem pairs. • Long term and seasonal glacial flow using optical feature tracking. • Quantifying spatial–temporal ice velocity changes. Himalayan glaciers have shown more sensitivity and visible changes to the climate change and global warming in the last 150 years. The highly rugged topography and inaccessible remote areas makes satellite images as the most appropriate source of information retrieval. We performed remote sensing based glacier change study for Samudra Tapu glacier, located in the Chandra basin of North-West Himalaya. In the present study, the capabilities of both optical and microwave remote sensing data was analysed in glacier change study in terms of its coverage, shift in equilibrium line altitude (ELA) and surface velocity over a period from 2000 to 2021. Multi Sensor (RISAT-1, Sentinel-1) time series of C-band SAR data along with a object oriented classification technique were used to identify different glacier facies such as percolation facies, icefalls, bare ice facies, refreeze snow and supraglacial debris. These classified maps were also used to detect the snow line and firn line along with ELA, aided with elevation information from digital elevation model (DEM). It was identified that more than 50 % of the total glacier area still lies into accumulation region. Further, we estimated the glacier surface velocity using Differential Interferometric Synthetic Aperture Radar (DInSAR) technique using European Remote Sensing Satellite (ERS-1/2) tandem data of 1996. High value of coherence was observed from the SAR return signal for one-day temporal difference. A mean velocity of 17–24 cm/day was found for the months of March and May 1996, highest flow rates were seen in the high accumulation area located in the Eastern and Southern Aspect of glacier. Spatial analysis of velocity patterns with respect to slope and aspect show that high rates of flow was found in southern slopes and movement rates generally increase with increase in slope. Feature tracking approach was used to estimate the glacier flow for long term and seasonal basis using optical and SAR datasets (IRS-1C, 1D PAN, Landsat-7, 8 PAN, and TANDEM-x) during 1999–2020 period. The results suggest that glacier flow varies with season, i.e., high velocity during spring-summer season, as compared to late summer or winter and, the rate of ice flow changes over the years. The mean glacier velocity reduced to 49.5 m/year during 2013–2020 time, as compared to 67.67 m/year during 1999–2003 time. These results of reducing glacier velocity and changing snow line altitude indicates enhanced glacier's melt rate and overall negative mass balance for Smudra tapu glacier. [ABSTRACT FROM AUTHOR]

Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere, and it is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of global climate processes, and is still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) is a suitable tool for tundra lakes recognition and monitoring of their changes. However, manual analysis of lake boundaries can be slow and inefficient; therefore, reliable automated algorithms are required. To address this issue, we propose a two-stage approach, comprising instance deep-learning-based segmentation by U-Net, followed by semantic segmentation based on a watershed algorithm for separating touching and overlapping lakes. Implementation of this concept is essential for accurate sizes and shapes estimation of an individual lake. Here, we evaluated the performance of the proposed approach on lakes, manually extracted from tens of C-band SAR images from Sentinel-1, which were collected in the Yamal Peninsula and Alaska areas in the summer months of 2015–2022. An accuracy of 0.73, in terms of the Jaccard similarity index, was achieved. The lake's perimeter, area and fractal sizes were estimated, based on the algorithm framework output from hundreds of SAR images. It was recognized as lognormal distributed. The evaluation of the results indicated the efficiency of the proposed approach for accurate automatic estimation of tundra lake shapes and sizes, and its potential to be used for further studies on tundra lake dynamics, in the context of global climate change, aimed at revealing new factors that could cause the planet to warm or cool. [ABSTRACT FROM AUTHOR]

The ancient Aniangzhai (ANZ) landslide in Danba County, Sichuan Province of southwest China was reactivated after a series of complex hazard events that occurred in June 2020. Since then, and until June 2021, emergency engineering work was carried out to prevent the further failure of the reactivated landslide. This study investigates the potential of joint use of time series Interferometric Synthetic Aperture Radar (InSAR) and optical pixel offset tracking (POT) to assess deformation characteristic and spatial-temporal evolution of the reactivated ANZ landslide during the post-failure stage. The relationships between sun illumination differences, temporal baseline of correlation pairs and the uncertainties were deeply explored. Surface deformation along the line-of-sight (LoS) direction was retrieved by the time series InSAR processing with the two Sentinel-1 datasets, revealing a maximum deformation rate up to 190 mm/year. The large horizontal displacements were also detected from the POT processing using 11 optical images acquired by the PlanetScope satellite (3 m spatial resolution), showing a significant increase of about 24 m between 24 June 2020 and 11 June 2021. The time series analysis from the InSAR and optical POT results revealed that the reactivated ANZ landslide body is gradually slowing down to a steady deformation status since its occurrence in August 2020, indicating the effectiveness of engineering work on the prevention of further landslide. A slight acceleration was detected from both InSAR and optical POT time series analysis between May 2021 and June 2021, which could be caused by the increased rainfall in May 2021. [ABSTRACT FROM AUTHOR]

In this study, Differential Interferometric Synthetic Aperture Radar Interferometry (DInSAR) of artificial Corner Reflectors (CRs) were validated in the area of fast and nonlinear deformation gradient caused by active coal longwall exploitation. Three Sentinel-1 datasets were processed using conventional DInSAR, Persistent Scatterer Interferometry (PSI), and Small BAseline Subset methods implemented in ENVI SARscape™. For evaluation, leveling and Global Navigation Satellite System (GNSS) measurements were used. Considering the challenge of snow cover, the removal of all winter images was not a successful strategy due to the long temporal baseline and strong movement, which cause phase unwrapping problems and underestimate the real deformation. The results indicate that only conventional DInSAR and SBAS with low network redundancy allow us to capture maximal deformation gradient and the root mean square error calculated between the CRs and the ground truth is on the level of 2–3 cm for the vertical and easting deformation component, respectively. For the small deformation gradient represented by the permanent GNSS station (4 cm/year), all SBAS techniques appeared to be more accurate than DInSAR, which corresponds to higher redundancy and better removal of the atmospheric signal. In contrast, DInSAR results allowed to capture information about two subsidence basins, which was not possible with SBAS and PSI approaches. [ABSTRACT FROM AUTHOR]

In order to change the energy supply structure of traditional thermal power generation with excessive carbon emissions, more and more offshore wind turbines are being installed and put into operation worldwide. However, the dynamic monitoring of offshore wind turbines is still an emerging problem to be solved at a global scale. This paper proposes an effective approach for dynamic detection of offshore wind turbines by machine learning from spaceborne synthetic aperture radar (SAR) Sentinel-1 satellite data. First, the radar images are preprocessed to reduce the speckle noise and ocean wave clutter. Specifically, the time series radar data are cumulatively averaged according to the monthly interval to eliminate the influence of moving temporary objects such as ships; then, the speckle noise is suppressed using refined lee filter; the imagery affected by ocean wave clutters are also mitigated by using a constant false alarm rate (CFAR) technique. Next, representative data are carefully selected as the labeled training data set; and the random forest (RF) model is trained on the Google Earth Engine (GEE) cloud computing platform. Validation demonstrates an overall accuracy of 99.99%, a producer accuracy of 100%, and an user accuracy of 94.12%. Then, mathematical morphology-based time series spatial data differentiation is proposed for monitoring the change of wind turbine. Finally, the dynamic change of the offshore wind farms in the Yellow Sea of China and the North Sea of Europe are monitored by using the proposed approach from 2015 to 2021. Cross-comparisons with ground truth data show an accuracy of 93.67% for dynamic detection of offshore wind turbines. From a large number of experimental results, it is shown that the proposed approach has the advantages of large scale monitoring and high precision, and can be used for global dynamic detection of offshore wind turbines. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the rock glacier destabilisation and permafrost thawing induced by warming climate represent a continuous threat to life, infrastructure and socio‐economic development in the mountainous regions of the Hindu Kush Himalaya. This study presents the first systematic rock glacier inventory for the Shigar and Shayok basins, quantifying rock glacier geomorphology and kinematics based on morphological evidence using Google Earth images and interferometric synthetic aperture radar (InSAR). The certainty index of each inventoried rock glacier is recorded, along with its geomorphological properties and kinematic attributes. The rock glacier velocity is estimated through the InSAR time series analysis of Sentinel‐1 images from 2020 to 2021, with temporal baselines at 12‐day intervals. We developed a rock glacier inventory consisting of 84 rock glaciers covering an area of 29 km2 for the Shigar Basin and 2206 rock glaciers encompassing 369 km2 for the Shayok Basin. Among these rock glaciers, 69% and 52% are categorised as active rock glaciers, respectively. Rock glaciers in both catchments are confined to elevations between 3600 and 5875 m a.s.l., with a mean area of 0.22 km2. The maximum recorded velocity for active rock glaciers in the Shigar Basin is 101 ± 9 cm year−1, with a median of 27 ± 10 cm year−1, and in the Shayok Basin 114 ± 10 cm year−1 (median of 29 ± 9 cm year−1). Temporal variations in the surface velocities of the rock glaciers reveal that they increase with rising temperatures in both catchments, highlighting the seasonality in the rock glacier surface velocity. In total, we recorded the kinematic attributes of 98% of the inventoried rock glaciers in the study area. [ABSTRACT FROM AUTHOR]

Background: The cloud-penetrating and fog-penetrating capability of Synthetic Aperture Radar (SAR) give it the potential for application in forest fire progress monitoring; however, the low extraction accuracy and significant salt-and-pepper noise in SAR remote sensing mapping of the burned area are problems. Aims: This paper provides a method for accurately extracting the burned area based on fully exploiting the changes in multiple different dimensional feature parameters of dual-polarised SAR images before and after a fire. Methods: This paper describes forest fire progress monitoring using dual-polarisation SAR images combined with multi-scale segmentation and unsupervised classification. We first constructed polarisation feature and texture feature datasets using multi-scene Sentinel-1 images. A multi-scale segmentation algorithm was then used to generate objects to suppress the salt-and-pepper noise, followed by an unsupervised classification method to extract the burned area. Key results: The accuracy of burned area extraction in this paper is 91.67%, an improvement of 33.70% compared to the pixel-based classification results. Conclusions: Compared with the pixel-based method, our method effectively suppresses the salt-and-pepper noise and improves the SAR burned area extraction accuracy. Implications: The fire monitoring method using SAR images provides a reference for extracting the burned area under continuous cloud or smoke cover. This paper describes a method to monitor forest fire progress using dual-polarisation Synthetic Aperture Radar (SAR) images combined with multi-scale segmentation and unsupervised classification. We aimed to take full advantage of the many different dimensions of feature parameter changes caused by forest fires, relying on time-series dual-polarised SAR imagery to achieve burned area extraction and forest fire progress monitoring. [ABSTRACT FROM AUTHOR]

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46×106 km 2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ -13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements. [ABSTRACT FROM AUTHOR]