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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]

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]

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]

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]

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]

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]

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]

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]

Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. In this study, we propose a Seamless Surface Water Mapping Framework (SSWMF) which synergizes both optical (MODIS, Landsat 8, Sentinel-2) and SAR (Sentinel-1) imageries. The validity of SSWMF was first proved over the middle and lower reaches of the Yangtze River of China with abundant lake resources, showing an overall accuracy of 90.72%, and the results indicate that SSWMF can provide surface water map with higher spatial and temporal continuity compared to the Joint Research Centre Global Surface Water dataset. Multi-source validation showed that the SSWMFderived surface water maps can well capture the temporal fluctuation and spatial heterogeneity of water resources over China, with an overall accuracy of 92.39%. Overall, our results suggest that the proposed water mapping framework is promising and is readily applicable to large-scale water resource management and drought/flood monitoring. [ABSTRACT FROM AUTHOR]

Affected by geographical location and climatic conditions, crop classification in the Yunnan Plateau of China is greatly restricted by the low utilization rate of annual optical data, complex crop planting structure, and broken cultivated land. This paper combines monthly Sentinel-2 optical remote sensing data with Sentinel-1 radar data to minimize cloud interference to conduct crop classification for plateau areas. However, pixel classification will inevitably produce a "different spectrum of the same object, foreign objects in the same spectrum". A principal component feature synthesis method is developed for multi-source remote sensing data (PCA-MR) to improve classification accuracy. In order to compare and analyze the classification effect of PCA-MR combined with multi-source remote sensing data, we constructed 11 classification scenarios using the Google Earth Engine platform and random forest algorithm (RF). The results show that: (1) the classification accuracy is 79.98% by using Sentinel-1 data and 91.18% when using Sentinel-2 data. When integrating Sentinel-1 and Sentinel-2 data, the accuracy is 92.31%. By analyzing the influence of texture features on classification under different feature combinations, it was found that optical texture features affected the recognition accuracy of rice to a lesser extent. (2) The errors will be reduced if the PCA-MR feature is involved in the classification, and the classification accuracy and Kappa coefficient are improved to 93.47% and 0.92, respectively. [ABSTRACT FROM AUTHOR]

Since the 1960s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air–sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can "see" through clouds and facilitate observations of the air–sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016 to 2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt (∼17 m s−1) contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in square kilometers. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data are useful for studying tropical cyclones at the air–sea interface and provide results of these behaviors closely to data derived from best track archives. [ABSTRACT FROM AUTHOR]

The wave-affected marginal ice zone (MIZ) is an essential part of the sea ice cover and crucial to the atmosphere–ice–ocean interaction in the polar region. While we primarily rely on in situ campaigns for studying MIZs, significant challenges exist for the remote sensing of MIZs by satellites. This study develops a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter on board CryoSat-2 (CS2). CS2 waveform power and waveform stack statistics are used to determine the part of the sea ice cover affected by waves. Based on the CS2 data since 2010, we generate a climate record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022 (10.5281/zenodo.8176585,). The MIZ record indicates no significant change in the mean MIZ width or the extreme width, although large temporal and spatial variability is present. In particular, extremely wide MIZ events (over 300 km) are observed in the Barents Sea, whereas in other parts of the Atlantic Arctic, MIZ events are typically narrower. We also compare the CS2-based retrieval with the retrievals based on the laser altimeter of ICESat2 and the synthetic aperture radar images from Sentinel-1. Under spatial and temporal collocation, we attain good agreement among the MIZ retrievals based on the three different types of satellite payloads. Moreover, the traditional sea-ice-concentration-based definition of MIZ yields systematically narrower MIZs than CS2, and no statistically significant correlation exists between the two. Beyond its application to CS2, the proposed retrieval algorithm can be adapted to historical and future radar altimetry campaigns. The synergy of multiple satellites can improve the spatial and temporal representation of the altimeters' observation of the MIZs. [ABSTRACT FROM AUTHOR]

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l'Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data. [ABSTRACT FROM AUTHOR]

This paper examines a feature-level fusion framework for detecting and mapping land degradation (LD) and enabling sustainable land management (SLM) in semi-arid areas using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The objectives of this review were to (i) determine the trends and geographical location of land degradation mapping publications, (ii) to identify and report current challenges pertaining to mapping LD using multiscale remote sensing data, (iii) to recommend a way forward for monitoring LD using multiscale remote sensing data. The study reviewed 78 peer-reviewed research articles published over the past 24 years (1998–2022). Image fusion has the potential to be more useful in various remote sensing applications than individual sensor image data, making it more informative and valuable in the interpretation process. In addition, this review discusses the importance of SAR and optical image fusion, pixel-level techniques, applications, and major classes of quality metrics for objectively assessing fusion performance. The literature review alluded that the SAR and optical image fusion in the detection and mapping of land degradation and enabling sustainable land management has not been fully explored. Advanced techniques such as the fusion of SAR and optical satellite imageries need to be incorporated for the detection and mapping of LD, as well as the promotion of SLM in halting LD in South African drylands and around the world. We conclude that there is scope for further research on the fusion of SAR and optical images, as new micro-wave and optical sensors with higher resolution are introduced on a regular basis. The results of this review contribute to a better understanding of the applications of SAR and optical image fusion in future research in the severely degraded drylands of southern Africa. The fusion of SAR and optical data still remains an open challenge. The future of different remote sensing applications lies in this kind of fusion. Land degradation is one of the greatest challenges amongst the environmental problems in South Africa, causing a reduction in the capacity of the land to perform ecosystem functions and services that support society and development. Yet, in South Africa, there are no studies that have widely investigated the potential for a fusion of SAR and optical data to detect and map land degradation and SLM practices. This paper established a baseline for understanding the application of a fusion of SAR and optical data as rapid tools for mapping, monitoring, and evaluating LD, as well as the impacts of SLM practices in South Africa's degraded drylands. [ABSTRACT FROM AUTHOR]

Generation of digital elevation models (DEM) over densely vegetated humid tropics using Sentinel-1 data and Interferometry synthetic aperture radar (InSAR) technique is revised based on recommended optimum perpendicular baselines, and accuracy assessment based on coherence cells is introduced. Six pairs of Sentinel-1 data within the optimum perpendicular baseline of 150( 400 m and different temporal baselines acquired between 2015 and 2021 were processed using Sentinel Application Platform. Results revealed: that due to the extreme difficulty of achieving good coherence, interferograms are characterized by noise, generated DEMs are dominated by artefacts, higher elevation accuracy can be achieved within coherence cells, and within the recommended optimum perpendicular baseline range, elevation accuracy within coherence cells is mainly determined by the perpendicular baseline compared to temporal baseline. Therefore, this study can provide practical guide for InSAR generated DEMs evaluation in densely vegetated environments, and remote sensing method for regional-scale measurement and mapping of spots heights. [ABSTRACT FROM AUTHOR]

Precise and regularly updated maps of surface water extent are essential for wetland management. Since it is often challenging to obtain water extent information through ground surveys due to accessibility, satellite remote sensing has become a critical and cost-effective tool for acquiring this information in a temporal context. The methods that are commonly used include supervised and unsupervised multispectral classification, density slicing of a single band and spectral indices. However, optical data are substantially affected by cloud cover and solar illumination, thus, radar data comprise an ideal option. This article presents a methodological framework for rapid delineation of waterbodies' boundaries. For that purpose, an object-based approach is implemented. Sentinel-1 data were obtained and the Mean-Shift segmentation algorithm was employed. The proposed methodology produced promising results achieving an overall accuracy of 98%, a producer's accuracy for waterbodies of 90% and a user's accuracy for waterbodies of 95%. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion and image degradation or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this article, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder–decoder architecture, which incorporates the atrous spatial pyramid pooling, depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixelwise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples. [ABSTRACT FROM AUTHOR]

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) satellite images are used to monitor Arctic sea ice, with systematic data records dating back to 1991. We propose a semi-supervised classification method that separates open water from sea ice and can utilise ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1 SAR images. The classification combines automatic segmentation with a manual segment selection stage. The segmentation algorithm requires only the backscatter intensities and incidence angle values as input, therefore can be used to establish a consistent decadal sea ice record. In this study we investigate the sea ice conditions in two Svalbard fjords, Kongsfjorden and Rijpfjorden. Both fjords have a seasonal ice cover, though Rijpfjorden has a longer sea ice season. The satellite image dataset has weekly to daily records from 2002 until now, and less frequent records between 1991 and 2002. Time overlap between different sensors is investigated to ensure consistency in the reported sea ice cover. The classification results have been compared to high-resolution SAR data as well as in-situ measurements and sea ice maps from Ny-Ålesund. For both fjords the length of the sea ice season has shortened since 2002 and for Kongsfjorden the maximum sea ice coverage is significantly lower after 2006. [ABSTRACT FROM AUTHOR]

Deep-seated gravitational slope deformations (DsGSDs) are slope-scale phenomena which are widespread in mountainous regions. Despite interacting with human infrastructures and settlements, only a few cases are monitored with ground-based systems. Remote sensing technologies have recently become a consolidated instrument for monitoring and studying such widespread and slow processes. This paper proposes a three-step novel methodology to analyze the morpho-structural domain of DsGSDs by exploiting the advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) technique through (i) the analysis of A-DInSAR measurement point density and distribution defining a coverage threshold; (ii) the assessment of the actual ground deformation with respect to the orientation of phenomena based on slope, aspect, and C-index; and (iii) ground deformation mapping with previously ranked velocity interpolation. The methodology was tested on two differently oriented phenomena: the mainly north–south-oriented Croix de Fana and the mainly east–west-oriented Valtournenche DsGSD, located in the Aosta Valley Region, northern Italy. The results show a variation in the kinematic behavior between the morpho-structural domains, while also considering any other superimposed surficial deformations. This work provides the lines for the implementation of a rapid and low-cost tool based on the use of A-DInSAR measurements which are suitable for assessing the impact of any type of DsGSD on the anthropic facilities and infrastructures in mountainous areas. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. [ABSTRACT FROM AUTHOR]

Abstract In the event of a flood disaster, first response agencies need inundation maps produced in near real time (NRT). Such maps can be generated using satellite-based information. In this study, we developed mapping techniques that rely on synthetic aperture radar (SAR) on-board earth-orbiting platforms. SAR provides valid ground surface measurements through cloud cover with high resolution and sampling frequency that has recently increased through multiple missions. Despite numerous efforts, automatic processing of SAR data to derive accurate inundation maps still poses challenges. To address them, we have developed an NRT system named RAdar-Produced Inundation Diary (RAPID). RAPID integrates four processing steps: classification based on statistics, morphological processing, multi-threshold-based compensation, and machine-learning correction. Besides SAR data, the system integrates multisource remote-sensing data products, including land cover classification, water occurrence, hydrographical, water type, and river width products. In comparison to expert handmade flood maps, the fully-automated RAPID system exhibited "overall," "producer," and "user" accuracies of 93%, 77%, and 75%, respectively. RAPID accommodates commonly encountered over- and under-detections caused by noise-like speckle, water-like radar response areas, strong scatterers, and isolated inundation areas—errors that are in common practice to ignore, mask out, or be filtered out by coarsening the effective resolution. RAPID can serve as the kernel algorithm to derive flood inundation products from satellites—both existing and to be launched—equipped with high-resolution SAR sensors, including Envisat, Radarsat, NISAR, Advanced Land Observation Satellite (ALOS)-1/2, Sentinel-1, and TerraSAR-X. Highlights • We built a fully automated system to retrieve inundation maps from high resolution SAR images. • Statistical, morphological and machine learning approaches are integrated. • The system extensively uses multi-source remote sensing ancillary data. • The system can detect both connected and isolated open flood areas. • The system shown reliable performance in state scale during multiple flood events. [ABSTRACT FROM AUTHOR]

Wave-affected marginal ice zone (MIZ) is an integral part of the sea ice cover and key to the atmosphere-ice-ocean interaction in the polar region. While we mainly rely on in-situ campaigns for studying MIZs, great challenges still exist for the remote sensing of the MIZs by satellites. In this study we develop a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter onboard CryoSat-2 (CS2). CS2 waveform power and the waveform stack statistics are used to determine the part of the ice cover affected by waves. Based on the CS2 data since 2010, we generate a climate record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022. As indicated by the MIZ record, no significant change of either the mean MIZ width or the extreme width is detected, although large temporal and spatial variability is present. In particular, extremely wide MIZs (over 300 km) are observed in the Barents Sea, while in other part of the Atlantic Arctic, MIZs are generally narrower. We also compare the CS2-based retrieval with those based on the laser altimeter of ICESat2 and the synthetic aperture radar images from Sentinel-1. Under spatial and temporal collocation, we attain good agreement among the MIZ retrievals based on the three different types of satellite payloads. Moreover, the traditional sea ice concentration based definition MIZ yields systematically narrower MIZs than CS2, and there is no statistically significant correlation between the two. Besides CS2, the proposed retrieval algorithm can be adapted for various historical and future radar altimetry campaigns. The synergy of multiple satellites can further improve the spatial and temporal representation of the altimeters' observation of the MIZs.c [ABSTRACT FROM AUTHOR]

Ulva prolifera (U. prolifera), a global eco-environmental issue, has been recurring annually in the Yellow Sea of China since 2007, leading to significant impacts on the coastal ecosystem and the economies of coastal cities. To enhance the frequency of daily monitoring for U. prolifera and to advance the multi-source remote sensing monitoring system, a combination of the Sentinel-1 SAR remote sensing satellite and the Sentinel-3 OLCI and NPP VIIRS optical remote sensing satellites was employed. This comprehensive analysis encompassed the examination of Sentinel-1 C band characteristics, the range of influence of U. prolifera, and the migration trajectory of its enrichment zones. On 6 June 2021, three satellite images depicted the northwest drift of U. prolifera, followed by a southward movement after making contact with the coast of Qingdao, China, on 12 June. The most extensive impact area caused by U. prolifera was observed on 18 June. Subsequently, the images revealed a contraction and enrichment of U. prolifera in an eas–-west direction. The amalgamation of radar and optical remote sensing satellites in a multi-frequency monitoring approach allows for a continuous all-weather surveillance mechanism for U. prolifera. This mechanism serves to provide timely alerts for the prevention and management of U. prolifera outbreaks. [ABSTRACT FROM AUTHOR]

Consistent estimation of water surface area from remote sensing remains challenging in regions such as South Asia with vegetation, mountainous topography, and persistent monsoonal cloud cover. High-resolution optical imagery, which is often used for global inundation mapping, is highly impacted by clouds, while synthetic aperture radar (SAR) imagery is not impacted by clouds and is affected by both topographic layover and vegetation. Here, we compare and contrast inundation extent measurements from visible (Landsat-8 and Sentinel-2) and SAR (Sentinel-1) imagery. Each data type (wavelength) has complementary strengths and weaknesses which were gauged separately over selected water bodies in Bangladesh. High-resolution cloud-free PlanetScope imagery at 3-m resolution was used as a reference to check the accuracy of each technique and data type. Next, the optical and radar images were fused for a rule-based water area classification algorithm to derive the optimal decision for the water mask. Results indicate that the fusion approach can improve the overall accuracy by up to 3.8%, 18.2%, and 8.3% during the wet season over using the individual products of Landsat8, Sentinel-1, and Sentinel-2, respectively, at three sites, while providing increased observational frequency. The fusion-derived products resulted in overall accuracy ranging from 85.8% to 98.7% and Kappa coefficient varying from 0.61 to 0.83. The proposed SAR-visible fusion technique has potential for improving satellite-based surface water monitoring and storage changes, especially for smaller water bodies in humid tropical climate of South Asia. [ABSTRACT FROM AUTHOR]

Due to extreme weather, occasionally Hungary’s main rivers and lakes grow an ice cover causing severe damage to infrastructure and increased flood hazard. During cold periods in 2017 and 2022, a dangerous layer of ice developed on the main rivers in the country. Since river ice is rare in this region, no permanent ice monitoring system is in operation. Due to their all weather capabilities, active remote sensing instruments provide a good opportunity to monitor ice coverage. ESA’s Sentinel-1 radar satellites acquire data with a relatively high spatial and temporal resolution. A method was developed to provide ice coverage information at a regular interval; depending on the satellite revisit, at least once every 5 days, but often also on a daily basis. In 2017, maps were created for sections along the Danube and in 2022 for another section of the Tisza river. The ice coverage was calculated with a spatial resolution of 10 metre and visualised with a spatial density of 100 metre along the rivers. The mapping procedure provides visual information to give a fast overview of the spatial extent of ice coverage and quantitative, tabular information for operational activities to mitigate the damage due to ice packs and ice jams. [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]

This work deals with the evaluation of the potential of multi-temporal and multi-sensor (optical and radar) images of the satellites Sentinel-1 and Sentinel-2 satellites for land cover mapping and coastline evolution monitoring in a tropical ecosystem over the period 2015 to 2020. The study area chosen is the Cameroon Estuary. This area represents a major ecological transition in the sub-region with the presence of protected natural reserves (mangroves, dense forest, wetlands) but also a strong anthropic activity (construction, agriculture, degraded forest). The methodological approach consisted of a chain of pre-processing and visual image analysis, followed by a combination of the bands from each sensor, a supervised Random Forest classification to draft a land use map and finally a digitization of the coastline. Overall, the results show that the classification with the Sentinel- 2 images using 10 bands and adding 4 vegetation indices is slightly more accurate (95.75 %) than the classification with the 13 initial bands (91.78 %). The classification performed with the Sentinel-1A multi-temporal image series (dual polarization (VV, VH) and VV/VH ratio) leads to an accuracy of 78.44 %. The combination of Sentinel-2 A&B bands (10 bands and 4 indices) and Sentinel-1 improves the results and leads to an accuracy of 98.76 %. Our results also show that the use of multi-temporal image time series significantly improves the classification accuracy compared to the use of a single image (mono-date), and this for both sensors, i.e. an additional gain of 13 % and 10 % respectively for Sentinel-2 and Sentinel-1. However, this gain remains small for temporally stable classes. The results of the coastline evolution analysis show that the Cameroon estuary is disturbed by different levels of erosion (Cape Cameroon, North Manoka Island, Sanaga River mouth), and accretion (Limbe, and South Manoka Island) and also by weak internal variations. The fusion of optical and radar remote sensing data in the discrimination of land use classes, has shown that the construction zones are the most vulnerable to coastal erosion. On the other hand, the presence of vegetation (mangrove, forest) stabilizes and protects the coast from possible risks of this nature. [ABSTRACT FROM AUTHOR]

Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution of 40min this study. Snowmelt detected by the special sensor microwave/imager (SSM/I) is used to study the relationship between summer snowmelt and winter synthetic aperture radar (SAR) backscatter. Radar glacier zones (RGZs) classifications were conducted based on their differences in liquid snow content, snow grain size, and the relative elevations. We developed a practical method based on the simulations of a microwave scattering model to classify RGZs by using Sentinel-1 images in the AP. The summer snowmelt detected by SSM/I and Sentinel-1 data are compared between 2014 and 2015. The SSM/I-derived melting days is used to validate the winter dry snow line (DSL). RGZs derived from Sentinel-1 images suggest that snowmelt expanded from inland of the Larsen C Ice Shelf to the coastal area, whereas an opposite direction was found in the George VI Ice Shelf. The long melting season in the grounding zone of the Larsen C Ice Shelf may result from the adiabatically-dried föhn winds on the east side of the AP. As the uppermost limit of summer snowmelt, DSL was mapped based on the winter Sentinel-1 mosaic of the AP. Compared with the SSM/I-derived melting days, the winter DSL mainly distributed in the areas melted for one to three days in summer. DSL elevations on the Palmer Land increased from south to north. [ABSTRACT FROM AUTHOR]

Satellite images are widely used for change detection in various applications, like, agricultural and forest cover area monitoring and management, urban planning, disaster management etc. which require prior information about the timeline of changes. However, it is very challenging to identify precise timeline of changes and to acquire corresponding labelled reference of changes. Considering this, Convolutional Auto-Encoder (CAE)-based approaches has been proposed for unsupervised change detection from bi-temporal remote sensing images. However, these type of approaches require prior knowledge about the exact moment the change occurs in order to employ the bi-temporal images, one pre-change and one post-change. This work introduces the utilization of satellite image time-series (SITS) data in a Joint 3D CAE-based unsupervised approach to detect annual changes. Our study focuses on a mining area where the changes mostly occurs due to tailings, waste and ore deposit, mining pit, mining residue, and so on. Here, we have explored Synthetic Aperture Radar (SAR) (i.e. Sentinel-1) data along with optical (i.e. Sentinel-2) data, since optical data alone was not able to detect all the changes. The recall of detected change areas during 2016–2018 has improved by about 16% with the use of combination of Sentinel-1&2 data as compared to utilization of only Sentinel-2 images. [ABSTRACT FROM AUTHOR]

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas. [ABSTRACT FROM AUTHOR]

Global sea wave monitoring is of utmost importance for tasks such as analysis of ocean climate change, offshore fisheries, and early warning of marine disasters. Significant wave height (SWH) is one of the most vital and widely used metrics for measuring sea waves in marine research. Hence, obtaining high precision and extensive coverage measurements of SWH is of great significance for comprehensive sea wave studies. A data set is constructed by combining wave spectrometer and scatterometer data from China‐French Ocean Satellite, synthetic aperture radar (SAR) wave mode data from Sentinel‐1, and altimeter data from Jason‐3 and HY‐2B through space‐time matching method. The multi‐sources integrated data set is used to complete the reconstruction of the wide swath SWH. A model based on stacked autoencoder and deep neural network (SAE‐DNN) is developed. The SWH reconstructed by the model is evaluated with the training‐independent test set. The results demonstrate that the accuracy of the SAE‐DNN model is significantly improved by incorporating SAR joint quasi‐synchronous observations and the root mean square error can reach 0.217 m, which is comparable to the SWH measured by altimeter and highlights the effectiveness and reliability of the model in accurately reconstructing SWH. We further examine and analysis the distance variations between SWH reconstruction sites and Surface Wave Investigation and Monitoring observations, the influence of different SAR features, and the influence of sea state, highlighting the benefits of incorporating SAR data into SAE‐DNN model. Plain Language Summary: Using remote sensing satellites to observe significant wave height (SWH) with high precision and extensive coverage can provide more valuable data sets for sea wave research. A model for retrieving the total wave height is developed based on a stacked autoencoder and deep neural network (SAE‐DNN). Sentinel‐1 synthetic aperture radar (SAR) data is introduced to simulate the quasi‐synchronous observations with China‐French Ocean Satellite and the altimeter observations of Jason‐3 and HY‐2B are used as the target value for model training. After evaluation and analysis, satisfactory results are found. The results also demonstrate that the addition of joint SAR quasi‐synchronous observations can have a highly positive impact on SWH reconstruction. Key Points: Quasi‐synchronous observations of multisource satellite sensors are matched and leveraged to construct the data setA model of autoencoder deep neural network is developed for the reconstruction of wide swath significant wave heightThe joint synthetic aperture radar enables the model to better learn the swell features and provides effective information for the external swath [ABSTRACT FROM AUTHOR]

Pantana phyllostachysae Chao (PPC) is one of the deadliest defoliators of Moso bamboo. Accurately locating and evaluating PPC damage is essential for the management of bamboo forests. Moso bamboo has a unique biennial growth cycle, consisting of the on-year period (bamboo shoots are incubated and then produced) and the off-year period (old leaves are dropped and then new leaves are grown, and no bamboo shoots are produced in the coming year). The similar physiological characteristics of off-year bamboo and damaged on-year bamboo create difficulties in monitoring PPC damage using remote sensing data. In this study, we synergistically used Sentinel-1, Sentinel-2, and field inventory data to construct machine learning (extreme gradient boosting, XGBoost) models monitoring PPC damage. The results show that the single-time observation feature-based model (using images from October) outperformed the double-time observation feature-based model (using the differences between remote sensing signals from October and February or April) due to the interference from other disturbance agents (e.g., logging and weeding). The overall accuracy (OA) values of the single-time observation feature-based model were at least 3% and 10% higher than those for double-time observation feature-based models for on- and off-year samples, respectively. With the consideration of the on- and off-year phenological differences, OA was improved by over 4%. The model without differentiation of the phenological difference tended to underestimate the damaged area of on-year bamboo and overestimate that of off-year bamboo. We also found that the responses of optical and SAR (synthetic aperture radar) features to PPC damage were different. The optical features increased or decreased with increasing damage severity. SAR features decreased significantly at the initial stage of PPC damage and then changed marginally with the increase in damage severity. The addition of SAR features to optical features improved the model performance, mainly for healthy and mildly damaged samples. The methodology developed in this study provides technical and theoretical support for the pest monitoring of bamboo forests using remote sensing data. [ABSTRACT FROM AUTHOR]

The analysis of ocean eddies in the marginal ice zone via remote sensing and modeling data is a challenging task. However, it is of crucial importance for various scientific applications and anthropogenic activities in the Arctic. Models often struggle to accurately represent eddies near the MIZ due to the intricate nature of sea ice-ocean interactions, unresolved small-scale processes, and coarse resolution. Nevertheless, combining the information provided from both SAR and model data offers promising results that can potentially improve eddy detection accuracy near the MIZ. Furthermore, accurate characterization of the spatial and temporal variability of eddies near the MIZ demands a holistic approach that incorporates multi-platform observations, including numerical models and remote sensing data. This study demonstrates a specific test case on the intercomparison of the eddy signatures located in the MIZ in the Fram Strait based on remote sensing SAR scenes and Lagrangian modeling data from the two global oceanographic reanalysis GOFS 3.1 and GLORYS 12 V1. The study specifically displays the strong agreement in the eddy polarity and synchronism with reality, as well as differences in spatial scales and location of eddy centers. Overall, the obtained results support the further use of the presented approach for studying the eddies in the MIZ regions in the Arctic. • The lack of climate-scale evaluation of different modeling studies in the MIZ derives from an absence of observational data. • We compare eddies in the MIZ acquired from SAR and Lagrangian modeling data based on GOFS 3.1 and GLORYS 12 V1 reanalyses. • This is the first study where such an approach is implemented for the eddy analysis in the Arctic MIZ. • Agreement in eddy structure and polarity supports further use of the presented approach for studying eddies in MIZ. [ABSTRACT FROM AUTHOR]

Environmental effects and climate change are lately representing an increasing strain on coastal areas, whose topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. Continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, and tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relationship with precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content. [ABSTRACT FROM AUTHOR]

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. [ABSTRACT FROM AUTHOR]

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection. [ABSTRACT FROM AUTHOR]

The combined use of the data from Sentinel-1 (S1) dual-polarized Synthetic Aperture Radar (SAR) and Sentinel-2 (S2) Multispectral Sensor Images (MSI) has been successfully applied in many remote sensing applications. However, when it comes to the mapping of impervious surface (IS), the non-negligible shadows on S2 images can severely degrade the mapping accuracy. How to effectively incorporate S1 and S2 data to precisely extract IS from cities has not been well investigated yet. Besides, it still remains challenging to extract appropriate polarimetric features of S1 to discriminate IS and supplement the drawbacks of S2. In this research, we explore more effective polarimetric features of S1 and propose a hierarchical framework for IS mapping at the city scale by synergetic fusion of dual polarized SAR and multispectral information. The new polasrimetric features of S1 is initially explored based on the backscattering mechanism of dual polarized SAR. Then the IS is identified from shadow-free and shadow areas in two consecutive steps with different feature sets. The result indicates that our new polarimetric features of S1, i.e., the diagonal elements of coherence matrix, are more suitable for IS mapping due to their associated physical meaning with different land covers. With suitable feature sets, our proposed methods performed well on the urban area with abundant shadows, with an Overall Accuracy (OA) of 93.9% and a Kappa coefficient of 0.92 achieved. [ABSTRACT FROM AUTHOR]

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σº). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σº and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σº ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σº where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R²) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R² between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R² of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m³ m-3). [ABSTRACT FROM AUTHOR]

Observing Earth from the space is key for monitoring land-surface dynamics and probing near-surface geomorphic and geological features, including those obscured by sand sheets/dunes and human activity. The hyper-arid area of the Arabian Peninsula was selected for detecting and extracting obscure paleohydrological systems and modeling the spatio-temporal dynamics of sand dunes using synthetic aperture radar (SAR) and optical high-resolution remote sensing images. Interferometric synthetic aperture radar (InSAR) coherence change detection (CCD) using Sentinel-1 imagery was performed, revealing subtle changes in the surficial landforms of the study area. These images also discriminated between active and inactive geomorphic features. In addition, low-frequency L-band Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) and Shuttle Radar Topography Mission (SRTM) data revealed the details of paleochannels in the basins that drain into the Arabian Gulf. These ephemeral rivers are the source of sand that was later reshaped into dunes by aeolian processes. The results also revealed that the morphology and extension of dunes were clearly influenced by topography, slope, and wind velocity and direction, rather than human activities. Knowledge gained by exploring desert features is essential for planning settlements and development, and integrated approaches using a variety of remote sensing datasets are key for enhancing Earth observation across a range of spatial and temporal scales. • InSAR quantified the land surface changes in spatio-temporal scale. • L-band ALOS/PALSAR obscured the buried paleohydrologic systems. • High image resolution of Sentinel-1&2 and Google Earth well successfully defined the dynamic of sand dunes. [ABSTRACT FROM AUTHOR]

Crop lodging reduces yield quantity and grain quality of cereal crops. Understanding seasonal variation in crop lodging susceptibility enables lodging risk assessments and predictions of associated crop yield losses. We demonstrate a novel remote sensing-based approach, using sparse field observations and widely available synthetic aperture radar (SAR) satellite imagery, to map a safety factor against root lodging (SF A) in wheat. SF A quantifies the ability of the rooting system to support the self-weight moment of the whole plant and can be used as an indicator of in-season root lodging susceptibility. SAR satellite images, from Sentinel-1 and RADARSAT-2, were acquired synchronously with field measurements in Jolanda di Savoia, Ferrara, Italy during the 2018 winter wheat growing season. The field data included measurements from non-lodged (healthy) wheat such as plant height, height at the centre of gravity, self-weight moment of the whole plant, soil anchorage strength, SF A and those from lodged wheat such as crop angle of inclination, lodged area and the point of plant failure (stem or root). Field measurements confirmed that SF A decreased progressively through the season and was consistent with the observed lodging. Strong and significant correlations through the season were observed between SF A and SAR satellite image metrics. The validated regression models showed a strong relationship between field-measured SF A and the metrics from RADARSAT-2 (R 2 CV = 0.84, RMSE CV = 0.54) and Sentinel-1 data (R 2 CV = 0.73, RMSE CV = 0.59). However, it is possible that the relationship between SF A and SAR parameters is confounded by the variations in crop biomass, which is highly correlated with SF A (r = −0.71, p < 0.0001). Our study, for the first time, demonstrates the use of remote sensing SAR data for lodging susceptibility assessment. Current and planned satellite platforms have the potential for large scale, operational assessment of lodging susceptibility in cereal crops. • We model safety factor as an indicator of root lodging susceptibility in wheat. • The performances of Sentinel-1 and RADARSAT-2 data for SF A estimation are compared. • Gradient boosting regression models were coupled with parameters extracted from SAR data. • Safety factor values are consistent with the observed lodging. • Multi-incidence angle RADARSAT-2 SAR data can be used to track safety factor. [ABSTRACT FROM AUTHOR]

Garlic has been one of the most widely planted cash crops in Henan and Shandong provinces, China. The garlic plantation varies greatly in the frequently fluctuated price over the past few years, further dominating the decision-making on the planting for the healthy and sustainable development of the garlic market. However, there is a great challenge on the rapid and accurate extraction of garlic with a high precision, where the garlic crop planting is often mixed with other ground crops. Taking the Four-Huaiqing Chinese medicine base in Kaifeng City, Henan Province of China as a study area, this research aims to improve the identification accuracy and efficiency of fragmented garlic with the complex planting structure using remote sensing data. An object-oriented model was also proposed to integrate the active and passive remote sensing data of Sentinel satellites using the Google Earth Engine (GEE) platform and random forest (RF). The normalized difference vegetation index (NDVI) time-series data was selected to separate the phenological features of garlic and other land cover types. The maximum difference of NDVI and the features of RF were used to screen the monthly mean Sentinel-2 optical data in March 2021. A time series of Sentinel-1 synthetic aperture radar (SAR) backscattering coefficients were selected for the monthly mean data from November 2020 to May 2021. Two steps were then implemented before classification. The first one was to integrate the data segmentation using the simple non-iterative clustering (SNIC), further to select the best segmentation scale with the highest classification accuracy and Kappa coefficient. The second one was to select the best within three neighborhood values (4, 8, and 16) using the gray-level co-occurrence matrix (GLCM), thereby calculating the seven texture features of synthetic optical data, and finally to reduce the data dimensions using the principal components analysis (PCA). The red edge bands of vegetation were integrated with the Sentinel passive and active remote sensing data in the10 or 20 m spatial resolution. As such, the identification accuracy of garlic was improved to combine various groups of spectral features, backscattering coefficients, vegetation indexes, and different principal component groups of texture features. The result showed that the highest overall accuracy of classification and Kappa coefficient reached 94.54%, and 0.93, respectively, using the active and passive Sentinel remote sensing data in the 10 m spatial resolution, where the producer’s and user’s accuracies of garlic were 97.83% and 96.38%, respectively, at the SNIC segmentation scale of 5, and the GLCM neighborhood value of 4, as well as the first and second principal components of the 7 texture features. In the case of active and passive Sentinel remote sensing data with three vegetation red edge bands in the 20 m spatial resolution, the highest overall accuracy of classification and Kappa coefficient reached 94.14%, and 0.92, respectively, where the producer’s and user’s accuracies of garlic were 95.72% and 98.81%, respectively, at the SNIC segmentation scale of 3, and the GLCM neighborhood value of 4, as well as the first and second principal components of the 7 texture features. The producer’s and user’s accuracies in the 20m spatial resolution Sentinel-2 data with three vegetation red edge bands were improved 0.49% and 4.38%, respectively, compared with the 10m spatial resolution. In the time series of Sentinel-1 SAR data, the producer’s and user’s accuracies in 10 m spatial resolution were improved 0.66% and 2.03%, respectively, compared with the 20m. Correspondingly, the Sentinel active and passive remote sensing data can be effectively integrated to fully represent the spectral and structural information. Moreover, the overall accuracy and Kappa coefficient were much higher than those using the optical or time series SAR data alone, particularly the maximum in the 10 m high spatial resolution. Therefore, the SNIC, GLCM, PCA, RF, and GEE platforms can be widely expected to accurately and efficiently extract the garlic planting areas using GF satellites data. [ABSTRACT FROM AUTHOR]

Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices. [ABSTRACT FROM AUTHOR]

The identification of morphological changes occurring along river channels is essential to support river process understanding, assess sediment budgets and evaluate the effectiveness of river management. Among available remote sensing techniques, space-borne synthetic aperture radar (SAR) could potentially provide a powerful complement to optical imagery for this task. However, very few studies have been carried out on the use of SAR datasets to study erosion and deposition processes in river channels. In this work, we investigate the potential of change detection analysis based on Sentinel-1 data, by comparing variations of radar backscattering to river morphology changes identified through high-resolution drone acquisitions. We considered a time series of two years of Sentinel-1 data relative to a period where, despite a moderate fluvial event occurred, morphological changes have been significantly detected in multitemporal drone point clouds. Satellite optical imagery (planet.com) and hydro-meteorological data were used to support the analysis and interpret results. The results show that the spatial and temporal resolution of Sentinel-1 is currently not suitable for accurate discrimination of morphological changes related to river dynamics at local scale. Other spaceborne sensors with sub-metric ground sampling distance and/or daily revisit time would be probably suitable; however, so far, this option would need the use of commercial solutions with a consistent increase of the costs of the investigation. First attempt to evaluate whether Sentinel-1 data can provide quantitative estimates of fluvial morphological changes; Multitemporal comparison of Sentinel-1 backscattering and coherence data against UAV-SfM 3D point clouds difference; Sentinel-1 data found to be not suitable to track relatively small morphological changes (<2 m in the vertical direction) and the consequent changes in the surface characteristics which are typical of river dynamics; SAR-based detection of fluvial changes in rivers are intrinsically affected by severe limitations due to varying soil water content and vegetation cover; Higher resolution and/or different spectral sensor may lead to different results but, so far, would require important acquisition costs. [ABSTRACT FROM AUTHOR]

Delimitation of areas subject to flooding is crucial to understand water dynamics and fluvial changes. This study analyzed the potential of C-band Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1 satellite in 2017, 2018, and 2019 to delineate flooded areas in the Central Amazon. The images were processed by the Artificial Neural Network MultiLayer Perceptron (ANN-MLP) and two KNearest Neighbor (KNN-7 and KNN-11) machine learning (ML) classifiers. Pre-processing of Single Look Complex (SLC) SAR images involved the following methodological steps: orbitfile application; radiometric calibration (σ0); RangeDoppler terrain correction; speckle noise filtering; and conversion of linear data to backscattering coefficients (units in dB). We applied the Lee filter, with a window size of 3x3, for speckle filtering. A set of 6000 randomly distributed samples for training (70%), validation (20%), and test (10%) was obtained based on visual interpretation of Sentinel-2 optical satellite image acquired in the same years of SAR images. We found the largest flooded areas in 2019 in the study area (municipality of Parintins and Urucará, Amazonas River, Brazil): 6244km² by the ANN-MLP classifier; 6268km² by KNN-7; and 6290km² by KNN-11, while the smallest flooded areas were found in 2018: 5364km² by ANN-MLP; 5412km² by KNN-7; and 5535km² by KNN-11. The three classifiers presented Kappa coefficients between 0.77 and 0.91. ANN-MLP showed the best accuracy. The presence of shadow effects in the SAR images increased the commission errors. [ABSTRACT FROM AUTHOR]

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user's and producer's accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user's accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve. [ABSTRACT FROM AUTHOR]

Collecting a complete dataset of snow avalanche activity in a given avalanche forecasting region throughout an entire winter is a seemingly simple task that is very difficult to accomplish. Traditional field-based methods are too time and cost consuming and limited to good visibility and accessible terrain. Different remote sensing methods have been proposed to assist in monitoring of snow avalanche activity, with satellite-borne radar remote sensing allowing for all-weather and all-light conditions monitoring. Here we present a complete, two-year snow avalanche activity record from a Norwegian forecasting region using Sentinel-1A radar satellite imagery. We used a change detection method to manually identify avalanche debris based on localized backscatter increase in avalanche runout zones and assess these detections using multi-temporal and multi-sensor datasets. We then used the complete dataset of snow avalanche activity to exemplify different applications from validating issued avalanche bulletins, to generating avalanche path maps, and creating a snow climate classification. Comparing manual identification in Sentinel-1A images and a very high resolution Radarsat-2 image suggest overall underestimation of avalanche activity, also when considering the non-detectability of small avalanche debris. We foresee that with the further increase in satellite-radar data availability and the improvement of automatized snow avalanche detection, the presented methodologies will be able to assist in operational avalanche forecasting worldwide in the near future. [ABSTRACT FROM AUTHOR]

The variation of the azimuth to Doppler center frequency of the terrain observation by progressive scans (TOPS) mode leads to the relative distortion of the phase of the interferogram. Therefore, high registration accuracy is required for interference processing. The Canadian Synthetic Aperture Radar Remote Sensing Satellite (RADARSAT‐2) TOPS mode image is less useful due to poor orbital accuracy and geometric registration accuracy, which makes the interference registration more complicated than it in sentinel SAR remote sensing (Sentinel‐1) satellite TOPS mode. In order to perform the interference processing of the TOPS mode data, this paper uses the cross‐correlation registration to correct the geometric registration results, and then uses the spectral diversity for the second geometric registration correction, and finally uses the enhanced spectral diversity to implement the final azimuth offset correction. Typically, the Doppler centroid estimation from satellite ephemeris parameters and focusing imaging parameters alone does not provide sufficient accuracy. Therefore, a phase increment method is proposed to optimize the Doppler center estimation and the estimated Doppler center is verified by the image bearing power spectrum in this paper. [ABSTRACT FROM AUTHOR]

Delineation of shellfish beds through field surveys is time consuming. Remote sensing can help in detecting the location and boundaries of shellfish beds. This can be achieved through the use of aerial photographs and optical satellite sensors during cloud-free and low-tide conditions. Cloud penetrating radar is an alternative, but still requires careful selection of low tide imagery. Manual selection becomes cumbersome for large areas, such as the entire Dutch Wadden Sea. We therefore developed a method that automatically combines dense time series of Sentinel-1 radar imagery into a useful mosaic that allows for effective delineation of shellfish beds. The method consists of temporal compositing many images in the cloud-based Google Earth Engine. We evaluated different combinations of 1) compositing options (average backscatter and various percentiles), 2) temporal periods for compositing, 3) different polarizations, and 4) imagery from ascending versus descending satellite paths. The resulting composite images were visually compared with in-situ records to identify which composite visually best allowed for shellfish bed delineation. Although the average VH-polarized backscatter for one full year provided effective demarcation, an RGB color composite containing three average VH images for four months each provided improved visibility of the shellfish beds. We then quantitatively compared which compositing option was most effective in separating pixels with shellfish beds from those without. For this purpose, the unsupervised classification approach of K-means clustering was applied to the different composite images, and outcomes (i.e. selected classes) were compared with field data derived from the annual in-situ survey. The multi-season image composites (i.e. Jan–Apr, May–Aug, and Sep–Dec) that were made by combining VH image acquisitions of only descending and of combined ascending and descending paths resulted in higher accuracies than other compositing options tested. The producer and user accuracy of the multi-season descending composite were respectively 40% and 70% for the full Dutch Wadden Sea, against 59% and 73% for a smaller subset. These accuracies are similar to what is previously achieved for small study areas with carefully selected radar images acquired around low tide. Moreover, our approach could identify various shellfish beds that were initially missed by the in-situ surveys, but for which later the presence was confirmed (so the actual producer accuracy would increase to about 82%). The composite images can be created on the fly with Google Earth Engine shortly before a field campaign and used as a base for sampling design. • We performed rapid temporal compositing of Sentinel-1 images in Google Earth Engine. • We evaluated different compositing options for the entire Dutch Wadden Sea. • A multi-season VH composite performed best for shellfish bed classification. • Producer and user accuracies reached 82% and 73% respectively, for a smaller subset. • Our approach allows for effective monitoring and already guides field inventories. [ABSTRACT FROM AUTHOR]

Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values. [ABSTRACT FROM AUTHOR]