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Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. [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]

As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent years. However, existing transform domain interference detection methods exhibit unsatisfactory performance in complex interference environments. Moreover, most of them rely on label information, while existing publicly available interference datasets are limited. To solve these problems, this paper proposes an SAR unsupervised interference detection model that combines Canny edge detection with vision transformer (CEVIT). Using a time–frequency spectrogram as input, CEVIT realizes interference detection in complex interference environments with multi-interference and multiple types of interference by means of a feature extraction module and a detection head module. To validate the performance of the proposed model, experiments are conducted on airborne SAR interference simulation data and Sentinel-1 real interference data. The experimental results show that, compared with the other object detection models, CEVIT has the best interference detection performance in a complex interference environment, and the key evaluation indexes (e.g., Recall and F1-score) are improved by nearly 20%. The detection results on the real interfered echo data have a Recall that reaches 0.8722 and an F1-score that reaches 0.9115, which are much better than those of the compared methods, and the results also indicate that the proposed model achieves good detection performance with a fast detection speed in complex interference environments, which has certain practical application value in the interference detection problem of the SAR system. [ABSTRACT FROM AUTHOR]

Accurate detection of sea ice leads is essential for safe navigation in polar regions. In this paper, a shape-aware (SA) network, SA-DeepLabv3+, is proposed for automatic lead detection from synthetic aperture radar (SAR) images. Considering the fact that training data are limited in the task of lead detection, we construct a dataset fusing dual-polarized (HH, HV) SAR images from the C-band Sentinel-1 satellite. Taking the DeepLabv3+ as the baseline network, we introduce a shape-aware module (SAM) to combine multi-scale semantic features and shape information and, therefore, better capture the shape characteristics of leads. A squeeze-and-excitation channel-position attention module (SECPAM) is designed to enhance lead feature extraction. Segmentation loss generated by the segmentation network and shape loss generated by the shape-aware stream are combined to optimize the network during training. Postprocessing is performed to filter out segmentation errors based on the aspect ratio of leads. Experimental results show that the proposed method outperforms the existing benchmarking deep learning methods, reaching 96.82% for overall accuracy, 93.01% for F1-score, and 91.48% for mIoU. It is also found that the fusion of dual-polarimetric SAR channels as the input could effectively improve the accuracy of sea ice lead detection. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha. [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]

This paper designed a Generative Adversarial Network (GAN)-based super-resolution framework for scatterometer ocean surface wind speed (OSWS) mapping. An improved GAN, WSGAN, was well-trained to generate high-resolution OSWS (~1/64 km) from low-resolution OSWS (~12.5 km) retrieved from scatterometer observations. The generator of GAN incorporated Synthetic Aperture Radar (SAR) information in the training phase. Therefore, the pre-trained model could reconstruct high-resolution OSWS with historical local spatial and texture information. The training experiments were executed in the South China Sea using the OSWS generated from the Advanced SCATterometer (ASCAT) scatterometer and Sentinel-1 SAR OSWS set. Several GAN-based methods were compared, and WSGAN performed the best in most sea states, enabling more detail mining with fewer checkerboard artifacts at a scale factor of eight. The model reaches an overall root mean square error (RMSE) of 0.81 m/s and an overall mean absolute error (MAE) of 0.68 m/s in the collocation region of ASCAT and Sentinel-1. The model also exhibits excellent generalization capability in another scatterometer with an overall RMSE of 1.11 m/s. This study benefits high-resolution OSWS users when no SAR observation is available. [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]

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]

Floods are devastating phenomena that occur almost all around the world and are responsible for significant losses, in terms of both human lives and economic damages. When floods occur, one of the challenges that emergency response agencies face is the identification of the flooded area so that access points and safe routes can be determined quickly. This study presents a flood detection methodology that combines transfer learning with vision transformers and satellite images from open datasets. Transformers are powerful models that have been successfully applied in Natural Language Processing (NLP). A variation of this model is the vision transformer (ViT), which can be applied to image classification tasks. The methodology is applied and evaluated for two types of satellite images: Synthetic Aperture Radar (SAR) images from Sentinel-1 and Multispectral Instrument (MSI) images from Sentinel-2. By using a pre-trained vision transformer and transfer learning, the model is fine-tuned on these two datasets to train the models to determine whether the images contain floods. It is found that the proposed methodology achieves an accuracy of 84.84% on the Sentinel-1 dataset and 83.14% on the Sentinel-2 dataset, revealing its insensitivity to the image type and applicability to a wide range of available visual data for flood detection. Moreover, this study shows that the proposed approach outperforms state-of-the-art CNN models by up to 15% on the SAR images and 9% on the MSI images. Overall, it is shown that the combination of transfer learning, vision transformers, and satellite images is a promising tool for flood risk management experts and emergency response agencies. [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]

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops. [ABSTRACT FROM AUTHOR]

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop. [ABSTRACT FROM AUTHOR]

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels' in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin. [ABSTRACT FROM AUTHOR]

Net-pen aquaculture, one of the primary forms of aquaculture in shallow lakes, has generated substantial economic benefits but has also triggered eutrophication and ecological degradation. Therefore, the accurate and scientific dynamic monitoring of aquaculture areas is of the utmost importance. However, existing studies have predominantly employed annual monitoring, failing to fully exploit the unique advantage of synthetic aperture radar (SAR) imagery for seasonal, monthly or more frequent time scale monitoring. This study aimed to develop an approach for monitoring the monthly dynamics of net-pen aquaculture using Sentinel-1 time series images. We integrated threshold segmentation and temporal consistency checking to extract spatial data on the net-pen aquaculture from eight typical shallow lakes in Jiangsu Province, one of the eastern coastal provinces of China, from 2016 to 2021. This study yielded the following results: (1) The VV polarization image offers a particularly noticeable contrast between net-pen aquaculture structures and their surroundings and checking for temporal consistency can help alleviate the problem of distinguishing between changes in error caused by misclassification and actual changes that occurred. The overall accuracy of the net-pen aquaculture extraction results was over 85%. (2) This approach not only enables the analysis of changes in the aquaculture area and fence length but can also identify key time points. For instance, the net-pen aquaculture area in Gehu Lake experienced a rapid decline from May to November 2018, the decrease in while Eastern Taihu Lake occurred from September 2018 to June 2019. (3) The removal of net-pen aquaculture in lakes within the Jiangsu Province is primarily attributed to policy responses. In response to the ecological river and lake action plan implemented in Jiangsu Province, a decrease was observed in both the aquaculture areas and fence lengths across most lakes from 2016 to 2021. These results can provide a reference for lake management and ensuring the effective implementation of related policies. [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]

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]

Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency's GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area. [ABSTRACT FROM AUTHOR]

Lithium mining has become a controversial issue in the transition to green technologies due to the intervention in natural basins that impact the native flora and fauna in these environments. Large resources of this element are concentrated in Andean salt flats in South America, where extraction is much easier than in other geological configurations. The Pozuelos highland salt flat, located in northern Argentina (Salta's Province), was chosen for this study due to the presence of different evaporitic crusts and its proven economic potential in lithium-rich brines. A comprehensive analysis of a 5.5-year-long time series of its microwave backscatter with Synthetic Aperture Radar (SAR) images yielded significant insights into the dynamics of their crusts. During a field campaign conducted near the acquisition of three SAR images (Sentinel-1, ALOS-2/PALSAR-2, and SAOCOM-1), field measurements were collected for computational modeling of the SAR response. The temporal backscattering coefficients for the crusts in the salt flat are directly linked to rainfall events, where changes in surface roughness, soil moisture, and water table depth represent the most critical variables. Field parameters were employed to model the backscattering response of the salt flat using the Small Slope Approximation (SSA) model. Salt concentration of the subsurface brine and the water table depth over the slightly to moderately roughed crusts were quantitatively derived from Bayesian inference of the ALOS-2/PALSAR-2 and SAOCOM-1 SAR backscattering coefficient data. The results demonstrated the potential for subsurface estimation with L-band dual-polarization images, constrained to crusts compatible with the feasibility range of the layered model. [ABSTRACT FROM AUTHOR]

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integration was evaluated over crop-dominated regions (corn, soybean and wheat) in southwest Ontario, Canada. Comparative analyses against several machine learning and deep learning methods revealed the superiority of the LWAT, achieving an impressive F1-score of 97.96% and a Kappa coefficient of 97.08% for the northern crop region and F1-scores of 98.07% and 97.02% for the southern crop region when leveraging time-series data from RCM and Sentinel-1, respectively. Additionally, by the incremental procedure, the evolution of accuracy determined by RCM and Sentinel-1 was analyzed, which demonstrated that RCM performed better at the beginning of the season and could achieve comparable accuracy to that achieved by utilizing both datasets. Moreover, the beginning of stem elongation of corn was identified as a crucial phenological stage to acquire acceptable crop maps in the early season. This study explores the potential of RCM to provide reliable prior information early enough to assist with in-season production forecasting and decision making. [ABSTRACT FROM AUTHOR]

Deformation monitoring plays a pivotal role in assessing dam safety. Interferometric Synthetic Aperture Radar (InSAR) has the advantage of obtaining an extensive range of deformation, regardless of weather conditions. The Datengxia Water Conservancy Hub is the largest in-construction dam in China. To effectively assess the in-construction dam safety, the SBAS-InSAR (Small Baseline Subset-InSAR) technique and 86 Sentinel-1 images (from 11 February 2020, to 16 January 2023) have been employed in this study to monitor the deformation over the reservoir and its surrounding areas. The reliability of the SBAS-InSAR monitoring results over the study area was demonstrated by the in situ monitoring results. And the InSAR results show that the central section of the left dam exhibits the most substantial cumulative deformation, attributed to the maximal water pressure. This is closely followed by the left end of the dam, which reflects a similar but smaller deformation. However, the in-construction cofferdam facilities make the right-end section of the left dam more robust, and the deformation is the most stable. Additionally, significant deformation of the auxiliary dam slope has been identified. Moreover, the analysis indicated that the deformation of the four upstream slopes is closely related to the precipitation, which potentially poses a threat to the safety of the Datengxia Dam. [ABSTRACT FROM AUTHOR]

This study employs advanced synthetic aperture radar (SAR) techniques, specifically the small baseline subset (SBAS) method, to analyze ground deformation dynamics on Aegina, a volcanic island within the Hellenic Volcanic Arc. Using Sentinel-1 satellite data spanning January 2016 to May 2023, this research reveals different deformation behaviors. The towns of Aegina and Saint Marina portray regions of stability, contrasting with central areas exhibiting subsidence rates of up to 1 cm/year. The absence of deformation consistent with volcanic activity on Aegina Island aligns with geological records and limited seismic activity, attributing the observed subsidence processes to settlement phenomena from past volcanic events and regional geothermal activity. These findings reinforce the need for continuous monitoring of the volcanic islands located in the Hellenic Volcanic Arc, providing important insights for local risk management, and contributing to our broader understanding of geodynamic and volcanic processes. [ABSTRACT FROM AUTHOR]

In this study, the full-focusing (FF) algorithm is reviewed with the objective of optimizing it for processing data from different types of surfaces probed in altimetry. In particular, this work aims to provide a set of optimal FF processing parameters for the Sentinel-6 Michael Freilich (S6-MF) mission. The S6-MF satellite carries an advanced radar altimeter offering a wide range of potential FF-based applications which are just beginning to be explored and require prior optimization of this processing. In S6-MF, the Synthetic Aperture Radar (SAR) altimeter acquisitions are known to be aliased in the along-track direction. Depending on the target, aliasing can be tolerated or may be a severe impairment to provide the level of performance expected from FF processing. Another key aspect to consider in this optimization study is the unprecedented resolution of the FF processing, which results in a higher posting rate than the standard SAR processing. This work investigates the relationship between posting rate and noise levels and provides recommendations for optimal algorithm configurations in various scenarios, including transponder, open ocean, and specular targets like sea-ice and inland water scenes. The Omega–Kappa (WK) algorithm, which has demonstrated superior CPU efficiency compared to the back-projection (BP) algorithm, is considered for this study. But, unlike BP, it operates in the Doppler frequency domain, necessitating further precise spectral and time domain settings. Based on the results of this work, real case studies using S6-MF acquisitions are presented. We first compare S6-MF FF radargrams with Sentinel-1 (S1) images to showcase the potential of optimally configured FF processing. For highly specular surfaces such as sea-ice, distinct techniques are employed for lead signature identification. S1 relies on image-based lineic reconstruction, while S6-MF utilizes phase coherency of focalized pulses for lead detection. The study also delves into two-dimensional wave spectra derived from the amplitude modulation of image/radargrams, with a focus on a coastal example. This case is especially intriguing, as it vividly illustrates different sea states characterized by varying spectral peak positions over time. [ABSTRACT FROM AUTHOR]

The Amazon, the world's largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The accuracy of the water surface model was high, with an F1-score of 0.93. A 12-day mosaic time series of the water surface was generated from the Sentinel-1 prediction. The water surface mask demonstrated relatively consistent agreement with the global surface water (GSW) product from the Joint Research Centre (F1-score: 0.708) and with the Brazilian MapBiomas Water initiative (F1-score: 0.686). The main errors of the map were omission errors in flooded woodland, in flooded shrub, and because of clouds. Rio Negro water surfaces reached their lowest level around the 25th of November 2023 and were reduced to 68.1% (9559.9 km2) of the maximum water surfaces observed in the period 2022–2023 (14,036.3 km2). Synthetic aperture radar (SAR) data, in conjunction with deep learning techniques, can significantly improve near-real-time mapping of water surfaces in tropical regions. [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]

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 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]

High-resolution synthetic aperture radars (SARs) are becoming an indispensable environmental monitoring system to capture the important geophysical phenomena on the earth and sea surface. However, there is a lack of comprehensive models that can orchestrate such large-scale datasets from numerous satellite missions such as GaoFen-3 and Sentinel-1. In addition, these SAR images of different ocean scenes need to convey a variety of high-level classification features in oceanic and atmospheric phenomena. In this study, we propose a multichannel neural network (MCNN) that supports oceanic SAR scene classification for limited oceanic data samples according to multi-feature fusion, data augmentation, and multichannel feature extraction. To exploit the multichannel semantics of SAR scenes, the multi-feature fusion module effectively combines and reshapes the spatiotemporal SAR images to preserve their structural properties. This fine-grained feature augmentation policy is extended to improve the data quality so that the classification model is less vulnerable to both small- and large-scale data. The multichannel feature extraction also aggregates different oceanic features convolutionally extracted from ocean SAR scenes to improve the classification accuracy of oceanic phenomena with different scales. Through extensive experimental analysis, our MCNN framework has demonstrated a commendable classification performance, achieving an average precision rate of 96%, an average recall rate of 95%, and an average F-score of 95% across ten distinct oceanic phenomena. Notably, it surpasses two state-of-the-art classification techniques, namely, AlexNet and CMwv, by margins of 23.7% and 18.3%, respectively. [ABSTRACT FROM AUTHOR]

Many previous studies have primarily focused on the use of deep learning for interferometric processing or separate recognition purposes rather than targeted measurements of detected wellpads. The present study centered around the integration of deep learning recognition and interferometric measurements for Tengiz oilfield wellpads. This study proposes the optimization, automation, and acceleration of targeted ground deformation wellpad monitoring. Mask Region-based Convolutional Neural Network (R-CNN)-based deep learning wellpad recognition and consequent Small Baseline Subset Synthetic Aperture Radar Interferometry (SBAS-InSAR) analyses were used for the assessment of ground deformation in the wellpads. The Mask R-CNN technique allowed us to detect 159 wells with a confidence level of more than 95%. The Mask R-CNN model achieved a precision value of 0.71 and a recall value of 0.91. SBAS-InSAR interferometric measurements identified 13 wells for Sentinel-1 (SNT1), 8 wells for COSMO-SkyMed (CSK), and 20 wells for TerraSAR-X (TSX) located within the −54–−40 mm/y class of vertical displacement (VD) velocity. Regression analyses for the annual deformation velocities and cumulative displacements (CD) of wells derived from SNT1, CSK, and TSX satellite missions showed a good agreement with R2 > 95. The predictions for cumulative displacements showed that the vertical subsidence processes will continue and reach −339 mm on 31 December 2023, with increasing spatial coverage and the potential to impact a higher number of wells. The hydrological analyses in the Tengiz oilfield clearly demonstrated that water flow has been moving towards the detected hotspot of subsidence and that its accumulation will increase with increasing subsidence. This detected subsidence hotspot was observed at a crossing with a seismic fault that might always be subject to reactivation. The role of this seismic fault should also be investigated as one of the ground deformation-controlling factors, even though this area is not considered seismically active. The primary practical and scientific values of these studies were identified for the operational risk assessment and maintenance needs of oilfield and gas field operators. [ABSTRACT FROM AUTHOR]

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR's performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring. [ABSTRACT FROM AUTHOR]

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters. [ABSTRACT FROM AUTHOR]

South China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation information. However, SAR data may be interfered with by noise, i.e., radar shadows and permanent water bodies. Existing cropland data derived from open-access landcover data are not accurate enough to mask out these noises mainly due to insufficient spatial resolution. This study proposed a method that extracted cropland inundation with a high spatial resolution cropland mask. First, the Proportional–Integral–Derivative Network (PIDNet) was applied to the sub-meter-level imagery to identify cropland areas. Then, Sentinel-1 dual-polarized water index (SDWI) and change detection (CD) were used to identify flood area from open water bodies. A case study was conducted in Fujian province, China, which endured several heavy rainfalls in summer 2022. The result of the Intersection over Union (IoU) of the extracted cropland data reached 89.38%, and the F1-score of cropland inundation achieved 82.35%. The proposed method provides support for agricultural disaster assessment and disaster emergency monitoring. [ABSTRACT FROM AUTHOR]

Multispectral remote sensing (RS) data and synthetic aperture radar (SAR) data can provide horizontal and vertical information about forest AGB under different stand conditions. With the abundance of RS features extracted from multispectral and SAR datasets, a key point for accurate forest AGB estimation is to use suitable feature optimization inversion algorithms. In this study, feature optimization inversion algorithms including multiple linear stepwise regression (MLSR), K-nearest neighbor with fast iterative feature selection (KNN-FIFS), and random forest (RF) were explored, with a total of 93 RS features working as inversion model input for forest AGB inversion. The results showed that KNN-FIFS with the combination of Sentinel-1 and Sentinel-2 performed best at both test sites (R2 = 0.568 and RMSE = 15.05 t/hm2 for Puer and R2 = 0.511 and RMSE = 32.29 t/hm2 for Genhe). Among the three feature optimization inversion algorithms, RF performed worst for forest AGB estimation with R2 = 0.348 and RMSE = 18.06 t/hm2 for Puer and R2 = 0.345 and RMSE = 35.98 t/hm2 for Genhe using the feature combination of Sentinel-1 and Sentinel-2. The results indicated that a combination of features extracted from Sentinel-1 and Sentinel-2 can improve the inversion accuracy of forest AGB, and the KNN-FIFS algorithm has robustness and transferability in forest AGB inversions. [ABSTRACT FROM AUTHOR]

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention. [ABSTRACT FROM AUTHOR]

Remote sensing technologies have been successfully used for deforestation monitoring, and with the wide availability of satellite products from different platforms, forest monitoring applications have grown in recent years. The observed potential in these technologies motivates the development of forest mapping and monitoring tools that could also be used for neighboring applications like agriculture or land-use mapping. A literature review confirmed the research areas of interest in deforestation monitoring using synthetic aperture radar (SAR) and data fusion techniques, which guided the formulation of the method developed in this article consisting of a data preprocessing workflow for SAR (Sentinel-1) and multispectral (Sentinel-2) data and a procedure for the selection of a machine learning model for forest/nonforest segmentation evaluated in different combinations of Sentinel-1 and Sentinel-2 bands. The selected model is a random forest algorithm that uses C-band SAR dual-polarimetric bands, intensity features, and vegetation indices derived from optical/multispectral data. The selected random forest classifier's balanced accuracies were 79–81%, and the f1-scores were 0.72–0.76 for the validation set. The results allow the obtention of yearly forest/nonforest and forest loss maps in the study area of Bajo Cauca in Colombia, a region with a documented high deforestation rate. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imagers are active microwave sensors that could overcome many challenges of passive optical bathymetry inversion, yet their capacity to yield accurate high-resolution bathymetric mapping is not studied sufficiently. In this study, we evaluate the feasibility of applying fast Fourier transform (FFT) to SAR data in coastal nearshore bathymetry derivation in Florida's coastal waters. The study aims to develop a robust SAR bathymetry inversion framework across extensive spatial scales to address the dearth of bathymetric data in deeper nearshore coastal regions. By leveraging the Sentinel-1 datasets as a rich source of training data, our method yields high-resolution and accurate depth extraction up to 80 m. A comprehensive workflow to determine both the wavelength and peak wave period is associated with the proposed automated model compilation. A novel contour geometry-based spectral analysis technique for wavelength retrieval is presented that enables an efficient and scalable SAR bathymetry model. Multi-date SAR images were used to assess the robustness of the proposed depth-retrieval model. An accuracy assessment against the GMRT data demonstrated the high efficacy of the proposed approach, achieving a coefficient of determination ( R 2 ) above 0.95, a root-mean-square error (RMSE) of 1.56–10.20 m, and relative errors of 3.56–11.08% in automatically extracting the underwater terrain at every 50 m interval. A sensitivity analysis was conducted to estimate the uncertainty associated with our method. Overall, this study highlights the potential of SAR technology to produce updated, cost-effective, and accurate bathymetry maps of high resolution and to fill bathymetric data gaps worldwide. The code and datasets are made publicly available. [ABSTRACT FROM AUTHOR]