SAR Datasets Research Articles

Spatial variability in paddy intensity and pattern of groundwater trend in part of Lower Gangetic Plain.

Evaluating the intensity of paddy cultivation and tracking the evolving patterns of groundwater levels is essential for a comprehensive assessment of the impact of paddy farming on water resources across diverse geographic regions. Due to the notable water-intensive nature of paddy cultivation, it is imperative to take into account the condition of groundwater resources. In regions such as the cloud-prone Lower Gangetic Plain, characterized by small landholdings, traditional coarse-resolution optical remote sensing methods are inadequate in delivering sufficient information. Therefore, a comprehensive framework that integrates advanced machine learning techniques with multi-temporal SAR datasets becomes essential for assessing the spatio-temporal variations in paddy intensity. In this investigation, Sentinel SAR images of 2022 were employed to evaluate the effectiveness of a machine learning model for mapping paddy rice at a 10-m scale. The analysis of multi-temporal Sentinel-1A data revealed a trajectory of rice growth phases that aligns with the crop calendar of the study area. Notably, the characteristics of σ VH (backscattering coefficient) demonstrated a strong correlation with the growth stages of rice throughout the Aus, Aman, and Boro seasons. This study highlighted the superiority of the machine learning-based Random Forest (RF) model, which achieved an overall accuracy of ≥ 90% in identifying areas under paddy cultivation and discerning their seasonal distribution in cloud-prone regions. Regarding groundwater variability, an assessment was conducted using Central Groundwater Board (CGWB) data spanning from 2002 to 2022. The results indicated that 70% of the paddy-cultivated area in the study region exhibited a decreasing trend in groundwater levels. This integrated assessment provides a holistic understanding of the interplay between paddy cultivation intensity and its impact on groundwater resources, serving as a valuable resource for sustainable land and water management.

GSCAT-UNET: Enhanced U-Net model with spatial-channel attention gate and three-level attention for oil spill detection using SAR data.

Marine pollution due to oil spills presents major risks to coastal areas and aquatic life, leading to serious environmental health concerns. Oil Spill detection using SAR data has transitioned from traditional segmentation to a variety of machine learning & deep learning models like UNET proving its efficiency for the task. This research paper proposes a GSCAT-UNET model for efficient oil spill detection and discrimination from lookalikes. The GSCAT-UNET is an advanced UNET architecture comprising of Spatial-Channel Attention Gates(SCAG), Three Level Attention Module(TLM) and Global Feature Module(GFM) for global level oil spill feature enhancement leading to effective oil spill detection and discrimination from lookalikes. Sentinel-1 Dual-Pol SAR dataset of 1112 images and respective labeled images (5 classes) including confirmed oil spills and lookalikes is used to demonstrate the efficacy of the GSCAT-UNET model. The GSCAT-UNET model significantly enhances segmentation accuracy and robustness for oil spill detection with 5% higher accuracy and 29% higher IoU i.e. 93.7% compared to the UNET segmentation model, addressing the challenges of SAR data complexities and imbalanced datasets. The strong performance of the GSCAT-UNET model demonstrates its potential as a critical tool for disaster response and environmental monitoring.

Long-Term Ground Deformation Monitoring and Quantitative Interpretation in Shanghai Using Multi-Platform TS-InSAR, PCA, and K-Means Clustering

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas.

Identifying Potential Landslides in Low-Coherence Areas Using SBAS-InSAR: A Case Study of Ninghai County, China

The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Circular synthetic aperture radar sub‐aperture angle information complementation based on azimuth‐controllable generative adversarial network

AbstractA conditional generative adversarial network (CGAN) framework is proposed to address the issue of incomplete circular synthetic aperture radar (CSAR) azimuthal information due to motion errors. Specifically, the authors propose a novel CGAN architecture that can control the azimuth angle for arbitrary angle generation, capable of complementing missing CSAR sub‐aperture information. The network incorporates angular labels for various scenarios and integrates a dynamic region‐aware convolution (DRconv) module. Additionally, to counteract the common challenge of mode collapse in GAN training, a mode seeking regularisation technique is innovativrly introduced into the authors’ loss function. The efficacy of the proposed network is rigorously tested using both the MSTAR dataset and an X‐band SAR dataset. The results demonstrate that the authors’ network can generate high‐fidelity SAR images with controllable azimuths, closely resembling authentic images. Furthermore, the proposed method excels in complementing missing CSAR sub‐aperture information, effectively supplying the lost angular information due to motion errors. A new technical approach for SAR image generation is not only offered but it also has the potential to significantly expand SAR datasets. This advancement is expected to enhance the quality and utility of SAR imagery in applications such as surveillance, reconnaissance, and environmental monitoring.

A Priori Estimation of Radar Satellite Interferometry’s Sensitivity for Landslide Monitoring in the Italian Emilia-Romagna Region

The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry.

Modeling, Mapping and Analysis of Floods Using Optical, Lidar and SAR Datasets—a Review

Modeling, Mapping and Analysis of Floods Using Optical, Lidar and SAR Datasets—a Review

Utilising InSAR for monitoring infrastructure at national scales

Interferometric SAR (InSAR) is now a widely used tool for the monitoring of infrastructure, in particular mines, dams, and oil and gas facilities. The technology is also widely used for the monitoring of construction activities such as tunnelling. With the growth of globally available and continuously updated SAR datasets, the monitoring of entire regional and national infrastructure networks is now feasible. Here we show how continuously updated, network-wide, InSAR datasets can be used to proactively manage large infrastructure networks such as roads, railways, and water pipelines. In the UK continuously updated InSAR data (derived from Sentinel-1 SAR data) is being effectively used across the entire railway network to proactively monitor potential embankment failures, large-scale landslides, and potential hazards outside of the boundary fence. A challenge of using InSAR data in this way is the huge volume of data that is available, as such custom risk metrics and machine learning algorithms are applied to highlight some of the areas at heightened risk. InSAR is also being applied to tackle the issue of water pipe leakage caused by natural ground movements, such as soil shrink-swell. In this case, regularly updated InSAR data has been used in the UK and Australia to detect areas at elevated risk of pipeline failures based on ground movement patterns, and by combining historical movement data and known failure events it is possible to refine such models. With expanding global SAR constellations proactive, nationwide, monitoring of critical assets is now possible, with the challenge being how we derive insight from these very large datasets.

Analysis of the Dihedral Corner Reflector’s RCS Features in Multi-Resource SAR

Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR’s axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX–spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12°, which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX–spotlight data.

Monitoring slope stabilization of a reactivated landslide in the Three Gorges Reservoir Region (China) with multi-source satellite SAR and optical datasets

Monitoring slope stabilization of a reactivated landslide in the Three Gorges Reservoir Region (China) with multi-source satellite SAR and optical datasets

Few-shot SAR vehicle target augmentation based on generative adversarial networks

Abstract. The study of few-shot SAR image generation is an effective way to expand the SAR dataset, which not only provides diversified data support for SAR target classification, but also provides a high-fidelity false image template for SAR deceptive jamming. In this paper, we have constructed a multi-frequency and multi-target type SAR vehicle imagery dataset that encompasses frequencies such as X, Ka, P, and S bands. The vehicle types are coaster, suv and cabin. Subsequently, we utilized various Generative Adversarial Networks for image generation from the SAR vehicle dataset. The experimental result indicates that the images generated by the DCGAN and the LSGAN models are of superior quality. Furthermore, we employed different recognition networks to evaluate the classification accuracy of the generated images. Of all the frequency bands, the Ka band generated images achieved the highest recognition rate, with an accuracy of up to 99%. Under conditions of a limited number of samples, the LSGAN model performed the best, reaching a classification recognition rate of 71.48% with a dataset of only 20 samples. Finally, we use a conditional network generation model to generate conditions based on target categories and frequency bands, providing high fidelity samples for SAR deception jamming.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment

As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification.

Ground Subsidence, Driving Factors, and Risk Assessment of the Photovoltaic Power Generation and Greenhouse Planting (PPG&GP) Projects in Coal-Mining Areas of Xintai City Observed from a Multi-Temporal InSAR Perspective

In recent years, photovoltaic power generation and greenhouse planting (PPG&GP) have become effective approaches for reconstructing and restoring the ecological environment of old coal-mining industry bases, such as Xintai City. However, the ecological impacts or improvements of the PPG&GP projects and their daily operations on the local environment are still unclear. To solve these problems, this study retrieved the ground deformation velocities and time series of the study region by performing the Small-Baseline Subset (SBAS)-Interferometric Synthetic Aperture Radar (InSAR) technique on the Advanced Land Observing Satellite (ALOS) PALSAR and Sentinel-1 SAR datasets. With these deformation results, the spatial analysis indicated that the area of the subsidence region within the PPG&GP projects reached 10.70 km2, with a magnitude of approximately −21.61 ± 12.10 mm/yr. Also, even though the ground deformations and their temporal changes were both visible in the construction and operation stages of the PPG&GP projects, the temporal analysis demonstrated that most observation points finally entered into the stationary phases in the late stage of the observation period. This phenomenon validated the effectiveness of the PPG&GP projects in enhancing the ground surface stability in coal-mining areas. Additionally, the precipitation, geological structure, increased coal-mining depths, and emergent agricultural modes were assumed to be the major impact factors controlling the ground deformation within the local PPG&GP projects. Finally, a novel risk assessment method with a designed index of IRA was utilized to classify the ground subsidence risks of the PPG&GP projects into three levels: Low (69.7%), Medium (16.9%), and High (9.4%). This study sheds a bright light on the ecological monitoring and risk management of the burgeoning industrial and agricultural infrastructures, such as the PPG&GP projects, constructed upon the traditional coal-mining areas in China from a multi-temporal InSAR perspective.

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities.

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications.

Flood Mapping Using the Sentinel-1 SAR Dataset and Application of the Change Detection Approach Technique (CDAT) to the Google Earth Engine In Sindh Province, Pakistan

Flooding is a natural disaster characterised by overflowing water onto normally dry land, resulting in widespread damage and disruption to communities. Using Geographic Information Systems (GIS) and Remote Sensing (RS) technologies, accurate flood maps can be created. The Change Detection approach was employed using pre-processed Sentinel-1 SAR data. Pre-processing steps, including Thermal-Noise Removal, Radiometric Calibration, and Terrain Correction, were carried out using the Google Earth Engine (GEE) platform. This methodology allows for accurate and reliable analysis of land cover changes and provides valuable information for land management and decision-making processes in the region. The purpose of this study was to estimate the damage caused by flooding. The flood status between 18 August 2022 and 28 August 2022 was analysed. The estimated flood extent based on Sentinel-1 SAR data is almost 759,642 ha. The estimated number of people exposed is almost 663,797, estimated using the Global Human Settlement Layer (2015), with a resolution of about 250 m. Based on a MODIS land cover dataset with a resolution of 500 m, the estimated area of crops damaged is almost 415,450 ha and estimated urban area affected is almost 8435 ha.