Sentinel-1 Synthetic Aperture Radar Data Research Articles

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

A Comprehensive Evaluation of Dual-Polarimetric Sentinel-1 SAR Data for Monitoring Key Phenological Stages of Winter Wheat

Large-scale crop phenology monitoring is critical for agronomic planning and yield prediction applications. Synthetic Aperture Radar (SAR) remote sensing is well-suited for crop growth monitoring due to its nearly all-weather observation capability. Yet, the capability of the dual-polarimetric SAR data for wheat phenology estimation has not been thoroughly investigated. Here, we conducted a comprehensive evaluation of Sentinel-1 SAR polarimetric parameters’ sensibilities on winter wheat’s key phenophases while considering the incidence angle. We extracted 12 polarimetric parameters based on the covariance matrix and a dual-pol-version H-α decomposition. All parameters were evaluated by their temporal profile and feature importance score of Gini impurity with a decremental random forest classification process. A final wheat phenology classification model was built using the best indicator combination. The result shows that the Normalized Shannon Entropy (NSE), Degree of Linear Polarization (DoLP), and Stokes Parameter g2 were the three most important indicators, while the Span, Average Alpha (α2¯), and Backscatter Coefficient σVH0 were the three least important features in discriminating wheat phenology for all three incidence angle groups. The smaller-incidence angle (30–35°) SAR images are better suited for estimating wheat phenology. The combination of NSE, DoLP, and two Stokes Parameters (g2 and g0) constitutes the most effective indicator ensemble. For all eight key phenophases, the average Precision and Recall scores were above 0.8. This study highlighted the potential of dual-polarimetric SAR data for wheat phenology estimation. The feature importance evaluation results provide a reference for future phenology estimation studies using dual-polarimetric SAR data in choosing better-informed indicators.

DUAL-POLARIMETRIC DECOMPOSITION OF SENTINEL-1 SAR IMAGE AND MACHINE LEARNING MODEL FOR OIL SPILL DETECTION: CASE OF MINDORO OIL SPILL

Abstract. Oil spills represent a significant environmental hazard necessitating timely detection to mitigate their detrimental effects. Synthetic Aperture Radar (SAR) technology serves as a remote sensing (RS)-based tool capable of detecting oil spills under varying weather conditions and at all times of day. SAR polarimetry, which assesses the polarization of the backscattered SAR signal, can effectively discriminate oil spills from other features that may manifest as dark regions in the SAR images. The integration of machine learning algorithms offers significant potential for enhancing the accuracy and efficiency of oil spill detection through SAR polarimetry. In recent years, several studies have introduced machine learning-based methodologies for this purpose, yet a comprehensive evaluation of their real-world performance remains essential. This study aimed to assess the efficacy of a machine learning (ML)-based approach for oil spill detection utilizing features derived from a dual-polarimetric decomposition method applied to Sentinel-1 SAR data. Results show that the machine learning-based approach achieved notable accuracy in oil spill detection reaching a score of 0.569 for intersection over union and 72.50 for f1-score of oil spill areas. Overall, this research underscores the potential of ML techniques as valuable tools for oil spill detection via SAR polarimetry.

Exploring optimal integration schemes for Sentinel-1 SAR and Sentinel-2 multispectral data in land cover mapping across different atmospheric conditions

Integrating remote sensing data, such as Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral data, can provide valuable insights for this task. However, in tropical regions, such as Yogyakarta, Indonesia, atmospheric interference from cloud cover and haze can result in spectral confusion and decreased usability of multispectral data, leading to reduced classifier accuracy. This study investigates the impact of integrating Sentinel-1 SAR and Sentinel-2 multispectral data and their derivatives on land cover mapping in part of Yogyakarta under hazy and clear atmospheric conditions. This study employed four Schemes: (1) using only SAR data and its derivatives, (2) using only multispectral data and its derivatives, (3) combining SAR, multispectral, and their derivatives, and (4) combining SAR, multispectral, and their derivatives with feature selection using Recursive Feature Elimination. The XGBoost classification algorithm tuned using Bayesian optimization was used for classification, and the accuracy and efficiency (processing time) of each Scheme were evaluated. The results showed that integrating SAR data and optical data with clear and hazy condition increased the accuracy by 4 % (clear) to 14.58 % (hazy). The integration of SAR and optical data had the highest accuracy (79.58% on hazy imagery and 84.58% on clear imagery) but longer processing time. However, the feature selection conducted in decreased model complexity and processing time, with a reduction in processing time of 78.74% on hazy imagery and 49.89% as compared to utilizing the full SAR and optical data variables. Lastly, the McNemar test concluded the significance of integrating SAR data for mapping land cover, especially with hazy optical data.

CROP HEIGHT ESTIMATION OF WHEAT USING SENTINEL-1 SATELLITE IMAGERY: PRELIMINARY RESULTS

Abstract. Wheat is one of the primary crop productions for half of the global production. It is the most exported cereal, which reached up to 40% according to the records of the Food and Agriculture Organization of the United Nations (FAO) in 2020. The relationship between the crop parameters and remote sensing tools has become essential for the temporal monitoring and estimations of crop growth. In this study, we investigate the relationship between crop height and Synthetic Aperture Radar (SAR) backscatter. Plant height was collected twice, in the early (13 April 2023) and late stages (21 June 2023) of wheat, for a total of 70 samples. Sentinel-1 SAR data was also attempted to be synchronized with ground measurements. For this purpose, 8 images were obtained, 4 in both ascending (8 and 15 April, 16 and 26 June) and descending (9 and 14 April, 20 and 25 June) directions. The basic steps of image pre-processing, calibration, filtering, and topography correction, were applied to each image. By evaluating the correlation coefficients between plant height and images, data with low correlations were excluded and the first three data, dated 8 April and 19 June, were used for plant height estimation. For prediction purposes, Linear Regression (LR) and Random Forest (RF) methods were evaluated with three different training data sets. The highest correlation and minimum error value were achieved by VH polarization with random forest (r = 0.868, RMSE = 5.377 cm) in the early stage and LR (r = 0.616, RMSE = 14.451 cm) in the late stage. In general, higher training (80%) and lower test data (20%) produced better results.

Estimating vegetation water content from Sentinel-1 C-band SAR data over savanna and grassland ecosystems

Studying vegetation water content (VWC) dynamics is essential for understanding plant growth, water and carbon cycles, and ecosystem stability. However, acquiring field-based VWC estimates, consistently through space and time, is challenging due to time and resource constraints. This study investigates the potential of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data for estimating VWC in natural ecosystems in central Brazil. We assessed (i) how well Sentinel-1 SAR data can capture variations in VWC over three different vegetation types (i.e. dry and waterlogged grasslands, and savannas) and (ii) how the studied vegetation types respond to seasonal dry periods in terms of water content. Field data from 82 plots, distributed across the three vegetation types and revisited in four different seasons, were used to calibrate and validate a model for VWC estimation. The calibrated model, with an R 2 of 0.52 and RMSE of 0.465 kg m−2, was then applied to Sentinel-1 SAR backscatter data to generate monthly VWC maps for grassland and savanna ecosystems at 30 m spatial resolution between April 2015 and September 2023. These maps, combined with rainfall and potential evapotranspiration data, provided insights into how the studied vegetation types respond to water shortage during the dry season at the community scale. More specifically, savannas showed to be better able to retain higher levels of water content during the dry season, probably due to a higher water holding capacity of the woody component together with its deep-root system ability to access deeper groundwater. This research demonstrates the potential of Sentinel-1 SAR data for monitoring VWC in natural ecosystems, allowing for future studies to assess ecosystems’ response to drought events and changes in their functioning, ultimately supporting land management decisions.

High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau

ABSTRACT As a key climate variable, soil moisture plays a crucial role in drought detection, flood warning, and crop yield prediction. In recent years, the demand for high-spatial-resolution soil moisture has increased, particularly in environmental management. In this study, Copernicus Sentinel-1 synthetic aperture radar data, Sentinel-2 multi-spectral data, and other auxiliary data (land cover types, soil texture, etc.) were used to retrieve surface soil moisture (10 m) in the cloud environment (Google Earth Engine + Google Colab + Google Drive) over the Tibetan Plateau, and an entirely data-driven machine learning-based model called Deep Forest was adopted. We discussed the application of the Deep Forest model and compared it with other machine learning models. Overall, on the basis of 10-fold cross-validation, the modified Deep Forest model performed the best, with estimate accuracy of 0.834 and 0.038 m3·m−3 in terms of coefficient of determination ( R 2 ) and unbiased Root Mean Square Error (ubRMSE), respectively. It also demonstrated the best performance in site-based validation ( R 2 of 0.606 and ubRMSE of 0.092 m3·m−3). In addition, the framework for the data acquisition, data preprocessing, model training, and soil moisture mapping in this study was completed in the cloud environment, which facilitated the entire retrieval process. This work provides new ideas beyond the retrieval model for other related studies.

MSwinUNet: A multi-modal U-shaped swin transformer for supervised change detection

Convolutional neural networks (CNNs) have received significant attention for change detection (CD) on multimodal remote sensing images, but they struggle to capture global cues due to the locality of convolution operations. In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self-attention mechanism. Motivated by this, we propose mSwinUNet, a novel end-to-end multi-modal model with swin-transformer-based and U-shaped siamese network architectures for supervised CD using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager (MSI) data. mSwinUNet contains multi-modal encoder with difference module, bottleneck, and fused decoder, and all of them are based on swin transformer. Firstly, tokenized multi-modal bitemporal image patches are fed into multiple Siamese encoder branches to extract multi-level multi-modal difference feature maps in parallel. Subsequently, the last level multi-modal difference maps are fused to generate the smallest scale change map in the bottleneck. Then, the hierarchical decoder incorporates patch expansion and fusion operations to fuse multi-scale difference and change maps, effectively recuperating the details of the change information. Finally, the last patch expansion and a linear projection are applied to output the final change map, which preserves the identical spatial resolution as the input image. Extensive experiments have shown that mSwinUNet outperforms several the state-of-the-art multi-modal CD methods on OSCD dataset and the corresponding Sentinel-1 SAR data.

Detection of Coastal Erosion and Progradation in the Colombian ‘Atrato River’ Delta by Using Sentinel-1 Synthetic Aperture Radar Data

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.

An investigation of inversion method to measure the radial velocity of Kuroshio from Sentinel-1 SAR data

ABSTRACT Studies on the retrieval of ocean surface current by Synthetic Aperture Radar (SAR) mainly concentrate on coastal regions with land coverage, allowing for continuous land to estimate non-geophysical Doppler shifts. As a strong western boundary in the Western Pacific, the Kuroshio flows through mostly islands or open sea, and lacks land coverage for range bias correction. We propose a non-geophysical Doppler shift correction algorithm suitable for Kuroshio observation. Three solutions matching different land coverage were adopted in the range direction, and the scalloping was removed by using average filtering in the azimuth direction. Comparison between SAR radial velocity and Global Surface Lagrangian Drifter (GLD) reveals that the correlation coefficient (R 2) increase to 0.686, and root mean square error (RMSE) of 0.215 m/s after non-geophysical corrections, while the wind-wave bias corrections make R 2 increase to 0.806 and RMSE decrease to 0.13 m/s. There is also a good consistency between SAR inversion and Regional Ocean Circulation Model (ROMS). Sensitivity analysis illustrates that ascending pass is more suitable for monitoring Kuroshio, and high incidence angles are less affected by ocean conditions and non-geophysical factors. The algorithm can be used in regions with discontinuous or absent land.

Sustainable flood hazard mapping with GLOF: A Google Earth Engine approach

This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.

Use of Synthetic Aperture Radar (SAR) imagery on oil spill detection: A case study on the MV X-Press Pearl container ship incident in 2021 in Sri Lanka

Oil spills on the ocean from ships have a strong impact on the environment as well as on human societies. The MV X-Press Pearl container ship incident in 2021 became the worst marine environmental disaster in Sri Lanka due to its powerful impact. Oil leakage from the ship to the ocean continued for months. Synthetic Aperture Radar (SAR) imagery has the advantage of penetrating cloud cover, as well as day and night operating capabilities. Therefore, SAR imagery can be used to detect as well as observe the temporal changes of oil spills. The study in this article aimed to detect the oil spill caused by the MV X-Press Pearl container ship on Sri Lanka’s west coast from May to August 2021 using Sentinel-1 SAR data and measure the temporal changes of the oil slick. Three SAR imageries with Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarization from June, July, and August of 2021 have been processed by applying the orbit file, running the multi-looking algorithm, and making the ellipsoid correction. The possible oil spills have been identified from each set of imagery as dark linear shapes with lengths of 3.1 ±0.012 km with an average reflectance of 15.6 decibels for June and 3.6 ±0.012 km with an average reflectance of 16.5 decibels for the other two months, and the shapes correspond with the oil slick classifications from other literature. The results indicate that the VV polarization is the most suitable to detect oil spills, and that the oil spill from the X-Press Pearl continued from June to the end of August 2021.

Automatic wide area land cover mapping using Sentinel-1 multitemporal data

This study introduces a methodology for land cover mapping across extensive areas, utilizing multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) data. The objective is to effectively process SAR data to extract spatio-temporal features that encapsulate temporal patterns within various land cover classes. The paper outlines the approach for processing multitemporal SAR data and presents an innovative technique for the selection of training points from an existing Medium Resolution Land Cover (MRLC) map. The methodology was tested across four distinct regions of interest, each spanning 100 × 100 km2, located in Siberia, Italy, Brazil, and Africa. These regions were chosen to evaluate the methodology’s applicability in diverse climate environments. The study reports both qualitative and quantitative results, showcasing the validity of the proposed procedure and the potential of SAR data for land cover mapping. The experimental outcomes demonstrate an average increase of 16% in overall accuracy compared to existing global products. The results suggest that the presented approach holds promise for enhancing land cover mapping accuracy, particularly when applied to extensive areas with varying land cover classes and environmental conditions. The ability to leverage multitemporal SAR data for this purpose opens new possibilities for improving global land cover maps and their applications.

Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia

Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring.

Sentinel-1 Imagery Used for Estimation of Soil Organic Carbon by Dual-Polarization SAR Vegetation Indices

Despite optical remote sensing (and the spectral vegetation indices) contributions to digital soil-mapping studies of soil organic carbon (SOC), few studies have used active radar remote sensing mission data like that from synthetic aperture radar (SAR) sensors to predict SOC. Bearing in mind the importance of SOC mapping for agricultural, ecological, and climate interests and also the recently developed methods for vegetation monitoring using Sentinel-1 SAR data, in this work, we aimed to take advantage of the high operationality of Sentinel-1 imaging to test the accuracy of SOC prediction at different soil depths using machine learning systems. Using linear, nonlinear, and tree regression-based methods, it was possible to predict the SOC content of soils from western Bahia, Brazil, a region with predominantly sandy soils, using as explanatory variables the SAR vegetation indices. The models fed with SAR sensor polarizations and vegetation indices produced more accurate results for the topsoil layers (0–5 cm and 5–10 cm in depth). In these superficial layers, the models achieved an RMSE in the order of 5.0 g kg−1 and an R2 ranging from 0.16 to 0.24, therefore explaining about 20% of SOC variability using only Sentinel-1 predictors.

A New ground open water detection scheme using Sentinel-1 SAR images

ABSTRACT The detection of groundwater is essential not only for scientific research but also for agricultural purposes. This research aims to improve the accuracy and reliability of detecting ground standing water in cropland during the spring/early summer season in eastern South Dakota, USA, by reducing misclassification between water and vegetation. The study utilizes Sentinel-1 synthetic aperture radar (SAR) data. We selected and surveyed 159 ground sites, comprising 78 water sites and 81 nonwater sites, located between Brookings and Sioux Falls, SD, USA. The proposed scheme consists of three steps: 1) developing a modified speckle filter to reduce speckle noise while preserving image details, 2) characterizing the data for water and nonwater sites and providing parameter estimation using the Method of log-cumulants (MoLC) with generalized Gamma distribution (GΓD), and 3) applying Markov random field (MRF) for SAR data classification. The developed scheme demonstrates good performance with a site-based overall detection accuracy of 93.7%. In addition, it provides a computationally efficient solution for water detection, which can be applied in various applications such as crop insurance, precision agriculture and drought monitoring.

Crop NDVI time series construction by fusing Sentinel-1, Sentinel-2, and environmental data with an ensemble-based framework

Satellite-based Normalized Difference Vegetation Index (NDVI) time series data are conducive for near-real-time (NRT) monitoring of crop progress, but they commonly suffer from data gaps due to cloudy weather conditions. Fortunately, Synthetic Aperture Radar (SAR) data, which are cloud-insensitive, has high potentials to fill these data gaps. However, the relationship between NDVI and SAR data is not generic and affected by various factors (e.g., crop canopy structure and phenology, soil exposure and humidity). Therefore, it is worth exploring to evaluate the relationships and develop a more robust SAR-NDVI fusion method for NDVI time series construction. In this study, we proposed and evaluated an ensemble-based data fusion framework that accounts for these factors, based on which dense NDVI time series for crop monitoring were constructed. The framework consisted of three steps: (1) NDVI was predicted from Sentinel-1 SAR data and auxiliary environmental factors using a random forest (RF) model; (2) an improved feature importance measurement method was proposed to reveal the different contributions of multisource data to the modeling, and suggestions for selecting optimal input parameters were presented; (3) the uncertainty of the predicted data was quantified by an ensemble-based method and incorporated into a Weighted Least Squares (WLS) method to construct dense NDVI time series. Results showed that the RF models were generally improved by the auxiliary data and achieved a satisfactory accuracy of NDVI estimation (R2 > 0.93, RMSE < 0.075) for both corn and soybean crops in Southwestern Ontario, Canada. The proposed method performed well in filling data gaps in both the vegetative and reproductive stages for corn and soybean, providing a practical and promising solution for continuous crop monitoring.

An Assessment of the Epicenter Location and Surroundings of the 24 January 2020 Sivrice Earthquake, SE Türkiye

The Sivrice earthquake (Mw 6.8) occurred on 24 January 2020 along the East Anatolian Fault (EAF) zone of Türkiye, and epicentral information and focal mechanism solutions were published by two national and six international seismic stations. Here, we analyzed epicentral locations and the major fault trace using aerial photogrammetric images taken two days after, and synthetic aperture radar (SAR) interferometry. Although the focal mechanism solutions were similar, the epicenters were largely displaced. Several bright lineaments with a stair-like geometry were observed in aerial images of the Euphrates River channel along the fault trace. These lineaments, also called en echelon fractures in structural geology, are like right-lateral segments of a fault plane aligning the river channel, cut and offset by those similar in trend with the EAF and with alignments of a left lateral sense, as is the EAF motion sense. We interpret that the river local channel follows a right-lateral fault structure. The traces were lost a few days later, which proves the essentiality of remote sensing technologies for obtaining precise information in large regions. The time series analysis for one year period from Sentinel-1 SAR data also illustrated the displacements in the region sourced from the earthquake.

Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data

Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available.

“Investigating and mapping day-night urban heat island and its driving factors using Sentinel/MODIS data and Google Earth Engine. Case study: Greater Cairo, Egypt”

Urban heat islands (UHI) represent one of the substantial human-induced challenges endangering urban livelihoods. UHI and climate change have significant interactions. Progressively rising warming tendencies are exacerbating higher temperatures in UHI regions. Using time-series data, we examined annual and seasonal day-night UHI intensity and its driving factors in Greater Cairo (GC), which is considered Egypt's megacity, between April 2021 and March 2022. Since Sentinel-1 synthetic aperture radar (SAR) proposes unique horizons for mapping urban areas, we used SAR data and the Random Forest (RF) algorithm for urban footprint extraction in the search region. Sentinel-3 SLSTR thermal images were utilized to derive annual and seasonal day-night land surface temperatures (LST) during the research period. A spatio-temporal assessment of tropospheric NO2, SO2, CO, and aerosol concentrations was performed via the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), MODIS, and Google Earth Engine (GEE) applications. The annual average UHI intensity was higher in Egypt's megacity during the nighttime (3.27 K) than during the daytime (0.50 K). This finding indicates that driving forces are boosting temperatures in urban regions at night. The seasonal average day-night UHI intensities have the highest values during the winter, followed by the summer. Significant influencing factors concerning UHI intensity varied between the daytime and nighttime. Moreover, NDBI, PV, NDVI, altitude, and wind speed were the driving factors that were influential during the day and at night. These results can be used to establish effective adaptation measures for combating the impacts of climatic change and UHI.