Synthetic Aperture Radar Sensors Research Articles

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022–2023 using a PSInSAR-based approach, varies from − 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas − ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016–2017, 2018–2019, and 2020–2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022–2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022–2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022–2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to − 13.13 mm/year (2022–2023). Towards the south-east, the town experienced rapid land subsidence, − 13.13 mm/year to − 5 mm/year (2022–2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

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.

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

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

The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests

ABSTRACT Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% ~50% of the variance compared to 17% ~25%. The combination of optical and SAR time-series data produced the best performance (R2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R2 of 0.657 and rRMSE of 35.317%; SAR: R2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.

Comparison of Synthetic Aperture Radar Sentinel-1 and ALOS-2 observations for lake monitoring

This work investigates the efficacy of L-band and C-band Synthetic Aperture Radar (SAR) sensors onboard ALOS-2 and Sentinel-1 satellites, as compared to optical sensors onboard Sentinel-2 satellite, for mapping open water of the Tri An reservoir, one of the largest artificial reservoirs in South Vietnam, during the 2016-2023 period. The Google Earth Engine (GEE) was the primary computing platform to pre-process all satellite observations. The Otsu threshold algorithm was employed to generate water/non-water maps derived from the VH- and HH-polarized backscatter coefficient data acquired by Sentinel-1 and ALOS-2 satellites and from the Modified Normalized Difference Water Index (MNDWI) data acquired by Sentinel-2 satellite, respectively. The findings reveal the stability of Tri An reservoir’s surface water extent from 2017 to 2022, followed by a significant decline of nearly 70% during the dry season of 2023 to approximately 100 km2. This substantial decrease can be explained by the impact of a robust El Niño phase occurring in the region simultaneously. Overall, there is a high consistency between results derived from SAR and optical sensors, but the correlation between Sentinel-1 and Sentinel-2 (R = 0.9774) was higher than that between ALOS-2 and Sentinel-2 (R = 0.9145). During the drought period, both C-band and L-band SAR sensors overestimate the reservoir’s surface water extent due to the similarity in their backscatter coefficient between water and dry flat soil surfaces. This misclassification is more pronounced in ALOS-2 data than Sentinel-1 data, suggesting that the C-band sensor is more suitable than the L-band sensor for mapping the lake’s open water areas.

Mission operations management of a regional coverage highly agile SAR satellite: Few key aspects

The regional coverage satellite constellations are in demand for fulfilling earth observation needs of specific user regions of interest as opposed to global coverage thus enhancing the revisit period for the area. In this paper, we present a few key mission aspects of the regional coverage constellation designed and operated by the Indian Space Research Organization (ISRO) in low earth orbit. ISRO developed three X-Band Synthetic Aperture Radar (SAR) Earth Observation satellites and placed them in three inclined orbit planes to enhance the Indian region's coverage. This is India's first inclined orbit constellation designed for remote sensing applications in low earth orbit. The satellites were built around a standard bus carrying a Synthetic Aperture Radar payload with a deployable Radial Rib Antenna (RRA). They were designed to achieve a high agility capability. As SAR sensors are capable of all-weather, day, and night imaging; thus, most suitable in inclined orbits where the illumination conditions vary continuously for imaging. Though the inclined orbits provide more revisit opportunities to the region of interest, their characteristics pose challenges in mission design and operations. Though the mission design and planning involved a host of elements, this paper brings out the details of a few key mission analysis elements, planning aspects and operations that were critical in the course of various mission phases. The mission analysis included mission-specific requirements like launch windows selection for the constellation and reference attitude generation specific to inclined orbit geometry. Mission planning had to take care of Radial Rib Antenna (RRA) deployment requirements which were very critical for the mission, apart from taking care of individual subsystem requirements. The SAR payload data product quality and targeting accuracy are very sensitive to the satellite image point range (vis-a-vis SAR operation data window). Extensive post-launch calibration activity was carried out to meet all these mission requirements and data product specifications. The challenge was in fixing the payload beam alignment and fixing the uncertainties in onboard and ground computations. The paper also describes the methods of fixing all possible error sources affecting the high-resolution SAR payload performance.

Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling

Surface water is a vital component of the Earth’s water cycle and characterizing its dynamics is essential for understanding and managing our water resources. Satellite-based remote sensing has been used to monitor surface water dynamics, but cloud cover can obscure surface observations, particularly during flood events, hindering water identification. The fusion of optical and synthetic aperture radar (SAR) data leverages the advantages of both sensors to provide accurate surface water maps while increasing the temporal density of unobstructed observations for monitoring surface water spatial dynamics. This paper presents a method for generating dense time series of surface water observations using optical–SAR sensor fusion and gap filling. We applied this method to data from the Copernicus Sentinel-1 and Landsat 8 satellite data from 2019 over six regions spanning different ecological and climatological conditions. We validated the resulting surface water maps using an independent, hand-labeled dataset and found an overall accuracy of 0.9025, with an accuracy range of 0.8656–0.9212 between the different regions. The validation showed an overall false alarm ratio (FAR) of 0.0631, a probability of detection (POD) of 0.8394, and a critical success index (CSI) of 0.8073, indicating that the method generally performs well at identifying water areas. However, it slightly underpredicts water areas with more false negatives. We found that fusing optical and SAR data for surface water mapping increased, on average, the number of observations for the regions and months validated in 2019 from 11.46 for optical and 55.35 for SAR to 64.90 using both, a 466% and 17% increase, respectively. The results show that the method can effectively fill in gaps in optical data caused by cloud cover and produce a dense time series of surface water maps. The method has the potential to improve the monitoring of surface water dynamics and support sustainable water management.

A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding

Determining the timing of landslide occurrence is crucial for establishing an accurate, comprehensive and systematic landslide inventory while assessing the potential for reducing landslide risk. Unfortunately, many existing landslide inventories lack temporal information such as the precise time of landslide events. Optical and Synthetic Aperture Radar (SAR) sensors are the most commonly used remote sensing technologies for landslide detection. Unlike optical sensors, SAR sensors are not affected by cloudy conditions and provide valuable imagery regardless of sunlight availability. Therefore, SAR-derived parameters, i.e., SAR amplitude, interferometric coherence, and polarimetric features (alpha and entropy), offer a higher temporal resolution for detecting landslide occurrence times compared to optical data. Despite the advantages, there is currently no universally accepted automatic method for determining the time of landslide events using SAR data. This is due to the lack of anomaly labels and the high time-series volatility in detecting landslide occurrence times. Despite advances in deep-learning methods for anomaly detection in time-series, only a few of them can address these challenges in our case. In this paper, we propose an unsupervised multivariate transformed-based deep-learning model to automatically and efficiently estimate landslide occurrence times using multivariate SAR-derived parameters time-series analysis. The designed gated relative position can increase robustness and temporal context information, by learning global temporal trends in the time-series. Subsequently, the time-series of the anomaly score derived from the proposed Transformer model is analyzed using an adaptive thresholding strategy to dynamically and automatically mark anomalies related to the landslide occurrence. Our research focuses on collapsed landslides characterized by dramatic changes in ground surface topography, with a particular attention for the need of a prior knowledge about landslide boundaries. We assess the performance of the proposed methodology for several collapsed landslides including the July 21, 2020 Shaziba and 23 July, 2019 Shuicheng landslides in China, March 19, 2019 Takht landslide in Iran, June 15, 2018 Jalgyz-Jangak and May 25, 2018 Kugart landslides in Kyrgyzstan, July 7, 2018 Hitardalur landslide in Iceland, and January 25, 2019 Brumadinho landslide in Brazil. In comparison to commonly used neural networks like the LSTM algorithm, our proposed framework leads to a more accurate estimate for the time of landslide failure using time-series of SAR-derived parameters. Furthermore, our results suggest the great potential of SAR data to narrow the time period detected from optical data when used in conjunction with them.

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

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

Study on LOS to Vertical Deformation Conversion Model on Embankment Slopes Using Multi-Satellite SAR Interferometry

Slant range geometry plays a crucial role in interpreting synthetic aperture radar (SAR) observations, especially in converting line-of-sight (LOS) surface deformations to actual vertical subsidence. This paper proposes a new conversion model to retrieve vertical settlements of the embankment slopes using the geometrical parameters of the dam and the SAR sensor. The simulation results highlight the impact of slope foreshortening and heading direction of the satellite on deformation retrieval. Various SAR data with different resolutions and bands are used to analyze the model’s performance, revealing a high conformity of the model with practical conversion parameters exceeding 80% for TerraSAR-X and Cosmo-SkyMed data.

Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.

Flood Detection in Polarimetric SAR Data Using Deformable Convolutional Vision Model

Floods represent a significant natural hazard with the potential to inflict substantial damage on human society. The swift and precise delineation of flood extents is of paramount importance for effectively supporting flood response and disaster relief efforts. In comparison to optical sensors, Synthetic Aperture Radar (SAR) sensor data acquisition exhibits superior capabilities, finding extensive application in flood detection research. Nonetheless, current methodologies exhibit limited accuracy in flood boundary detection, leading to elevated instances of both false positives and false negatives, particularly in the detection of smaller-scale features. In this study, we proposed an advanced flood detection method called FWSARNet, which leveraged a deformable convolutional visual model with Sentinel-1 SAR images as its primary data source. This model centered around deformable convolutions as its fundamental operation and took inspiration from the structural merits of the Vision Transformer. Through the introduction of a modest number of supplementary parameters, it significantly extended the effective receptive field, enabling the comprehensive capture of intricate local details and spatial fluctuations within flood boundaries. Moreover, our model employed a multi-level feature map fusion strategy that amalgamated feature information from diverse hierarchical levels. This enhancement substantially augmented the model’s capability to encompass various scales and boost its discriminative power. To validate the effectiveness of the proposed model, experiments were conducted using the ETCI2021 dataset. The results demonstrated that the Intersection over Union (IoU) and mean Intersection over Union (mIoU) metrics for flood detection achieved impressive values of 80.10% and 88.47%, respectively. These results surpassed the performance of state-of-the-art (SOTA) models. Notably, in comparison to the best results documented on the official ETCI2021 dataset competition website, our proposed model in this paper exhibited a remarkable 3.29% improvement in flood prediction IoU. The experimental outcomes underscore the capability of the FWSARNet method outlined in this paper for flood detection using Synthetic Aperture Radar (SAR) data. This method notably enhances the accuracy of flood detection, providing essential technical and data support for real-world flood monitoring, prevention, and response efforts.

Permanent & Non-Permanent Water Identification Based on Image Fusion of Sentinel 1 & 2 Data (Case Study: Lamongan Regency)

The Earth’s surface consists of land and water. Surface water is separated into two groups: permanent water and temporary water. Flooding is temporary water that is often experienced in many places in Indonesia, including Lamongan Regency. In this area, at the beginning of 2022, seasonal floods caused many residents’ houses, agricultural land, and access roads to be submerged in water. This is due to its topography, which is characterized by lowlands and water. Land cover monitoring in large areas can be identified and analyzed using remote sensing. In this case, remote sensing is used to obtain information about surface water for separating permanent water and temporary water for comprehensive treatment in the long term. This study uses two primary sources of data: Sentinel-1 and Sentinel-2 satellite images from five years of observation from January 2018 to December 2022. Sentinel-1 is used to exploit its Synthetic Aperture Radar (SAR) sensor, which is sensitive to dielectric properties; thus, the water objects can be identified easily. Meanwhile, the Sentinel-2 satellite provides up to 10 meters of spatial-resolution satellite data. Processing was carried out using Image Fusion with Fuzzy Logic Min to merge the advantages of different data sources. The result of processing obtained that the probability of permanent water distribution in Lamongan Regency from 2018 to 2022 has differences in the total area every year. The total area of permanent water from Image Fusion is 12.769 km2. Meanwhile, the total area for flood inundation is 57.023 km2. A comparison of the processing results with comparative data is carried out for the calculation of the accuracy test. Overall accuracy for permanent water is 91%, with a kappa coefficient of 0.82. Then an overall accuracy for flood inundation obtained 90% with a kappa coefficient is 0.79. The result of this study is a permanent water map and flood inundation map of the Lamongan Regency, which is expected to be a reference in flood disaster mitigation activities.

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.

Long-term vertical-land-motion investigation with space and terrestrial geodetic techniques near San Leon, Texas, USA

Monitoring vertical land motion (VLM) along coastlines, which influences the dynamics of sea level changes in relation to the land, is a challenging task due to its inherent high spatiotemporal variability and limited availability of observations. This study aimed to investigate the rates, patterns, and drivers of land subsidence near the coastal town of San Leon, TX, United States, since the 1990s, utilizing a range of space and terrestrial geodetic techniques. These techniques included interferometric synthetic aperture radar (InSAR), global navigation satellite systems (GNSS), tide gauge (TG), and satellite radar altimetry (SRA). The small baseline subset (SBAS) InSAR method was adopted to process 254 images from three synthetic aperture radar (SAR) sensors, i.e., ERS-2 between 1995 and 1999, ALOS-1 PALSAR between 2006 and 2011, and Sentinel-1 between 2016 and 2020. The results from InSAR subsidence maps were verified by comparing with high-accuracy vertical positioning observations at ten continuously operating GNSS (cGNSS) stations. Within the study area, a special attention was given to the Eagle Point TG station where sea level has been significantly rising relative to the sinking land. Long-term time series of land subsidence at Eagle Point obtained from sea-level difference between TG and SRA observations were confirmed and compared against InSAR and the cGNSS station observations recorded in close proximity. Gaussian Process regression (GPR) was then employed to model the VLM processes at Eagle Point using: (1) the combined results of InSAR and sea-level difference (i.e., GPR 1), and (2) the InSAR results alone (i.e., GPR 2). A 0.9 mm/yr divergence was found between GPR 1 and GPR 2 models, indicating the potential to accurately estimate long-term VLM with InSAR standalone measurements even if multi-year observation gaps intermittently occur, especially for inland areas where measurement data from other geodetic techniques, such as GNSS, TG, and SRA, are not available. Further investigations suggest that land subsidence around Eagle Point since 1998 was related to anthropogenic activities such as hydrocarbon pumping from oil and gas wells that were situated in close proximity to the TG station.

Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques

Quantifying the forage biomass in pastoral systems can be used for enhancing farmers’ decision-making in precision management and optimizing livestock feeding systems. In this study, we assessed the feasibility of integrating Sentinel-1 and Sentinel-2 satellite imagery with machine learning techniques to estimate the aboveground biomass and forage quality of bale grazing and sacrificed grassland areas in Virginia. The workflow comprised two steps, each addressing specific objectives. Firstly, we analyzed the temporal variation in spectral and synthetic aperture radar (SAR) variables derived from Sentinel-1 and Sentinel-2 time series images. Subsequently, we evaluated the contribution of these variables with the estimation of grassland biomass using three machine learning algorithms, as follows: support vector regression (SVR), random forest (RF), and artificial neural network (ANN). The quantitative assessment of the models demonstrates that the ANN algorithm outperforms the other approaches when estimating pasture biomass. The developed ANN model achieved an R2 of 0.83 and RMSE of 6.68 kg/100 sq. meter. The evaluation of feature importance revealed that VV and VH polarizations play a significant role in the model, indicating the SAR sensor’s ability to perceive changes in plant structure during the growth period. Additionally, the blue, green, and NIR bands were identified as the most influential spectral variables in the model, underscoring the alterations in the spectrum of the pasture over time.

Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σVV0, σVH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σVH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σVH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σVV0, σVH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture.

Integrating Target and Shadow Features for SAR Target Recognition.

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.