Synthetic Aperture Radar Satellite Research Articles

Patterns and impacts of an unprecedented outbreak of bark beetles in Central Europe: A glimpse into the future?

Natural disturbances have significantly intensified across European forests, with bark beetle outbreaks being the most rapidly escalating disturbance type. Since 2018, the Czech Republic (Central Europe) has become a Europe's disturbance epicentre due to the unprecedented outbreak of spruce bark beetle Ips typographus in the forests dominated by Norway spruce Picea abies. Here we provide novel insights into the impacts and dynamics of this disturbance from 2016 to 2022. The investigation is based on annual forest change maps developed by the classification of optical and Synthetic Aperture Radar satellite imagery. We identified seven major outbreak foci across the country, where the outbreaks culminated between 2018 and 2021. Most of the outbreak waves exhibited a symmetric shape, characterized by a three-year build-up phase, a single culmination year, and the subsequent decline. The substantial proportion of spruce remaining in the outbreak areas after the culmination point implies that resource depletion is an improbable cause for the outbreak's retreat. In the year of retreat, the proportion of spruce in the forest ranged between 26% and 36% in most of the outbreak areas. The disturbance dynamics manifested a transition from the emergence of new tree mortality spots in the early outbreak phase to their short-range expansion, suggesting density-dependent changes in bark beetle dispersal during the studied period. The core disturbance zone, pinpointed in 2022, covered an area of 9,000 ​km2 and experienced a 38% loss in forest cover. Within this area, forest fragmentation increased significantly, leading to a greater forest patch complexity and reduced connectivity among the patches. The presented findings can serve as a glimpse into the future for other European regions, revealing the potential impacts of natural disturbances amplified by climate change.

Assessing the Morphometry of Pit Craters on Venus From Orbital Synthetic Aperture Radar Data

Assessing the Morphometry of Pit Craters on Venus From Orbital Synthetic Aperture Radar Data

Multi‐Temporal InSAR, GNSS and Seismic Measurements Reveal the Origin of the 2021 Vulcano Island (Italy) Unrest

AbstractLa Fossa Caldera at Vulcano (Italy) has been showing signs of unrest since September 2021. To investigate this phenomenon, we conducted an analysis of geodetic and seismological data from July to December 2021. In particular, we analyzed Multi Temporal Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data, showing a pronounced elliptical uplift signal, which we elaborated using analytical source modeling. Additionally, seismic data were used to identify seismicity associated with hydrothermal system activity and assess its temporal evolution. The results indicate that the observed deformation is consistent with the expansion of the hydrothermal system within the La Fossa Caldera. These findings align with the analysis of seismic data, revealing signals indicative of hydrothermal activity, such as Very Long Period events. The results suggest that the ongoing phenomenon since 2021 represents a hydrothermal unrest, similar to the one observed during the late 1970s to early 1990s.

A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet’s capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work.

DEEP AND MACHINE LEARNING FOR MONITORING GROUNDWATER STORAGE BASINS AND HYDROLOGICAL CHANGES USING THE GRAVITY RECOVERY AND CLIMATE EXPERIMENT (GRACE) SATELLITE MISSION AND SENTINEL-1 DATA FOR THE GANGA RIVER BASIN IN THE INDIAN REGION

Abstract. Accurate estimation of groundwater levels in river basins is paramount for hydro-geological research and sustainable water resource management. In this paper, we introduce a deep learning framework explicitly developed for precise groundwater level estimation in the Ganga River Basin. Leveraging the combined band information of Sentinel-1 synthetic aperture radar (SAR) and GRACE satellite data, our approach capitalizes on the trans-formative capabilities of Vision Transformers (ViT) and their variants, with a particular focus on Swin-Transformer variant enriched with Normalization Attention Modules (NAMs).To address the unique challenges of the Ganga River Basin, we curated a comprehensive dataset, forming a robust foundation for training computer vision models tailored to this distinct geographical region. Through rigorous experiments, our state-of-the-art Vision Transformers demonstrated significant potential in groundwater level estimation, with the Swin-Transformer NAM-based model achieving an outstanding Mean Absolute Error (MAE) of 1.2. These remarkable results surpass conventional methodologies and underscore the substantial advancements achieved through advanced transformer-based architectures in this domain. Moreover, this research contributes a robust dataset for future endeavours, fostering further advancements in groundwater estimation and related fields. This study represents a substantial step towards advancing sustainable groundwater utilization practices in the Ganga River Basin and beyond.

The Use of Satellite Synthetic Aperture Radar Imagery to Assist in the Monitoring of the Time Evolution of Challenging Coastal Environments: A Case Study of the Basilicata Coast

This study focuses on a very complex environment, namely the Ionian coast of the Basilicata region, Southern Italy, which includes different kinds of beaches, river mouths and built-up areas. This complex environment is used as a test case to analyze the time variability of the coastline using measurements that were remotely sensed by the satellite European Copernicus Synthetic Aperture Radar (SAR) mission. First, the accuracy of the coastline, extracted by the SAR, is discussed with respect to finer-spatial-resolution drone-based light detection and ranging (LIDAR) measurements. Then, a time series of SAR dual-polarimetric measurements acquired by the European Copernicus mission is used to discuss the time variability of the coastline of the area of interest in a time period spanning from 2015 to 2021. The experimental results show that the accuracy of the SAR-based coastline is better than 15 m, which is reasonably good precision for monitoring the erosion/accretion processes that characterize the area of interest at a moderate scale. The estimated time variability of the extracted coastline suggests a dominant erosion process, which is always within 60 m.

Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R2 = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ◦) for VV polarization, which is used as one of the inputs in universal Topp’s model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m3m-3) with R2 = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m3m-3 with R2 = 0.73 and RMSE=0.003 followed by 0.015-0.6 m3m-3 with R2 = 0.81 and RMSE=0.001 and 0.11-0.18 m3m-3 with R2 = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture.

Coseismic Deformation of the 2023 Türkiye Earthquake Doublet from Sentinel-1 InSAR and Implications for Earthquake Hazard

Abstract The 6 February 2023 Türkiye earthquake doublet occurred on the east Anatolian fault system, which marks the tectonic boundary between the Arabia plate and the Anatolian microplate. This earthquake doublet consists of the Mw 7.8 Pazarcik earthquake along the east Anatolian fault and the Mw 7.6 Çardak earthquake along the Savrun–Çardak fault. Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) satellite successfully imaged the surface deformation caused by this earthquake doublet. The pixel offset from cross correlation of two Synthetic Aperture Radar images complements the interferograms in mapping the surface ruptures and the near-field deformation. We inverted for a coseismic slip model in elastic half-space using the InSAR phase and the range offset data. The variance reduction of the inversion reaches ∼90%. The coseismic slip model shows that the 2023 Türkiye earthquake doublet are left-lateral strike-slip events. The peak slip is located near Nurhak in southern Türkiye along the Savrun–Çardak fault. From measuring discontinuities in the pixel offset images we found that the surface rupture length of the Pazarcik earthquake is ∼300 km and the surface rupture length of the Çardak earthquake is ∼100 km. To first order, the faults are dipping vertically. “Slip gaps” are identified by our modeling, and they might be the source regions of future large earthquakes.

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.

Rapid Saline Permafrost Thaw Below a Shallow Thermokarst Lake in Arctic Alaska

AbstractPermafrost warming and degradation is well documented across the Arctic. However, observation‐ and model‐based studies typically consider thaw to occur at 0°C, neglecting the widespread occurrence of saline permafrost in coastal plain regions. In this study, we document rapid saline permafrost thaw below a shallow arctic lake. Over the 15‐year period, the lakebed subsided by 0.6 m as ice‐rich, saline permafrost thawed. Repeat transient electromagnetic measurements show that near‐surface bulk sediment electrical conductivity increased by 198% between 2016 and 2022. Analysis of wintertime Synthetic Aperture Radar satellite imagery indicates a transition from a bedfast to a floating ice lake with brackish water due to saline permafrost thaw. The regime shift likely contributed to the 65% increase in thermokarst lake lateral expansion rates. Our results indicate that thawing saline permafrost may be contributing to an increase in landscape change rates in the Arctic faster than anticipated.

Capturing the Onset of Mountain Snowmelt Runoff Using Satellite Synthetic Aperture Radar

AbstractThe timing of snowmelt runoff is critical for water resource applications, but its spatiotemporal evolution remains poorly understood. We present a scalable approach to map snowmelt runoff onset using Sentinel‐1 synthetic aperture radar data for the past 8 years with 10 m spatial resolution and a median temporal resolution of 3.9 days. A systematic analysis of stratovolcanoes in the Western United States showed that snowmelt runoff onset is strongly dependent on elevation (r = 0.81), with a median runoff onset lapse rate of 4.9 days per 100 m of elevation gain. During the 2015 snow drought, we observed snowmelt runoff onset 25 days early relative to the 2015–2022 median. We document a median shift in snowmelt runoff onset of +2.0 days later in the year per year between 2016 and 2022. Our open‐source tools can be used to create snowmelt runoff onset maps anywhere on Earth.

An Accurate and Efficient BP Algorithm Based on Precise Slant Range Model and Rapid Range History Construction Method for GEO SAR

The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target’s movement, caused by the Earth’s rotation, is influenced by the Earth’s curvature. The back-projection (BP) algorithm has been proven to be a highly effective technique for precise imaging with GEO SAR by processing these specific echo signals. However, this approach necessitates considerable computational resources. Existing BP algorithms, such as the fast BP algorithm, do not consider the “Stop-and-Go” error present in GEO SAR. Consequently, we developed a Precise Slant Range Model that considers the motion of both the satellite and targets. The model incorporates velocity and acceleration factors to accurately represent the signal transmission from transmission to reception. Additionally, we propose a Rapid Range History Construction Method to lessen the computational burden of generating the three-dimensional range history array. By utilizing the Precise Slant Range Model and the Rapid Range History Construction Method, and employing parallel processing through aperture segmentation, we propose an Accurate and Efficient BP imaging algorithm suitable for GEO SAR applications. To validate its effectiveness, simulations were conducted using the parameters of a GEO SAR system. The results indicated that the proposed algorithm enhances the imaging quality of GEO SAR, reduces the processing time, and achieves high-precision rapid imaging, thereby improving operational efficiency.

Estimating mangrove aboveground biomass in the Colombian Pacific coast: A multisensor and machine learning approach

The Colombian Pacific Coast is renowned for its exceptional biodiversity and hosts vital mangrove ecosystems that benefit local communities and contribute to climate change mitigation. Therefore, estimating mangrove aboveground biomass (AGB) in this region is crucial for planning and managing these coastal forest covers, ensuring the long-term sustainability of the essential environmental services provided by the Colombian Pacific Coast (CPC). This study employed a spatial estimation approach to assess mangrove AGB, evaluating various parametric and non-parametric models using a multisensor combination and machine learning on the Google Earth Engine (GEE) platform within the CPC. Synthetic aperture radar (SAR) satellite imagery (ALOS-2/PALSAR-2, SRTM, NASADEM, and ALOSDSM) and optical data (Landsat 8) were utilized to quantify mangrove AGB in 2022 across the four departments of the CPC. The Random Forest model exhibited superior predictive performance compared to the other models evaluated, achieving values of R2 = 0.783, RMSE = 38.239 [Mg/ha], MAE = 27.409 [Mg/ha], and BIAS = 0.164. Our findings reveal that the mangrove AGB map for the CPC exhibits a mean ± standard deviation of 181.236 ± 28.939 [Mg/ha] across eight classes, ranging from 88.622 [Mg/ha] to 378.21 [Mg/ha]. This research provides valuable information to inform and strengthen various management strategies and decision-making processes for the mangrove forests of the CPC.

Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure

There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards this ambition. However, extracting meaningful information and interpreting the deformation patterns from InSAR data can be a challenging task. One approach to address this challenge is through the use of data science techniques. In this study, the representation of InSAR metadata as Entity Embeddings within a Deep Learning framework (EE-DL) is investigated for modelling the spatio-temporal deformation response. Entity embeddings are commonly used in natural-language-processing tasks. They represent discrete objects, such as words, as continuous, low-dimensional vectors that can be manipulated mathematically. We demonstrate that EE-DL can be used to predict anomalous patterns in the InSAR time series. To evaluate the performance of the EE-DL approach in SAR interferometry, we conducted experiments over a mining test site (Cadia, Australia), which has been subject to a TSF failure. This study demonstrated that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compared the results with deformation predictions from common baseline models, the Random Forest model and Gaussian Process Regression (GPR). Both EE-DL and GPR greatly outperform Random Forest. While GPR is also able to predict displacement patterns with millimetric accuracy, it detects a significantly lower number of anomalies compared to EE-DL. Overall, our study showed that EE-DL is a promising approach for building early-warning systems for critical infrastructures that use InSAR to predict ground deformations.

Satellite imagery in evaluating oil spill modelling scenarios for the Syrian oil spill crisis, summer 2021

The second-largest oil pollution incident in the Eastern Mediterranean Levantine basin, following the oil pollution crisis in Lebanon in 2006, is considered to be the oil leakage from the Syrian Baniyas power plant (summer 2021), during which 12,000 tons of oil were released. At the operational phase, the everyday predictions of oil drift were provided using the MEDSLIK and MEDSLIK-II models in the framework of an agreement between the Mediterranean Operational Network for Global Ocean Observing System (MONGOOS) and the Regional Marine Pollution Emergency Response Centre for the Mediterranean (REMPEC). In this work, we present a novel post-operational comprehensive model-based analysis, conducting a SAR validation in two model outputs: the MEDSLIK and the OpenDrift models. Each simulation is initiated with the operationally acquired EMSA-CSN and ESA SAR images. Moreover, the high-resolution met-ocean fields (CYCOFOS, SKIRON) are used to force the oil drift and transformation in both models. The spill was developed under the calm-wind conditions that prevailed during the incident. We found that the boundary sea currents developed on the periphery of the Lattakia eddies (anticyclonic and cyclonic) were responsible for the fast westward spreading of the oil spill offshore in the NE Levantine, the north-south pathway bifurcation, and re-landing of oil in the extended coastal area of Lattakia. Model outputs were validated against Synthetic-aperture radar (SAR) images with appropriate performance metrics, used for the first time, to assess the capacity of a reliable representation of oil spill drift. The intercomparison between the two oil spill models indicated that both models produce almost similar results, while their validation against the satellite SAR observations illustrates moderate accuracy.

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night.

Nyiragongo Crater Collapses Measured by Multi‐Sensor SAR Amplitude Time Series

AbstractCrater morphology undergoes rapid changes at active volcanoes, and quantifying these changes during volcanic unrest episodes is crucial for assessing volcanic activity levels. However, various limitations, including restricted crater access, cloud cover, haze, and intra‐crater eruptive activity, often impede regular optical or on‐site crater monitoring. To overcome these challenges, we utilize multi‐sensor satellite Synthetic Aperture Radar (SAR) imagery to generate dense time series of quantitative indicators for monitoring crater morphological changes. By combining images from diverse satellites and acquisition modes, we achieve high temporal resolution. Nevertheless, due to variations in acquisition geometries, direct image comparisons become impractical. To address this, we develop PickCraterSAR, an open‐access Python tool that employs basic trigonometry assumptions to measure crater radius and depth from SAR amplitude images in radar geometry. We apply our methodology to study the crater collapse associated with the May 2021 and January 2002 eruptions of Nyiragongo volcano. Following the 2021 collapse, we estimate the maximum depth of the crater to be 850 m below the rim, with a total volume of 84 ± 10 Mm3. Notably, the post‐2021 eruption crater was 270 m deeper but only 15%–20% more voluminous compared to the post‐2002 eruption crater. Additionally, we demonstrate that the 2021 crater collapse occurred progressively while a dike intrusion migrated southward as a consequence of the drainage of the lava lake system. Overall, our study showcases the utility of multi‐sensor SAR imagery and introduces PickCraterSAR as a valuable tool for monitoring and analyzing crater morphological changes, providing insights into the dynamics of volcanic activity.

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses

This study developed the European Space Agency (ESA) Setinel-1 Ground Range Detected (GRD) time series analysis model for monitoring floating debris in lake areas through Google Earth Engine Application Programming Interface. The study aims to monitor floating debris caused by heavy rainfall efficiently. Regarding water resources and water quality management, floating debris from multipurpose dams requires continuous monitoring from the initial generation stage. In the study, a Synthetic Aperture Radar (SAR) time series analysis model that is easy to identify water bodies was developed due to low accessibility in large areas. Although SAR satellite images could be used to observe inland water environments, debris detection on water surface surfaces has yet to be studied. For the first time, this study detected floating debris patches in a wide range of lakes from GRD imagery acquired by ESA’s Sentinel-1 satellite. It demonstrated the potential to distinguish them from naturally occurring materials such as invasive floating plants. In this study, the case of Daecheong Dam, in which predicted floating debris was detected after heavy rain using Sentinel-1 GRD data, is presented. It could quickly detect various floating debris flowing into dams used as a source of drinking water and serve as a reference for establishing a collection plan.