Sentinel-1 Synthetic Aperture Radar Research Articles

Multi-wave characteristics associated with January 15, 2022 Hunga-Tonga volcanic eruption: A global observation

The eruption of Hunga-Tonga Volcano on January 15, 2022 has stimulated a wide spectrum of atmospheric waves globally. To probe the surface deformation pattern, Sentinel-1 Synthetic Aperture Radar (SAR) data has been analyzed. It has been approximated that an overall area of about 2.47 square kilometres experienced deformation in conjunction with this event. To characterize the atmospheric wave propagation, barometric pressure data from 1814 stations distributed all around the globe have been examined. This study encompassed with the propagation characteristics of the waves over four zones including Indian and Polar regions for the first time using barometric data. Time-series observations indicate that the waves propagated globally multiple times. Within the Indian region, three minor arc passages and one major arc passage were identified. In Japan, two minor arc passages and one major arc were present. Conversely, in North America, both minor and major arc passages were detected, occurring a minimum of three times. Moreover, the attributes of these waves, such as their propagation speed and periodicity, were compared across these four regions. The estimated phase speed and periodicity fall within the ranges of approximately 291–314 m/s and 10–180 min, respectively including Polar Regions. These speed and periodicity measurements of the observed waves suggest that the dominant mode of wave propagation generated during the Tonga volcanic eruption is that of Lamb waves. In addition, a slower propagation phase speed of about 226.6 m/s was identified in Japan which corresponds to Pekeris mode of waves.

The 2022 Har Lake earthquake sequence highlights a complex fault system in the western Qilian Shan, northeastern Tibetan Plateau

SUMMARY The 2022 Har Lake earthquake sequence, which began in 2022 January and lasted for ∼70 d, jolted the Har Lake area, which is located in the western Qilian Shan, northeastern Tibetan Plateau. Two Mw > 5.5 earthquakes occurred during the earthquake sequence, among which the March 25 Mw 5.8 event is considered the largest event recorded in the area. However, determining the seismogenic faults of the earthquake sequence, as well as the detailed rupture features, is difficult due to the lack of geological data and near-field seismological observations. In this study, we use Sentinel-1 synthetic aperture radar (SAR) data to obtain the coseismic deformation field, identify possible ruptured faults and associated fault geometries, and further estimate detailed coseismic slip models of the two Mw > 5.5 earthquakes. The results show that the January 23 Mw 5.6 earthquake (Earthquake A) occurred on a N15° W-trending dextral-slip fault with a dip angle of ∼61°. For the March 25 Mw 5.8 earthquake (Earthquake B), the interferometric synthetic aperture radar (InSAR) data can be described by either an ∼N–S-trending dextral-slip fault or an ∼E–W-trending sinistral-slip fault. The ∼N–S-trending fault better describes the aftershock distribution, while the ∼E–W-trending model is more consistent with the regional geological setting. We suggest that the complex coseismic ruptures in the multiple-fault system are driven by widespread NE–SW-trending compression in the western Qilian Shan. This study demonstrates the importance of integrating geodetic and seismological observations to capture the full complexity of moderate earthquakes and further suggests potential seismic hazards in the Har Lake area.

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

ABSTRACT Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery

This study proposes a dual-branch encoder U-Net (DBU-Net) deep learning model to classify sea ice types based on synthetic aperture radar (SAR) images in the Beaufort Sea. The DBU-Net can segment multi-year ice (MYI), first-year ice (FYI), open water (OW), and leads on SAR images. We design a dual-branch encoder to fuse the polarization and the grey-level co-occurrence matrix (GLCM) information of SAR images to improve the model's classification capability. The model is subsequently fine-tuned using lead samples to identify leads. 24 Sentinel-1 SAR images acquired in the Beaufort Sea are utilized for model training and testing. The accuracy (Acc), mean intersection over union (mIoU), and kappa coefficient (Kappa) are employed as evaluation metrics. Experiments show that DBU-Net achieves 91.83%/0.841/0.849 in Acc/mIoU/Kappa in classifying MYI, FYI, and OW, significantly outperforming three traditional models based on support vector machine, random forest, or convolutional neural network. Compared with the original U-Net, the dual-branch encoder and the GLCMs improve 1.45%/4.4%/2.8% in Acc/mIoU/Kappa in MYI, FYI, and OW. Acc/mIoU/Kappa metrics of leads detection is 99.49%/0.801/0.754. Besides, 454 Sentinel-1 SAR images are fed into the optimal DBU-Net to generate 80 m sea ice products in the Beaufort Sea for winters 2018–2022. As the MYI draws wide attention and the FYI and MYI are complementary in the area during the Winter, we discuss the variation of MYI based on the generated sea ice products and explore the relationship between MYI's variation and the Beaufort High. We found that the MYI export in the 2018/19 winter was due to large summer sea ice remains and the abnormal sea ice motion caused by the southeast shifting Beaufort Atmospheric Pressure High (Beaufort High). The MYI import in the 2020/21 winter was due to a strong northward MYI import caused by the powerful Beaufort High.

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.

An Advanced Quality Assessment and Monitoring of ESA Sentinel-1 SAR Products via the CyCLOPS Infrastructure in the Southeastern Mediterranean Region

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA’s Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure’s reliability.

Shoreline position trends in the Niger Delta: analyzing spatial and temporal changes through Sentinel-1 SAR imagery

Various natural and human-induced processes operating across different temporal scales contribute to the spatial and temporal evolution of shoreline alterations. Deltaic coasts, particularly dynamic geomorphic systems, experience continuous changes at various spatial and temporal dimensions. This investigation underscores the effectiveness of Sentinel-1 Synthetic Aperture Radar (SAR) data in assessing long-term shifts in shoreline positions, focusing on the Niger Delta region. Out of 255 images from 2015 to 2020, 36 were selected based on high tide conditions for each year. This study has pioneered a straightforward and systematic approach rooted in Geographic Information Systems (GIS). This approach offers an accessible pathway for evaluating coastal changes in regions with limited data availability. To better comprehend the heterogeneity of change processes along the shoreline and to pinpoint their causes and mechanisms, we divided the shoreline into distinct sediment cells. This division adheres to a convenient and well-established methodology. This study identifies prevailing erosion trends outweighing accretion, with specific areas showing distinct erosion (cells II, IV, VI, VII, and VIII) and accretion (cells I, III, and V) patterns. Furthermore, coastal areas near river mouths exhibit a higher susceptibility to change. This approach offers a pathway for evaluating coastal changes in data-limited regions and can establish an effective monitoring system benefiting coastal modellers, scientists, and government agencies. It lays the groundwork for comprehensive coastal zone frameworks, facilitating improved planning and management strategies.

Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics

Arctic sea ice leads are linear fractures in pack ice, which provide narrow windows for the enhanced exchange of mass, energy, and momentum between the atmosphere and ocean. However, the parameterisations of the sub-grid (< 1 km) lead distribution and its associated dynamic processes are still challenging for numerical sea ice modelling. This study explores the dynamics governing lead formation in the Arctic Ocean using multiple data sources including Sentinel-1 synthetic aperture radar (SAR) images, buoy array, and atmospheric reanalysis. Random forest (RF) models trained based on the polarisation and texture features of SAR images were compared, and an optimal RF model sensitive to narrow leads was selected with implementation of screening based on lead geometry. As a case study, the lead distribution along the trajectory of buoy array in the central Arctic Ocean during the freezing period of 2018–2019 was obtained. An enhanced lead opening was observed as the sea ice motion swerved from clockwise circulation following the Beaufort Gyre (BG) to meridional advection when assembled into the Transpolar Drift (TPD). Synoptically, active lead-opening events associated with ice divergence were driven by episodic cyclones. We noticed a dramatic change in the backscatter signal over the leads caused by the growth of thin ice and formation of frost flowers. The identification and the evolution of narrow leads during the freezing season given in this study are conducive to increasing our understanding of the response mechanism of sea ice to atmospheric dynamic processes and supporting the numerical simulation of leads.

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.

Identification of mangrove forests using Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery in Medan Belawan District, Medan City

Mangroves are a forest ecosystem tolerant to salt (halophytic) and influenced by sea tides. Mangrove forests are producers of ecosystem services that all living things need. The use of remote sensing can be used to identify mangrove forests in large areas in a short time. Sentinel-1 is a satellite product widely used to monitor land cover, including mangrove forests. The research objective was to identify mangrove forests in the Medan Belawan District using Sentinel-1 satellite imagery. Land cover classification in digital images was done using supervised classification with the maximum likelihood and random forest method. The results showed that random forest methods are more accurate than the maximum likelihood of identifying mangrove forests in Medan Belawan. The kappa accuracy value of the digital satellite image classification using the random forest method is 85.09%, and the maximum likelihood method is 77.4%. Based on the random forest method, the mangrove forest area is 1,325.81 hectares or 43.64% of the Belawan District area.

Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions.

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods-LSM, R-Index, and P-NG-against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.

DEVELOPMENT OF METHODOLOGY FOR MONITORING AND ASSESSING THE NATIONAL GREENING PROGRAM USING OPTICAL AND SAR DATA

Abstract. Launched primarily to address the growing concern of environmental sustainability and to combat climate change, the National Greening Program (NGP) is the most ambitious reforestation program undertaken in the Philippines. Given the enormous number of resources invested in the program, it is crucial to monitor and assess the effectiveness of this initiative. To do this, the success of the selected NGP site in Zambales Province was evaluated using land cover maps, Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and MODIS, and Radar Vegetation Index (RVI) and Radar Forestation Degradation Index (RFDI) from Sentinel-1 Synthetic Aperture Radar (SAR) images. The index time series were processed in Google Earth Engine cloud computing platform. The findings indicate that the rise in vegetation density shown by the generally increasing trend in the NDVI and RVI was consistent with the increase in vegetation cover indicated by the land cover maps. This work contributes towards the field of environmental monitoring and resource management, with potential applications that extend beyond monitoring and assessment of the NGP and towards dealing with global issues regarding land use and environmental protection.

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.

Mapping tobacco planting areas in smallholder farmlands using Phenological-Spatial-Temporal LSTM from time-series Sentinel-1 SAR images

Phenological information on crop growth aids in identifying crop types from remote sensing images, but its incorporation into classification models is insufficiently exploited, especially in deep learning frameworks. This study presents a new model, Phenological-Temporal-Spatial LSTM (PST-LSTM), for mapping tobacco planting areas in smallholder farmlands using time-series Sentinel-1 Synthetic Aperture Radar (SAR) images. The PST-LSTM model is built on a multi-modal learning framework that fuses phenological information with deep spatial–temporal features. We applied the model to extract tobacco planting areas in Ninghua, Pucheng, and Shanghang Counties, in Fujian Province, and Luoping County in Yunnan Province, China. We compared PST-LSTM with existing methods based on phenological rules and Dynamic Time Warping (DTW) methods, and analyzed its strength in feature fusion. Results showed that our model outperformed these methods, achieving an overall accuracy (OA) of 93.16% compared to 86.69% and 85.93% for the phenological rules and DTW methods, respectively, in the Ninghua area. PST-LSTM effectively integrated time-series data with phenological information derived from different strategies at the feature level and performed better than existing feature fusion methods (based upon fuzzy sets) at the decision level. It also demonstrated a better spatial transferability than other methods when applied to different study areas, achieving an OA of 90.95%, 91.41%, and 80.75% for the Pucheng, Shanghang, and Luoping areas, respectively, using training samples from Ninghua. We conclude that PST-LSTM can effectively extract tobacco planting areas in smallholder farming from time-series SAR images and has the potential for mapping other crop types.

Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1

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

Sentinel-1-Based Soil Freeze–Thaw Detection in Agro-Forested Areas: A Case Study in Southern Québec, Canada

Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) data time series at two agro-forested study sites, St-Marthe and St-Maurice, in southern Québec, Canada. In total, 18 plots were instrumented to monitor soil temperature and derive soil freezing probabilities at 2 and 10 cm depths during 2020–21 and 2021–22. Three change detection algorithms were tested: backscatter differences (∆σ) derived from thawed reference (Delta), the freeze–thaw index (FTI), and a newly developed exponential freeze–thaw algorithm (EFTA). Various probabilistic mixed models were compared to identify the model and predictor variables that best predicted soil freezing probability. VH polarization backscatter signals processed with the EFTA and used as predictors in a logistic model led to improved predictions of soil freezing probability at 2 cm (Pseudo-R2 = 0.54) compared to other approaches. The EFTA could effectively address the limitations of the Delta algorithm caused by backscatter fluctuations in the shoulder seasons, resulting in more precise estimates of FT events. Furthermore, the inclusion of crop types as plot-level effects within the probabilistic model also slightly improved the soil freezing probability prediction at each monitored plot, with marginal and conditional R2 values of 0.59 and 0.61, respectively. The model accurately classified observed binary ‘frozen’ or ‘thawed’ states with 85.2% accuracy. Strong cross-level interactions were also observed between crop types and the EFTA derived from VH backscatter, indicating that crop type modulated the backscatter response to soil freezing. This study represents the first application of the EFTA and a probabilistic approach to detect frozen soil conditions in agro-forested areas in southern Quebec, Canada.

Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)

Abstract. Melting of sea ice in the Arctic has accelerated due to global warming. The Fram Strait (FS) serves as a crucial pathway for sea ice export from the Arctic to the North Atlantic Ocean. Monitoring sea ice drift (SID) in the FS provides insight into how Arctic sea ice responds to the climate change. The SID has been retrieved from Sentinel-1 synthetic aperture radar (SAR), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and further exploration is needed for the retrieval of SID using optical imagery. In this paper, we retrieve SID in the FS using the Chinese HaiYang1-D (HY-1D) satellite equipped with the Coastal Zone Imager (CZI). A multi-template matching technique is employed to calculate the cross-correlation, and subpixel estimation is used to locate displacement vectors from the cross-correlation matrix. The dataset covering March to May 2021 was divided into hourly and daily intervals for analysis, and validation was performed using Copernicus Marine Environment Monitoring Service (CMEMS) SAR-based product and International Arctic Buoy Programme (IABP) buoy. A comparison with the CMEMS SID product revealed a high correlation with the daily interval dataset; however, due to the spatial and temporal variability in the sea ice motion, differences are observed with the hourly interval dataset. Additionally, validation with the IABP buoys yielded a velocity bias of −0.005 m s−1 and RMSE of 0.031 m s−1 for the daily interval dataset, along with a flow direction bias of 0.002 rad and RMSE of 0.009 rad, respectively. For the hourly interval dataset, the velocity bias was negligible (0 m s−1), with a RMSE of 0.036 m s−1, while the flow direction bias was 0.003 rad, with a RMSE of 0.010 rad. In addition, during the validation with buoys, we found that the accuracy of retrieving the SID flow direction is distinctly interrelated with the sea ice displacement.

Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop–rice cropping systems using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang-Gan-Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites, with an overall accuracy of 91.92%. There were 7348 km2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one-tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.