Sentinel-1 Synthetic Aperture Radar Research Articles

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

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

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

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

Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region

An extensive number of farmlands in the Poyang Lake region of China have been submerged due to the impact of flood disasters, resulting in significant agricultural economic losses. Therefore, it is of great importance to conduct the long-term temporal monitoring of flood-induced water body changes using remote sensing technology. However, the scarcity of optical images and the complex, fragmented terrain are pressing issues in the current water body extraction efforts in southern hilly regions, particularly due to difficulties in distinguishing shadows from numerous mountain and water bodies. For this purpose, this study employs Sentinel-1 synthetic aperture radar (SAR) data, complemented by water indices and terrain features, to conduct research in the Poyang Lake area. The results indicate that the proposed multi-source data water extraction method based on microwave remote sensing data can quickly and accurately extract a large range of water bodies and realize long-time monitoring, thus proving a new technical means for the accurate extraction of floodwater bodies in the Poyang Lake region. Moreover, the comparison of several methods reveals that CAU-Net, which utilizes multi-band imagery as the input and incorporates a channel attention mechanism, demonstrated the best extraction performance, achieving an impressive overall accuracy of 98.71%. This represents a 0.12% improvement compared to the original U-Net model. Moreover, compared to the thresholding, decision tree, and random forest methods, CAU-Net exhibited a significant enhancement in extracting flood-induced water bodies, making it more suitable for floodwater extraction in the hilly Poyang Lake region. During this flood monitoring period, the water extent in the Poyang Lake area rapidly expanded and subsequently declined gradually. The peak water area reached 4080 km2 at the height of the disaster. The severely affected areas were primarily concentrated in Yongxiu County, Poyang County, Xinjian District, and Yugan County.

Monitoring maize lodging severity based on multi-temporal Sentinel-1 images using Time-weighted Dynamic time Warping

Lodging is one of the most important stresses in maize growth, which affects the yield, quality and mechanical harvesting ability of maize. Monitoring the lodging range and severity of large-scale maize by satellite remote sensing technology can support agricultural insurance claims, planting structure adjustment and field management. The purpose of this study is to investigate the application of Synthetic Aperture Radar (SAR) remote sensing images in monitoring the severity of maize lodging at the county scale. A time series feature dataset was obtained from multi-temporal Sentinel-1 SAR images before and after lodging. The sensitive features of maize lodging severity were screened by Jeffreys-Matusita (J-M) distance. The study compared the performance of three different methods for monitoring maize lodging severity, including Time-weighted Dynamic Time Warping (TwDTW), random forest (RF) and minimum distance (MinD). The results showed that all time phases of SAR images selected in the study had an important contribution to the differentiation of maize lodging severities. Among all features, the VH (Vertical Send and Horizontal Receive) time series features composed of five time-phase had better discrimination for different maize lodging severities. The accuracy of extracting maize lodging severities based on TwDTW was 76% and the Kappa coefficient was 0.70, which was higher than RF and MinD. Thus, by utilizing the multi-time phase features of Sentinel-1, we can effectively monitor the grades of maize lodging at the county scale.

Sentinel-1 InSAR-derived land subsidence assessment along the Texas Gulf Coast

Mapping large-scale coastal subsidence is significant in providing valuable support to decision-making stakeholders to recognize impacts of potential natural disasters. However, this task presents significant challenges due to its highly complex nature of the spatial–temporal variability. Recent advances in the observation capability of synthetic aperture radar (SAR) missions and the processing algorithms of interferometric SAR (InSAR) techniques have made it possible to efficiently map large-scale subsidence from space. This study utilized the persistent scatterer (PS) InSAR method to process four swathes of Sentinel-1 SAR images captured between 2016 and 2022 to map large-scale subsidence along the Texas coastline. The subsidence map, encompassing over 1.7 million PS points, was calibrated/validated with observations of 115 continuously operating global navigation satellite system (cGNSS) stations. Nineteen subsiding hotspots were identified and potential subsidence drivers were analyzed, including hydrocarbon extraction (HE), groundwater withdrawal (GW), and salt domes. The results suggest that HE activities were the primary driver of observed subsidence in the Coastal Bend and South Texas, as well as in the Southeast Texas regions. In the Houston-Galveston Area, subsidence appears to be influenced by a combination of GW, HE, and salt dome movements. Within 50 km of the Texas shorelines, subsidence rates were approximately −1.0 mm/yr within the coastal vicinity, while inland areas experience a gradually increasing trend. Specific coastal areas, such as Corpus Christi, Freeport, Seabrook, San Leon, and others, may face elevated flooding risks during high tide and storm events with the sea-level rise trend up to 24.0 mm/yr in relation to coastal subsiding land.

A generic framework for glacial lake outburst flood investigation: A case study of Zalai Tsho, Southeast Tibet

Glacial lake outburst floods (GLOFs) are one of the most severe natural disasters, resulting in enormous socio-economic losses as well as threatening downstream settlements and infrastructure in the alpine region. Understanding historical GLOFs benefits disaster reduction and landscape conservation, but many GLOF events remain unreported or lack critical attributes. In this study, a novel framework was proposed to detect historical GLOFs and populate related attributes using a combination of Landsat, Sentinel-2 and Planet optical images, Sentinel-1 Synthetic Aperture Radar (SAR) images, field surveys, and documentation. The framework was implemented successfully on the case of Zalai Tsho, which revealed a previously unknown GLOF with critical attributes including the associated lake evolution, the flood process, and the impacts. The Zalai Tsho GLOF event in Bomi County, Southeast Tibet, occurred on July 7, 2018. Zalai Tsho emerged in 1994, and its surface area increased by 178% between 1994 and 2018, followed by a complete disappearance after the dam failure. A total water volume of 2.7 ± 0.2 × 105 m3 was released from Zalai Tsho to generate a peak discharge of 558.4 m3/s estimated by hydrological models, which destroyed a bridge, 23.2 ha of woodland, and 1.7 ha of farmland. The Zalai Tsho GLOF was initiated by intense melting waters, superimposed on water from an upstream supraglacial lake, eventually resulted in serious socio-economic losses and long-term environmental effects. Post-GLOF conservation projects play a positive role in protecting residents against flooding, while soil erosion has been exacerbated in certain areas without artificial countermeasures. This study provided an up-to-date, prototype framework for the identification of historical GLOF events to enrich the GLOF dataset and contributed to the risk assessment of GLOF and landscape conservation.

Wind field reconstruction based on dual-polarized synthetic aperture radar during a tropical cyclone

ABSTRACT A wind field reconstruction method for dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) synthetic aperture radar (SAR) images collected during tropical cyclones (TCs) that does not require external information is proposed. Forty S-1 images acquired in interferometric-wide (IW) and extra-wide (EW) modes during the Satellite Hurricane Observation Campaign in 2015–2022 were collected. Stepped-frequency microwave radiometer (SFMR) observations made onboard the National Oceanic and Atmospheric Administration’s hurricane aircraft are available for 13 images. The geophysical model functions, namely VV-polarized C-SARMOD and cross-polarized S-1 IW/EW mode wind speed retrieval model after noise removal (S1IW.NR/S1EW.NR), were employed to invert the wind fields from the collected images. TC wind fields were reconstructed based on SAR-derived winds, enhancing TC intensity representation in the VV-polarized SAR retrievals and minimizing the error of the VH-polarized SAR retrievals at the sub-swath edge. The wind speeds retrieved from the SAR IW image were validated against the remote-sensing products from the soil moisture active passive (SMAP) radiometer, yielding a root mean squared error (RMSE) of approximately 4.3 m s−1, which is slightly smaller than the RMSE (4.4 m s−1) for the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Sea (IFREMER). However, the CyclObs wind product has better performance than the approach proposed in this paper for the S-1 EW mode. Moreover, the RMSE of the wind speed between SAR-derived wind speed obtained using the proposed approach and the CyclObs wind product is within 3 m s−1 in all flow directions clockwise relative to north centered on the TC’s eye. This study provides an alternative method for TC wind retrieval from dual-polarized S-1 images without suffering saturation problem and external information; however, the pattern of the wind field around the TC’s eye needs to be further improved, especially at the head and back of the TC’s eye.

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

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

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

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

Evaluating SAR Radiometric Terrain Correction Products: Analysis-Ready Data for Users

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging from ecosystem to hazard monitoring. Various open-source software packages exist to create RTC products from Single Look Complex (SLC) or Ground Range Detected (GRD) level SAR data, including the Interferometric SAR Computing Environment (ISCE), and the Sentinel-1 Toolbox from the European Space Agency (SNAP 8). Despite the growing availability of RTC software solutions, little work has been performed to identify differences between RTC products generated using different software packages. This work evaluates several Sentinel-1 RTC products and two other Sentinel-1 Analysis Ready Data (ARD) to address the following questions: (1) Which software provides the most accurate RTC product? and (2) how appropriate for analysis are other non-RTC products that are readily available? The RTCs are produced with GAMMA, ISCE-2, and SNAP 8. The other two ARD products evaluated consisted of an angular-based radiometric slope correction produced in Google Earth Engine (GEE) following Vollrath et al., and the Sentinel-1 GRD product. Products are evaluated across 10 sites in a single image approach for (1) radiometric calibration, (2) geometric corrections, and for (3) geolocation quality. In addition, time-series stacks over two sites representing varied terrain and ecosystems are evaluated. The GAMMA-derived RTC product implemented by the Alaska Satellite Facility (ASF) is used as a reference for some of the time-series metrics. The results provide direct guidance and recommendations about the quality of the RTC and ARD products obtained from open source methods. The results indicate that it is not recommended to use the GRD product with no radiometric or geometric corrections for any applications given low performance in multiple metrics. The radiometric calibration and geometric corrections have overall good performance for all open-source solutions, only the non-RTC products (Vollrath et al. and GRD) portray some significant variances in steep terrain. The geolocation assessment indicated that the GRD product has the most significant displacement errors, followed by SNAP 8 with Digital Elevation Model (DEM) matching, and ISCE-2. RTCs created without DEM-matching performed better for both GAMMA and SNAP 8. The time-series results indicate that SNAP 8 products align more closely to GAMMA products than other open-source software in terms of radiometric and geometric quality. This understanding of software performance for SAR image processing is key to designing the affordable and scalable solutions needed for the operational application of SAR Sentinel-1 data.

Synthetic aperture radar polarised backscattering behaviour in partially inundated agricultural fields

ABSTRACT A methodology for classifying rainfed paddy fields based on their hydrological conditions is lacking. This study analysed the behaviour of synthetic aperture radar (SAR) backscatter coefficients at each polarisation and cross-polarisation ratio index in a rainfed rice paddy field with a partially waterlogged surface. The SAR polarisations used were the VH and VV of the C-band SAR Sentinel-1 and the HV and HH of the L-band SAR Palsar-2. The relationship between backscatter coefficient and terrains including topographic categories and local relative elevation, which affect waterlogging conditions in rainfed paddy fields, were evaluated. The VV of C-band, and HH and HV of L-band showed different patterns in the time series variation according to the topographic categories. We observed that the combination of L-band HH and HV could be used to assess waterlogging conditions because they differ depending on variations in local elevation. The study results suggest that future studies can evaluate the microtopography and associated local hydrological environment using the combination of L-band HH and HV backscatter coefficient.

Reconstructing flood level timeseries at seasonal wetlands in Ireland using Sentinel-1

Wetlands are vital habitats which play a critical role in global biodiversity. Their functioning is intrinsically linked with their hydrological regime and so the monitoring of water level data is critical to their management and conservation. However, monitoring the hydrology of wetlands can be challenging. Their large number and wide distribution can make them impractical to monitor using traditional field instrumentation. In this context, this paper presents a novel approach for monitoring water levels in gauged and ungauged seasonally flooded wetlands using Sentinel-1 Synthetic Aperture Radar (SAR) data. The methodology, which was fully automated, used multitemporal sequences of SAR imagery to reconstruct hydrometric data at 44 seasonally flooded wetland sites across the Republic of Ireland. The procedure downloaded, processed and checked the suitability of SAR images for water classification. Suitable images were classified and filtered to remove common sources of misclassification, and the flood area in each image was cross-referenced against a predefined stage-area relationship (derived from a digital terrain model) to determine water level. The methodology was calibrated using observed field data and performance evaluated in two ways: 1) generic calibration whereby the process was optimised collectively to achieve the best accuracy for ungauged sites, and 2) site-specific calibration whereby the process was optimised for each site individually to achieve the best possible accuracy for each gauged site. The generic calibration yielded an average Nash-Sutcliffe Efficiency of 0.80 for both calibration and validation datasets while the site-specific optimisation produced results of 0.87 for both calibration and validation datasets. Results show the methodology was capable of accurately reproducing water levels in easonal wetlands for floods as small as 3 Ha. This demonstrates its potential as a viable tool for the remote monitoring of ecologically significant wetlands, and a means of providing the observational data necessary for their long-term sustainable management.

Time-series Sentinel-1A SAR remote sensing of rice planting methods in Ebonyi State, Nigeria

Timely and reliable agricultural data are essential to monitoring the implementation of rice intervention programs and policies aimed at sustainably reducing hunger and poverty. The difficulties associated with traditional data-gathering methods have made using satellite imagery for data acquisition imperative due to its coverage of large agricultural areas, high repetitive coverage, and cost-effectiveness. However, the use of satellite images for rice research in Nigeria is hampered by the peculiarities associated with methods of rice crop establishment, where manual transplanting and direct seeding methods are practiced in single rice plots due to financial constraints. To examine the effect of different planting methods on sensor signals, we analysed the temporal variation of Sentinel-1 synthetic aperture radar (SAR) data. Also, the relationship between SAR backscatter coefficients and field-measured rice plant parameters (wet biomass and plant height) as a function of planting methods was determined in contrast to similar studies where only the temporal variations in the planting methods were investigated. Multi-temporal, dual-polarised C-band Sentinel-1A backscatter data at 20 m spatial resolution and field measurements were simultaneously acquired every 12 days over the study area during the rainy season, from August to December 2019, in Ebonyi state, Nigeria. Average backscatter values were derived per sampling rice plot, with the temporal profiles of rice SAR backscatter plotted to show variability due to planting methods. Simple regression analysis determined the relationships between measured rice parameters and VH (Vertical transmit, Horizontal receive) and VV (Vertical transmit, Vertical receive) polarisation backscatter coefficients. Results revealed variations in the temporal profile and responses of transplanted and direct-seeded rice fields at different crop growth stages. Regression analysis showed that VH and VV backscatter values for transplanted rice were significantly related to wet rice biomass (R2 = 0.6; 0.51) and rice plant height (R2 = 0.5; 0.59). Conversely, wet biomass (R2 = 0.24; 0.19) and plant height (R2 = 0.18; 0.2) were reported for broadcasted rice for VH and VV polarisations, respectively. These findings show that significant differences exist in the SAR backscatter responses of these crop establishment methods, affecting the retrieved data's accuracy and reliability. Therefore, a cautious approach is advocated in using SAR data from small rice plots with multiple planting methods.

Monitoring selective logging intensities in central Africa with sentinel-1: A canopy disturbance experiment

Forest degradation is a major threat to tropical forests, and effective monitoring using remotely sensed data is subject to significant challenges. In particular, consistent methods for detecting subtle changes in the forest canopy structure caused by selective logging are lacking. Here, using a unique dataset collected in southeastern Cameroon, covering over 22,000 ha of monthly harvesting areas, >6000 locations of harvested trees, and an independent canopy gap dataset developed from an uninhabited aerial vehicle flight (UAV; RGB camera) of approximately 1500 ha, a new method was designed to monitor canopy disturbance and logging intensity in Central Africa. Using Sentinel-1 synthetic aperture radar (SAR) data, the method was conceptualised using a two-step, two-scale approach, which better matched logging practices. First, (non-)harvesting activity areas were identified using textural indices at a spatial resolution of 300 m (step 1), and within these harvesting activity areas, canopy gaps were detected at a resolution of 10 m (step 2). Both steps were based on monthly differences in the Sentinel-1 SAR time series computed using the average of the 12 months preceding and the average of the three months following the month of interest. This method identified harvesting activity areas (step 1 at 300 m resolution) of over 12,004 km2 with high accuracy (omission and commission errors for both classes ≤0.05) and, within them, detected canopy gaps (step 2 at 10 m resolution) with a global accuracy of 0.89. Although some canopy gaps were subject to omission and commission errors (0.39 and 0.05, respectively), this method yielded better results than other available approaches. Compared to the UAV canopy gaps, this method detected most of the small gaps (≤ 500 m2), which represent 80% of all disturbed areas, whereas other available approaches missed at least 70% of these and consequently missed most of the disturbance events occurring in a selectively logged forest. Furthermore, the predictions were correlated with logging intensity, i.e., the number of trees and volume cut per hectare, which are two important criteria for assessing the sustainability of logging activities. This two-step two-scale method for short-term, monthly monitoring of logging disturbances and intensity has strong practical implications for forest administration and certification bodies in Central Africa.

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data

Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

Detecting Wetlands within the Gomoa East District of Ghana through the Lenses of Sentinel-1 SAR Data Using Google Earth Engine for Ecosystem Conservation and Water Resource Management

This study presents a comprehensive analysis of wetlands within The Gomoa East District of Ghana using the powerful synergy of Sentinel-1 Synthetic Aperture Radar (SAR) data and the Google Earth Engine platform. The primary objective was to assess the status of wetlands in the region and evaluate their implications for ecosystem conservation and water resource management. Through the integration of multi-temporal SAR data, the study identified wetland areas and changes in their spatial distribution over time. Notably, the results revealed a concerning degradation of wetlands, with a loss of 260040 square meters in wetland areas, underscoring the critical need for conservation efforts. The study showcases the potential of Sentinel-1 SAR data and Google Earth Engine as valuable tools for monitoring wetlands, emphasizing their pivotal role in environmental sustainability and community well-being. Conservation strategies are imperative to safeguard these vital ecosystems and ensure a sustainable future. This research contributes to the knowledge base for wetland preservation and supports informed decision-making for the environmental management and ecological health of Gomoa East District in Ghana.

A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images

High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016–2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies.

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

ABSTRACT Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification.

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

ReCuSum: A polyvalent method to monitor tropical forest disturbances

Change detection methods based on Earth Observations are increasingly used to monitor rainforest in the intertropical band. Until recently, deforestation monitoring was mainly based on remotely sensed optical images which often face limitations in humid tropical areas due to frequent cloud coverage. This leads to late detections of disturbance events. Since the launch of Sentinel-1 acquiring images with a revisit time of 12 days and a spatial resolution of 5 × 20 m in Brazil, Synthetic Aperture Radar (SAR) images have been increasingly used to monitor deforestation. In this study, we propose a multitemporal version of the change detection method we previously applied to timeseries of Sentinel-1 SAR images, to monitor deforestation/degradation in the Congo rainforest. Our approach is based on a Cumulative Sum (CuSum) method combined with a spatial recombination of Critical Thresholds (CuSum cross-Tc). The newly developed multitemporal CuSum method (ReCuSum) was applied to a time-series of 82 dual polarization (VH, VV) Ground Range Detected (GRD) Sentinel-1 images acquired in the Para State, in the Brazilian Amazonia, from 29/09/2016 to 01/07/2019. The ReCuSum method consists of iteratively applying the CuSum cross-Tc to monitor multiple changes in a time-series by splitting the time-series at each date of detected change and by independently iterating over the time periods resulting from the splits. The number of changes in the time-series was then analysed according to the vegetation type (Forest, non-forest vegetation) determined by visual inspection of optical Sentinel-2 image and PlanetScope monthly mosaic. This showed a difference between non-forest vegetation and forested areas. A threshold based on the number of changes (Tnbc) was then developed to differentiate forest from non-forest disturbances. The ability to monitor non-forest vegetation was analysed: the CuSum cross-Tc detected up to 90% of the total non-forest vegetation area over the study region in the past period. After removing past disturbances and past non-forest vegetation, then removing the pixels covered with non-forest vegetations based on Tnbc, the application of the ReCuSum led to a precision of 81%, a recall of 68%, a kappa coefficient of 0.72 and a F1- score of 0.74 in forest disturbance monitoring. According to these results, ReCuSum applied to Sentinel-1 time-series of images can be used for efficient forest disturbance monitoring and for generating a forest / non-forest map after the application of newly developed post-processing steps. Sentinel-1 imagery can be used for both Forest / Non-forest mapping and for forest disturbance detection. ReCuSum was released as an open-source GIT project available at: https://forgemia.inra.fr/bertrand.ygorra/cusum-deforestation_monitoring.