Sentinel-1 Sentinel-1 Synthetic Aperture Radar Data Research Articles

Monitoring Cover Crop Biomass in Southern Brazil Using Combined PlanetScope and Sentinel-1 SAR Data

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management.

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

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

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

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

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

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

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.

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.

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.

Flood cascading on critical infrastructure with climate change: A spatial analysis of the extreme weather event in Xinxiang, China

Floods caused by extreme weather events and climate change have increased in occurrence and severity all over the world, resulting in devastation and disruption of activities. Researchers and policy practitioners have increasingly paid attention to the role of critical infrastructure (CI) in disaster risk reduction, flood resilience and climate change adaptation in terms of its backbone functions in maintaining societal services in hazard attacks. The analysed city in this study, Xinxiang (Henan province, China), was affected by an extreme flood event that occurred on 17–23 July 2021, which caused great socio-economic losses. However, few studies have focused on medium-sized cities and the flood cascading effects on CI during this event. Therefore, this study explores the damages caused by this flooding event with links to CI, such as health services, energy supply stations, shelters and transport facilities (HEST infrastructure). To achieve this, the study first combines RGB (red, green blue) composition and supervised classification for flood detection to monitor and map flood inundation areas. Second, it manages a multiscenario simulation and evaluates the cascading effects on HEST infrastructure. Diverse open-source data are employed, including Sentinel-1 synthetic aperture radar (SAR) data and Landsat-8 OIL data, point-of-interest (POI) and OpenStreetMap (OSM) data. The study reveals that this extreme flood event has profoundly affected croplands and villagers. Due to the revisiting period of Sentinel-1 SAR data, four scenarios are simulated to portray the retreated but ‘omitted’ floodwater: Scenario 0 is the flood inundation area on 27 July, and Scenarios 1, 2 and 3 are built based on this information with a buffer of 50, 100 and 150 m outwards, respectively. In the four scenarios, as the inundation areas expand, the affected HEST infrastructure becomes more clustered at the centre of the core study area, indicating that those located in the urban centre are more susceptible to flooding. Furthermore, the affected transport facilities assemble in the north and east of the core study area, implying that transport facilities located in the north and east of the core study area are more susceptible to flooding. The verification of the flood inundation maps and affected HEST infrastructure in the scenario simulation support the series method adopted in this study. The findings of this study can be used by flood managers, urban planners and other decision makers to better understand extreme historic weather events in China, improve flood resilience and decrease the negative impacts of such events on HEST infrastructure.

Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia

Abstract. Snowmelt runoff serves both human needs and ecosystem services and is an important parameter in operational forecasting systems. Sentinel-1 synthetic-aperture-radar (SAR) observations can estimate the timing of melt within a snowpack; however, these estimates have not been applied on large spatial scales. Here we present a workflow to combine Sentinel-1 SAR and optical data from Landsat-8 and Sentinel-2 to estimate the onset and duration of snowmelt in the La Joie Basin, a 985 km2 watershed in the southern Coast Mountains of British Columbia. A backscatter threshold is used to infer the point at which snowpack saturation occurs and the snowpack begins to produce runoff. Multispectral imagery is used to estimate snow-free dates across the basin to define the end of the snowmelt period. SAR estimates of snowmelt onset form consistent trends in terms of elevation and aspect on the watershed scale and reflect snowmelt records from continuous snow water equivalence observations. SAR estimates of snowpack saturation are most effective on moderate to low slopes (< 30∘) in open areas. The accuracy of snowmelt duration is reduced due to persistent cloud cover in optical imagery. Despite these challenges, snowmelt duration agrees with trends in snow depths observed in the La Joie Basin. This approach has high potential for adaptability to other alpine regions and can provide estimates of snowmelt timing in ungauged basins.

Monitoring of Paddy and Maize Fields Using Sentinel-1 SAR Data and NGB Images: A Case Study in Papua, Indonesia

This study addresses the question of how to evaluate the growth stage of food crops, for instance, paddy (Oryza sativa) and maize (Zea mays), from two different sensors in selected developed areas of Papua Province of Indonesia. Level-1 Ground Range Detected (L1 GRD) images from Sentinel-1 Synthetic Aperture Radar (SAR) data were used to investigate the growth of paddy and maize crops. An NGB camera was then used to obtain the Green Normalized Difference Vegetation Index (GNDVI), and the Enhanced Normalized Difference Vegetation Index (ENDVI) as in situ measurement. Afterwards, the results were analyzed based on the Radar Vegetation Index (RVI) and the Vertical-Vertical (VV) and Vertical Horizontal (VH) band backscatters at incidence angles of 30.55°–45.88°, and 30.59°–46.16° in 2021 and 2022, respectively. The findings showed that Sigma0_VV_db and sigma0_VH_db had a strong correlation (R2 above 0.900); however, polarization modification is required, specifically in the maize field. The RVI calculated and backscatter changes in this study were comparable to the in situ measurements, specifically those of paddy fields, in 2022. Even though the results of this study were not able to prove the RVI values from the two relative orbits (orbit31 and orbit155) due to the different angle incidences and the availability of the Sentinel-1 SAR data set over the study area, the division of SAR image data based on each relative orbit adequately represents the development of crops in our study areas. The significance of this study is expected to support food crop security and the implementation of development plans that contribute to the local government’s goals and settings.

Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique

Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, the country became the first one in southern Africa to be hit by two cyclones in the same raining season. Aiming to provide the local authorities with tools to yield better responses before and after any disaster event, and to mitigate the impact and support in decision making for sustainable development, it is fundamental to continue investigating reliable methods for disaster management. In this paper, we propose a fully automated method for flood mapping in near real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in the Beira municipality and Macomia district. The procedure exploits the processing capability of the Google Earth Engine (GEE) platform. We map flooded areas by finding the differences of images acquired before and after the flooding and then use Otsu’s thresholding method to automatically extract the flooded area from the difference image. To validate and compute the accuracy of the proposed technique, we compare our results with the Copernicus Emergency Management Service (Copernicus EMS) data available in the study areas. Furthermore, we investigated the use of a Sentinel-2 multi-spectral instrument (MSI) to produce a land cover (LC) map of the study area and estimate the percentage of flooded areas in each LC class. The results show that the combination of Sentinel-1 SAR and Sentinel-2 MSI data is reliable for near real-time flood mapping and damage assessment. We automatically mapped flooded areas with an overall accuracy of about 87–88% and kappa of 0.73–0.75 by directly comparing our prediction and Copernicus EMS maps. The LC classification is validated by randomly collecting over 600 points for each LC, and the overall accuracy is 90–95% with a kappa of 0.80–0.94.

Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban development. Earth Observation (EO) is a valuable data source to cover this demand. In particular, Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions offer new opportunities to map built-up areas on a global scale. Using Sentinel-2 images, recent urban mapping efforts achieved promising results by training Convolutional Neural Networks (CNNs) on available built-up data. However, these results strongly depend on the availability of local reference data for fully supervised training or assume that the application of CNNs to unseen areas (i.e. across-region generalization) produces satisfactory results. To alleviate these shortcomings, it is desirable to leverage Semi-Supervised Learning (SSL) algorithms that can take advantage of unlabeled data, especially because satellite data is plentiful. In this paper, we propose a novel Domain Adaptation (DA) approach using SSL that jointly exploits Sentinel-1 SAR and Sentinel-2 MSI to improve across-region generalization for built-up area mapping. Specifically, two identical sub-networks are incorporated into the proposed model to perform built-up area segmentation from SAR and optical images separately. Assuming that consistent built-up area segmentation should be obtained across data modality, we design an unsupervised loss for unlabeled data that penalizes inconsistent segmentation from the two sub-networks. Therefore, we propose to use complementary data modalities as real-world perturbations for consistency regularization. For the final prediction, the model takes both data modalities into account. Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements (F1 score 0.694) over fully supervised learning from Sentinel-1 SAR data (F1 score 0.574), Sentinel-2 MSI data (F1 score 0.580) and their input-level fusion (F1 score 0.651). To demonstrate the effectiveness of DA, we also performed a comparison with two state-of-the-art products, namely GHS-BUILT-S2 and WSF 2019, on the test set. The comparison showed that our model is capable of producing built-up area maps with comparable or even better quality than the state-of-the-art global human settlement maps. Therefore, the multi-modal DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale.

Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine

Accurate information on the dynamic change of wet-snow facies (WSF) in Greenland is important in investigating the mass balance of Greenland ice sheets (GrIS) and assessing global sea level rise. In this study, we proposed a rapid WSF extraction algorithm, referred to as difference method, for extracting WSF at a continental scale and with high spatial resolution using the Sentinel-1 synthetic Aperture radar (SAR) data available on Google Earth Engine (GEE). We used this method to extract the WSF in Greenland from May to October in recent 6 years. Accuracy of the extracted results was evaluated using air temperature data from Greenland Automatic Weather Station (AWS). Results suggested that the difference method has good precision in extracting WSF from Sentinel-1 SAR data, and can process a large amount of data simply, quickly, and effectively.

Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net

Urbanization is progressing rapidly around the world. With sub-weekly revisits at global scale, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imager (MSI) data can play an important role for monitoring urban sprawl to support sustainable development. In this letter, we proposed an urban change detection (CD) approach featuring a new network architecture for the fusion of SAR and optical data. Specifically, a dual stream concept was introduced to process different data modalities separately, before combining extracted features at a later decision stage. The individual streams are based on U-Net architecture that is one of the most popular fully convolutional networks used for semantic segmentation. The effectiveness of the proposed approach was demonstrated using the Onera Satellite CD (OSCD) dataset. The proposed strategy outperformed other U-Net-based approaches in combination with unimodal data and multimodal data with feature level fusion. Furthermore, our approach achieved state-of-the-art performance on the urban CD problem posed by the OSCD dataset. Our Sentinel-1 SAR data and code are available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SebastianHafner/DS_UNet</uri> .

Retrieval of nearshore bathymetry from Sentinel-1 SAR data in high energetic wave coasts: The Portuguese case study

The ability to derive bathymetry using remote sensing techniques enables rapid and cost-efficient mapping of large coastal areas. This contribution focuses on the application of both fast Fourier transform (FFT) and wavelet spectral analysis to obtain satellite-derived bathymetry maps of the nearshore, from freely available and easily accessible Sentinel-1 synthetic aperture radar (SAR) data with 10 m pixel resolution. For this purpose, an extension of 220 km of the Portuguese west coast is analyzed using six satellite images obtained during the years 2018, 2019 and 2020. This extension allows to assess the applicability to coastal sectors with distinct geomorphological constraints. The peak wave periods corresponding to the acquisition of these images approximately range between 11 and 16 s. The spectral analysis is carried to estimate the water depths in near-shore water regions from the observed wavelength patterns. This signature of the sea surface, reflected in the variations of the wavelengths, is captured by the satellite images, making it possible to infer the underlying bathymetry. The bathymetric estimates obtained from both methodologies are compared with data extracted from the Coastal Nautical Charts provided by the Portuguese Hydrographic Institute. Wavelet image processing methodology shows very positive results, particularly extending the depth inversion limits of the FFT methodology, allowing to obtain bathymetric data for the entire shoaling zone where the wavelength patterns are visible. The achieved results also highlight that both FFT and wavelet methodologies are dependent from the seabed slope. For gentle slopes, the inferred depths from 2018 SAR images lead to relative errors between 2.5 and 20% when compared with the observed isobaths (10, 20 and 30 m). For steeper slopes, the errors are generally greater than 20% and increase with depth. The capabilities of the wavelet methodology to map shallow marine environments for high energetic coasts seems promising, regarding research purposes and management interventions.

Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform

As one of the widely concerned urban climate issues, urban heat island (UHI) has been studied using the local climate zone (LCZ) classification scheme in recent years. More and more effort has been focused on improving LCZ mapping accuracy. It has become a prevalent trend to take advantage of multi-source images in LCZ mapping. To this end, this paper tried to utilize multi-source freely available datasets: Sentinel-2 multispectral instrument (MSI), Sentinel-1 synthetic aperture radar (SAR), Luojia1-01 nighttime light (NTL), and Open Street Map (OSM) datasets to produce the 10 m LCZ classification result using Google Earth Engine (GEE) platform. Additionally, the derived datasets of Sentinel-2 MSI data were also exploited in LCZ classification, such as spectral indexes (SI) and gray-level co-occurrence matrix (GLCM) datasets. The different dataset combinations were designed to evaluate the particular dataset’s contribution to LCZ classification. It was found that: (1) The synergistic use of Sentinel-2 MSI and Sentinel-1 SAR data can improve the accuracy of LCZ classification; (2) The multi-seasonal information of Sentinel data also has a good contribution to LCZ classification; (3) OSM, GLCM, SI, and NTL datasets have some positive contribution to LCZ classification when individually adding them to the seasonal Sentinel-1 and Sentinel-2 datasets; (4) It is not an absolute right way to improve LCZ classification accuracy by combining as many datasets as possible. With the help of the GEE, this study provides the potential to generate more accurate LCZ mapping on a large scale, which is significant for urban development.

Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery

Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K: 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region.

Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.

Using remote sensing to assess peatland resilience by estimating soil surface moisture and drought recovery

Peatland areas provide a range of ecosystem services, including biodiversity, carbon storage, clean water, and flood mitigation, but many areas of peatland in the UK have been degraded through human land use including drainage. Here, we explore whether remote sensing can be used to monitor peatland resilience to drought. We take resilience to mean the rate at which a system recovers from perturbation; here measured literally as a recovery timescale of a soil surface moisture proxy from drought lowering. Our objectives were (1) to assess the reliability of Sentinel-1 Synthetic Aperture Radar (SAR) backscatter as a proxy for water table depth (WTD); (2) to develop a method using SAR to estimate below-ground (hydrological) resilience of peatlands; and (3) to apply the developed method to different sites and consider the links between resilience and land management. Our inferences of WTD from Sentinel-1 SAR data gave results with an average Pearson's correlation of 0.77 when compared to measured WTD values. The 2018 summer drought was used to assess resilience across three different UK peatland areas (Dartmoor, the Peak District, and the Flow Country) by considering the timescale of the soil moisture proxy recovery. Results show clear areas of lower resilience within all three study sites, which often correspond to areas of high drainage and may be particularly vulnerable to increasing drought severity/events under climate change. This method is applicable to monitoring peatland resilience elsewhere over larger scales, and could be used to target restoration work towards the most vulnerable areas.

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine.

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel—1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year’s flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year’s same season (August 2015–2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.