Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms

Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency's GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area.

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 1833.03 ha (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 62.33 ha 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.

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.

Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from -59 to 17 mm/yr. The results show that subsidence is -28.48 mm/yr in 2018, the subsidence reached -49.02 mm/yr in 2019, while in 2020, the subsidence reached -49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence.

Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

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

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

SBAS-InSAR Based Deformation Monitoring of Tailings Dam: The Case Study of the Dexing Copper Mine No.4 Tailings Dam

The No.4 tailings pond of the Dexing Copper Mine is the second largest in Asia. The tailing pond is a dangerous source of man-made debris flow with high potential energy. In view of the lack of effective and low-cost global safety monitoring means in this region, in this paper, the time-series InSAR technology is innovatively introduced to monitor the deformation of tailings dam and significant key findings are obtained. First, the surface deformation information of the tailings pond and its surrounding areas was extracted by using SBAS-InSAR technology and Sentinel-1A data. Second, the cause of deformation is explored by analyzing the deformation rate, deformation accumulation, and three typical deformation rate profiles of the representative observation points on the dam body. Finally, the power function model is used to predict the typical deformation observation points. The results of this paper indicated that: (1) the surface deformation of the tailings dam can be categorized into two directions: the upper portion of the dam moving away from the satellite along the Line of Sight (LOS) at a rate of -40 mm/yr, whereas the bottom portion approaching the satellite along the LOS at a rate of 8 mm/yr; (2) the deformation of the dam body is mainly affected by the inventory deposits and the construction materials of the dam body; (3) according to the current trend, deformation of two typical observation points in the LOS direction will reach the cumulative deformation of 80 mm and -360 mm respectively. The research results can provide data support for safety management of No.4 tailings dam in the Dexing Copper Mine, and provide a method reference for monitoring other similar tailings dams.

Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events.

Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data

Rangelands play a vital role in developing countries' biodiversity conservation and economic growth, since most people depend on rangelands for their livelihood. Aboveground-biomass (AGB) is an ecological indicator of the health and productivity of rangeland and provides an estimate of the amount of carbon stored in the vegetation. Thus, monitoring seasonal AGB is important for understanding and managing rangelands' status and resilience. This study assesses the impact of seasonal dynamics and fire on biophysical parameters using Sentinel-1 (S1) and Sentinel-2 (S2) image data in the mesic rangeland of Limpopo, South Africa. Six sites were selected (3/area), with homogenous vegetation (10 plots/site of 30m2). The seasonal measurements of LAI and biomass were undertaken in the early summer (December 2020), winter (July-August 2021), and late summer (March 2022). Two regression approaches, random forest (RF) and stepwise multiple linear regression (SMLR), were used to estimate seasonal AGB. The results show a significant difference (p < 0.05) in AGB seasonal distribution and occurrence between the fire (ranging from 0.26 to 0.39 kg/m2) and non-fire areas (0.24-0.35 kg/m2). In addition, the seasonal predictive models derived from random forest regression (RF) are fit to predict disturbance and seasonal variations in mesic tropical rangelands. The S1 variables were excluded from all models due to high moisture content. Hence, this study analyzed the time series to evaluate the correlation between seasonal estimated and field AGB in mesic tropical rangelands. A significant correlation between backscattering, AGB and ecological parameters was observed. Therefore, using S1 and S2 data provides sufficient data to obtain the seasonal changes of biophysical parameters in mesic tropical rangelands after disturbance (fire) and enhanced assessments of critical phenology stages.

Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia

Continuous mapping of the height and canopy cover of forests is vital for measuring forest biomass, monitoring forest degradation and restoration. In this regard, the contribution of Light Detection and Ranging (LiDAR) sensors, which were developed to obtain detailed data on forest composition across large geographical areas, is immense. Accordingly, this study aims to predict forest canopy cover and height in tropical forest areas utilizing Global Ecosystem Dynamics Investigation (GEDI) LIDAR, multisensor images, and random forest regression. To achieve this, we gathered predictor variables from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Sentinel-2 multispectral datasets, and Sentinel-1 synthetic aperture radar (SAR) backscatters. The model's accuracy was evaluated based on a validation dataset of GEDI Level 2A and Level 2B. The random forest method was used the combination of data layers from Sentinel-1, Sentinel-2, and topographic measurements to model forest canopy cover and height. The produced canopy height and cover maps had a resolution of 30 m with R2 = 0.86 and an RMSE of 3.65 m for forest canopy height and R2 = 0.87 and an RMSE of 0.15 for canopy cover for the year 2022. These results suggest that combining multiple variables and data sources improves canopy cover and height prediction accuracy compared to relying on a single data source. The output of this study could be helpful in creating forest management plans that support sustainable utilization of the forest resources.

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology's effectiveness and usefulness in the region.

Country-scale assessment of urban areas, population, and households exposed to land subsidence using Sentinel-1 InSAR, and GPS time series

The increased need for water resources in urban sprawls and intense droughts has forced more aggressive groundwater extraction resulting in numerous urban areas undergoing land subsidence. In most cases, only some large metropolitan areas have been well-characterized for subsidence. However, there is no existing country-wide assessment of urban areas, population, and households exposed to this process. This research showcases a methodology to systematically evaluate urban localities with land subsidence higher than - 2.8 cm/year throughout Mexico. We used Interferometric Synthetic Aperture Radar (InSAR) tools with a dataset of 4611 scenes from European Space Agency's Sentinel-1 A/B SAR sensors acquired from descending orbits from September 2018 through October 2019. This dataset was processed at a supercomputer using InSAR Scientific Computing Environment and the Miami InSAR Time Series software in Python software. The quality and calibration of the resulting velocity maps are assessed through a large-scale comparison with observations from 100 continuous GPS sites throughout Mexico. Our results show that an urban area of 3797 km2, 6.9 million households, and 17% of the total population in Mexico is exposed to subsidence velocities of faster than - 2.8 cm/year, in more than 853 urban localities within 29 land subsidence regions. We also confirm previous global potential estimations of subsidence occurrence in low relief areas over unconsolidated deposits and where groundwater aquifers are under stress. The presented research demonstrates the capabilities for surveying urban areas exposed to land subsidence at a country-scale level by combining Sentinel-1 velocities with spatial national census data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11069-023-06259-5.

Comparing NISAR (Using Sentinel-1), USDA/NASS CDL, and Ground Truth Crop/Non-Crop Areas in an Urban Agricultural Region

A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA's Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017-2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91-98%) than for the CDL (79-93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery

Flood inundation mapping and satellite imagery monitoring are critical and effective responses during flood events. Mapping of a flood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV+VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The normalized difference water index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identified as 440.3 km2 in 2018, 268.58 km2 in 2019, 178.40 km2 in 2020, 203.79 km2 in 2021, and 321.33 km2 in 2022, respectively. The overlap of flood maps on the LULC map indicated that flooding highly affected agriculture and urban areas in these years. The approach using the near-real-time Sentinel-1 SAR imagery and GEE platform can be operationalized for periodic flood mapping, helps develop flood control measures, and helps enhance flood management. The generated annual flood inundation maps are also useful for policy development, agriculture yield estimation, crop insurance framing, etc.

Water distribution based on SAR and optical data to improve hazard mapping

Climate projections foresee intense precipitation and long-term drought events is increasing with consequent rapid changes in surface water bodies in a short period. In areas with drastic hydrological changes, achieving accurate and rapid mapping of these phenomena in combination with hydrologic variability characteristics is a key of effective emergency management and disaster risk reduction plans. This study presents an automatic method for mapping drought and flood hazards, particularly in regions with significant hydrological changes. We use Sentinel-1/2 and Landsat data to extract surface water and classify permanent and seasonal water bodies in historical periods, which serve as the basis for identifying flood or drought areas. The water extraction method combines index-based analysis for optical data and the region-Otsu method for radar data, ensuring accurate identification of water. The effectiveness of this approach is demonstrated through comparisons with existing products in Poyang Lake (China), the Po River Plain (Italy), and the Indus River Plain (Pakistan). Findings show a high similarity between the two, and our results can provide more specific details. Our method is particularly well-suited for areas with fluctuating hydrological conditions, can also map quickly without optical data. By effectively identifying areas affected by drought and flood hazards while mitigating errors from natural hydrological dynamics, this methodology contributes valuable insights to enhance emergency management and disaster risk reduction plans.

Using Sentinel images for analyzing water and land separability in an agricultural river basin

The presence or absence of water can result in floods or droughts, potentially impacting agricultural productivity to a great extent. With advancements in remote sensing technology, the reliability of identifying water bodies has significantly improved, particularly in terms of distinguishing between water and land. This study introduced remote sensing methods to improve the accuracy of differentiating water within the Dawenhe River basin. Various water body scenarios were examined, and the performance of these methods was evaluated to determine the proper approach for water-land separation. In applying water body indices to Sentinel-2 images, it was found that the normalized difference water index (NDWI) outperformed the modified normalized difference water index (MNDWI) in identifying water bodies. Consequently, histograms of frequency distribution for Sentinel-1 were generated, revealing that water and land were more distinguishable in VV polarization than in VH polarization. Using histogram thresholding on VV polarized images in Dongping Lake resulted in an overall classification accuracy of 97.58%, surpassing that of Otsu's method at 97.36%. To address the persisting misclassifications, this study identified three leading causes and proposed corresponding solutions. These solutions included (1) employing the morphological dilation algorithm to expand the water area, mitigating pixel mixing issues at the water-land boundary that caused the water bodies to appear smaller; (2) utilizing incidence angles and digital elevation model (DEM) to locate and remove shadows; and (3) slightly lowering the thresholds and manually correcting misclassifications. As a result, the average accuracy of the four areas increased from 95.56 to 96.94%.

Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area

Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1), Sentinel-2-MSI (S2), RapidEye (RE), and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope.

Variable-complexity machine learning models for large-scale oil spill detection: The case of Persian Gulf

Oil spill is the main cause of marine pollution in the waterbodies with rich oil resources. In this study, we developed and compared the performance of variable-complexity machine-learning models to detect oil spill origin, extent, and movement over large scales. To this end, we trained Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) models by using the statistical, geometrical, and textural features of Sentinel-1 SAR data. Our results in the Persian Gulf showed that CNN is superior to RF and SVM classifiers in oil spill detection, as evidenced by the testing accuracy of 95.8 %, 86.0 %, and 78.9 %, respectively. The results suggested utilizing both ascending and descending orbit pass directions to track the movement of oil spill and the underlying transport rate. The proposed methodology enables the detection of probable leaking tankers and platforms, which aids in identifying other sources of oil pollution than tankers and platforms.

Monitoring early-season agricultural drought using temporal Sentinel-1 SAR-based combined drought index

Early-season agricultural drought is frequent over South Asian region due to delayed or deficient monsoon rainfall. These drought events often cause delay in sowing and can even result in crop failure. The present study focuses on monitoring early-season agricultural drought in a semi-arid region of India over 5-year period (2016-2020). It utilizes hydro-climatic and biophysical variables to develop a combined drought index (CDI), which integrates anomalies in soil moisture conditions, rainfall, and crop-sown area progression. Synthetic aperture radar (SAR)-based soil moisture index (SMI) represents in situ measured soil moisture with reasonable accuracy (r=0.68). Based on the highest F1-score, SAR backscatter in VH (vertical transmit-horizontal receive) polarization with specific values for parameter threshold (-18.63 dB) and slope threshold (-0.072) is selected to determine the start of season (SoS) with a validation accuracy of 73.53%. The CDI approach is used to monitor early-season agricultural drought and identified drought conditions during June-July in 2019 and during July in 2018. Conversely, 2020 experienced consistently wet conditions, while 2016 and 2017 had near-normal conditions. Overall, the study highlights the use of SAR data for early-season agricultural drought monitoring, which is mainly governed by soil moisture-driven crop-sowing progression. The proposed methodology holds potential for effective monitoring, management, and decision-making in early-season agricultural drought scenarios.

Utilising Sentinel-1's Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite's high orbital stability. By propagating Sentinel-1's orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10-50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Across Canada, farmers are encouraged to adopt beneficial management practices (BMPs) to protect soil heath, reduce green house gas emissions and mitigate off-site impacts from agriculture. Measuring the uptake of BMPs, including the implementation of conservation tillage, helps gauge the success of policies and programs to promote adoption. Satellites are one way to monitor BMP adoption and Synthetic Aperture Radars (SARs) are of particular interest given their all-weather data collection capability. This research investigated coherent change detection (CCD) to determine when farmers harvest and till their fields. A time series of both Sentinel-1 and RADARSAT Constellation Mission (RCM) images was acquired over a site in the Canadian Lake Erie basin, during the autumn of 2021, when farmers were harvesting and tilling fields of corn, soybeans and wheat. 16 CCD pairs were created and coherence values were interpreted based on observations collected for 101 fields. An m-chi decomposition was applied to the RCM data, and the Volume/Surface (V/S) ratio was calculated as an additional source of information to interpret results. Change events due to harvest, tillage, autumn seeding and chemical termination resulted in coherence values below 0.20. The mean and standard deviation for fields with observed change was 0.18 ± 0.03. Coherence values were 0.42 ± 0.15 for fields where no change was noted. Tests confirmed that the coherence associated with changed and unchanged fields was significantly different. Coherence values could also differentiate between some types of management events, including tillage and harvest. CCD could also separate harvest as a function of crop type (corn or soybeans). V/S ratios declined after tillage events but increased after both harvesting and chemical termination. Narrowing the date of harvest and tillage is as important as detecting change. To meet this requirement, Sentinel-1 and RCM CCD products with values below 0.20 (indicating change had occurred), were graphically overlaid. With this approach, the timing of corn harvest was identified as occurring within a 5-day window. The tilling of corn, soybeans and wheat was narrowed to a 4-day window. The results of this research confirmed that CCD can be used to capture change due to autumn agricultural activities, and this technique can also separate change due to harvest and tillage. Finally, this study demonstrated that when data from different SAR missions are combined in a virtual constellation, timing of harvest and tillage can be more precisely defined.

Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform

Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth's surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologies capturing high-resolution and multi-temporal views of landscapes are incredibly beneficial in wetland mapping compared to traditional methods. Taking advantage of the Google Earth Engine's computational power and multi-source earth observation data from Sentinel-1 multi-spectral sensor and Sentinel-2 radar, we generated a 10 m nationwide wetlands inventory map for Iran. The whole country is mapped using an object-based image processing framework, containing SNIC superpixel segmentation and a Random Forest classifier that was performed for four different ecological zones of Iran separately. Reference data was provided by different sources and through both field and office-based methods. Almost 70% of this data was used for the training stage and the other 30% for evaluation. The whole map overall accuracy was 96.39% and the producer's accuracy for wetland classes ranged from nearly 65 to 99%. It is estimated that 22,384 km² of Iran are covered with water bodies and wetland classes, and emergent and shrub-dominated are the most common wetland classes in Iran. Considering the water crisis that has been started in Iran, the resulting ever-demanding map of Iranian wetland sites offers remarkable information about wetland boundaries and spatial distribution of wetland species, and therefore it is helpful for both governmental and commercial sectors.

Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park

Continuous monitoring of oil discharges in coastal and open ocean waters using Earth Observation (EO) has undeniably contributed to diminishing their occurrence wherever a detection system was in place, such as in Europe (EMSA's CleanSeaNet) or in the United States (NOAA's OR&R). This study describes the development and testing of a semi-automated oil slick detection system tailored to the Great Barrier Reef (GBR) marine park solely based on EO data as no such service was routinely available in Australia until recently. In this study, a large, curated, historical global dataset of SAR imagery acquired by Sentinel-1 SAR, now publicly available, is used to assess classification techniques, namely an empirical approach and a deep learning model, to discriminate between oil-like features and look-alikes in the scenes acquired over the marine park. An evaluation of this detection system on 10 Sentinel-1 SAR images of the GBR using two performance metrics - the detection accuracy and the false-positive rate (FPR) - shows that the classifiers perform best when combined (accuracy >98 %; FPR 0.01) rather than when used separately. This study demonstrates the benefit of sequentially combining classifiers to improve the detection and monitoring of unreported oil discharge events in SAR imagery. The workflow has also been tested outside the GBR, demonstrating its robustness when applied to other regions such as Australia's Northwest Shelf, Southeast Asia and the Pacific.

A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The use-case calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management.

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence.

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R C V 2 = 0.67 and RMSE _CV = 0.88 m² m^-2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloudprone agri-environments.

Lava Mapping Using Sentinel-1 Data after the Occurrence of a Volcanic Eruption-The Case of Cumbre Vieja Eruption on La Palma, Canary Islands, Spain

Volcanic eruptions pose a great threat to humans. In this context, volcanic hazard and risk assessment constitute crucial issues with respect to mitigating the effects of volcanic activity and ensuring the health and safety of inhabitants. Lava flows directly affect communities living near active volcanoes. Nowadays, remote sensing advances make it possible to effectively monitor eruptive activity, providing immediate and accurate information concerning lava evolution. The current research focuses on the mapping of the surface deformation and the analysis of lava flow evolution occurred on the island of La Palma, during the recent (2021) eruptive phase of the volcano. Sentinel-1 data covering the island were collected throughout the entire eruptive period, i.e., September 2021 until January 2022. The processing was based on amplitude-based and phase-based detection methods, i.e., Synthetic Aperture Radar interferometry (InSAR) and offset tracking. In particular, ground deformation occurred on the island, while Line-Of-Sight (LOS) displacements were derived from Sentinel-1 interferograms. Moreover, the evolution of lava flow velocity was estimated using Sentinel-1 imagery along with offset tracking technique. The maximum lava flow velocity was calculated to be 2 m/day. It was proved that both approaches can provide rapid and useful information in emergencies, especially in inaccessible areas. Although offset tracking seems a quite promising technique for the mapping of lava flows, it still requires improvement.

Impact of extreme weather events on cropland inundation over Indian subcontinent

Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The "Gulab" cyclonic storm formed on the 24^th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26^th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the "Gulab" cyclonic event. The Otsu's thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km²), followed by Telangana (~2040 km²) and Odisha (~1132 km²), and the least in Chhattisgarh (~93.75 km²). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years.

Flood mapping and damage assessment due to the super cyclone Yaas using Google Earth Engine in Purba Medinipur, West Bengal, India

This study maps flood inundation and estimates the damage caused by super cyclone Yaas in Purba Medinipur, India. We used Google Earth Engine (GEE) to create a flood inundation map of the research area using pre and post-cyclone Sentinel-1 SAR data. Using ESRI 2020 land cover data, flood damage was analysed. The flood affected 5% (239.69 km²) of the land of Purba Medinipur. The northern and southern regions were affected the most. 95% and 3% of the total flooded area are comprised of agricultural and vegetation, respectively. Kolaghat (24 km²) and Nandigram-II (1 km²) sustained the greatest damage to both agriculture and vegetation. The areas below 18 m were impacted by flooding, with the worst damage occurring below 5 m. The GEE platform was cost-effective, efficient, and faster at calculating with enhanced precision. The outcomes of this study will aid in the management of cyclone-induced hazards. We advocate planting native and salt-tolerant crops to reduce flood damage.

Using remote sensing to identify liquid manure applications in eastern North Carolina

Nutrient pollution from farm fertilizers and manure is a global concern. Excess nitrogen and phosphorous has been linked to algal blooms and a host of other water quality issues. In the U.S., most animal production occurs in concentrated animal feeding operations (CAFOs) housing a significant number of animals in a confined space. CAFOs tend to cluster in space and thus generate large quantities of manures within a small area. Liquid manure from CAFOs is often stored in open-air lagoons and then applied via irrigation to crops on nearby 'sprayfields'. The full scope and extent of CAFO impacts remain unclear because of the paucity of public information regarding animal numbers, barn and lagoon locations, and manure management practices. Where and when manure is applied on the landscape is key missing data that is needed to better understand and mitigate consequences of CAFO management practices. The aim of this study was to detect land applications of liquid manure using a remote sensing approach. We used random forest models incorporating C-Band synthetic-aperture radar, multispectral imagery, and other predictors to examine soil moisture conditions indicating probable liquid manure applications across known sprayfields in eastern North Carolina. Our models successfully distinguished saturated and unsaturated soils within corn, soybean, grassland, and 'other' crops, with 93-98% accuracy against validation for clear weather periods during the dormant, early, and late growing seasons. A Kruskal-Wallis test revealed that the mean soil saturation frequency was significantly higher on sprayfields than non-sprayfields of the same crop type (p < 2.2e-16). We also found that manure applications were concentrated within ∼1 km from the point of generation. This is the first application of satellite-based radar for identifying the location and timing of manure applications over broad areas. Future work can build on these methods to further understand manure management at CAFOs, as well as to improve pollution source tracking and modeling.

Quantifying Two-Dimensional Surface Displacements Using High-Resolution Cosmo-SkyMed, TerraSAR-X and Medium-Resolution Sentinel-1 SAR Interferometry: Case Study for the Tengiz Oilfield

The present study was aimed at comparing vertical and horizontal surface displacements derived from the Cosmo-SkyMED, TerraSAR-X and Sentinel-1 satellite missions for the detection of oil extraction-induced subsidence in the Tengiz oilfield during 2018-2021. The vertical and horizontal surface displacements were derived using the 2D decomposition of line-of-sight measurements from three satellite missions. Since the TerraSAR-X mission was only available from an ascending track, it was successfully decomposed by combining it with the Cosmo-SkyMED descending track. Vertical displacement velocities derived from 2D Decomposition showed a good agreement in similar ground motion patterns and an average regression coefficient of 0.98. The maximum average vertical subsidence obtained from the three satellite missions was observed to be -57 mm/year. Higher variations and deviations were observed for horizontal displacement velocities in terms of similar ground motion patterns and an average regression coefficient of 0.80. Fifteen wells and three facilities were observed to be located within the subsidence range between -55.6 mm/year and -42 mm/year. The spatial analyses in the present studies allowed us to suspect that the subsidence processes occurring in the Tengiz oilfield are controlled not solely by oil production activities since it was clearly observed from the detected horizontal movements. The natural tectonic factors related to two seismic faults crossing the oilfield, and terrain characteristics forming water flow towards the detected subsidence hotspot, should also be considered as ground deformation accelerating factors. The novelty of the present research for Kazakhstan's Tengiz oilfield is based on the cross-validation of vertical and horizontal surface displacement measurements derived from three radar satellite missions, 2D Decomposition of Cosmo-SkyMED descending and TerraSAR-X ascending line-of-sight measurements and spatial analysis of man-made and natural factors triggering subsidence processes.

Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model

Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples' lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images.

Evaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic

A recently conducted study by the Centers for Disease Control and Prevention encouraged access to urban green space for the public over the prevalence of COVID-19 in that exposure to urban green space can positively affect the physical and mental health, including the reduction rate of heart disease, obesity, stress, stroke, and depression. COVID-19 has foregrounded the inadequacy of green space in populated cities. It has also highlighted the extant inequities so as to unequal access to urban green space both quantitatively and qualitatively. In this regard, it seems that one of the problems related to Malatya is the uncoordinated distribution of green space in different parts of the city. Therefore, knowing the quantity and quality of these spaces in each region can play an effective role in urban planning. The aim of the present study has been to evaluate urban green space per capita and to investigate its distribution based on the population of the districts of Battalgazi county in Malatya city through developing an integrated methodology (remote sensing and geographic information system). Accordingly, in Google Earth Engine by images of Sentinel-1 and PlanetScope satellites, it was calculated different indexes (NDVI, EVI, PSSR, GNDVI, and NDWI). The data set was prepared and then by combining different data, classification was performed according to support vector machine algorithm. From the landscaping maps obtained, the map was selected with the highest accuracy (overall accuracy: 94.43; and kappa coefficient: 90.5). Finally, by the obtained last map, the distribution of urban green space per capita and their functions in Battalgazi county and its districts were evaluated. The results of the study showed that the existing urban green spaces in the Battalgazi/Malatya were not distributed evenly on the basis of the districts. The per capita of urban green space is twenty-four regions which is more than 9m² and in twenty-three ones is less than 9m². The recommendation of this study was that Türkiye city planners and landscape designers should replan and redesign the quality and equal distribution of urban green spaces, especially during and following COVID-19 pandemic. Additionally, drawing on the Google Earth Engine cloud system, which has revolutionized GIS and remote sensing, is recommended to be used in land use land cover modeling. It is straightforward to access information and analyze them quickly in Google Earth Engine. The published codes in this study makes it possible to conduct further relevant studies.

Extreme rainfall-induced urban flood monitoring and damage assessment in Wuhan (China) and Kumamoto (Japan) cities using Google Earth Engine

Floods in urban areas result in a detrimental impact on the natural environment and human health and pose major risks to assets and communication systems. In cities with high population density, the magnitude of flood damage largely depends upon flood inundation as well as floodwater depths. The present study compared recent flood inundation extent, damages caused, and possible floodwater depth in two highly developed metropolises of China and Japan, i.e., Wuhan and Kumamoto cities, for the year 2020. Sentinel-1 satellite data-driven change detection algorithm in Google Earth Engine (GEE) was applied to identify potentially flooded regions. Major land use land cover classes such as urban areas and croplands affected by the flood were mapped in conjunction with the exposed population. ALOS PALSAR digital elevation model (DEM) was used to study the inundation depth. The study revealed that 322 km² of the area has been inundated by floodwater in Wuhan city with 230 km² and 140 km² areas under damaged croplands and urban regions. Around 817,095 people were exposed to this natural catastrophe in Wuhan. The city Kumamoto has witnessed an inundation area of about 505 km² with damaged cropland of 350 km² and an urban area of 83 km².

Field test of the surface soil moisture mapping using Sentinel-1 radar data

Soil surface moisture is one of the key parameters for describing the hydrological state and assessing the potential availability of water for irrigated plants. Because the radar backscattering coefficient is sensitive to soil moisture, the application of Sentinel-1 data may support soil surface moisture mapping at high spatial resolution by detecting spatial and temporal changes at the field scale for precision irrigation management. This mapping is required to control soil water erosion and preferential water flow to improve irrigation water efficiency and minimise negative impacts on surface and ground water bodies. Direct observations of soil surface moisture (5-cm thickness) were performed at an experimental plot in the study site of the All-Russian Scientific Research Institute of Irrigated Agriculture, near the village Vodnyy, Volgograd region. Soil surface moisture retrieval from Sentinel-1 was performed at the same location. A second set of soil surface moisture was calculated for the soil sampling sites using the permittivity model, based on the estimates of soil surface characteristics: a) reflectivity, obtained by the neural network method from Sentinel-1 observations; b) roughness, obtained from the geodata of the stereoscopic survey with unmanned aerial vehicle Phantom 4 Pro. The raster set of soil surface moisture geodata was obtained based on the reflectivity geodata raster set to solve the inverse problem using a permittivity model that considers the soil texture of the experimental plot. The determination coefficient (0.948) and standard deviation (2.04%) were obtained by comparing both sets of soil moisture point geodata taken from the same soil sampling sites. The values confirmed a satisfactory linear correlation between the directly measured and indirectly modelled sets. A comparison of the two sets of geodata indicated a satisfactory reproduction of the first set by the second set. As a result, the developed method can be considered as the scientific and methodological basis of the new technology of soil surface moisture monitoring by radar, which is one of the basic characteristics used in precision irrigation management.

Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

The objective of this paper was to estimate soil moisture in pepper crops with drip irrigation in a semi-arid area in the center of Tunisia using synthetic aperture radar (SAR) data. Within this context, the sensitivity of L-band (ALOS-2) in horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations and C-band (Sentinel-1) data in vertical-vertical (VV) and vertical-horizontal (VH) polarizations is examined as a function of soil moisture and vegetation properties using statistical correlations. SAR signals scattered by pepper-covered fields are simulated with a modified version of the water cloud model using L-HH and C-VV data. In spatially heterogeneous soil moisture cases, the total backscattering is the sum of the bare soil contribution weighted by the proportion of bare soil (one-cover fraction) and the vegetation fraction cover contribution. The vegetation fraction contribution is calculated as the volume scattering contribution of the vegetation and underlying soil components attenuated by the vegetation cover. The underlying soil is divided into irrigated and non-irrigated parts owing to the presence of drip irrigation, thus generating different levels of moisture underneath vegetation. Based on signal sensitivity results, the potential of L-HH data to retrieve soil moisture is demonstrated. L-HV data exhibit a higher potential to retrieve vegetation properties regarding a lower potential for soil moisture estimation. After calibration and validation of the proposed model, various simulations are performed to assess the model behavior patterns under different conditions of soil moisture and pepper biophysical properties. The results highlight the potential of the proposed model to simulate a radar signal over heterogeneous soil moisture fields using L-HH and C-VV data.

Fragmented landscapes affect honey bee colony strength at diverse spatial scales in agroecological landscapes in Kenya

Landscape fragmentation and habitat loss at multiple scales directly affect species abundance, diversity, and productivity. There is a paucity of information about the effect of the landscape structure and diversity on honey bee colony strength in Africa. Here, we present new insights into the relationship between landscape metrics such as patch size, shape, connectivity, composition, and configuration and honey bee (Apis mellifera) colony strength characteristics. Remote-sensing-based landscape variables were linked to honey bee colony strength variables in a typical highly fragmented smallholder agroecological region in Kenya. We examined colonies in six sites with varying degrees of land degradation during the period from 2017 to 2018. Landscape structure was first mapped using medium resolution bitemporal Sentinel-1 and Sentinel-2 satellite imagery with an optimized random forest model. The influence of the surrounding landscape matrix was then constrained to two buffer distances, i.e., 1 km representing the local foraging scale and 2.5 km representing the wider foraging scale around each investigated apiary and for each of the six sites. The results of zero-inflated negative binomial regression with mixed effects showed that lower complexity of patch geometries represented by fractal dimension and reduced proportions of croplands were most influential at local foraging scales (1 km) from the apiary. In addition, higher proportions of woody vegetation and hedges resulted in higher colony strength at longer distances from the apiary (2.5 km). Honey bees in moderately degraded landscapes demonstrated the most consistently strong colonies throughout the study period. Efforts towards improving beekeeper livelihoods, through higher hive productivity, should target moderately degraded and heterogeneous landscapes, which provide forage from diverse land covers.

Mapping flood by the object-based method using backscattering coefficient and interference coherence of Sentinel-1 time series

The SAR has the ability of all-weather and all-time data acquisition, it can penetrate the cloud and is not affected by extreme weather conditions, and the acquired images have better contrast and rich texture information. This paper aims to investigate the use of an object-oriented classification approach for flood information monitoring in floodplains using backscattering coefficients and interferometric coherence of Sentinel-1 data under time series. Firstly, the backscattering characteristics and interference coherence variation characteristics of SAR time series are used to analyze whether the flood disaster information can be accurately reflected and provide the basis for selecting input classification characteristics of subsequent SAR images. Subsequently, the contribution rate index of the RF model is used to calculate the importance of each index in time series to convert the selected large number of classification features into low dimensional feature space to improve the classification accuracy and reduce the data redundancy. Finally, the SAR image features in each period after multi-scale segmentation and feature selection are jointly used as the input features of RF classification to extract and segment the water in the study area to monitor floods' spatial distribution and dynamic characteristics. The results showed that the various attributes of backscatter coefficients and interferometric coherence under time series could accurately correspond with the actual flood risk, and the combined use of backscattering coefficient and interferometric coherence for flood extraction can significantly improve the accuracy of flood information extraction. Overall, the object-based random forest method using the backscattering coefficient and interference coherence of Sentinel-1 time series for flood extraction advances our understanding of flooding's temporal and spatial dynamics, essential for the timely adoption of adaptation and mitigation strategies for loss reduction.

A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

[Estimating average tree height in Xixiaoshan Forest Farm, Northeast China based on Sentinel-1 with Sentinel-2A data]

Forest resource survey is important for the sustainable development of forest ecosystem in China. The average tree height is a main structural parameter of forest resource survey, and also one of the key parameters with greatest difficulty to obtain. The purpose of this study was to explore the potential of joint active and passive remote sensing technology in estimating forest average height. Taking Xixiaoshan Forest Farm in Linjiang City of Jilin Province as the research area, we used Sentinel-1 SAR and Sentinel-2A data, extracted two backscatter coefficients and eight texture information of Sentinel-1, ten spectral bands and texture information of Sentinel-2A and eleven vegetation index variables, constructed five groups of average tree height estimation models based on above variables and fusion of four variables by multiple linear regression method. We further evaluated the influence of each variable on the inversion accuracy. The results showed that the texture information extracted from the Sentinel-2A spectral band of a single data source variable had a better modeling effect and could be used as effective data to estimate the average tree height. The height estimation model of the integrated four variables was optimal, with a R² vaule of 0.56, a root mean square error of leave-one-out cross-validation of 2.92 m, and a relative root mean square error of leave-one-out cross-validation of 21.5%. The forest average height model based on Sentinel-1 and Sentinel-2a characteristic variables could improve the estimation accuracy of forest height, which could be used for regional forest average height estimation and mapping.

Detecting cocoa plantations in Côte d'Ivoire and Ghana and their implications on protected areas

Côte d'Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation of sustainable cocoa practices and for the periodic and effective monitoring of forests. In this study, a method for cocoa plantation identification was developed based on a multi-temporal stack of Sentinel-1 and Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. The Normalized Difference Vegetation Index (NDVI) and second-order texture features were assessed for their importance in an RF classification, and their optimal combination was used as input variables for the RF model to identify cocoa plantations in both countries. The RF model-based cocoa map achieved 82.89% producer's and 62.22% user's accuracy, detecting 3.69 million hectares (Mha) and 2.15 Mha of cocoa plantations for Côte d'Ivoire and Ghana, respectively. The results demonstrate that a combination of an RF model and multi-feature classification can distinguish cocoa plantations from other land cover/use, effectively reducing feature dimensions and improving classification efficiency. The results also highlight that cocoa farms largely encroach into protected areas (PAs), as 20% of the detected cocoa plantation area is located in PAs and almost 70% of the PAs in the study area house cocoa plantations.

Dataset of Sentinel-1 surface soil moisture time series at 1 km resolution over Southern Italy

This paper describes the specifications of the surface soil volumetric water content (Θ) [m³/m³] product derived from Sentinel-1 (S-1) data and assessed in the study “Sentinel-1 soil moisture at 1 km resolution: a validation study” [1]. The S-1 Θ product consists of Θ mean and standard deviation values at 1 km spatial resolution and is expected to support applications in agriculture and hydrology as well as the Numerical Weather Prediction at regional scale [2]. The retrieval algorithm is a time series based short term change detection that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (v2.0). The provided dataset represents an example of the developed S-1 Θ product and consists of a time series of 183 S-1 Θ images over Southern Italy from January 2015 to December 2018. The maps were produced for each ascending S-1 acquisition date on the Relative Orbit Number (RON) 146 and the temporal gap between consecutive maps is 6 days (when both S-1A and S-1B data are available) or 12 days.

Exploring potential of C band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity

Development of satellite technology over decades established unique approach to analyze rice crop phenological parameters and supervise growth and production. Advancement in technology leads to the development of microwave remote sensing that is operational round the clock irrespective of weather conditions. An attempt has been carried out in the present study to classify and map phenological stages, namely, transplanting stage, heading stage, and harvesting stage of rice crop using Sentinel-1, MODIS Enhanced vegetation index (EVI) data. Puddling stage, transplanting stage, heading stage, and harvesting stages are identified on 05th and 15th January, 28th February, and 27th March of the year 2017, respectively. Field visits are performed frequently at sampling locations for an effective study on rice phenological stages, rice yield estimation, and mapping large-scale area using regression analysis. Estimated yield is compared with ground truth data; average yield produced from study area is 3.03 tons/acre. Methodology adopted reflected satisfactory performance with minimal error for mapping of rice phenological stages, and it is suggested to experiment with other agricultural crops.

Novel Weight-Based Approach for Soil Moisture Content Estimation via Synthetic Aperture Radar, Multispectral and Thermal Infrared Data Fusion

Though current remote sensing technologies, especially synthetic aperture radars (SARs), exhibit huge potential for soil moisture content (SMC) retrievals, such technologies also present several performance disadvantages. This study explored the merits of proposing a novel data fusion methodology (partly decision level and partly feature level) for SMC estimation. Initially, individual estimations were derived from three distinct methods: the inversion of an Empirically Adapted Integral Equation Model (EA-IEM) applied to SAR data, the Perpendicular Drought Index (PDI), and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat-8 data. Subsequently, three feature level fusions were performed to produce three different novel salient feature combinations where said features were extracted from each of the previously mentioned methods to be the input of an artificial neural network (ANN). The latter underwent a modification of its performance function, more specifically from absolute error to root mean square error (RMSE). Eventually, all SMC estimations, including the feature level fusion estimation, were fused at the decision level through a novel weight-based estimation. The performance of the proposed system was analysed and validated by measurements collected from three study areas, an agricultural field in Blackwell farms, Guildford, United Kingdom, and two different agricultural fields in Sidi Rached, Tipasa, Algeria. Those measurements contained SMC levels and surface roughness profiles. The proposed SMC estimation system yielded stronger correlations and lower RMSE values than any of the considered SMC estimation methods in the order of 0.38%, 1.4%, and 1.09% for the Blackwell farms, Sidi Rached 1, and Sidi Rached 2 datasets, respectively.

Assessing the impacts of Amphan cyclone over West Bengal, India: a multi-sensor approach

Landfall of the Amphan (very severe cyclonic storm) occurred at 1730 hrs Indian Standard Time (IST) on May 20, 2020, near the West Bengal (W.B.) coast of India. High wind speed, storm surge, and torrential rainfall-induced flooding caused devastation in W.B. The present study aims to analyse the impacts of Amphan cyclone on land use/land cover (LULC) such as built-up area, cropland, brick-kiln industries and vegetation cover of nine districts of W.B. namely, Barddhaman, Nadia, North 24 Parganas, South 24 Parganas, Purba Medinipur, Paschim Medinipur, Haora, and Kolkata. Flood extent has been mapped using Sentinel-1A and B interferometric wide swath (IW) ground range detected (GRD) VV polarisation images dated May 22, 2020. The total actual flooded area covers 488 km² of the study area. For the pre-cyclone period, LULC classification and normalised difference vegetation index (NDVI) have been done using Sentinel-2B multispectral instrument (MSI) images dated May 14, 2020. Post-cyclone NDVI has been computed using Sentinel-2B MSI images dated June 3, 2020. Flood-affected cropland covers a large chunk (88.2%) of the total actual flooded area. Mean NDVI values of non-flooded and flooded cropland and vegetation cover have been reduced between May 14, 2020, and June 3, 2020. District, block and pixel-wise changes in pre- and post-cyclone NDVI values have also been analysed. This study helps planners and policy makers to understand the district-wise flooding behavior, severity of damage to cropland and vegetation cover and to plan restriction on high-value land use in flooded low-lying areas.

Automatic flood detection using sentinel-1 images on the google earth engine

Flood is considered to be one of the most destructive natural disasters. It is important to detect the flood-affected area in a reasonable time. In March 2019, a severe flood occurred in the north of Iran and lasted for 2 months. In the present paper, this flood event has been monitored by Sentinel-1 images. The Otsu thresholding algorithm has been applied to separate flooded areas from remaining land covers. The threshold value of -14.9 dB was derived and applied to each scene to delineate flooded areas. There was high variability of the inundated area; however, the presented threshold correctly represented the variation of the flood. The resultant maps were further verified by independent datasets. The overall accuracies were higher than 90%, confirming the applicability of the Otsu automatic thresholding method in flood mapping. The automatic approach is efficient in rapid fold mapping across complex landscapes.

Impact of physical process on propagating oil spills in the Caspian Sea

This study aims to inspect the oil spill propagation in the Caspian Sea using Sentinel-1 data as well as wind and observation data. Detection processes clearly show that although the north and middle basin are the main sources of oil pollution, the southern basin would be the final destination of these oil slicks. Comparison of oil spill clusters in the southern and on the Apsheron indicates that the size of these clusters decreases under the physical process of the southern basin like eddies. Further, the mixed layer is estimated at 25-35 m in the southern basin. After applying the analytical formulas, the eddy diffusivity profile is plotted, leading to an estimate of nearly 5 × 10^-4 m²/s on the surface water. The droplet oil diameters are calculated which vary from 150 μm to 250 μm based on an analytical model in a steady-state mode.

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.

Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015-2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform

Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015-2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of "Amathus" and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.