Remote Sensing Applications-Society and Environment最新文献

{"title":"Highly turbid and eutrophic small water bodies in West Africa well identified by a CNN U-Net algorithm","authors":"Mathilde de Fleury ,&nbsp;Manuela Grippa ,&nbsp;Martin Brandt ,&nbsp;Rasmus Fensholt ,&nbsp;Florian Reiner ,&nbsp;Gyula Maté Kovacs ,&nbsp;Laurent Kergoat","doi":"10.1016/j.rsase.2024.101412","DOIUrl":"10.1016/j.rsase.2024.101412","url":null,"abstract":"<div><div>Although high-resolution multispectral optical imagery is increasingly being used to monitor continental surface waters more easily than ever before, there are still limitations to the methods used to extract water bodies. Detecting water becomes particularly difficult in the presence of aquatic vegetation or trees, or when spectral variations across the water surface are high. These limitations pose significant challenges in West Africa, where such cases are numerous, hindering the application of widely used methods and leading to a reduced quality of various existing datasets. As a result, the region lacks comprehensive information on the number of water bodies, their surface area, their spatial distribution and their typology. In this study, we propose a method based on a convolutional neural network based on a U-net architecture, which we apply to images from the Sentinel-2 multispectral instrument acquired in November 2020 and March 2018, corresponding to the maximum and minimum water area extent during the 2016–2020 period. We observe a much larger number of lakes than in current datasets, a large proportion of which are small and temporary. Overall, 29,265 water bodies were classified in November 2020 and 8,093 in March 2018 over an area of 1,340,450 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> in the central Sahel, with sizes ranging from 0.002 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> to 1,162 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. In addition, a wide diversity of optical water types was found across the water bodies: hypereutrophic water bodies dominate, accounting for 67.9% in November 2020, followed by very turbid water bodies representing 26.1%. The Convolutional Neural Network U-Net algorithm successfully identified water bodies with aquatic vegetation or obscured by trees, as well as extremely turbid small lakes and reservoirs, which are often missing in global datasets. Such improved mapping capability has important implications for the monitoring of water resources and water quality, which are pivotal for the livelihoods of the region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101412"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal variations and trends in solar UV spectral irradiances based on data from the Ozone Monitoring Instrument at solar noon in Southern Amazonas, Brazil","authors":"Péricles Vale Alves ,&nbsp;Vandoir Bourscheidt ,&nbsp;Luiz Octávio Fabrício dos Santos ,&nbsp;Paula Regina Humbelino de Melo","doi":"10.1016/j.rsase.2024.101423","DOIUrl":"10.1016/j.rsase.2024.101423","url":null,"abstract":"<div><div>Ultraviolet (UV) radiation has significant implications for public health and the environment, making it crucial to understand the dynamics of UV irradiances, particularly in sensitive regions such as the southern mesoregion of Amazonas. This study aimed to analyze the seasonal variations and trends in UV irradiances (305, 310, 324, and 380 nm) in the mentioned region using remote sensing data. The data were derived from satellite-mounted sensors, covering the period from January 2005 to December 2022. The results indicate a well-defined seasonality of UV irradiances, with intensity peaks in summer and spring. The largest and smallest monthly variations in UV irradiances (305 and 310 nm) occurred in February and September, respectively, while for UV irradiances (324 and 380 nm), these variations were observed in November and September. As for the trends, the most significant findings included substantial increases in UV irradiances (324 and 380 nm) and a reduction in Cloud Optical Thickness (COT). A significant negative correlation between ozone and UV irradiance (305 nm) was also observed, along with a strong correlation between COT and UV irradiances (324 and 380 nm). The study revealed a critical situation in July, emphasizing the need for additional precautions regarding UV exposure. While the results indicate concerning behaviors in irradiances and COT, the lack of spectral UV sensors on the ground in the southern Amazon region highlights the urgent need for investment in advanced monitoring technologies so that further studies can describe these dynamics more precisely.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101423"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in UAV remote sensing for agricultural yield estimation: A systematic comprehensive review of platforms, sensors, and data analytics","authors":"Shubham Anil Gade ,&nbsp;Mallappa Jadiyappa Madolli ,&nbsp;Pedro García‐Caparrós ,&nbsp;Hayat Ullah ,&nbsp;Suriyan Cha-um ,&nbsp;Avishek Datta ,&nbsp;Sushil Kumar Himanshu","doi":"10.1016/j.rsase.2024.101418","DOIUrl":"10.1016/j.rsase.2024.101418","url":null,"abstract":"<div><div>Traditional yield estimation approaches are quite tedious, time-consuming, and labor-intensive. Unmanned aerial vehicles (UAVs) present an exciting opportunity to estimate crop yield with high spatial and temporal resolution in agriculture. The objective of this article is to review current studies and research works in agriculture that employ the use of different UAV platforms, sensors, data acquisition, machine learning and photogrammetry techniques, and vegetation indices in UAV-based crop yield prediction. Furthermore, the article also explores the challenges and limitations in yield estimation. Hundred different studies from Google Scholar, Scopus, and Web of Science are presented and reviewed. The result demonstrated that most of the studies are centered on China and USA. Supervised learning models are widely used and exhibit better accuracy in yield estimation. The normalized difference vegetation index (NDVI) is preferred by researchers and emerges as a widely used vegetation index (60 studies). The study concluded that UAV-based crop remote sensing can be an effective method for improving yield estimation. The integration of multimodal data, including textural, structural, thermal, and meteorological features, along with key spectral bands such as near-infrared (NIR) and red-edge (RE), has demonstrated potential for improving the accuracy of yield estimation models. Moreover, supervised models have shown great suitability for cereal crops. Random Forest and linear regression emerge as reliable options for estimating yields of major crops, such as wheat, rice, and maize. However, challenges in yield estimation with UAV-based remote sensing include regulatory constraints, weather conditions, data storage and management, high initial costs, and technical limitations.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101418"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications based on EGMS products: A review","authors":"M. Crosetto ,&nbsp;B. Crippa ,&nbsp;M. Mróz ,&nbsp;M. Cuevas-González ,&nbsp;S. Shahbazi","doi":"10.1016/j.rsase.2025.101452","DOIUrl":"10.1016/j.rsase.2025.101452","url":null,"abstract":"<div><div>The European Ground Motion Service (EGMS) represents the largest wide area Persistent Scatterer Interferometry service ever conceived. It is part of the Copernicus Land Monitoring Service's product portfolio. Thanks to its technical characteristics, and the fact that EGMS products are made available on a full, open, and free-access principle, the EGMS has the potential to become a game changer in the way ground motion data are used in Europe. Three years after the publication of the first EGMS products, this initial review studies the scope and impact of this service in terms of applications. After a brief introduction to the service, the paper describes the main EGMS trends, including the procedures to analyse and exploit its data, and a review of its main applications. The quantity and quality of the applications are useful ways to show the potential of the service. This can open the door to a future widespread use of EGMS data. Next, the paper features a technical discussion on the main characteristics of the EGMS products, the main EGMS validation activities, and future research lines.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101452"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Error-reduced digital elevation models and high-resolution land cover roughness in mapping tsunami exposure for low elevation coastal zones","authors":"Rajuli Amra ,&nbsp;Susumu Araki ,&nbsp;Christian Geiß ,&nbsp;Gareth Davies","doi":"10.1016/j.rsase.2024.101438","DOIUrl":"10.1016/j.rsase.2024.101438","url":null,"abstract":"<div><div>This study presents a systematic exposure assessment by reconstructing the impact of the 2004 Indian Ocean Tsunami using a wide range of inundation scenarios and multiresolution exposure layers. To develop inundation and exposure models, we employed the error-reduced global digital elevation models (DEMs) and geospatially consistent multiresolution datasets: land cover roughness (LCR) models, built-up areas, and gridded population layers. We implemented three sequential validation assessments to evaluate the performance of inundation models, incorporating satellite observations, post-tsunami measurements, and the confidence level associated with inherent DEM error characteristics. The results demonstrated that the error-reduced variants of Copernicus DEM (i.e., FABDEM and DiluviumDEM) satisfied all reliability criteria. Incorporating these elevation models with LCR models improved the accuracy of inundation depth estimates; however, it reduced the agreement between simulated and observed inundation extents. We observed that applying high-resolution LCR models had a minimal impact on overland inundation extents but still influenced the exposure assessment, especially in high-density urban areas.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101438"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-derived shallow water depths estimation using remote sensing and artificial intelligence models, a case study: Darbandikhan Lake Upper, Kurdistan Region, Iraq","authors":"Arsalan Ahmed Othman ,&nbsp;Salahalddin S. Ali ,&nbsp;Ahmed K. Obaid ,&nbsp;Sarkawt G. Salar ,&nbsp;Omeed Al-Kakey ,&nbsp;Younus I. Al-Saady ,&nbsp;Sarmad Dashti Latif ,&nbsp;Veraldo Liesenberg ,&nbsp;Silvio Luís Rafaeli Neto ,&nbsp;Fabio Marcelo Breunig ,&nbsp;Syed E. Hasan","doi":"10.1016/j.rsase.2024.101432","DOIUrl":"10.1016/j.rsase.2024.101432","url":null,"abstract":"<div><div>Bathymetric mapping provides valuable information for the estimation of the depth and volume of enclosed inland water bodies that are useful in the planning and management of water resources. The use of conventional methods for the detection of shallow water depth, specifically in flooded areas, has been challenging. However, advances in remote sensing technology combined with artificial intelligence (AI) offer a reliable method. This study presents a reliable method to estimate water depth, using the Darbandikhan Lake Upper (DLU) as a test site. The novelty of this work lies in using a combination of Quantile Regression Forests (QRF), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) approaches together with the reflectance of Sentinel-2 and the ICESat-2 LiDAR data to estimate the depth of the water in the DLU during the 2019 spring flood. Our results gave the coefficient of determination (R²) and root mean square error (RMSE) between the actual depth obtained from the ICESat-2 and the estimated depth from the applied artificial intelligence models of 0.984, 0.983, 0.868, and 0.809; and 0.545, 0.569, 1.618, and 2.143 for the QRF, RF, SVM, and ANN models, respectively. This study, which applied the QRF model for the first time to determine the satellite-derived water depths, produced the most accurate result, with the maximum and mean estimated depth of DLU being 19.93 and 6.29 m, respectively. This study shows that the most sensitive bands to estimate the bathymetry are Band 9 (940 nm), Band 3 (560 nm), and Band 5 (705 nm) of the Sentinel-2, while the less sensitive bands are Band 2 (490 nm) and Band 11 (1610 nm). We argue that this technique can be applied to estimate the depth of shallow water bodies using passive satellite imageries in other regions of the world regardless of the full coverage availability of ICESat-2.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101432"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal variation and trend analysis of ground-level ozone in major Indian metropolitan cities: A geospatial approach","authors":"Roshan George Moncy,&nbsp;Aneesh Mathew,&nbsp;Padala Raja Shekar","doi":"10.1016/j.rsase.2024.101395","DOIUrl":"10.1016/j.rsase.2024.101395","url":null,"abstract":"<div><div>Air pollution refers to any chemical, physical, or biological contamination that contaminates an interior or outdoor environment and modifies the intrinsic qualities of the atmosphere. It can be produced by natural or anthropogenic activities. Among those pollutants mentioned by the World Health Organization (WHO), ground-level ozone, also known as tropospheric ozone, possesses a significant impact on human life. The current study was developed in response to the need to study ground-level ozone concentrations around India and metropolitan cities. The spatiotemporal variation across India was analyzed using geospatial methods. Using trend tests, trend analysis of the main metropolises in Bangalore, Chennai, Delhi, Hyderabad, Kolkata, and Mumbai was presented. 18 years of data (2005–2022) from the Ozone Monitoring Instrument (OMI) were used to conduct the test. According to geospatial research results, the northern region of India has a higher concentration of ozone than other locations. Delhi has a higher ozone rate than other metropolitan cities, ranging from 0.1219 to 0.1567 mol/m², followed by Kolkata (0.1085–0.1418 mol/m²). In these cities, summertime is often the time of year when the ground-level ozone concentration is at its maximum. Trend analysis using the Mann-Kendall and modified Mann-Kendall tests from 2005 to 2022 shows that the concentration increases with each year that goes by, even though there isn't a significant trend (p &lt; 0.05) across all of the monthly, seasonal, or annual periods. The research identifies high ozone areas and seasons, guiding policies, health advisories, urban planning, and accurate pollution forecasts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101395"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CCD-Conv1D: A deep learning based coherent change detection technique to monitor and forecast floods using Sentinel-1 images","authors":"Mohammed Siddique ,&nbsp;Tasneem Ahmed","doi":"10.1016/j.rsase.2024.101440","DOIUrl":"10.1016/j.rsase.2024.101440","url":null,"abstract":"<div><div>Floods are among the most common natural disasters affecting human lives and public amenities. In the North-Indian region, the situation is severe as floods continue to create havoc with flood fatalities and huge infrastructure damages every year. To mitigate this risk, flood monitoring based on detecting the changes in land cover and future predictions is required to be developed using Synthetic Aperture Radar (SAR) images. In this paper, a novel DL-based coherent change detection (CCD-Conv1D) model comprising a combination of coherent change detection technique, deep learning (DL) models based analysis, and flood forecasting implementation on the obtained change patterns, which pave the way to generate flood maps and identify the flooded areas has been developed. The proposed coherent change detection technique on Sentinel-1 images using image segmentation generated a log ratio image with statistics creating a changed band. An enhanced accuracy achieved in detecting changes from log-ratio-based temporal composition for Ayodhya and Basti cities shows positive threshold values of 2.96 and 2.01 during and after the crisis which is higher than 2.34 and 1.46 before and during the crisis respectively. The experimental outcomes demonstrated that the inundation concentrated mostly over the vegetation region of these cities. Additionally, the DL-based flood prediction performed through the Convolutional Neural Network (Conv1D) and Naïve Forecast (NF) model demonstrated that the positive changes for Ayodhya city were 31.4 and 31.8 and for Basti city were 30.40 and 35.04 respectively, depicting larger variation inferring that significant area is expected to be inundated. The outcomes from CCD-Conv1D based on the analysis of results, accuracy in change detection, and DL-based flood predictions confirmed that it is more reliable when compared with individual traditional approaches. In the future, more DL models can be explored for a wider insight and for comparative analysis of the outcomes from CCD-Conv1D implementation to develop an efficient flood monitoring and early warning system (FMEWS).</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101440"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of transplanting and harvest dates of rice crops in the Philippines using Sentinel-1 data","authors":"Arturo G. Cauba ,&nbsp;Roshanak Darvishzadeh ,&nbsp;Michael Schlund ,&nbsp;Andrew Nelson ,&nbsp;Alice Laborte","doi":"10.1016/j.rsase.2024.101435","DOIUrl":"10.1016/j.rsase.2024.101435","url":null,"abstract":"<div><div>Rice is a staple crop in the Philippines, thus, identifying the ideal window to carry out crop management activities is valuable for efficient monitoring and resource allocation. This study used Sentinel-1A and 1B Synthetic Aperture Radar (SAR) data to estimate the transplanting and harvesting dates of paddy rice under dry and wet seasons and varying climatic conditions. A total of 99 rice fields in three provinces with distinct climatic patterns were considered in this study.</div><div>From Sentinel-1, we extracted the mean backscatter coefficients in VV, VH, and VH/VV polarizations for each field to generate time series curves with a temporal resolution of 6 days. To mitigate noise, locally weighted scatterplot smoothing (LOWESS) was applied. Periodogram analysis and the Breusch-Godfrey test were used to identify repetitive patterns and their statistical significance. Local extrema and corresponding dates suggest potential transplanting and harvesting dates. The identified dates were then compared with field data from farmer interviews. The root mean squared difference (RMSD) for transplanting ranged from 9 to 16 days and 14–29 days for dry and wet seasons, respectively. Harvest estimates followed similar trends with generally less scattered RMSD during the dry season (16–17.5 days) compared to the wet season values (8–22 days). Results show that VH and VV polarizations are promising for estimating transplanting and harvest dates during the dry season, whereas, VH/VV polarization were better during the wet season. The study emphasized the importance of SAR data for monitoring crop management strategies which are important for the agricultural sector.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101435"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra- and inter-annual spatiotemporal variations and climatic driving factors of surface water area in the Irtysh River Basin during 1985–2022","authors":"Enzhao Zhu ,&nbsp;Alim Samat ,&nbsp;Wenbo Li ,&nbsp;Ren Xu ,&nbsp;Junshi Xia ,&nbsp;Yinguo Qiu ,&nbsp;Jilili Abuduwaili","doi":"10.1016/j.rsase.2025.101455","DOIUrl":"10.1016/j.rsase.2025.101455","url":null,"abstract":"<div><div>Climate change and human activities have significantly altered the dynamics of surface water area (SWA) in the Irtysh River Basin (IRB). While inter-annual trends in SWA can be detected using Landsat imagery, the characteristics of seasonal SWA changes under long-term scenarios remain uncertain due to reduced data availability caused by cloud cover. In this study, we propose a time-disaggregated water frequency (TWF) that is more suitable for seasonal surface water analysis and develop a cloud-filling algorithm utilizing a Random Forest approach. The results demonstrate that the TWF effectively represents seasonal surface water distribution and achieves high cloud-filling accuracy. Using this method, we reconstructed monthly cloud-filled SWA series for the IRB from 1985 to 2022 at a spatial resolution of 30 m with high accuracy (&gt;94%). Analysis indicates that the multi-year average SWA of the IRB was 41,003 km², reflecting a decrease of 22%. The peak SWA occurs in spring (May), following the general trend of spring &gt; summer &gt; fall &gt; winter. Surface water loss primarily occurs during summer and fall, particularly in the middle reaches of the Irtysh River Basin (35%). Time-series correlation analysis reveals that snowmelt, precipitation, and temperature are the most significant climatic factors affecting SWA in spring, summer, and fall.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101455"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}