[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5091":3,"related-5091":88},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":21,"secondary_topics":27,"classification":39,"search_phrases":66,"slug":67,"view_count":15,"doi":68,"paper":69,"created_at":87},5091,"Assessing the impacts of gold mining on vegetation and soil degradation using multi-temporal remote sensing in Kintinian, Siguiri prefecture, Guinea","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101570","Gold mining, while economically significant, has increasingly driven environmental degradation in many parts of West Africa, including Kintinian in the Siguiri Prefecture of the Republic of Guinea. This study evaluated the extent and environmental impacts of gold mining on soil and vegetation dynamics between 1990 and 2016, with an emphasis on landscape transformation and ecological degradation. The study integrated field observations and ground-truthing with multi-temporal satellite imagery (Landsat 1990, 2000, 2010, and 2016; and Sentinel-1) within the Geographical Pathways to Environmental Sustainability (GPES) framework. Geographic Information Systems (GIS) were employed for change detection analysis, while the Soil-Adjusted Vegetation Index (SAVI) quantified vegetation dynamics and Analytical Hierarchical Principles (AHP) weighed the vulnerability degradation threshold. Findings reveal substantial land-use dynamics associated with intensified mining activities. Agricultural land declined by 13%, while forest cover decreased by 23% over the study period. Vegetation condition deteriorated as SAVI values declined from 0.928 (1990) to 0.602 (2016), indicating reduced vegetative vigor and increasing soil exposure. Soil vulnerability was weighted using Land Use Land Cover (10%), Iron Oxide (35%), Enhanced Built-up and Bare Soil (15%), Ground Soil Index (15%), and Land Surface Temperature (25%), demonstrating significant degradation found at the abandoned industrial mining sites that were reconverted for artisanal mining purposes. The study concluded that gold mining has significantly accelerated landscape transformation, vegetation loss, and soil exposure, suggesting sustainable mining practices and targeted land restoration strategies in the mining-affected regions.","金矿开采虽具有重要经济意义，却在包括几内亚共和国锡吉里省金蒂尼安在内的西非许多地区日益加剧环境退化。本研究评估了1990年至2016年间金矿开采对土壤和植被动态的影响范围及环境效应，重点关注景观转变与生态退化。研究在“环境可持续性地理路径”（GPES）框架内，将实地观测和地面验证与多时相卫星影像（1990、2000、2010和2016年Landsat影像；以及Sentinel-1影像）相结合。采用地理信息系统（GIS）进行变化检测分析，利用土壤调节植被指数（SAVI）量化植被动态，并运用层次分析法（AHP）对脆弱性退化阈值进行加权。研究结果揭示了与采矿活动加剧相关的显著土地利用动态。研究期内，农用地减少13%，森林覆盖减少23%。植被状况恶化，SAVI值从0.928（1990年）降至0.602（2016年），表明植被活力下降、土壤裸露增加。土壤脆弱性采用土地利用\u002F土地覆盖（10%）、氧化铁（35%）、增强型建成区与裸土（15%）、地表土壤指数（15%）和地表温度（25%）进行加权，结果显示，在重新转为手工采矿用途的废弃工业采矿场址，土壤退化显著。研究结论认为，金矿开采显著加速了景观转变、植被丧失和土壤裸露，建议在受采矿影响地区推行可持续采矿实践并实施有针对性的土地恢复策略。",null,"Environmental and Sustainability Indicators","2026-10-15T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究几内亚金矿开采对植被与土壤退化的遥感评估，与三农、农业信息化、智慧农业等主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[12],{"code":22,"slug":23,"name":24,"path":25,"parent_code":26},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[28,34],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":35,"slug":36,"name":37,"path":38,"parent_code":33},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60],{"code":22,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},14,true,[23],[50,23],"土壤",[],{"code":35,"score":53,"supported":47,"title":54,"abstract":55,"tags":59},9,[],[56,57,58],"地理信息","GIS","geographic information",[],{"code":29,"score":61,"supported":47,"title":62,"abstract":64,"tags":65},8,[63],"remote sensing",[],[],[],"Assessingtheimpactsofgoldminingonvegetat-5091","10.1016\u002Fj.indic.2026.101570",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":15,"oa_url":6,"card":80,"direction":84,"ingested_from":86},"W7222305702",[72,74,77],{"name":73,"orcid":9},"Mohamed Traoré",{"name":75,"orcid":76},"Opeyemi Aniramu","https:\u002F\u002Forcid.org\u002F0000-0002-5111-0221",{"name":78,"orcid":79},"Oluwagbenga O. I. Orimoogunje","https:\u002F\u002Forcid.org\u002F0000-0001-8070-5040",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"评估几内亚金矿开采对植被与土壤退化的影响。","多时相Landsat\u002FSentinel-1影像、GIS变化检测、SAVI与AHP","1990-2016年农地减13%、林地减23%，SAVI由0.928降至0.602，土壤退化严重。","农业遥感与作物表型","可结合时序遥感与机器学习，构建矿区复垦动态监测与生态恢复评估模型。","openalex","2026-10-16T23:30:27.217565Z",{"total":89,"page":90,"page_size":89,"items":91},6,1,[92,186,257,331,388,460],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":107,"tags":109,"primary_topic":115,"secondary_topics":116,"classification":119,"search_phrases":170,"slug":173,"view_count":174,"doi":9,"paper":175,"created_at":185},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":102,"substance":103,"depth":104,"authority":105,"freshness":61,"relevant":90,"comment":106},16,20,17,13,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[108],{"name":98,"url":95},[110,111,112,113,114],"智慧农业","精准农业","遥感","盐碱地治理","土壤监测",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[117,118],{"code":35,"slug":36,"name":37,"path":38,"parent_code":33},{"code":22,"slug":23,"name":24,"path":25,"parent_code":26},{"status":40,"locked":14,"version":41,"method":42,"evidence":120},{"candidates":121,"agricultural_evidence":47},[122,128,133,138,144,151,158,164],{"code":29,"score":123,"supported":47,"title":124,"abstract":125,"tags":127},18,[63],[112,63,126],"multispectral",[112],{"code":35,"score":104,"supported":47,"title":129,"abstract":130,"tags":132},[57],[56,131,57,58],"空间分析",[],{"code":22,"score":134,"supported":47,"title":135,"abstract":136,"tags":137},15,[23],[50,23],[113],{"code":139,"score":13,"supported":47,"title":140,"abstract":142,"tags":143},"I1",[141],"precision agriculture",[],[110,111],{"code":145,"score":89,"supported":47,"title":146,"abstract":147,"tags":150},"I2",[],[148,149],"机器学习","machine learning",[],{"code":152,"score":153,"supported":47,"title":154,"abstract":155,"tags":157},"H3",3,[],[156],"灌溉",[],{"code":159,"score":153,"supported":47,"title":160,"abstract":161,"tags":163},"H7",[],[162],"干旱",[],{"code":165,"score":153,"supported":47,"title":166,"abstract":167,"tags":169},"I7",[],[168],"methodological framework",[],[171,172],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":176,"authors":177,"venue":98,"cited_by_count":15,"oa_url":95,"card":180,"direction":84,"ingested_from":86},"W7221843179",[178],{"name":179,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":181,"method":182,"finding":183,"direction":84,"opportunity":184},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":194,"is_selected":14,"score":195,"score_detail":196,"sources":199,"tags":203,"primary_topic":208,"secondary_topics":209,"classification":212,"search_phrases":237,"slug":240,"view_count":228,"doi":241,"paper":242,"created_at":256},3279,"ASSESSMENT OF SOIL SALINIZATION IN IRRIGATED LANDS BASED ON REMOTE SENSING AND GIS TECHNOLOGIES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900049","This thesis analyzes modern approaches to monitoring and assessing soil salinity in irrigated areas of Uzbekistan and Central Asia. Soil salinization is one of the major land degradation processes affecting the reclamation condition of irrigated lands, crop growth and agricultural productivity. Although conventional field and laboratory surveys provide detailed information, they have limitations in terms of regular monitoring of large areas. Therefore, remote sensing and geographic information systems provide promising tools for rapid and spatial assessment of soil salinity. The study considers the application of satellite imagery, particularly Sentinel-2 and Landsat data, for soil salinity assessment, including the use of spectral bands and salinity indices, GIS-based thematic mapping, and validation using field and laboratory observations. Recent studies demonstrate the potential of remote sensing data for spatial assessment and mapping of soil salinity under the conditions of Uzbekistan. Such an integrated approach can support rapid monitoring of the reclamation status of irrigated lands, identification of salt-affected areas and spatial planning of appropriate reclamation measures.","本论文分析了乌兹别克斯坦和中亚灌溉区土壤盐分监测与评估的现代方法。土壤盐渍化是影响灌溉土地改良状况、作物生长和农业生产力的主要土地退化过程之一。尽管传统的野外调查和实验室分析能够提供详细信息，但在大范围定期监测方面存在局限性。因此，遥感与地理信息系统为土壤盐分的快速空间评估提供了有前景的工具。本研究探讨了卫星影像，特别是Sentinel-2和Landsat数据在土壤盐分评估中的应用，包括光谱波段和盐分指数的使用、基于GIS的主题制图，以及利用野外和实验室观测数据进行验证。近期研究表明，遥感数据在乌兹别克斯坦条件下用于土壤盐分空间评估与制图具有潜力。这种综合方法可支持灌溉土地改良状况的快速监测、盐渍化区域的识别以及适当改良措施的空间规划。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,66,{"impact":197,"substance":123,"depth":134,"authority":197,"freshness":53,"relevant":90,"comment":198},12,"基于遥感与GIS的灌溉区土壤盐渍化监测论文，方法成熟、结论可靠，对盐碱地治理与农业信息化有参考价值，但属区域性研究，影响层级有限。",[200,201],{"name":192,"url":189},{"name":192,"url":202},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[204,205,57,206,207],"土地退化","遥感监测","土壤盐渍化","灌溉农业",{"code":35,"slug":36,"name":37,"path":38,"parent_code":33},[210,211],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"code":22,"slug":23,"name":24,"path":25,"parent_code":26},{"status":40,"locked":14,"version":41,"method":42,"evidence":213},{"candidates":214,"agricultural_evidence":47},[215,219,223,227,232],{"code":35,"score":123,"supported":47,"title":216,"abstract":217,"tags":218},[57],[56,57,58],[57],{"code":29,"score":134,"supported":47,"title":220,"abstract":221,"tags":222},[63],[112,63],[205],{"code":22,"score":46,"supported":47,"title":224,"abstract":225,"tags":226},[23],[50,23],[],{"code":152,"score":228,"supported":47,"title":229,"abstract":230,"tags":231},4,[],[156],[207],{"code":233,"score":90,"supported":14,"title":234,"abstract":235,"tags":236},"H1",[],[],[204],[238,239],"乌兹别克斯坦 土壤盐渍化 遥感","Sentinel-2 盐渍化 灌溉","乌兹别克斯坦土壤盐渍化遥感-3279","10.5281\u002Fzenodo.22900049",{"doi":241,"openalex_id":243,"authors":244,"venue":192,"cited_by_count":15,"oa_url":189,"card":251,"direction":84,"ingested_from":86},"W7214052773",[245,247,249],{"name":246,"orcid":9},"Mardiyev",{"name":248,"orcid":9},"Ergashaliyev",{"name":250,"orcid":9},"G'aniyev",{"tldr":252,"method":253,"finding":254,"direction":84,"opportunity":255},"综述遥感与GIS评估乌兹别克斯坦及中亚灌溉土壤盐渍化的方法。","Sentinel-2与Landsat影像、光谱盐分指数、GIS制图及野外验证。","遥感与GIS可快速空间评估盐渍化，支持灌溉地改良状态监测与规划。","可探索多源遥感与机器学习融合的盐分指数模型，提升干旱区盐渍化反演精度与时效。","2026-09-23T23:30:19.341486Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":268,"tags":270,"primary_topic":274,"secondary_topics":275,"classification":278,"search_phrases":308,"slug":311,"view_count":174,"doi":312,"paper":313,"created_at":330},1768,"Remote Sensing and GIS-Based Spatial Planning of Soil and Water Conservation Measures in a Semi-Arid Watershed Maliwadgaon of Maharashtra, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95662","Site-specific planning of water harvesting and soil and water conservation (SWC) measures is important for improving water availability and watershed resilience in semi-arid regions. This study aimed to identify and prioritise suitable locations for SWC structures in the 43 km² Maliwadgaon watershed of Chhatrapati Sambhajinagar district, Maharashtra, India, using an integrated remote sensing (RS) and Geographic Information System (GIS) approach. A 30 m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model and Landsat-8 imagery were used to derive terrain, drainage, land use\u002Fland cover (LULC), and vegetation characteristics. Elevation, slope, drainage density, stream order, dissection index, LULC, and normalized difference vegetation index (NDVI) were integrated through multi-criteria evaluation (MCE) for site-suitability assessment. The watershed has an elevation range of approximately 509–591 m and a dendritic, fourth-order drainage network with a drainage density of 0.991 km\u002Fkm². The MCE identified 40 potential conservation structures: 13 farm ponds, 13 cement nala bunds, 5 percolation tanks, 4 underground barriers, and 5 gully plugs. The proposed locations provide a spatial basis for runoff interception, erosion control, water storage, groundwater recharge, and agricultural water management. The findings demonstrate the practical utility of RS–GIS-based MCE as a decision-support approach for prioritising site-specific SWC interventions in a semi-arid watershed.","在半干旱地区，水资源收集及水土保持（SWC）措施的定点规划对于改善水资源可利用性和增强流域韧性至关重要。本研究旨在利用遥感（RS）与地理信息系统（GIS）集成方法，识别并优先确定印度马哈拉施特拉邦恰特拉帕蒂·桑巴吉纳加尔区Maliwadgaon流域（面积43平方公里）内适宜建设水土保持设施的位置。研究采用30米分辨率的航天飞机雷达地形任务（SRTM）数字高程模型和Landsat-8影像，提取地形、水系、土地利用\u002F土地覆盖（LULC）及植被特征。通过多准则评价（MCE）整合高程、坡度、排水密度、河流等级、切割指数、土地利用\u002F土地覆盖及归一化差异植被指数（NDVI），进行选址适宜性评估。该流域海拔范围约为509–591米，水系呈树枝状，为四级排水网络，排水密度为0.991公里\u002F平方公里。多准则评价识别出40处潜在的水土保持设施：13座田间池塘、13座水泥沟壑拦水坝、5座渗滤池、4座地下屏障及5处冲沟堵截设施。所提出的选址为径流拦截、侵蚀控制、蓄水、地下水补给及农业水资源管理提供了空间依据。研究结果表明，基于遥感与地理信息系统的多准则评价作为一种决策支持方法，在半干旱流域优先确定定点水土保持干预措施方面具有实际应用价值。","International Journal of Environment and Climate Change","2026-09-05T00:00:00Z",60,{"impact":197,"substance":123,"depth":102,"authority":197,"freshness":174,"relevant":90,"comment":267},"研究利用遥感和GIS技术在半干旱流域进行水土保持措施选址，方法实用，但时效性较低。",[269],{"name":263,"url":260},[271,112,57,272,273],"农业水资源","水土保持","半干旱区",{"code":35,"slug":36,"name":37,"path":38,"parent_code":33},[276,277],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"code":22,"slug":23,"name":24,"path":25,"parent_code":26},{"status":40,"locked":14,"version":41,"method":42,"evidence":279},{"candidates":280,"agricultural_evidence":47},[281,285,289,294,299,304],{"code":35,"score":123,"supported":47,"title":282,"abstract":283,"tags":284},[57],[56,57,58],[57],{"code":29,"score":134,"supported":47,"title":286,"abstract":287,"tags":288},[63],[112,63],[112],{"code":22,"score":290,"supported":47,"title":291,"abstract":292,"tags":293},11,[23],[23],[],{"code":152,"score":228,"supported":47,"title":295,"abstract":296,"tags":298},[],[297],"水资源",[271],{"code":300,"score":228,"supported":47,"title":301,"abstract":302,"tags":303},"H6",[],[272],[272],{"code":159,"score":153,"supported":47,"title":305,"abstract":306,"tags":307},[],[162],[],[309,310],"农业水资源 半干旱区 水土保持 遥感","农业水资源 半干旱区","农业水资源半干旱区水土保持遥感-1768","10.9734\u002Fijecc\u002F2026\u002Fv16i95662",{"doi":312,"openalex_id":314,"authors":315,"venue":263,"cited_by_count":15,"oa_url":260,"card":325,"direction":84,"ingested_from":86},"W7208812908",[316,319,321,323],{"name":317,"orcid":318},"Prasannajit D. Gangurde","https:\u002F\u002Forcid.org\u002F0009-0002-9266-8931",{"name":320,"orcid":9},"Sanjay N. Pawar",{"name":322,"orcid":9},"M. S. Pendke",{"name":324,"orcid":9},"Anilkumar M. Kamble",{"tldr":326,"method":327,"finding":328,"direction":84,"opportunity":329},"利用遥感和GIS对半干旱流域进行水土保持措施的空间规划与选址。","集成SRTM DEM、Landsat-8影像，通过多准则评价进行选址。","识别出40个潜在保护结构，包括池塘、堤坝等，用于水资源管理。","可扩展至其他半干旱流域，结合高分辨率影像和机器学习优化选址精度。","2026-09-06T23:30:27.290464Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":338,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":339,"sources":341,"tags":343,"primary_topic":347,"secondary_topics":348,"classification":354,"search_phrases":370,"slug":373,"view_count":15,"doi":374,"paper":375,"created_at":387},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":340},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[342],{"name":337,"url":334},[344,205,345,346],"防灾减灾","洪水风险评估","GIS应用",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[349,350],{"code":35,"slug":36,"name":37,"path":38,"parent_code":33},{"code":159,"slug":351,"name":352,"path":353,"parent_code":26},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":40,"locked":14,"version":41,"method":42,"evidence":355},{"candidates":356,"agricultural_evidence":47},[357,361,365],{"code":29,"score":134,"supported":47,"title":358,"abstract":359,"tags":360},[63],[112,63],[205],{"code":35,"score":46,"supported":47,"title":362,"abstract":363,"tags":364},[57],[56,57],[],{"code":159,"score":61,"supported":47,"title":366,"abstract":367,"tags":369},[],[162,368],"drought",[345,344],[371,372],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":374,"openalex_id":376,"authors":377,"venue":337,"cited_by_count":15,"oa_url":381,"card":382,"direction":84,"ingested_from":86},"W7222885988",[378],{"name":379,"orcid":380},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":383,"method":384,"finding":385,"direction":84,"opportunity":386},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":396,"sources":398,"tags":400,"primary_topic":404,"secondary_topics":405,"classification":407,"search_phrases":436,"slug":439,"view_count":15,"doi":440,"paper":441,"created_at":459},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",{"impact":61,"substance":123,"depth":134,"authority":13,"freshness":53,"relevant":90,"comment":397},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[399],{"name":394,"url":391},[112,57,401,402,403],"农用地适宜性","土地覆被","孟加拉农业",{"code":35,"slug":36,"name":37,"path":38,"parent_code":33},[406],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":40,"locked":14,"version":41,"method":42,"evidence":408},{"candidates":409,"agricultural_evidence":47},[410,414,418,425,432],{"code":35,"score":123,"supported":47,"title":411,"abstract":412,"tags":413},[57],[56,57,58],[57],{"code":29,"score":134,"supported":47,"title":415,"abstract":416,"tags":417},[63],[112,63],[112],{"code":419,"score":89,"supported":47,"title":420,"abstract":421,"tags":424},"A1",[],[422,423],"稻米","rice",[],{"code":426,"score":89,"supported":47,"title":427,"abstract":428,"tags":431},"G",[],[429,430],"农村发展","rural development",[],{"code":22,"score":89,"supported":47,"title":433,"abstract":434,"tags":435},[],[50,23],[],[437,438],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":440,"openalex_id":442,"authors":443,"venue":453,"cited_by_count":15,"oa_url":391,"card":454,"direction":84,"ingested_from":86},"W7222895468",[444,446,448,450],{"name":445,"orcid":9},"Raju Ahmed",{"name":447,"orcid":9},"Shaikh Mehdee Mohammad",{"name":449,"orcid":9},"Ummul Momanin Coalee",{"name":451,"orcid":452},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":455,"method":456,"finding":457,"direction":84,"opportunity":458},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z",{"id":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":9,"source_name":466,"source_url":463,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":470,"tags":472,"primary_topic":476,"secondary_topics":477,"classification":483,"search_phrases":498,"slug":501,"view_count":15,"doi":502,"paper":503,"created_at":543},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science",69,{"impact":197,"substance":103,"depth":102,"authority":105,"freshness":61,"relevant":90,"comment":469},"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[471],{"name":466,"url":463},[148,112,473,474,475],"土壤碳","Landsat","数字土壤制图",{"code":22,"slug":23,"name":24,"path":25,"parent_code":26},[478,482],{"code":145,"slug":479,"name":480,"path":481,"parent_code":33},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":40,"locked":14,"version":41,"method":42,"evidence":484},{"candidates":485,"agricultural_evidence":47},[486,490,494],{"code":22,"score":46,"supported":47,"title":487,"abstract":488,"tags":489},[23],[50,23],[],{"code":145,"score":53,"supported":47,"title":491,"abstract":492,"tags":493},[149],[],[148],{"code":29,"score":61,"supported":47,"title":495,"abstract":496,"tags":497},[],[112,63],[474,112],[499,500],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":502,"openalex_id":504,"authors":505,"venue":466,"cited_by_count":15,"oa_url":9,"card":538,"direction":84,"ingested_from":86},"W7222107492",[506,509,512,515,518,521,524,527,529,532,535],{"name":507,"orcid":508},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":510,"orcid":511},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":513,"orcid":514},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":516,"orcid":517},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":519,"orcid":520},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":522,"orcid":523},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":525,"orcid":526},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":528,"orcid":9},"A. E. Shatunov",{"name":530,"orcid":531},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":533,"orcid":534},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":536,"orcid":537},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":539,"method":540,"finding":541,"direction":84,"opportunity":542},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z"]