[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5013":3,"related-5013":130},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":50,"search_phrases":111,"slug":114,"view_count":115,"doi":9,"paper":116,"created_at":129},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。",null,"Arabian journal of science.","2026-10-13T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","遥感","盐碱地治理","土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,72,78,84,92,99,105],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":63},18,true,[60],"remote sensing",[29,60,62],"multispectral",[29],{"code":46,"score":19,"supported":58,"title":65,"abstract":67,"tags":71},[66],"GIS",[68,69,66,70],"地理信息","空间分析","geographic information",[],{"code":40,"score":73,"supported":58,"title":74,"abstract":75,"tags":77},15,[41],[76,41],"土壤",[30],{"code":79,"score":13,"supported":58,"title":80,"abstract":82,"tags":83},"I1",[81],"precision agriculture",[],[27,28],{"code":85,"score":86,"supported":58,"title":87,"abstract":88,"tags":91},"I2",6,[],[89,90],"机器学习","machine learning",[],{"code":93,"score":94,"supported":58,"title":95,"abstract":96,"tags":98},"H3",3,[],[97],"灌溉",[],{"code":100,"score":94,"supported":58,"title":101,"abstract":102,"tags":104},"H7",[],[103],"干旱",[],{"code":106,"score":94,"supported":58,"title":107,"abstract":108,"tags":110},"I7",[],[109],"methodological framework",[],[112,113],"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":117,"authors":118,"venue":10,"cited_by_count":121,"oa_url":6,"card":122,"direction":126,"ingested_from":128},"W7221843179",[119],{"name":120,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",0,{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"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。","农业遥感与作物表型","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。","openalex","2026-10-15T23:30:32.401626Z",{"total":86,"page":22,"page_size":86,"items":131},[132,208,262,339,424,493],{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":9,"source_name":138,"source_url":135,"published_at":139,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":140,"score_detail":141,"sources":144,"tags":146,"primary_topic":150,"secondary_topics":151,"classification":154,"search_phrases":185,"slug":188,"view_count":115,"doi":189,"paper":190,"created_at":207},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":142,"substance":57,"depth":17,"authority":142,"freshness":115,"relevant":22,"comment":143},12,"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。",[145],{"name":138,"url":135},[147,29,66,148,149],"农业水资源","水土保持","半干旱区",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[152,153],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":155},{"candidates":156,"agricultural_evidence":58},[157,161,165,170,176,181],{"code":46,"score":57,"supported":58,"title":158,"abstract":159,"tags":160},[66],[68,66,70],[66],{"code":33,"score":73,"supported":58,"title":162,"abstract":163,"tags":164},[60],[29,60],[29],{"code":40,"score":166,"supported":58,"title":167,"abstract":168,"tags":169},11,[41],[41],[],{"code":93,"score":171,"supported":58,"title":172,"abstract":173,"tags":175},4,[],[174],"水资源",[147],{"code":177,"score":171,"supported":58,"title":178,"abstract":179,"tags":180},"H6",[],[148],[148],{"code":100,"score":94,"supported":58,"title":182,"abstract":183,"tags":184},[],[103],[],[186,187],"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":189,"openalex_id":191,"authors":192,"venue":138,"cited_by_count":121,"oa_url":135,"card":202,"direction":126,"ingested_from":128},"W7208812908",[193,196,198,200],{"name":194,"orcid":195},"Prasannajit D. Gangurde","https:\u002F\u002Forcid.org\u002F0009-0002-9266-8931",{"name":197,"orcid":9},"Sanjay N. Pawar",{"name":199,"orcid":9},"M. S. Pendke",{"name":201,"orcid":9},"Anilkumar M. Kamble",{"tldr":203,"method":204,"finding":205,"direction":126,"opportunity":206},"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":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":216,"sources":218,"tags":220,"primary_topic":221,"secondary_topics":222,"classification":225,"search_phrases":242,"slug":243,"view_count":121,"doi":244,"paper":245,"created_at":261},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。","Environmental and Sustainability Indicators","2026-10-15T00:00:00Z",{"impact":121,"substance":121,"depth":121,"authority":121,"freshness":121,"relevant":121,"comment":217},"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。",[219],{"name":214,"url":211},[12],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[223,224],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":226},{"candidates":227,"agricultural_evidence":58},[228,233,238],{"code":40,"score":229,"supported":58,"title":230,"abstract":231,"tags":232},14,[41],[76,41],[],{"code":46,"score":234,"supported":58,"title":235,"abstract":236,"tags":237},9,[],[68,66,70],[],{"code":33,"score":21,"supported":58,"title":239,"abstract":240,"tags":241},[60],[],[],[],"Assessingtheimpactsofgoldminingonvegetat-5091","10.1016\u002Fj.indic.2026.101570",{"doi":244,"openalex_id":246,"authors":247,"venue":214,"cited_by_count":121,"oa_url":211,"card":256,"direction":126,"ingested_from":128},"W7222305702",[248,250,253],{"name":249,"orcid":9},"Mohamed Traoré",{"name":251,"orcid":252},"Opeyemi Aniramu","https:\u002F\u002Forcid.org\u002F0000-0002-5111-0221",{"name":254,"orcid":255},"Oluwagbenga O. I. Orimoogunje","https:\u002F\u002Forcid.org\u002F0000-0001-8070-5040",{"tldr":257,"method":258,"finding":259,"direction":126,"opportunity":260},"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。","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。","2026-10-16T23:30:27.217565Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":273,"tags":275,"primary_topic":278,"secondary_topics":279,"classification":281,"search_phrases":321,"slug":324,"view_count":22,"doi":325,"paper":326,"created_at":338},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",72,{"impact":142,"substance":271,"depth":19,"authority":20,"freshness":234,"relevant":22,"comment":272},21,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[274],{"name":268,"url":265},[27,28,29,276,277],"土壤健康","覆盖作物",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[280],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":282},{"candidates":283,"agricultural_evidence":58},[284,288,292,296,303,310,316],{"code":40,"score":73,"supported":58,"title":285,"abstract":286,"tags":287},[41],[76,41],[276],{"code":33,"score":142,"supported":58,"title":289,"abstract":290,"tags":291},[60],[29],[29],{"code":79,"score":21,"supported":58,"title":293,"abstract":294,"tags":295},[],[28,81],[27,28],{"code":297,"score":86,"supported":58,"title":298,"abstract":299,"tags":302},"A4",[],[300,301],"油菜","rapeseed",[],{"code":304,"score":86,"supported":58,"title":305,"abstract":306,"tags":309},"H8",[],[307,308],"可持续农业","sustainable agriculture",[],{"code":311,"score":94,"supported":58,"title":312,"abstract":313,"tags":315},"B1",[],[314],"萝卜",[],{"code":317,"score":22,"supported":14,"title":318,"abstract":319,"tags":320},"A7",[],[],[277],[322,323],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":325,"openalex_id":327,"authors":328,"venue":268,"cited_by_count":121,"oa_url":9,"card":333,"direction":126,"ingested_from":128},"W7221669942",[329,331],{"name":330,"orcid":9},"Ali Mirzakhani Nafchi",{"name":332,"orcid":9},"Salman Mirzaee",{"tldr":334,"method":335,"finding":336,"direction":126,"opportunity":337},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":58,"score":347,"score_detail":348,"sources":351,"tags":353,"primary_topic":356,"secondary_topics":357,"classification":359,"search_phrases":387,"slug":390,"view_count":22,"doi":391,"paper":392,"created_at":423},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data","2026-10-06T00:00:00Z",81,{"impact":57,"substance":349,"depth":57,"authority":229,"freshness":234,"relevant":22,"comment":350},22,"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[352],{"name":345,"url":342},[27,354,355,29,31],"农业人工智能","高光谱",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[358],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":360},{"candidates":361,"agricultural_evidence":58},[362,366,371,376,380],{"code":40,"score":229,"supported":58,"title":363,"abstract":364,"tags":365},[41],[76,41],[],{"code":33,"score":20,"supported":58,"title":367,"abstract":369,"tags":370},[368],"hyperspectral",[368],[29,355],{"code":79,"score":372,"supported":58,"title":373,"abstract":374,"tags":375},7,[],[28,81],[27],{"code":85,"score":372,"supported":58,"title":377,"abstract":378,"tags":379},[],[89,90],[354],{"code":381,"score":86,"supported":58,"title":382,"abstract":383,"tags":386},"F3",[],[384,385],"施肥","fertilization",[],[388,389],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":391,"openalex_id":393,"authors":394,"venue":345,"cited_by_count":121,"oa_url":416,"card":417,"direction":422,"ingested_from":128},"W7220738771",[395,398,401,404,407,410,413],{"name":396,"orcid":397},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":399,"orcid":400},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":402,"orcid":403},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":405,"orcid":406},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":408,"orcid":409},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":411,"orcid":412},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":414,"orcid":415},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":418,"method":419,"finding":420,"direction":126,"opportunity":421},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","智慧农业 \u002F 农业物联网","2026-10-07T23:30:09.147409Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":431,"category":12,"cover_url":9,"hotness":432,"is_selected":14,"score":433,"score_detail":434,"sources":436,"tags":440,"primary_topic":445,"secondary_topics":446,"classification":449,"search_phrases":473,"slug":476,"view_count":171,"doi":477,"paper":478,"created_at":492},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":142,"substance":57,"depth":73,"authority":142,"freshness":234,"relevant":22,"comment":435},"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。",[437,438],{"name":430,"url":427},{"name":430,"url":439},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[441,442,66,443,444],"土地退化","遥感监测","土壤盐渍化","灌溉农业",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[447,448],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":450},{"candidates":451,"agricultural_evidence":58},[452,456,460,464,468],{"code":46,"score":57,"supported":58,"title":453,"abstract":454,"tags":455},[66],[68,66,70],[66],{"code":33,"score":73,"supported":58,"title":457,"abstract":458,"tags":459},[60],[29,60],[442],{"code":40,"score":229,"supported":58,"title":461,"abstract":462,"tags":463},[41],[76,41],[],{"code":93,"score":171,"supported":58,"title":465,"abstract":466,"tags":467},[],[97],[444],{"code":469,"score":22,"supported":14,"title":470,"abstract":471,"tags":472},"H1",[],[],[441],[474,475],"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":477,"openalex_id":479,"authors":480,"venue":430,"cited_by_count":121,"oa_url":427,"card":487,"direction":126,"ingested_from":128},"W7214052773",[481,483,485],{"name":482,"orcid":9},"Mardiyev",{"name":484,"orcid":9},"Ergashaliyev",{"name":486,"orcid":9},"G'aniyev",{"tldr":488,"method":489,"finding":490,"direction":126,"opportunity":491},"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":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":9,"source_name":499,"source_url":496,"published_at":500,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":501,"score_detail":502,"sources":504,"tags":506,"primary_topic":508,"secondary_topics":509,"classification":515,"search_phrases":545,"slug":548,"view_count":86,"doi":549,"paper":550,"created_at":581},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":142,"substance":57,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":503},"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[505],{"name":499,"url":496},[27,354,28,29,507],"土壤碳汇",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[510,514],{"code":85,"slug":511,"name":512,"path":513,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":516},{"candidates":517,"agricultural_evidence":58},[518,522,527,531,535,541],{"code":40,"score":229,"supported":58,"title":519,"abstract":520,"tags":521},[41],[76,41],[],{"code":85,"score":13,"supported":58,"title":523,"abstract":524,"tags":526},[],[89,90,525],"artificial intelligence",[354],{"code":33,"score":13,"supported":58,"title":528,"abstract":529,"tags":530},[],[29,60,368],[29],{"code":79,"score":21,"supported":58,"title":532,"abstract":533,"tags":534},[],[28,81],[27,28],{"code":536,"score":171,"supported":58,"title":537,"abstract":538,"tags":540},"H4",[],[539],"carbon sequestration",[507],{"code":100,"score":94,"supported":58,"title":542,"abstract":543,"tags":544},[],[103],[],[546,547],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":549,"openalex_id":551,"authors":552,"venue":499,"cited_by_count":121,"oa_url":496,"card":576,"direction":126,"ingested_from":128},"W7213561504",[553,556,559,562,565,568,570,572,574],{"name":554,"orcid":555},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":557,"orcid":558},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":560,"orcid":561},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":563,"orcid":564},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":566,"orcid":567},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":569,"orcid":9},"Almaha Jamal",{"name":571,"orcid":9},"Afra Rashed",{"name":573,"orcid":9},"Hamda Yousif",{"name":575,"orcid":9},"Sarah Sadeq",{"tldr":577,"method":578,"finding":579,"direction":126,"opportunity":580},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z"]