[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5487":3,"related-5487":115},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":75,"slug":78,"view_count":79,"doi":80,"paper":81,"created_at":114},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。",null,"Discover Soil.","2026-10-20T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,16,13,8,1,"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30],"遥感","土地利用","土壤侵蚀","水土保持",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57,64,70],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},14,true,[33],[49,33],"土壤",[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":56},"H6",5,[],[30],[28,30],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":63},"H3",3,[],[62],"水资源",[],{"code":65,"score":59,"supported":46,"title":66,"abstract":67,"tags":69},"I4",[],[68],"GIS",[],{"code":71,"score":22,"supported":14,"title":72,"abstract":73,"tags":74},"I3",[],[],[27],[76,77],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487",0,"10.1007\u002Fs44378-026-00347-1",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":79,"oa_url":105,"card":106,"direction":112,"ingested_from":113},"W7223058039",[84,87,90,93,96,99,102],{"name":85,"orcid":86},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":88,"orcid":89},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":91,"orcid":92},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":94,"orcid":95},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":97,"orcid":98},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":100,"orcid":101},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":103,"orcid":104},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","农业绿色发展与碳","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","农业遥感与作物表型","openalex","2026-10-21T23:30:23.420269Z",{"total":116,"page":22,"page_size":116,"items":117},6,[118,188,255,334,394,481],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":9,"content":9,"source_name":123,"source_url":121,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":136,"secondary_topics":142,"classification":149,"search_phrases":176,"slug":179,"view_count":22,"doi":180,"paper":181,"created_at":187},4084,"Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.31332.74887","Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area。OpenAlex","OpenAlex","2026-10-02T00:00:00Z",68,{"impact":17,"substance":127,"depth":19,"authority":20,"freshness":128,"relevant":22,"comment":129},18,9,"该论文将轮作与遥感结合评估有机土壤水土保持，方法有创新且数据可靠，但属区域研究，影响范围有限，时效性强，值得关注。",[131],{"name":123,"url":121},[133,134,27,30,135],"智慧农业","轮作","有机土壤",{"code":137,"slug":138,"name":139,"path":140,"parent_code":141},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[143,148],{"code":71,"slug":144,"name":145,"path":146,"parent_code":147},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":150},{"candidates":151,"agricultural_evidence":46},[152,157,162,167,171],{"code":137,"score":17,"supported":46,"title":153,"abstract":155,"tags":156},[154],"crop rotation",[154],[134],{"code":71,"score":17,"supported":46,"title":158,"abstract":160,"tags":161},[159],"remote sensing",[159],[27],{"code":32,"score":163,"supported":46,"title":164,"abstract":165,"tags":166},11,[33],[33],[],{"code":52,"score":22,"supported":14,"title":168,"abstract":169,"tags":170},[],[],[30],{"code":172,"score":22,"supported":14,"title":173,"abstract":174,"tags":175},"I1",[],[],[133],[177,178],"Everglades 农业区 遥感 轮作","有机土壤 水土保持 遥感","Everglades农业区遥感轮作-4084","10.13140\u002Frg.2.2.31332.74887",{"doi":180,"openalex_id":182,"authors":183,"venue":9,"cited_by_count":79,"oa_url":121,"card":9,"direction":112,"ingested_from":113},"W7216417358",[184],{"name":185,"orcid":186},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033","2026-10-03T23:30:32.356375Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":200,"tags":202,"primary_topic":205,"secondary_topics":206,"classification":207,"search_phrases":231,"slug":234,"view_count":219,"doi":235,"paper":236,"created_at":254},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":17,"substance":18,"depth":198,"authority":20,"freshness":21,"relevant":22,"comment":199},17,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[201],{"name":194,"url":191},[133,203,204,27,28],"农业遥感","农业信息化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":208},{"candidates":209,"agricultural_evidence":46},[210,214,218,223,227],{"code":32,"score":116,"supported":46,"title":211,"abstract":212,"tags":213},[],[49,33],[],{"code":58,"score":59,"supported":46,"title":215,"abstract":216,"tags":217},[],[62],[],{"code":172,"score":219,"supported":14,"title":220,"abstract":221,"tags":222},2,[],[],[204,133],{"code":71,"score":219,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[203,27],{"code":52,"score":22,"supported":14,"title":228,"abstract":229,"tags":230},[],[],[28],[232,233],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":235,"openalex_id":237,"authors":238,"venue":194,"cited_by_count":79,"oa_url":247,"card":248,"direction":253,"ingested_from":113},"W7213644047",[239,241,243,245],{"name":240,"orcid":9},"Sreerama Naik Naik S R",{"name":242,"orcid":9},"T K Prasad",{"name":244,"orcid":9},"Feba Jose Jasmine",{"name":246,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":249,"method":250,"finding":251,"direction":112,"opportunity":252},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":9,"source_name":261,"source_url":258,"published_at":262,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":266,"tags":268,"primary_topic":271,"secondary_topics":275,"classification":278,"search_phrases":311,"slug":314,"view_count":219,"doi":315,"paper":316,"created_at":333},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":17,"substance":127,"depth":19,"authority":17,"freshness":219,"relevant":22,"comment":265},"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。",[267],{"name":261,"url":258},[269,27,68,30,270],"农业水资源","半干旱区",{"code":65,"slug":272,"name":273,"path":274,"parent_code":147},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[276,277],{"code":71,"slug":144,"name":145,"path":146,"parent_code":147},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":279},{"candidates":280,"agricultural_evidence":46},[281,287,292,296,301,305],{"code":65,"score":127,"supported":46,"title":282,"abstract":283,"tags":286},[68],[284,68,285],"地理信息","geographic information",[68],{"code":71,"score":288,"supported":46,"title":289,"abstract":290,"tags":291},15,[159],[27,159],[27],{"code":32,"score":163,"supported":46,"title":293,"abstract":294,"tags":295},[33],[33],[],{"code":58,"score":297,"supported":46,"title":298,"abstract":299,"tags":300},4,[],[62],[269],{"code":52,"score":297,"supported":46,"title":302,"abstract":303,"tags":304},[],[30],[30],{"code":306,"score":59,"supported":46,"title":307,"abstract":308,"tags":310},"H7",[],[309],"干旱",[],[312,313],"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":315,"openalex_id":317,"authors":318,"venue":261,"cited_by_count":79,"oa_url":258,"card":328,"direction":112,"ingested_from":113},"W7208812908",[319,322,324,326],{"name":320,"orcid":321},"Prasannajit D. Gangurde","https:\u002F\u002Forcid.org\u002F0009-0002-9266-8931",{"name":323,"orcid":9},"Sanjay N. Pawar",{"name":325,"orcid":9},"M. S. Pendke",{"name":327,"orcid":9},"Anilkumar M. Kamble",{"tldr":329,"method":330,"finding":331,"direction":112,"opportunity":332},"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":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":9,"source_name":340,"source_url":337,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":348,"secondary_topics":349,"classification":350,"search_phrases":375,"slug":378,"view_count":79,"doi":379,"paper":380,"created_at":393},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":21,"substance":127,"depth":45,"authority":17,"freshness":128,"relevant":22,"comment":344},"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[346],{"name":340,"url":337},[27,28,29,68,30],{"code":65,"slug":272,"name":273,"path":274,"parent_code":147},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":351},{"candidates":352,"agricultural_evidence":46},[353,357,362,366,371],{"code":65,"score":13,"supported":46,"title":354,"abstract":355,"tags":356},[],[284,68,285],[68],{"code":71,"score":358,"supported":46,"title":359,"abstract":360,"tags":361},7,[],[27,159],[27],{"code":32,"score":116,"supported":46,"title":363,"abstract":364,"tags":365},[],[49,33],[],{"code":58,"score":59,"supported":46,"title":367,"abstract":368,"tags":370},[],[369],"灌溉",[],{"code":52,"score":219,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[28,30],[376,377],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":379,"openalex_id":381,"authors":382,"venue":340,"cited_by_count":79,"oa_url":9,"card":388,"direction":112,"ingested_from":113},"W7223117733",[383,385],{"name":384,"orcid":9},"MERİÇ KURT",{"name":386,"orcid":387},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":389,"method":390,"finding":391,"direction":110,"opportunity":392},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":402,"sources":404,"tags":406,"primary_topic":411,"secondary_topics":412,"classification":419,"search_phrases":435,"slug":438,"view_count":79,"doi":439,"paper":440,"created_at":480},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","2026-10-15T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":403},"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。",[405],{"name":400,"url":397},[407,27,408,409,410],"机器学习","土壤碳","Landsat","数字土壤制图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[413,418],{"code":414,"slug":415,"name":416,"path":417,"parent_code":147},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":71,"slug":144,"name":145,"path":146,"parent_code":147},{"status":39,"locked":14,"version":40,"method":41,"evidence":420},{"candidates":421,"agricultural_evidence":46},[422,426,431],{"code":32,"score":45,"supported":46,"title":423,"abstract":424,"tags":425},[33],[49,33],[],{"code":414,"score":128,"supported":46,"title":427,"abstract":429,"tags":430},[428],"machine learning",[],[407],{"code":71,"score":21,"supported":46,"title":432,"abstract":433,"tags":434},[],[27,159],[409,27],[436,437],"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":439,"openalex_id":441,"authors":442,"venue":400,"cited_by_count":79,"oa_url":9,"card":475,"direction":112,"ingested_from":113},"W7222107492",[443,446,449,452,455,458,461,464,466,469,472],{"name":444,"orcid":445},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":447,"orcid":448},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":450,"orcid":451},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":453,"orcid":454},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":456,"orcid":457},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":459,"orcid":460},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":462,"orcid":463},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":465,"orcid":9},"A. E. Shatunov",{"name":467,"orcid":468},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":470,"orcid":471},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":473,"orcid":474},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":476,"method":477,"finding":478,"direction":112,"opportunity":479},"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",{"id":482,"title":483,"url":484,"summary":485,"summary_zh":486,"content":9,"source_name":487,"source_url":484,"published_at":488,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":489,"score_detail":490,"sources":492,"tags":494,"primary_topic":499,"secondary_topics":500,"classification":502,"search_phrases":539,"slug":542,"view_count":79,"doi":543,"paper":544,"created_at":586},5102,"Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42729-026-03629-3","Abstract Soils are fundamental to support ecosystem functions. Therefore, understanding soil organic carbon stock (SCS) as a strategic environmental component for climate regulation, sustainable agricultural practices, and maximizing fertilizer application is essential. This study aimed to predict and map the SCS at Palmital Farm, Minas Gerais state, using land use, parent materials, soil classes, proximal and remote sensing data as predictor variables. A total of 76 soil samples were collected in the surface (A) and subsurface (B) horizons using a regular grid design (200 × 200 m). Carbon content was determined using the Walkley–Black method. Prediction models were created via Random Forest algorithm and validated through leave-one-out cross-validation. Environmental and anthropogenic influences on SCS were analyzed statistically and spatially. The highest mean SCS values were found at the surface horizon under native vegetation and pasture areas (~ 33 Mg ha⁻¹), due to continuous organic matter deposition and lack of disturbance. Charnockite-derived soils showed higher SCS (31.24 Mg ha⁻¹, surface). Among soil classes, Argissolos stood out reaching the highest SCS (35.86 Mg ha⁻¹). Remote sensing data and terrain attributes yielded the best SCS prediction (R²=0.43). The main factors influencing SCS were land use\u002Fmanagement and soil texture. Also, strong correlations were observed between vegetation indices, some terrain attributes and SCS. This approach, combining multiple variables, indicated a moderate potential to achieve accurate SCS predictions using these data, demonstrating the high variability and complexity of SCS spatial variation in detailed-scale mapping.","摘要 土壤是支撑生态系统功能的基础。因此，理解土壤有机碳储量（SCS）作为气候调节、可持续农业实践和最大化肥料施用的战略性环境组成部分至关重要。本研究旨在利用土地利用、成土母质、土壤类别、近端和遥感数据作为预测变量，对米纳斯吉拉斯州Palmital农场的SCS进行预测和制图。采用规则网格设计（200 × 200 m），在表层（A）和亚表层（B）共采集76个土壤样品。碳含量采用Walkley–Black法测定。通过随机森林算法建立预测模型，并采用留一交叉验证进行验证。对环境和人为因素对SCS的影响进行了统计和空间分析。表层中原生植被和牧草地利用方式下的SCS均值最高（约33 Mg ha⁻¹），这归因于有机质的持续沉积和缺乏扰动。源自紫苏花岗岩的土壤表现出较高的SCS（31.24 Mg ha⁻¹，表层）。在土壤类别中，Argissolos表现突出，达到最高SCS（35.86 Mg ha⁻¹）。遥感数据和地形属性获得了最佳的SCS预测效果（R²=0.43）。影响SCS的主要因素为土地利用\u002F管理和土壤质地。此外，植被指数、部分地形属性与SCS之间观察到较强的相关性。这种结合多变量的方法表明，利用这些数据实现准确SCS预测具有中等潜力，同时也揭示了详细尺度制图中SCS空间变异的高变异性和复杂性。","Journal of soil science and plant nutrition","2026-10-14T00:00:00Z",67,{"impact":21,"substance":18,"depth":198,"authority":20,"freshness":128,"relevant":22,"comment":491},"该研究结合近地与遥感数据及随机森林算法预测土壤有机碳储量，方法新颖、数据扎实，对农业信息化与精准农业有参考价值，但属区域性案例研究，影响范围有限。",[493],{"name":487,"url":484},[495,496,497,27,498],"数字农业","随机森林","土壤有机碳","土壤制图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[501],{"code":71,"slug":144,"name":145,"path":146,"parent_code":147},{"status":39,"locked":14,"version":40,"method":41,"evidence":503},{"candidates":504,"agricultural_evidence":46},[505,509,513,520,526,531,535],{"code":32,"score":19,"supported":46,"title":506,"abstract":507,"tags":508},[33],[49,33],[498,497],{"code":71,"score":288,"supported":46,"title":510,"abstract":511,"tags":512},[159],[27,159],[27],{"code":514,"score":116,"supported":46,"title":515,"abstract":516,"tags":519},"F3",[],[517,518],"肥料","fertilizer",[],{"code":521,"score":59,"supported":46,"title":522,"abstract":523,"tags":525},"H8",[],[524],"可持续农业",[],{"code":65,"score":59,"supported":46,"title":527,"abstract":528,"tags":530},[],[529],"空间分析",[],{"code":172,"score":22,"supported":14,"title":532,"abstract":533,"tags":534},[],[],[495],{"code":414,"score":22,"supported":14,"title":536,"abstract":537,"tags":538},[],[],[496],[540,541],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102","10.1007\u002Fs42729-026-03629-3",{"doi":543,"openalex_id":545,"authors":546,"venue":487,"cited_by_count":79,"oa_url":580,"card":581,"direction":112,"ingested_from":113},"W7222335269",[547,550,553,556,559,562,565,568,571,574,577],{"name":548,"orcid":549},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":551,"orcid":552},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":554,"orcid":555},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":557,"orcid":558},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":560,"orcid":561},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":563,"orcid":564},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":566,"orcid":567},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":569,"orcid":570},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":572,"orcid":573},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":575,"orcid":576},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":578,"orcid":579},"Sérgio Henrique Godinho Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2750-5976","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs42729-026-03629-3.pdf",{"tldr":582,"method":583,"finding":584,"direction":112,"opportunity":585},"结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。","76个样点、Walkley-Black法测碳，随机森林建模，留一交叉验证。","遥感与地形因子预测最佳（R²=0.43），土地利用和土壤质地主导碳储量空间变异。","可探索多源遥感与深度学习方法提升小尺度土壤碳制图精度，并耦合管理措施情景。","2026-10-16T23:30:31.433840Z"]