[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3363":3,"related-3363":97},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":96},3363,"Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02576-x","Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India。Journal of the Indian Society of Remote Sensing","基于RUSLE、遥感和情景机器学习的土壤侵蚀驱动因素综合评估——以印度东部乔塔纳格普尔高原数据稀缺流域为例。《印度遥感学会杂志》",null,"Journal of the Indian Society of Remote Sensing","2026-09-23T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,13,9,1,"方法组合有新意但属区域案例研究，影响范围有限，可作为遥感与水土保持主题的补充素材。",[25],{"name":10,"url":6},[27,28,29,30],"机器学习","遥感监测","土壤侵蚀","水土保持",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":65},[38,45,52,60],{"code":39,"score":19,"supported":40,"title":41,"abstract":43,"tags":44},"I2",true,[42],"machine learning",[27,42],[27],{"code":46,"score":19,"supported":40,"title":47,"abstract":49,"tags":51},"I3",[48],"remote sensing",[50,48],"遥感",[28],{"code":53,"score":54,"supported":40,"title":55,"abstract":57,"tags":59},"H2",14,[56],"soil",[58,56],"土壤",[],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"H6",[],[],[30],"农业相关性需要复核",[67,68],"Chota Nagpur Plateau 土壤侵蚀","RUSLE 遥感 机器学习","ChotaNagpurPlateau土壤侵蚀-3363",7,"10.1007\u002Fs12524-026-02576-x",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":88,"oa_url":9,"card":89,"direction":93,"ingested_from":95},"W7214122637",[75,78,81,83,85],{"name":76,"orcid":77},"Mukesh Kumar Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-0385-4426",{"name":79,"orcid":80},"Prabhat Kumar Guru","https:\u002F\u002Forcid.org\u002F0000-0002-9294-2091",{"name":82,"orcid":9},"Sanjeet Kumar",{"name":84,"orcid":9},"Yogesh A. Rajwade",{"name":86,"orcid":87},"Narendra Singh Chandel","https:\u002F\u002Forcid.org\u002F0000-0003-4850-4702",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"结合RUSLE、遥感与情景机器学习，评估印度Chota Nagpur高原缺数据流域的土壤侵蚀驱动因素","RUSLE模型、遥感数据与情景机器学习集成分析。","在数据稀缺流域识别出土壤侵蚀关键驱动因子并预测不同情景下的侵蚀风险。","农业遥感与作物表型","可探索缺数据区多源遥感与机器学习融合的土壤侵蚀动态监测与情景预警方法。","openalex","2026-09-24T23:30:21.065678Z",{"total":98,"page":22,"page_size":98,"items":99},6,[100,162,230,320,398,458],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":9,"secondary_topics":117,"classification":118,"search_phrases":141,"slug":144,"view_count":88,"doi":145,"paper":146,"created_at":161},4316,"GIS-BASED ASSESSMENT OF SOIL EROSION RISK IN GUBI DAM WATERSHED, NORTHEASTERN NIGERIA, USING THE REVISED UNIVERSAL SOIL LOSS EQUATION (RUSLE)","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fbejemcr.v13i4.040","Soil erosion within reservoir watersheds can degrade land resources and mobilise sediment through drainage networks, making spatial identification of erosion-prone areas important for watershed management. This study assessed the magnitude and spatial distribution of soil erosion in the Gubi Dam watershed, northeastern Nigeria, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) and remote-sensing techniques. A 30m Shuttle Radar Topography Mission (SRTM) digital elevation model was used for watershed delineation and terrain analysis, while rainfall records, FAO soil information and September 2021 Landsat 8 OLI imagery were used to derive rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and support-practice (P) factors. Vegetation protection was represented using a continuous NDVI-derived C-factor surface. The delineated watershed covered approximately 18,067.39 ha (180.67 km²). RUSLE estimated soil loss ranged from approximately 0 to 715.3 t ha⁻¹ yr⁻¹, with a watershed-wide mean of 5.590t ha⁻¹ yr⁻¹. The archived study-specific classification indicated that 98.6% of the watershed occurred within the two lower soil loss classes, whereas the three upper classes collectively occupied approximately 252.94 ha (1.4%). Elevated estimates were spatially concentrated, particularly in areas associated with steep terrain and major drainage pathways. The assessment provides a spatial screening basis for prioritising field investigation and soil conservation planning. The estimates represent potential sheet and rill soil loss rather than sediment delivery to Gubi Reservoir, and field validation is recommended.","水库流域内的土壤侵蚀会退化土地资源，并通过排水网络搬运泥沙，因此对易侵蚀区域进行空间识别对流域管理具有重要意义。本研究采用修订通用土壤流失方程（RUSLE），结合地理信息系统（GIS）和遥感技术，评估了尼日利亚东北部古比大坝流域土壤侵蚀的强度与空间分布。研究使用30 m航天飞机雷达地形测绘任务（SRTM）数字高程模型进行流域划分和地形分析，并利用降雨记录、FAO土壤信息和2021年9月Landsat 8 OLI影像提取降雨侵蚀力（R）、土壤可蚀性（K）、坡长与坡度（LS）、植被覆盖与管理（C）以及水土保持措施（P）因子。植被保护状况通过基于NDVI生成的连续C因子表面来表示。划分出的流域面积约为18 067.39 ha（180.67 km²）。RUSLE估算的土壤流失量约为0至715.3 t ha⁻¹ yr⁻¹，全流域平均值为5.590 t ha⁻¹ yr⁻¹。本研究采用的分类结果表明，流域内98.6%的区域属于两个较低土壤流失等级，而三个较高等级合计约占252.94 ha（1.4%）。高估算值在空间上较为集中，尤其出现在陡峭地形和主要排水通道相关区域。该评估为优先开展野外调查和水土保持规划提供了空间筛查依据。这些估算值代表潜在的片蚀和细沟土壤流失，而非输移至古比水库的泥沙量，建议进行野外验证。","Journal of Environmental Management and Construction Research","2026-10-04T00:00:00Z",61,{"impact":17,"substance":18,"depth":19,"authority":110,"freshness":17,"relevant":22,"comment":111},12,"基于RUSLE与GIS\u002F遥感的流域土壤侵蚀定量评估，方法规范、数据具体，但属区域性案例研究，对国内三农信息化仅有方法借鉴价值。",[113],{"name":106,"url":103},[28,29,115,30,116],"GIS","流域治理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":119},{"candidates":120,"agricultural_evidence":14,"reason":65},[121,128,132,137],{"code":122,"score":18,"supported":40,"title":123,"abstract":124,"tags":127},"I4",[115],[125,115,126],"地理信息","geographic information",[115],{"code":53,"score":54,"supported":40,"title":129,"abstract":130,"tags":131},[56],[58,56],[],{"code":61,"score":133,"supported":40,"title":134,"abstract":135,"tags":136},4,[],[30],[30],{"code":46,"score":133,"supported":40,"title":138,"abstract":139,"tags":140},[],[50],[28],[142,143],"Gubi Dam 流域 RUSLE","尼日利亚 土壤侵蚀 GIS","GubiDam流域RUSLE-4316","10.70382\u002Fbejemcr.v13i4.040",{"doi":145,"openalex_id":147,"authors":148,"venue":106,"cited_by_count":88,"oa_url":155,"card":156,"direction":93,"ingested_from":95},"W7219911927",[149,151,153],{"name":150,"orcid":9},"AHMED BABAYO",{"name":152,"orcid":9},"ABDULKADIR ABUBAKAR",{"name":154,"orcid":9},"ZAKARI DANLADI","https:\u002F\u002Fberkeleypublications.com\u002Fbjemcr\u002Farticle\u002Fdownload\u002F895\u002F790",{"tldr":157,"method":158,"finding":159,"direction":93,"opportunity":160},"用RUSLE结合GIS和遥感评估尼日利亚Gubi水库流域土壤侵蚀风险并制图。","RUSLE模型、30m SRTM DEM、Landsat 8 OLI、NDVI与","流域年均土壤流失5.59 t\u002Fha\u002Fyr，高侵蚀区集中于陡坡和主排水路径，仅1.4%面积属高侵蚀等级","可结合泥沙输移比与实地验证，将潜在侵蚀估算转化为水库淤积风险与保护措施优先区研究。","2026-10-06T23:30:21.805554Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":170,"sources":172,"tags":174,"primary_topic":176,"secondary_topics":181,"classification":182,"search_phrases":210,"slug":213,"view_count":88,"doi":214,"paper":215,"created_at":229},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",{"impact":17,"substance":18,"depth":54,"authority":110,"freshness":21,"relevant":22,"comment":171},"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。",[173],{"name":168,"url":165},[50,175,29,115,30],"土地利用",{"code":122,"slug":177,"name":178,"path":179,"parent_code":180},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":183,"locked":14,"version":34,"method":35,"evidence":184},"classified",{"candidates":185,"agricultural_evidence":40},[186,190,194,198,205],{"code":122,"score":13,"supported":40,"title":187,"abstract":188,"tags":189},[],[125,115,126],[115],{"code":46,"score":70,"supported":40,"title":191,"abstract":192,"tags":193},[],[50,48],[50],{"code":53,"score":98,"supported":40,"title":195,"abstract":196,"tags":197},[],[58,56],[],{"code":199,"score":200,"supported":40,"title":201,"abstract":202,"tags":204},"H3",3,[],[203],"灌溉",[],{"code":61,"score":206,"supported":14,"title":207,"abstract":208,"tags":209},2,[],[],[175,30],[211,212],"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":214,"openalex_id":216,"authors":217,"venue":168,"cited_by_count":88,"oa_url":9,"card":223,"direction":93,"ingested_from":95},"W7223117733",[218,220],{"name":219,"orcid":9},"MERİÇ KURT",{"name":221,"orcid":222},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":224,"method":225,"finding":226,"direction":227,"opportunity":228},"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。","农业绿色发展与碳","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":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":243,"tags":245,"primary_topic":249,"secondary_topics":255,"classification":264,"search_phrases":296,"slug":299,"view_count":88,"doi":300,"paper":301,"created_at":319},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":239,"substance":240,"depth":241,"authority":20,"freshness":13,"relevant":22,"comment":242},16,21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[244],{"name":236,"url":233},[246,27,28,247,248],"智慧农业","土壤墒情","冬小麦",{"code":250,"slug":251,"name":252,"path":253,"parent_code":254},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[256,260],{"code":53,"slug":56,"name":257,"path":258,"parent_code":259},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":261,"name":262,"path":263,"parent_code":180},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":183,"locked":14,"version":34,"method":35,"evidence":265},{"candidates":266,"agricultural_evidence":40},[267,273,277,281,286,291],{"code":250,"score":268,"supported":40,"title":269,"abstract":271,"tags":272},26,[270,251],"winter wheat",[252,270,251],[248],{"code":53,"score":19,"supported":40,"title":274,"abstract":275,"tags":276},[56],[58,56],[247],{"code":39,"score":19,"supported":40,"title":278,"abstract":279,"tags":280},[42],[27,42],[27],{"code":46,"score":13,"supported":40,"title":282,"abstract":283,"tags":285},[],[50,48,284],"multispectral",[28],{"code":199,"score":98,"supported":40,"title":287,"abstract":288,"tags":290},[],[203,289],"irrigation",[],{"code":292,"score":133,"supported":40,"title":293,"abstract":294,"tags":295},"I1",[],[246],[246],[297,298],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":300,"openalex_id":302,"authors":303,"venue":236,"cited_by_count":88,"oa_url":233,"card":313,"direction":318,"ingested_from":95},"W7223156204",[304,307,309,311],{"name":305,"orcid":306},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":308,"orcid":9},"Yi Zhang",{"name":310,"orcid":9},"WEI Lei",{"name":312,"orcid":9},"Chengcai Zhang",{"tldr":314,"method":315,"finding":316,"direction":93,"opportunity":317},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":9,"source_name":326,"source_url":323,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":332,"tags":334,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":362,"slug":365,"view_count":88,"doi":366,"paper":367,"created_at":397},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。","Discover Soil.","2026-10-20T00:00:00Z",69,{"impact":110,"substance":330,"depth":239,"authority":20,"freshness":17,"relevant":22,"comment":331},20,"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。",[333],{"name":326,"url":323},[50,175,29,30],{"code":53,"slug":56,"name":257,"path":258,"parent_code":259},[],{"status":183,"locked":14,"version":34,"method":35,"evidence":338},{"candidates":339,"agricultural_evidence":40},[340,344,349,354,358],{"code":53,"score":54,"supported":40,"title":341,"abstract":342,"tags":343},[56],[58,56],[],{"code":61,"score":345,"supported":40,"title":346,"abstract":347,"tags":348},5,[],[30],[175,30],{"code":199,"score":200,"supported":40,"title":350,"abstract":351,"tags":353},[],[352],"水资源",[],{"code":122,"score":200,"supported":40,"title":355,"abstract":356,"tags":357},[],[115],[],{"code":46,"score":22,"supported":14,"title":359,"abstract":360,"tags":361},[],[],[50],[363,364],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":366,"openalex_id":368,"authors":369,"venue":326,"cited_by_count":88,"oa_url":391,"card":392,"direction":93,"ingested_from":95},"W7223058039",[370,373,376,379,382,385,388],{"name":371,"orcid":372},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":374,"orcid":375},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":377,"orcid":378},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":380,"orcid":381},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":383,"orcid":384},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":386,"orcid":387},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":389,"orcid":390},"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":393,"method":394,"finding":395,"direction":227,"opportunity":396},"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。","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。","2026-10-21T23:30:23.420269Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":405,"sources":407,"tags":409,"primary_topic":411,"secondary_topics":412,"classification":414,"search_phrases":435,"slug":438,"view_count":88,"doi":439,"paper":440,"created_at":457},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE",{"impact":88,"substance":88,"depth":88,"authority":88,"freshness":88,"relevant":88,"comment":406},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[408],{"name":404,"url":401},[27,28,175,410],"城市扩张",{"code":39,"slug":261,"name":262,"path":263,"parent_code":180},[413],{"code":122,"slug":177,"name":178,"path":179,"parent_code":180},{"status":183,"locked":14,"version":34,"method":35,"evidence":415},{"candidates":416,"agricultural_evidence":40},[417,421,427,431],{"code":39,"score":19,"supported":40,"title":418,"abstract":419,"tags":420},[42],[27,42],[27],{"code":122,"score":21,"supported":40,"title":422,"abstract":423,"tags":426},[],[424,115,425],"空间分析","spatial analysis",[],{"code":46,"score":70,"supported":40,"title":428,"abstract":429,"tags":430},[],[50,48],[28],{"code":61,"score":22,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[175],[436,437],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":439,"openalex_id":441,"authors":442,"venue":404,"cited_by_count":88,"oa_url":401,"card":452,"direction":93,"ingested_from":95},"W7223022155",[443,446,448,450],{"name":444,"orcid":445},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":447,"orcid":9},"Goanar Jal Dup",{"name":449,"orcid":9},"Weyesa Merga Chemeda",{"name":451,"orcid":9},"Shankar Karuppannan",{"tldr":453,"method":454,"finding":455,"direction":93,"opportunity":456},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":459,"title":460,"url":461,"summary":462,"summary_zh":463,"content":9,"source_name":464,"source_url":461,"published_at":465,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":466,"sources":468,"tags":470,"primary_topic":472,"secondary_topics":476,"classification":477,"search_phrases":498,"slug":501,"view_count":88,"doi":502,"paper":503,"created_at":531},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":88,"substance":88,"depth":88,"authority":88,"freshness":88,"relevant":88,"comment":467},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[469],{"name":464,"url":461},[28,175,29,471],"乡村人口",{"code":46,"slug":473,"name":474,"path":475,"parent_code":180},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":183,"locked":14,"version":34,"method":35,"evidence":478},{"candidates":479,"agricultural_evidence":40},[480,484,488,494],{"code":46,"score":70,"supported":40,"title":481,"abstract":482,"tags":483},[],[50,48],[28],{"code":122,"score":200,"supported":40,"title":485,"abstract":486,"tags":487},[],[115],[],{"code":489,"score":200,"supported":40,"title":490,"abstract":491,"tags":493},"I7",[],[492],"methodological framework",[],{"code":61,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[175],[499,500],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":502,"openalex_id":504,"authors":505,"venue":464,"cited_by_count":88,"oa_url":461,"card":525,"direction":93,"ingested_from":95},"W7222945959",[506,509,512,514,517,520,523],{"name":507,"orcid":508},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":510,"orcid":511},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":513,"orcid":9},"Милан Радовић",{"name":515,"orcid":516},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":518,"orcid":519},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":521,"orcid":522},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":524,"orcid":9},"Manojlović Stefan",{"tldr":526,"method":527,"finding":528,"direction":529,"opportunity":530},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z"]