[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3073":3,"related-3073":96},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":66,"slug":69,"view_count":21,"doi":70,"paper":71,"created_at":95},3073,"Evolution of soil erosion and sediment delivery modelling over six decades: paradigms, limitations and new solutions","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15715124.2026.2717245","Modelling sediment delivery, namely the linkage between hillslope erosion and yield in river systems, is still a challenge. In this study, 1,075 publications from Elsevier's Scopus were analysed to trace the evolution from lumped to spatially distributed approaches. Results confirm that no single model type dominates, while several studies rely on simplified approaches, and data- and model-related errors remain unresolved. Artificial intelligence (AI) and remote sensing (RS) can address such limitations. However, although AI has emerged as either a complementary tool for generating new climate or land-use scenarios or a standalone framework, its adoption has not been transformative so far. Similarly, RS is increasingly used to characterise topography and land cover, but is poorly leveraged to determine other factors influencing sediment dynamics. This overview of existing and evolving methods provides useful insights to support river basin management and model selection. Overall, this study underscores the need for further integration of diverse data sources and processing methods, while future research should prioritise the trade-offs among model complexity, accuracy, and scalability.","模拟泥沙输移，即坡面侵蚀与河流系统产沙之间的联系，仍然是一项挑战。本研究分析了来自Elsevier Scopus数据库的1，075篇文献，以追溯从集总式方法到空间分布式方法的演变历程。结果证实，尚无单一模型类型占据主导地位，而若干研究依赖于简化方法，且与数据和模型相关的误差仍未得到解决。人工智能（AI）和遥感（RS）能够应对这些局限性。然而，尽管AI已作为生成新气候或土地利用情景的补充工具或独立框架出现，但其应用迄今尚未带来变革性影响。同样，RS越来越多地用于表征地形和土地覆盖，但在确定影响泥沙动力学的其他因素方面利用不足。本综述对现有及不断演变的方法进行了概述，为支持流域管理和模型选择提供了有益见解。总体而言，本研究强调了进一步整合多样化数据来源和处理方法的必要性，同时未来研究应优先考虑模型复杂性、精度和可扩展性之间的权衡。",null,"International Journal of River Basin Management","2026-09-19T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"基于1075篇文献的六十年土壤侵蚀与泥沙输移建模综述，指出AI与遥感应用尚未形成变革性突破，对农业水土保持与流域管理有参考价值，但属学术综述、影响面有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","农业遥感","土壤侵蚀","水土保持","流域管理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":65},[38,45,53,60],{"code":39,"score":17,"supported":40,"title":41,"abstract":43,"tags":44},"H2",true,[42],"soil",[],[],{"code":46,"score":47,"supported":40,"title":48,"abstract":49,"tags":52},"I3",7,[],[50,51],"遥感","remote sensing",[27],{"code":54,"score":55,"supported":40,"title":56,"abstract":57,"tags":59},"I2",4,[],[58],"artificial intelligence",[26],{"code":61,"score":21,"supported":14,"title":62,"abstract":63,"tags":64},"H6",[],[],[29],"农业相关性需要复核",[67,68],"土壤侵蚀 泥沙输移 模型","遥感 AI 流域管理","土壤侵蚀泥沙输移模型-3073","10.1080\u002F15715124.2026.2717245",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":86,"oa_url":87,"card":88,"direction":92,"ingested_from":94},"W7213758675",[74,77,80,83],{"name":75,"orcid":76},"Melissa Latella","https:\u002F\u002Forcid.org\u002F0000-0003-3678-6992",{"name":78,"orcid":79},"Monia Santini","https:\u002F\u002Forcid.org\u002F0000-0002-8041-8241",{"name":81,"orcid":82},"Pierfranco Costabile","https:\u002F\u002Forcid.org\u002F0000-0003-1147-9929",{"name":84,"orcid":85},"Roberta Padulano","https:\u002F\u002Forcid.org\u002F0000-0003-4881-4495",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15715124.2026.2717245?needAccess=true",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"分析1075篇文献，梳理六十年土壤侵蚀与泥沙输移模型从集总到分布式的演变。","Scopus文献计量分析，综述AI与遥感在泥沙输移建模中的应用。","无单一模型占主导，AI与遥感应用尚未变革性，需整合多源数据并权衡复杂度、精度与可扩展性。","农业遥感与作物表型","可探索AI与多源遥感深度融合的分布式泥沙输移模型，兼顾精度与可扩展性。","openalex","2026-09-21T23:30:25.637249Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,206,279,359,436,521],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":114,"tags":116,"primary_topic":120,"secondary_topics":126,"classification":138,"search_phrases":174,"slug":177,"view_count":86,"doi":178,"paper":179,"created_at":205},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":109,"substance":110,"depth":111,"authority":112,"freshness":13,"relevant":21,"comment":113},16,22,18,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[115],{"name":105,"url":102},[117,26,27,118,119],"智慧农业","氮素监测","紫花苜蓿",{"code":121,"slug":122,"name":123,"path":124,"parent_code":125},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[127,132],{"code":46,"slug":128,"name":129,"path":130,"parent_code":131},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":133,"slug":134,"name":135,"path":136,"parent_code":137},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":139,"locked":14,"version":34,"method":35,"evidence":140},"classified",{"candidates":141,"agricultural_evidence":40},[142,147,154,160,169],{"code":46,"score":111,"supported":40,"title":143,"abstract":145,"tags":146},[144],"hyperspectral",[50,51,144],[27],{"code":121,"score":148,"supported":40,"title":149,"abstract":151,"tags":153},15,[150],"alfalfa",[152,150],"苜蓿",[119],{"code":133,"score":13,"supported":40,"title":155,"abstract":156,"tags":159},[],[157,152,158],"饲草","forage",[119],{"code":54,"score":13,"supported":40,"title":161,"abstract":162,"tags":168},[],[163,164,165,166,167],"深度学习","机器学习","machine learning","deep learning","decision support",[26],{"code":170,"score":21,"supported":14,"title":171,"abstract":172,"tags":173},"I1",[],[],[117],[175,176],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":178,"openalex_id":180,"authors":181,"venue":105,"cited_by_count":86,"oa_url":102,"card":200,"direction":92,"ingested_from":94},"W7223154576",[182,184,186,188,190,193,195,198],{"name":183,"orcid":9},"Shuai Fu",{"name":185,"orcid":9},"Yunhao Li",{"name":187,"orcid":9},"Chunli Miao",{"name":189,"orcid":9},"Jie Liu",{"name":191,"orcid":192},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":194,"orcid":9},"Yongjia Hu",{"name":196,"orcid":197},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":199,"orcid":9},"吴彩霞",{"tldr":201,"method":202,"finding":203,"direction":92,"opportunity":204},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":9,"source_name":212,"source_url":209,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":213,"score_detail":214,"sources":218,"tags":220,"primary_topic":223,"secondary_topics":228,"classification":229,"search_phrases":259,"slug":262,"view_count":86,"doi":263,"paper":264,"created_at":278},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",61,{"impact":17,"substance":111,"depth":112,"authority":215,"freshness":216,"relevant":21,"comment":217},12,9,"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。",[219],{"name":212,"url":209},[50,221,28,222,29],"土地利用","GIS",{"code":224,"slug":225,"name":226,"path":227,"parent_code":131},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":139,"locked":14,"version":34,"method":35,"evidence":230},{"candidates":231,"agricultural_evidence":40},[232,238,242,247,254],{"code":224,"score":13,"supported":40,"title":233,"abstract":234,"tags":237},[],[235,222,236],"地理信息","geographic information",[222],{"code":46,"score":47,"supported":40,"title":239,"abstract":240,"tags":241},[],[50,51],[50],{"code":39,"score":97,"supported":40,"title":243,"abstract":244,"tags":246},[],[245,42],"土壤",[],{"code":248,"score":249,"supported":40,"title":250,"abstract":251,"tags":253},"H3",3,[],[252],"灌溉",[],{"code":61,"score":255,"supported":14,"title":256,"abstract":257,"tags":258},2,[],[],[221,29],[260,261],"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":263,"openalex_id":265,"authors":266,"venue":212,"cited_by_count":86,"oa_url":9,"card":272,"direction":92,"ingested_from":94},"W7223117733",[267,269],{"name":268,"orcid":9},"MERİÇ KURT",{"name":270,"orcid":271},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":273,"method":274,"finding":275,"direction":276,"opportunity":277},"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":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":290,"tags":292,"primary_topic":293,"secondary_topics":297,"classification":298,"search_phrases":323,"slug":326,"view_count":86,"doi":327,"paper":328,"created_at":358},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":215,"substance":18,"depth":109,"authority":20,"freshness":17,"relevant":21,"comment":289},"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。",[291],{"name":285,"url":282},[50,221,28,29],{"code":39,"slug":42,"name":294,"path":295,"parent_code":296},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":139,"locked":14,"version":34,"method":35,"evidence":299},{"candidates":300,"agricultural_evidence":40},[301,305,310,315,319],{"code":39,"score":112,"supported":40,"title":302,"abstract":303,"tags":304},[42],[245,42],[],{"code":61,"score":306,"supported":40,"title":307,"abstract":308,"tags":309},5,[],[29],[221,29],{"code":248,"score":249,"supported":40,"title":311,"abstract":312,"tags":314},[],[313],"水资源",[],{"code":224,"score":249,"supported":40,"title":316,"abstract":317,"tags":318},[],[222],[],{"code":46,"score":21,"supported":14,"title":320,"abstract":321,"tags":322},[],[],[50],[324,325],"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":327,"openalex_id":329,"authors":330,"venue":285,"cited_by_count":86,"oa_url":352,"card":353,"direction":92,"ingested_from":94},"W7223058039",[331,334,337,340,343,346,349],{"name":332,"orcid":333},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":335,"orcid":336},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":338,"orcid":339},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":341,"orcid":342},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":344,"orcid":345},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":347,"orcid":348},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":350,"orcid":351},"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":354,"method":355,"finding":356,"direction":276,"opportunity":357},"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":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":378,"classification":379,"search_phrases":410,"slug":413,"view_count":86,"doi":414,"paper":415,"created_at":435},5166,"Application of the SCS–CN Method Integrated with GIS and Remote Sensing for Watershed Runoff Estimation: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i105735","Accurate estimation of runoff is crucial for watershed planning, soil and water conservation, flood mitigation, and the management of ungauged watersheds. The Soil Conservation Service Curve Number (SCS-CN) method has been extensively used because of its minimal input requirements and its ability to model the spatial influence of land use, soil properties, and hydrologic soil groups. This review compiles applications of the GIS-based SCS-CN method reported for watersheds in semi-arid and monsoon-affected countries such as India, Saudi Arabia, Afghanistan, China, Iraq, and Ethiopia. The reviewed literature employed rainfall data, land-use\u002Fland-cover data, soil maps, hydrologic soil groups, digital elevation models, and watershed morphometry to calculate curve numbers and direct runoff. The reported CN values varied substantially, from about 40 under dry conditions to more than 90 under urban, rocky, agricultural, or poorly infiltrating conditions. Runoff estimates ranged from relatively low values in arid catchments to more than 1,300 mm per year in high-rainfall watersheds. Several studies reported high R2 correlations between observed and estimated runoff, typically ranging from 0.73 to 0.98. Important limitations identified in the review include the sensitivity of results to the choice of curve number, antecedent moisture condition, initial abstraction assumption, land-use classification, soil-group assignment, rainfall representation, and the lack of observed discharge data. The overall design of the SCS-CN method and its integration with RS and GIS offer a practical approach to assessing spatial runoff and prioritising watersheds, but the method requires local calibration, uncertainty analysis, and validation against observed rainfall–runoff data.","径流的准确估算对于流域规划、水土保持、洪水缓解以及无资料流域的管理至关重要。土壤保持服务曲线数（SCS-CN）方法因其输入需求少、能够模拟土地利用、土壤性质和水文土壤组的空间影响而被广泛使用。本综述汇编了基于GIS的SCS-CN方法在半干旱和受季风影响国家（如印度、沙特阿拉伯、阿富汗、中国、伊拉克和埃塞俄比亚）流域的应用报道。所综述的文献利用降雨数据、土地利用\u002F土地覆盖数据、土壤图、水文土壤组、数字高程模型和流域形态计量来计算曲线数和直接径流。报道的CN值差异很大，从干旱条件下的约40到城市、岩石、农业或入渗不良条件下的90以上不等。径流估算值从干旱集水区的相对较低值到高降雨流域的年径流量超过1300 mm不等。多项研究报告了观测径流与估算径流之间的高R²相关性，通常在0.73至0.98之间。本综述识别出的重要局限性包括：结果对曲线数选择、前期土壤湿润条件、初始损失假设、土地利用分类、土壤组赋值、降雨代表性以及缺乏实测流量数据的敏感性。SCS-CN方法的总体设计及其与遥感和GIS的集成，为评估空间径流和流域优先排序提供了一种实用方法，但该方法需要本地校准、不确定性分析，并依据实测降雨—径流数据进行验证。","International Journal of Environment and Climate Change","2026-10-16T00:00:00Z",63,{"impact":17,"substance":111,"depth":109,"authority":215,"freshness":216,"relevant":21,"comment":369},"综述系统梳理了GIS与遥感集成的SCS-CN径流估算方法及其局限，对农业信息化中的流域水资源与水土保持管理有参考价值，但属方法综述、影响面有限，未达每日精选门槛。",[371],{"name":365,"url":362},[50,222,29,30,373],"水文模型",{"code":131,"slug":375,"name":376,"path":377,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":380,"locked":14,"version":34,"method":35,"evidence":381},"partial",{"candidates":382,"agricultural_evidence":40,"reason":409},[383,387,391,395,399,405],{"code":46,"score":215,"supported":40,"title":384,"abstract":385,"tags":386},[51],[50],[50],{"code":224,"score":215,"supported":40,"title":388,"abstract":389,"tags":390},[222],[222],[222],{"code":39,"score":97,"supported":40,"title":392,"abstract":393,"tags":394},[],[245,42],[],{"code":61,"score":55,"supported":40,"title":396,"abstract":397,"tags":398},[],[29],[29],{"code":400,"score":249,"supported":40,"title":401,"abstract":402,"tags":404},"H7",[],[403],"干旱",[],{"code":248,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[373],"一级领域明确，二级方向需要细分",[411,412],"SCS-CN GIS 遥感 径流估算","水土保持 水文模型 流域管理 遥感","SCS-CNGIS遥感径流估算-5166","10.9734\u002Fijecc\u002F2026\u002Fv16i105735",{"doi":414,"openalex_id":416,"authors":417,"venue":365,"cited_by_count":86,"oa_url":362,"card":430,"direction":92,"ingested_from":94},"W7222632567",[418,420,422,424,426,428],{"name":419,"orcid":9},"Sudhir Lad",{"name":421,"orcid":9},"R. D. Bansod",{"name":423,"orcid":9},"Bhaupatil Gavit",{"name":425,"orcid":9},"Sachin Nandgude",{"name":427,"orcid":9},"Shailendra Gadge",{"name":429,"orcid":9},"Mahesh Patil",{"tldr":431,"method":432,"finding":433,"direction":92,"opportunity":434},"综述GIS与遥感集成的SCS-CN法在半干旱与季风区流域径流估算的应用。","汇总印度、中国等国文献，用降雨、土地利用、土壤图、DEM与流域形态计算CN和径流","CN约40至90以上，年径流差异大，R²多在0.73至0.98，但结果对CN与前期湿度等敏感。","可研究CN本地化校准与不确定性量化，并结合实测径流验证，提升无资料流域径流估算可靠性。","2026-10-17T23:30:28.373754Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":444,"score_detail":445,"sources":447,"tags":449,"primary_topic":453,"secondary_topics":457,"classification":458,"search_phrases":487,"slug":490,"view_count":21,"doi":491,"paper":492,"created_at":520},4992,"Evaluating tools and methods for agroecosystem sustainability and performance: a critical review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01121-4","The sustainability and performance of agroecosystems are critical for confronting global challenges, from food security to biodiversity loss. This critical narrative review synthesizes 137 peer-reviewed sources and grey literature documents (2000-2024) to interrogate the epistemological assumptions, power dynamics, and contextual appropriateness of sustainability assessment frameworks. This comprehensive review systematically analyzes the diverse tools and methodologies; including Life Cycle Assessment (LCA), Response-Inducing Sustainability Evaluation (RISE), Framework for the Evaluation of Natural Resource Management Systems Incorporating Sustainability Indicators (MESMIS), and Tool for Agroecology Performance Evaluation (TAPE); used to evaluate this sustainability. This analysis moves beyond a simple comparison to offer a critical interrogation of these frameworks, exposing a fundamental tension between reductionist tools that offer precise, quantifiable data but often miss systemic interactions, and holistic, participatory approaches that capture socio-ecological complexity but struggle with standardization and scalability. This review underscores that sustainability assessment is not a neutral technical exercise but a value-laden process fraught with trade-offs, power dynamics, and epistemological conflicts, particularly between scientific and indigenous knowledge systems. The transformative potential of emerging technologies like Artificial Intelligence (AI) and remote sensing is examined, while cautioning against technological optimism that risks exacerbating inequities. Ultimately, this review argues for a shift towards reflexive, pluralistic assessment processes that prioritize equity, adaptive learning, and critical independence over standardized scoring, providing practical heuristics for researchers and practitioners navigating methodological trade-offs in diverse contexts.","农业生态系统的可持续性与表现对于应对从粮食安全到生物多样性丧失等全球性挑战至关重要。本批判性叙事综述综合了137篇同行评审文献和灰色文献（2000—2024年），旨在审视可持续性评估框架的认识论假设、权力动态及情境适宜性。本综述系统分析了用于评估该可持续性的多种工具和方法，包括生命周期评价（LCA）、诱导响应可持续性评估（RISE）、纳入可持续性指标的自然资源管理系统评价框架（MESMIS）以及农业生态表现评估工具（TAPE）。本分析超越了简单的比较，对这些框架进行了批判性审视，揭示了还原论工具与整体性参与式方法之间的根本张力——前者提供精确、可量化的数据，但往往忽视系统性交互；后者捕捉社会生态复杂性，却在标准化和可扩展性方面面临困难。本综述强调，可持续性评估并非中性的技术操作，而是一个充满价值负载的过程，充斥着权衡取舍、权力动态和认识论冲突，尤其是科学知识体系与本土知识体系之间的冲突。本文审视了人工智能（AI）和遥感等新兴技术的变革潜力，同时警示技术乐观主义可能加剧不平等。最终，本综述主张转向反思性、多元化的评估过程，将公平、适应性学习和批判性独立置于标准化评分之上，并为研究者和实践者在不同情境中应对方法论权衡提供实用启发式指南。","Discover Environment","2026-10-13T00:00:00Z",71,{"impact":215,"substance":18,"depth":111,"authority":20,"freshness":17,"relevant":21,"comment":446},"系统梳理LCA、RISE、MESMIS、TAPE等农业生态可持续性评估工具，指出还原论与整体论方法的张力，并讨论AI与遥感的赋能潜力，对农业信息化与智慧农业评估体系有参考价值。",[448],{"name":442,"url":439},[26,27,450,451,452],"农业生态","生命周期评价","可持续评价",{"code":61,"slug":454,"name":455,"path":456,"parent_code":296},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",[],{"status":139,"locked":14,"version":34,"method":35,"evidence":459},{"candidates":460,"agricultural_evidence":40},[461,467,471,478,482],{"code":61,"score":13,"supported":40,"title":462,"abstract":463,"tags":466},[],[464,450,454,465],"生物多样性","agroecology",[450],{"code":46,"score":47,"supported":40,"title":468,"abstract":469,"tags":470},[],[50,51],[27],{"code":472,"score":97,"supported":40,"title":473,"abstract":474,"tags":477},"A8",[],[475,476],"粮食安全","food security",[],{"code":54,"score":55,"supported":40,"title":479,"abstract":480,"tags":481},[],[58],[26],{"code":483,"score":21,"supported":14,"title":484,"abstract":485,"tags":486},"H4",[],[],[451],[488,489],"TAPE 农业生态 评价","RISE MESMIS 可持续评估","TAPE农业生态评价-4992","10.1007\u002Fs44274-026-01121-4",{"doi":491,"openalex_id":493,"authors":494,"venue":442,"cited_by_count":86,"oa_url":513,"card":514,"direction":519,"ingested_from":94},"W7221633095",[495,498,501,504,506,508,510],{"name":496,"orcid":497},"Pierre Marie Chimi","https:\u002F\u002Forcid.org\u002F0000-0003-4520-4165",{"name":499,"orcid":500},"Karimou Ngamsou Abdel","https:\u002F\u002Forcid.org\u002F0000-0002-8126-6235",{"name":502,"orcid":503},"Baruch Batamack Nkoué","https:\u002F\u002Forcid.org\u002F0000-0001-5601-3327",{"name":505,"orcid":9},"Eusebe Ydelphonse Nyonce Pokam",{"name":507,"orcid":9},"Angele Marie Ngon Dikoume",{"name":509,"orcid":9},"Benjamin Loïc Ndong Ndzana",{"name":511,"orcid":512},"William Armand Mala","https:\u002F\u002Forcid.org\u002F0000-0002-2240-3746","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01121-4.pdf",{"tldr":515,"method":516,"finding":517,"direction":276,"opportunity":518},"综述137篇文献，批判性评估农业生态系统可持续性评价工具的方法论假设与权衡。","批判性叙事综述，分析LCA、RISE、MESMIS、TAPE等框架及AI与遥感。","评价工具在简化精确与整体参与间存在根本张力，评价是价值负载过程。","可开发兼顾标准化与参与式、融合本土知识并反思技术乐观的混合评价框架。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:10.405237Z",{"id":522,"title":523,"url":524,"summary":525,"summary_zh":526,"content":9,"source_name":527,"source_url":524,"published_at":528,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":529,"sources":531,"tags":533,"primary_topic":536,"secondary_topics":540,"classification":541,"search_phrases":594,"slug":597,"view_count":86,"doi":598,"paper":599,"created_at":619},4987,"Smart Agriculture Research in Date Palm (Phoenix dactylifera L.) Production: A Comprehensive Bibliometric Study","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15203137","The date palm (Phoenix dactylifera L.) is fundamental to food security, livelihoods, and agro-ecosystem sustainability across the Arabian Peninsula, yet its production is increasingly constrained by water scarcity, salinity, climate change, and biotic stresses. Smart agriculture technologies offer opportunities to improve crop monitoring, resource-use efficiency, and decision-making; however, their application in date palm production remains fragmented and insufficiently validated. This study evaluates the development, geographic distribution, technological focus, research approaches, and thematic structure of smart agriculture research on date palms in the Arabian Peninsula and other major date-producing countries. A bibliometric analysis was conducted using peer-reviewed English-language articles and reviews indexed in Scopus and Web of Science from 2016 to 2026, with 2026 representing a partial year. Following screening and duplicate removal, 164 published studies were retained. Publication output increased gradually, although the trend was not statistically significant (β = 1.25 articles year−1, p = 0.068; Mann–Kendall τ = 0.489, p = 0.060). The literature was concentrated in the Gulf Cooperation Council (GCC) region, with Saudi Arabia contributing 54 publications (32.9%). General agronomy\u002Fother topics accounted for 46.3% of publications, while remote sensing\u002FUAVs (17.1%), GIS\u002Fgeospatial technologies (14.0%), IoT\u002Fsensors (13.4%), and AI\u002Fmachine learning (9.8%) represented the leading specific technology categories. Water and irrigation management dominated thematic applications (46.3%), whereas Smart Farming\u002FIoT accounted for only 11.0%. Observational and monitoring studies comprised 50.0% of publications, compared with only 6.1% involving field experiments, indicating limited field-scale validation. Internationally co-authored studies received significantly more citations than domestically authored studies (16.55 vs. 8.68; p = 0.0007). Thematic analysis identified four major clusters: geospatial monitoring, water management, pest control, and sustainability, with AI\u002Fmachine learning showing marked growth after 2022. Altogether, the field is expanding but remains fragmented, highlighting the need for integrated IoT and sensor networks, rigorous field trials, AI-driven decision-support systems, and stronger regional and international collaboration to enable scalable, context-specific smart agriculture for date palm production.","海枣（Phoenix dactylifera L.）对阿拉伯半岛的粮食安全、生计和农业生态系统可持续性至关重要，然而其生产日益受到水资源短缺、盐碱化、气候变化和生物胁迫的制约。智慧农业技术为改善作物监测、资源利用效率和决策提供了机遇；然而，其在海枣生产中的应用仍然零散且缺乏充分验证。本研究评估了阿拉伯半岛及其他主要海枣生产国海枣智慧农业研究的发展、地理分布、技术重点、研究方法和主题结构。采用文献计量学分析方法，基于Scopus和Web of Science数据库中2016年至2026年索引的同行评审英文论文和综述，其中2026年为部分年份。经过筛选和去重后，共保留164篇已发表研究。发文量逐渐增长，但该趋势未达到统计学显著性（β = 1.25篇\u002F年，p = 0.068；Mann–Kendall τ = 0.489，p = 0.060）。文献集中于海湾合作委员会（GCC）地区，其中沙特阿拉伯贡献了54篇论文（32.9%）。一般农学\u002F其他主题占论文总数的46.3%，而遥感\u002F无人机（17.1%）、GIS\u002F地理空间技术（14.0%）、物联网\u002F传感器（13.4%）和人工智能\u002F机器学习（9.8%）为主要的具体技术类别。水资源与灌溉管理在主题应用中占主导地位（46.3%），而智慧农业\u002F物联网仅占11.0%。观测与监测类研究占论文总数的50.0%，而涉及田间试验的仅占6.1%，表明田间尺度的验证有限。国际合著论文的引用次数显著高于国内独著论文（16.55 vs. 8.68；p = 0.0007）。主题分析识别出四大聚类：地理空间监测、水资源管理、病虫害防治和可持续性，其中人工智能\u002F机器学习在2022年后呈现显著增长。总体而言，该领域正在扩展但仍显零散，凸显了整合物联网与传感器网络、严格的田间试验、人工智能驱动的决策支持系统以及加强区域和国际合作的必要性，以推动可规模化、因地制宜的海枣生产智慧农业。","Plants","2026-10-14T00:00:00Z",{"impact":111,"substance":110,"depth":111,"authority":112,"freshness":17,"relevant":21,"comment":530},"基于Scopus与WoS的164篇文献计量分析，揭示枣椰树智慧农业研究碎片化、田间验证不足，对干旱区智慧农业布局有参考价值。",[532],{"name":527,"url":524},[117,26,27,534,535],"农业物联网","枣椰树",{"code":248,"slug":537,"name":538,"path":539,"parent_code":296},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[],{"status":139,"locked":14,"version":34,"method":35,"evidence":542},{"candidates":543,"agricultural_evidence":40},[544,552,557,561,565,569,576,580,586,590],{"code":545,"score":19,"supported":40,"title":546,"abstract":548,"tags":551},"I7",[547],"bibliometric",[549,550,547],"文献计量","研究方法",[],{"code":170,"score":148,"supported":40,"title":553,"abstract":555,"tags":556},[554],"smart agriculture",[117,554],[117],{"code":248,"score":216,"supported":40,"title":558,"abstract":559,"tags":560},[],[252,313,537],[],{"code":54,"score":47,"supported":40,"title":562,"abstract":563,"tags":564},[],[164,165],[26],{"code":46,"score":47,"supported":40,"title":566,"abstract":567,"tags":568},[],[50,51],[27],{"code":570,"score":47,"supported":40,"title":571,"abstract":572,"tags":575},"I5",[],[573,574],"物联网","IoT",[534],{"code":472,"score":97,"supported":40,"title":577,"abstract":578,"tags":579},[],[475,476],[],{"code":400,"score":97,"supported":40,"title":581,"abstract":582,"tags":585},[],[583,584],"气候变化","climate change",[],{"code":61,"score":249,"supported":40,"title":587,"abstract":588,"tags":589},[],[450],[],{"code":224,"score":249,"supported":40,"title":591,"abstract":592,"tags":593},[],[222],[],[595,596],"枣椰树 智慧农业 文献计量","农业人工智能 农业物联网 农业遥感 智慧农业","枣椰树智慧农业文献计量-4987","10.3390\u002Fplants15203137",{"doi":598,"openalex_id":600,"authors":601,"venue":527,"cited_by_count":86,"oa_url":524,"card":614,"direction":519,"ingested_from":94},"W7221786893",[602,605,608,611],{"name":603,"orcid":604},"Sajid Safeer","https:\u002F\u002Forcid.org\u002F0000-0002-0120-5900",{"name":606,"orcid":607},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":609,"orcid":610},"Sajid Maqsood","https:\u002F\u002Forcid.org\u002F0000-0003-2099-8392",{"name":612,"orcid":613},"Cataldo Pulvento","https:\u002F\u002Forcid.org\u002F0000-0002-2650-1204",{"tldr":615,"method":616,"finding":617,"direction":519,"opportunity":618},"用文献计量分析2016-2026年枣椰树智慧农业研究，发现领域增长但碎片化、田间验证不足。","Scopus与Web of Science文献计量分析，164篇论文，含趋势检验","研究集中于海湾地区，遥感与灌溉为主，田间试验仅6.1%，国际合作论文引用更高。","枣椰树智慧农业缺乏IoT传感器集成与田间验证，AI决策支持系统是待填补的研究空白。","2026-10-15T23:30:08.558630Z"]