[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2168":3,"related-2168":103},{"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":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":102},2168,"From soil loss to sediment delivery: GeoAI-enhanced RUSLE modeling of erosion and sediment dynamics in a hyper-arid watershed","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeart.2026.1893163","In the delineated watershed containing Wadi Samnan near Az Zulfi, Saudi Arabia, soil erosion is a local management concern because sparse vegetation, erodible sandy surface materials, escarpment-influenced terrain, and episodic rainfall events can concentrate runoff and sediment movement along wadi channels and drainage corridors. These conditions make it difficult to identify erosion-prone zones using field observations alone, particularly in data-scarce hyper-arid environments. This study presents a GeoAI-enhanced RUSLE-based framework to model water-induced soil loss and sediment delivery dynamics in the study watershed. The Revised Universal Soil Loss Equation (RUSLE) was integrated with geospatial datasets, remote sensing products, digital elevation model-derived terrain attributes, and machine-learning-based spatial analysis to estimate the spatial distribution of water-induced erosion risk. Rainfall erosivity, soil erodibility, topographic influence, land-cover conditions, and conservation-practice factors were derived and mapped within a GIS environment. In addition, the Sediment Delivery Ratio (SDR) was incorporated to evaluate relative sediment-delivery potential and improve the interpretation of how estimated hillslope soil loss may be transferred into sediment yield within the drainage system. The results show that RUSLE-derived potential soil-loss estimates ranged from 0 to 319.5 t ha -1 yr -1 , with the slight erosion class covering 43.2% of the watershed and modeled severe-erosion hotspots covering 10.9%. High-risk areas were mainly concentrated along steep slopes, drainage corridors, sparsely vegetated surfaces, and erodible sandy soils. SDR results indicated low overall sediment connectivity, with approximately 85%–99% of eroded material likely retained locally within the watershed, depending on the spatially varying SDR values. The Random Forest internal consistency analysis explained 81.73% of the variance in RUSLE-derived soil-loss estimates, indicating that the input factors captured much of the modeled spatial variability; however, this result should not be interpreted as independent field validation. Overall, the integration of RUSLE, SDR, remote sensing, GIS, and GeoAI provides a useful framework for identifying erosion-prone zones, distinguishing soil-loss potential from sediment-delivery potential, prioritizing conservation interventions, and supporting sustainable watershed management in hyper-arid environments.","在沙特阿拉伯祖尔菲附近萨姆南干谷所在的划定流域内，土壤侵蚀是当地管理关注的问题，因为稀疏植被、易侵蚀的砂质表层物质、受崖坡影响的地形以及偶发性降雨事件会将径流和泥沙运动集中沿干谷河道和排水廊道输送。这些条件使得仅凭野外观测难以识别侵蚀易发区，尤其是在数据稀缺的极端干旱环境中。本研究提出了一种GeoAI增强的RUSLE框架，用于模拟研究流域内水力驱动的土壤流失和泥沙输移动态。修正通用土壤流失方程（RUSLE）与地理空间数据集、遥感产品、数字高程模型衍生的地形属性以及基于机器学习的空间分析相结合，以估算水力侵蚀风险的空间分布。降雨侵蚀力、土壤可蚀性、地形影响、土地覆盖条件和保护措施因子在GIS环境中被导出并制图。此外，引入泥沙输移比（SDR）以评估相对泥沙输移潜力，并改进对坡面土壤流失估算值如何转化为排水系统内产沙量的解释。结果表明，RUSLE衍生的潜在土壤流失估算值范围为0至319.5 t ha⁻¹ yr⁻¹，其中轻度侵蚀等级覆盖流域面积的43.2%，模拟的严重侵蚀热点区域覆盖10.9%。高风险区主要集中在陡坡、排水廊道、植被稀疏地表和易侵蚀砂质土壤沿线。SDR结果表明整体泥沙连通性较低，约85%–99%的侵蚀物质可能就地滞留于流域内，具体取决于空间变化的SDR值。随机森林内部一致性分析解释了RUSLE衍生土壤流失估算值中81.73%的方差，表明输入因子捕捉了大部分模拟的空间变异性；然而，该结果不应被解释为独立的野外验证。总体而言，RUSLE、SDR、遥感、GIS和GeoAI的整合为识别侵蚀易发区、区分土壤流失潜力与泥沙输移潜力、优先安排保护干预措施以及支持极端干旱环境中的可持续流域管理提供了一个有用的框架。",null,"Frontiers in Earth Science","2026-09-09T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"GeoAI融合RUSLE与SDR模型量化超干旱流域侵蚀与泥沙输移，方法新颖、数据扎实，但属区域案例研究，对国内三农实践的直接价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","遥感","水土保持","干旱区农业","流域治理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":81},[38,47,56,63,70,76],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":46},"H2",14,true,[43],"soil",[45,43],"土壤",[],{"code":48,"score":49,"supported":41,"title":50,"abstract":51,"tags":55},"I4",9,[],[52,53,54],"空间分析","GIS","spatial analysis",[],{"code":57,"score":58,"supported":41,"title":59,"abstract":60,"tags":62},"I3",7,[],[27,61],"remote sensing",[27],{"code":64,"score":65,"supported":41,"title":66,"abstract":67,"tags":69},"H7",4,[],[68],"干旱",[29],{"code":71,"score":65,"supported":41,"title":72,"abstract":73,"tags":75},"I2",[],[74],"机器学习",[26],{"code":77,"score":21,"supported":14,"title":78,"abstract":79,"tags":80},"H6",[],[],[28],"农业相关性需要复核",[83,84],"农业人工智能 干旱区农业 水土保持 流域治理","农业人工智能 干旱区农业","农业人工智能干旱区农业水土保持流域治理-2168",2,"10.3389\u002Ffeart.2026.1893163",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":93,"oa_url":94,"card":95,"direction":99,"ingested_from":101},"W7212020416",[91],{"name":92,"orcid":9},"Ali R. Alruzuq",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fearth-science\u002Farticles\u002F10.3389\u002Ffeart.2026.1893163\u002Fpdf",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"用GeoAI增强RUSLE与SDR模型，评估沙特超干旱流域土壤侵蚀与泥沙输移空间格局。","RUSLE结合遥感、DEM、GIS与随机森林空间分析，并引入泥沙输移比SDR。","侵蚀量0–319.5 t\u002Fha\u002Fyr，严重区占10.9%，约85%–99%侵蚀物就地滞留。","农业遥感与作物表型","可将GeoAI-RUSLE-SDR框架迁移至数据稀缺区，结合实地监测验证并耦合泥沙连通性指数。","openalex","2026-09-11T23:30:31.280278Z",{"total":104,"page":21,"page_size":104,"items":105},6,[106,179,246,299,412,476],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":131,"classification":132,"search_phrases":162,"slug":165,"view_count":93,"doi":166,"paper":167,"created_at":178},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":116,"substance":117,"depth":118,"authority":116,"freshness":13,"relevant":21,"comment":119},12,22,18,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[121],{"name":112,"url":109},[123,26,124,27,125],"智慧农业","粮食安全","气候分析",{"code":71,"slug":127,"name":128,"path":129,"parent_code":130},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":133,"locked":14,"version":34,"method":35,"evidence":134},"classified",{"candidates":135,"agricultural_evidence":41},[136,141,148,153,158],{"code":71,"score":49,"supported":41,"title":137,"abstract":139,"tags":140},[138],"artificial intelligence",[],[26],{"code":142,"score":143,"supported":41,"title":144,"abstract":145,"tags":147},"I5",3,[],[146],"农业传感",[],{"code":149,"score":21,"supported":14,"title":150,"abstract":151,"tags":152},"A8",[],[],[124],{"code":154,"score":21,"supported":14,"title":155,"abstract":156,"tags":157},"I1",[],[],[123],{"code":57,"score":21,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[27],[163,164],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":166,"openalex_id":168,"authors":169,"venue":112,"cited_by_count":93,"oa_url":109,"card":172,"direction":177,"ingested_from":101},"W7223111766",[170],{"name":171,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":173,"method":174,"finding":175,"direction":99,"opportunity":176},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":189,"tags":191,"primary_topic":194,"secondary_topics":198,"classification":199,"search_phrases":226,"slug":229,"view_count":93,"doi":230,"paper":231,"created_at":245},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":118,"depth":40,"authority":116,"freshness":49,"relevant":21,"comment":188},"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。",[190],{"name":185,"url":182},[27,192,193,53,28],"土地利用","土壤侵蚀",{"code":48,"slug":195,"name":196,"path":197,"parent_code":130},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":133,"locked":14,"version":34,"method":35,"evidence":200},{"candidates":201,"agricultural_evidence":41},[202,208,212,216,222],{"code":48,"score":13,"supported":41,"title":203,"abstract":204,"tags":207},[],[205,53,206],"地理信息","geographic information",[53],{"code":57,"score":58,"supported":41,"title":209,"abstract":210,"tags":211},[],[27,61],[27],{"code":39,"score":104,"supported":41,"title":213,"abstract":214,"tags":215},[],[45,43],[],{"code":217,"score":143,"supported":41,"title":218,"abstract":219,"tags":221},"H3",[],[220],"灌溉",[],{"code":77,"score":86,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[192,28],[227,228],"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":230,"openalex_id":232,"authors":233,"venue":185,"cited_by_count":93,"oa_url":9,"card":239,"direction":99,"ingested_from":101},"W7223117733",[234,236],{"name":235,"orcid":9},"MERİÇ KURT",{"name":237,"orcid":238},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"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":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":9,"source_name":112,"source_url":249,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":255,"tags":257,"primary_topic":260,"secondary_topics":261,"classification":266,"search_phrases":283,"slug":286,"view_count":93,"doi":287,"paper":288,"created_at":298},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":17,"substance":18,"depth":19,"authority":116,"freshness":13,"relevant":21,"comment":254},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[256],{"name":112,"url":249},[123,26,258,27,259],"产量预测","可解释性",{"code":71,"slug":127,"name":128,"path":129,"parent_code":130},[262],{"code":57,"slug":263,"name":264,"path":265,"parent_code":130},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":133,"locked":14,"version":34,"method":35,"evidence":267},{"candidates":268,"agricultural_evidence":41},[269,275,279],{"code":71,"score":116,"supported":41,"title":270,"abstract":272,"tags":274},[271],"computer vision",[273],"计算机视觉",[26],{"code":57,"score":17,"supported":41,"title":276,"abstract":277,"tags":278},[],[27,61],[258,27],{"code":154,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[123],[284,285],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":287,"openalex_id":289,"authors":290,"venue":112,"cited_by_count":93,"oa_url":249,"card":293,"direction":99,"ingested_from":101},"W7223114002",[291],{"name":292,"orcid":9},"Kadiatou Barry",{"tldr":294,"method":295,"finding":296,"direction":99,"opportunity":297},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":305,"source_url":302,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":309,"tags":311,"primary_topic":314,"secondary_topics":319,"classification":325,"search_phrases":382,"slug":385,"view_count":93,"doi":386,"paper":387,"created_at":411},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":118,"substance":117,"depth":118,"authority":116,"freshness":49,"relevant":21,"comment":308},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[310],{"name":305,"url":302},[123,26,27,312,313],"作物水分胁迫","精准灌溉",{"code":217,"slug":315,"name":316,"path":317,"parent_code":318},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[320,321],{"code":71,"slug":127,"name":128,"path":129,"parent_code":130},{"code":142,"slug":322,"name":323,"path":324,"parent_code":130},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":133,"locked":14,"version":34,"method":35,"evidence":326},{"candidates":327,"agricultural_evidence":41},[328,333,338,345,351,355,362,369,376],{"code":217,"score":329,"supported":41,"title":330,"abstract":331,"tags":332},15,[315],[220,315],[313],{"code":71,"score":13,"supported":41,"title":334,"abstract":335,"tags":337},[],[74,336,138],"machine learning",[26],{"code":142,"score":49,"supported":41,"title":339,"abstract":340,"tags":344},[],[341,342,343],"物联网","internet of things","IoT",[],{"code":154,"score":58,"supported":41,"title":346,"abstract":347,"tags":350},[],[348,349],"数字孪生","digital twin",[123],{"code":57,"score":58,"supported":41,"title":352,"abstract":353,"tags":354},[],[27,61],[27],{"code":356,"score":104,"supported":41,"title":357,"abstract":358,"tags":361},"A2",[],[359,360],"小麦","wheat",[],{"code":363,"score":104,"supported":41,"title":364,"abstract":365,"tags":368},"A3",[],[366,367],"玉米","maize",[],{"code":370,"score":104,"supported":41,"title":371,"abstract":372,"tags":375},"A5",[],[373,374],"马铃薯","potato",[],{"code":377,"score":143,"supported":41,"title":378,"abstract":379,"tags":381},"B2",[],[380],"葡萄",[],[383,384],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":386,"openalex_id":388,"authors":389,"venue":305,"cited_by_count":93,"oa_url":302,"card":405,"direction":410,"ingested_from":101},"W7223088921",[390,393,396,399,402],{"name":391,"orcid":392},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":394,"orcid":395},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":397,"orcid":398},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":400,"orcid":401},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":403,"orcid":404},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":406,"method":407,"finding":408,"direction":99,"opportunity":409},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":112,"source_url":415,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":418,"score_detail":419,"sources":422,"tags":424,"primary_topic":428,"secondary_topics":429,"classification":432,"search_phrases":460,"slug":463,"view_count":93,"doi":464,"paper":465,"created_at":475},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":118,"substance":18,"depth":420,"authority":116,"freshness":49,"relevant":21,"comment":421},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[423],{"name":112,"url":415},[26,425,27,426,427],"农业物联网","智慧灌溉","节水农业",{"code":217,"slug":315,"name":316,"path":317,"parent_code":318},[430,431],{"code":142,"slug":322,"name":323,"path":324,"parent_code":130},{"code":57,"slug":263,"name":264,"path":265,"parent_code":130},{"status":133,"locked":14,"version":34,"method":35,"evidence":433},{"candidates":434,"agricultural_evidence":41},[435,441,445,449,453],{"code":217,"score":436,"supported":41,"title":437,"abstract":438,"tags":440},19,[315],[220,439,315],"水资源",[426,427],{"code":142,"score":118,"supported":41,"title":442,"abstract":443,"tags":444},[343],[341,342,343],[425],{"code":57,"score":329,"supported":41,"title":446,"abstract":447,"tags":448},[61],[27,61],[27],{"code":71,"score":116,"supported":41,"title":450,"abstract":451,"tags":452},[138],[138],[26],{"code":454,"score":104,"supported":41,"title":455,"abstract":456,"tags":459},"H8",[],[457,458],"可持续农业","sustainable agriculture",[],[461,462],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":464,"openalex_id":466,"authors":467,"venue":112,"cited_by_count":93,"oa_url":415,"card":470,"direction":410,"ingested_from":101},"W7223090389",[468],{"name":469,"orcid":9},"Patil",{"tldr":471,"method":472,"finding":473,"direction":410,"opportunity":474},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":481,"content":9,"source_name":112,"source_url":479,"published_at":482,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":483,"score_detail":484,"sources":486,"tags":488,"primary_topic":491,"secondary_topics":495,"classification":496,"search_phrases":518,"slug":521,"view_count":93,"doi":522,"paper":523,"created_at":537},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","2026-10-20T00:00:00Z",70,{"impact":116,"substance":18,"depth":19,"authority":116,"freshness":49,"relevant":21,"comment":485},"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[487],{"name":112,"url":479},[26,27,53,489,490],"野生动物保护","人兽冲突",{"code":130,"slug":492,"name":493,"path":494,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":497,"locked":14,"version":34,"method":35,"evidence":498},"partial",{"candidates":499,"agricultural_evidence":41,"reason":517},[500,505,509,513],{"code":71,"score":13,"supported":41,"title":501,"abstract":502,"tags":504},[],[74,336,503],"decision support",[26],{"code":48,"score":13,"supported":41,"title":506,"abstract":507,"tags":508},[],[205,53,206],[53],{"code":57,"score":65,"supported":41,"title":510,"abstract":511,"tags":512},[],[27],[27],{"code":77,"score":21,"supported":14,"title":514,"abstract":515,"tags":516},[],[],[489],"一级领域明确，二级方向需要细分",[519,520],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":522,"openalex_id":524,"authors":525,"venue":112,"cited_by_count":93,"oa_url":479,"card":532,"direction":177,"ingested_from":101},"W7222999966",[526,528,530],{"name":527,"orcid":9},"Bhutia Dechen Doma",{"name":529,"orcid":9},"Swarup Das",{"name":531,"orcid":9},"Mandal Rakesh Kumar",{"tldr":533,"method":534,"finding":535,"direction":99,"opportunity":536},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","2026-10-21T23:30:43.936453Z"]