[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4101":3,"related-4101":87},{"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":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":86},4101,"Cloud-based remote sensing datasets used in hydrological studies: A review and examples","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1885422","Water resources are critically important for the continuity of ecosystems and the sustainability of human activities. However, climate change, population growth, and unplanned and rapid changes in land use are increasingly putting pressure on surface and groundwater resources. This situation necessitates an accurate assessment of the temporal and spatial distribution of water and its effective monitoring at the watershed scale. Traditional hydrological observation methods provide high accuracy but, due to their limited spatial coverage and prohibitive costs, they present significant limitations, particularly in regions with insufficient data. In this context, remote sensing technologies offer an approach that enables the acquisition of parameters represented directly and indirectly in hydrological analyses, covering large areas and providing continuity. This study examines the concept of water balance, which forms the basis of hydrology, within the framework of the hydrological cycle. It evaluates the contributions of different remote sensing datasets to the monitoring and assessment of water resources, as well as the limitations of these approaches, in light of studies in the literature. In addition, a representative study area encompassing the Hazar Lake Basin and the Behramaz Basin was selected, and analyses were conducted using the Google Earth Engine (GEE) platform to demonstrate the applicability of the available datasets.","水资源对于生态系统的延续和人类活动的可持续性至关重要。然而，气候变化、人口增长以及土地利用的无序快速变化正日益对地表水和地下水资源构成压力。这一形势要求对水资源在时间和空间上的分布进行准确评估，并在流域尺度上实施有效监测。传统水文观测方法虽然精度较高，但由于空间覆盖范围有限且成本高昂，存在明显局限性，在数据不足的地区尤为突出。在此背景下，遥感技术提供了一种途径，能够获取水文分析中直接和间接表示的参数，覆盖大范围区域并具有连续性。本研究在水文循环框架内考察了作为水文学基础的水量平衡概念，结合文献中的研究，评估了不同遥感数据集对水资源监测与评估的贡献及其局限性。此外，选取了涵盖哈扎尔湖流域和贝赫拉马兹流域的典型研究区，利用谷歌地球引擎（Google Earth Engine, GEE）平台进行分析，以验证现有数据集的适用性。",null,"OpenAlex","2026-09-30T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,6,1,"综述性论文，系统梳理云遥感数据集在水文水资源监测中的应用，并以GEE平台开展案例验证，对农业水资源信息化管理有参考价值，但非突破性成果。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业信息化","遥感","Google Earth Engine","水文模型","水资源监测",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":64},[38,45,52,59],{"code":39,"score":19,"supported":40,"title":41,"abstract":43,"tags":44},"I3",true,[42],"remote sensing",[27,42],[28,27],{"code":46,"score":20,"supported":40,"title":47,"abstract":48,"tags":51},"H7",[],[49,50],"气候变化","climate change",[],{"code":53,"score":54,"supported":40,"title":55,"abstract":56,"tags":58},"H3",4,[],[57],"水资源",[29],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"I1",[],[],[26],"农业相关性需要复核",[66,67],"Hazar Lake Basin 遥感 水文","Google Earth Engine 水资源监测","HazarLakeBasin遥感水文-4101",2,"10.53030\u002Ftjags.1885422",{"doi":70,"openalex_id":72,"authors":73,"venue":9,"cited_by_count":77,"oa_url":78,"card":79,"direction":83,"ingested_from":85},"W7214913155",[74],{"name":75,"orcid":76},"Ceren Sıla Özdemir","https:\u002F\u002Forcid.org\u002F0000-0003-0967-8179",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5701448",{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"综述云遥感数据集在水文研究中的应用，并以GEE平台示例分析。","文献综述结合Google Earth Engine平台，选取Hazar湖和Beh","云遥感数据集能有效弥补传统水文观测空间覆盖不足，支持流域尺度水资源监测。","农业遥感与作物表型","可探索云遥感数据集在农业灌溉用水估算与干旱监测中的集成应用与精度验证。","openalex","2026-10-03T23:30:37.766386Z",{"total":20,"page":21,"page_size":20,"items":88},[89,147,232,306,389,485],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":9,"content":9,"source_name":94,"source_url":92,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":100,"tags":102,"primary_topic":105,"secondary_topics":110,"classification":111,"search_phrases":125,"slug":128,"view_count":77,"doi":129,"paper":130,"created_at":146},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square","2026-10-21T00:00:00Z",38,{"impact":98,"substance":20,"depth":98,"authority":20,"freshness":13,"relevant":21,"comment":99},8,"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[101],{"name":94,"url":92},[103,26,27,104],"数字农业","地图评估",{"code":60,"slug":106,"name":107,"path":108,"parent_code":109},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":112,"locked":14,"version":34,"method":35,"evidence":113},"classified",{"candidates":114,"agricultural_evidence":40},[115,121],{"code":60,"score":116,"supported":40,"title":117,"abstract":119,"tags":120},13,[118],"digital agriculture",[118],[26,103],{"code":39,"score":21,"supported":14,"title":122,"abstract":123,"tags":124},[],[],[27],[126,127],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":129,"openalex_id":131,"authors":132,"venue":94,"cited_by_count":77,"oa_url":92,"card":9,"direction":145,"ingested_from":85},"W7223099987",[133,136,139,142],{"name":134,"orcid":135},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":137,"orcid":138},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":140,"orcid":141},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":143,"orcid":144},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":159,"tags":161,"primary_topic":165,"secondary_topics":169,"classification":170,"search_phrases":206,"slug":209,"view_count":77,"doi":210,"paper":211,"created_at":231},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":98,"substance":18,"depth":19,"authority":17,"freshness":157,"relevant":21,"comment":158},9,"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。",[160],{"name":153,"url":150},[27,162,163,164,29],"GIS","水土保持","流域管理",{"code":109,"slug":166,"name":167,"path":168,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":171,"locked":14,"version":34,"method":35,"evidence":172},"partial",{"candidates":173,"agricultural_evidence":40,"reason":205},[174,178,183,190,195,201],{"code":39,"score":17,"supported":40,"title":175,"abstract":176,"tags":177},[42],[27],[27],{"code":179,"score":17,"supported":40,"title":180,"abstract":181,"tags":182},"I4",[162],[162],[162],{"code":184,"score":20,"supported":40,"title":185,"abstract":186,"tags":189},"H2",[],[187,188],"土壤","soil",[],{"code":191,"score":54,"supported":40,"title":192,"abstract":193,"tags":194},"H6",[],[163],[163],{"code":46,"score":196,"supported":40,"title":197,"abstract":198,"tags":200},3,[],[199],"干旱",[],{"code":53,"score":21,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[29],"一级领域明确，二级方向需要细分",[207,208],"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":210,"openalex_id":212,"authors":213,"venue":153,"cited_by_count":77,"oa_url":150,"card":226,"direction":83,"ingested_from":85},"W7222632567",[214,216,218,220,222,224],{"name":215,"orcid":9},"Sudhir Lad",{"name":217,"orcid":9},"R. D. Bansod",{"name":219,"orcid":9},"Bhaupatil Gavit",{"name":221,"orcid":9},"Sachin Nandgude",{"name":223,"orcid":9},"Shailendra Gadge",{"name":225,"orcid":9},"Mahesh Patil",{"tldr":227,"method":228,"finding":229,"direction":83,"opportunity":230},"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":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":245,"tags":247,"primary_topic":251,"secondary_topics":255,"classification":256,"search_phrases":286,"slug":289,"view_count":21,"doi":290,"paper":291,"created_at":305},4799,"Applications of Neutrosophic Topology in the Indian Context: A Review and Emerging Research Directions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23318355","AbstractNeutrosophic topology is an extension of classical topology designed to represent informationcharacterized by truth, indeterminacy, and falsity simultaneously. Unlike classical mathematicalmodels, which generally assume that an element either belongs or does not belong to a set,neutrosophic approaches permit the coexistence of partial truth, uncertainty, and contradiction. Thisfeature makes neutrosophic topology potentially useful for modelling complex systems in whichinformation is incomplete, inconsistent, imprecise, or dynamically changing. India, with its largeand heterogeneous population, geographically diverse environment, rapidly expanding digitalinfrastructure, and complex decision-making systems, provides several important contexts in whichsuch mathematical frameworks may be investigated. This article reviews the development ofneutrosophic topology and examines its potential and emerging applications in the Indian context.Particular attention is given to geographical information systems, medical diagnosis, data mining,decision-making, smart-city management, environmental monitoring, agricultural informationsystems, transportation networks, and information systems. Existing Indian contributionsdemonstrate that neutrosophic topological structures are increasingly being studied in connectionwith data analysis and decision problems. The article also identifies limitations in current research,particularly the gap between theoretical topological constructions and validated real-world datasets.Finally, several future research directions are proposed, including integration with geographicinformation systems, artificial intelligence, Internet-of-Things data, remote sensing, healthcareanalytics, and multi-criteria decision-making. The study concludes that neutrosophic topology offersa promising mathematical framework for representing uncertainty and inconsistency in Indian socio-technical and environmental systems, provided that future research emphasizes empirical validation and computational implementation. Keywords: Neutrosophic topology; neutrosophic sets; uncertainty; indeterminacy; fuzzy topology; GIS; decision making; data mining; India; mathematical modelling.","摘要：中智拓扑学（Neutrosophic topology）是经典拓扑学的扩展，旨在同时表示由真、不确定和假所刻画的信息。与通常假设元素要么属于某集合、要么不属于某集合的经典数学模型不同，中智方法允许部分真、不确定性和矛盾同时存在。这一特性使中智拓扑学在建模信息不完整、不一致、不精确或动态变化的复杂系统方面具有潜在价值。印度拥有庞大且异质的人口、地理多样的环境、快速扩展的数字基础设施以及复杂的决策系统，为研究此类数学框架提供了若干重要场景。本文综述了中智拓扑学的发展，并考察其在印度语境下的潜力和新兴应用。特别关注地理信息系统、医学诊断、数据挖掘、决策、智慧城市管理、环境监测、农业信息系统、交通网络和信息系统等领域。印度已有的研究成果表明，中智拓扑结构正日益被用于数据分析和决策问题的研究。本文还指出了当前研究的局限性，尤其是理论拓扑构造与经过验证的现实世界数据集之间的差距。最后，提出了若干未来研究方向，包括与地理信息系统、人工智能、物联网数据、遥感、医疗分析和多准则决策的整合。研究认为，中智拓扑学为表示印度社会技术系统和环境系统中的不确定性与不一致性提供了一个有前景的数学框架，前提是未来研究应重视实证验证和计算实现。关键词：中智拓扑学；中智集；不确定性；不确定；模糊拓扑学；地理信息系统；决策；数据挖掘；印度；数学建模。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-12T00:00:00Z",57,{"impact":98,"substance":242,"depth":243,"authority":17,"freshness":98,"relevant":21,"comment":244},14,15,"综述性论文，将中智拓扑引入印度农业信息系统、遥感与决策场景，理论价值明确但缺乏实证数据，属细分领域参考。",[246],{"name":238,"url":235},[248,249,26,27,250],"智慧农业","农业人工智能","决策支持",{"code":39,"slug":252,"name":253,"path":254,"parent_code":109},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":112,"locked":14,"version":34,"method":35,"evidence":257},{"candidates":258,"agricultural_evidence":40},[259,264,269,276,282],{"code":39,"score":260,"supported":40,"title":261,"abstract":262,"tags":263},7,[],[27,42],[27],{"code":179,"score":20,"supported":40,"title":265,"abstract":266,"tags":268},[],[267,162],"地理信息",[],{"code":270,"score":271,"supported":40,"title":272,"abstract":273,"tags":275},"I2",5,[],[274],"artificial intelligence",[249,250],{"code":277,"score":196,"supported":40,"title":278,"abstract":279,"tags":281},"I5",[],[280],"物联网",[],{"code":60,"score":69,"supported":14,"title":283,"abstract":284,"tags":285},[],[],[26,248],[287,288],"中智拓扑 农业信息系统","农业人工智能 农业信息化 决策支持 智慧农业","中智拓扑农业信息系统-4799","10.5281\u002Fzenodo.23318355",{"doi":290,"openalex_id":292,"authors":293,"venue":238,"cited_by_count":77,"oa_url":235,"card":298,"direction":304,"ingested_from":85},"W7221514584",[294,296],{"name":295,"orcid":9},"N. Raksha Ben",{"name":297,"orcid":9},"G. Helen Rajapushpam",{"tldr":299,"method":300,"finding":301,"direction":302,"opportunity":303},"综述中智拓扑在印度多领域的应用潜力，并指出理论与实证数据脱节的局限。","文献综述，梳理中智拓扑在GIS、决策、农业信息系统等场景的应用。","中智拓扑能刻画不确定性，但缺乏真实数据验证与计算实现。","农业人工智能与决策模型","可将中智拓扑与农业物联网、遥感数据结合，构建可验证的不确定性决策模型。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:08.455353Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":9,"source_name":312,"source_url":309,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":327,"classification":328,"search_phrases":346,"slug":349,"view_count":69,"doi":350,"paper":351,"created_at":388},4686,"SAMWater: a prompt-guided multimodal framework for transferable water mapping with the segment anything model","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2745243","Accurate and transferable surface water mapping is critical for water resource assessment, flood monitoring, and aquatic ecosystem management, but remains difficult because SAR speckle, optical contamination, heterogeneous backgrounds, and scarce high-quality pixel-level annotations limit robust large-scale mapping. We propose SAMWater, a prompt-guided multimodal framework that integrates Sentinel-1 SAR, Sentinel-2 optical imagery, the Segment Anything Model (SAM), and Segment Anything Model 3 (SAM 3). Instead of using SAR and optical data as independent classifiers, SAMWater transforms their complementary information into structured prompts for coarse-to-refined water segmentation. A physically interpretable multisensor coarse water prior is first generated from Sentinel observations using an automatic threshold-based strategy. This prior is then refined through prompt-based boundary refinement, a multi-prompt excavation strategy that derives point, box, and Gaussian-style mask prompts, and Water-IoU fine-tuning for water-specific candidate ranking. Using globally distributed samples and manually interpreted PlanetScope reference labels, SAMWater achieved average Kappa coefficients of 90.71%, 85.63%, and 89.05% in evaluations stratified by water body type, land cover background, and climatic zone, respectively. It generally outperformed traditional thresholding and clustering methods, public deep learning models, and existing global water or land cover products, particularly in structurally complex river, reservoir, urban, agricultural, and mountainous scenes. Ablation experiments confirmed that multimodal prior fusion, structured prompt construction, and water-specific ranking jointly drive the performance gains. Moreover, SAMWater-generated refined labels improved the training of U-Net, DeepLabV3+, and SegFormer, indicating their utility for weakly supervised segmentation. SAMWater therefore demonstrates that integrating physically interpretable remote sensing priors with promptable foundation models provides a practical pathway for transferable surface water mapping, large-scale multisensor weak-label generation, and more consistent monitoring of surface water extent.","准确且具有可迁移性的地表水制图对水资源评估、洪水监测和水生生态系统管理至关重要，但由于合成孔径雷达（SAR）斑点噪声、光学影像污染、异质背景以及高质量像素级标注稀缺等因素，实现稳健的大尺度制图仍然困难。我们提出SAMWater，一种提示引导的多模态框架，集成了Sentinel-1 SAR、Sentinel-2光学影像、分割一切模型（SAM）和分割一切模型3（SAM 3）。SAMWater并非将SAR和光学数据用作独立分类器，而是将其互补信息转化为结构化提示，用于由粗到精的水体分割。首先，利用基于自动阈值的策略从Sentinel观测中生成具有物理可解释性的多传感器粗水体先验。随后，通过基于提示的边界细化、多提示挖掘策略（导出点、框和高斯式掩膜提示）以及面向水体的Water-IoU微调进行候选排序，对该先验进行精化。利用全球分布的样本和人工解译的PlanetScope参考标签，SAMWater在按水体类型、土地覆盖背景和气候带分层评估中分别取得了90.71%、85.63%和89.05%的平均Kappa系数。其总体优于传统阈值法和聚类方法、公开深度学习模型以及现有全球水体或土地覆盖产品，尤其是在结构复杂的河流、水库、城市、农业和山地场景中。消融实验证实，多模态先验融合、结构化提示构建和面向水体的排序共同驱动了性能提升。此外，SAMWater生成的精化标签改善了U-Net、DeepLabV3+和SegFormer的训练，表明其可用于弱监督分割。因此，SAMWater表明，将具有物理可解释性的遥感先验与可提示基础模型相结合，为可迁移地表水制图、大尺度多传感器弱标签生成以及更一致的地表水范围监测提供了一条实用路径。","GIScience & Remote Sensing","2026-10-09T00:00:00Z",81,{"impact":18,"substance":316,"depth":18,"authority":242,"freshness":157,"relevant":21,"comment":317},22,"将SAM基础模型与多源遥感先验结合实现可迁移水体制图，方法新颖、验证充分，对农业水资源与洪涝监测有实用价值。",[319],{"name":312,"url":309},[249,27,321,322,30],"洪涝监测","多模态大模型",{"code":270,"slug":324,"name":325,"path":326,"parent_code":109},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":112,"locked":14,"version":34,"method":35,"evidence":329},{"candidates":330,"agricultural_evidence":40},[331,337,341],{"code":270,"score":98,"supported":40,"title":332,"abstract":333,"tags":336},[],[334,335],"深度学习","deep learning",[249,322],{"code":39,"score":260,"supported":40,"title":338,"abstract":339,"tags":340},[],[27,42],[27],{"code":53,"score":20,"supported":40,"title":342,"abstract":343,"tags":345},[],[57,344],"water resource",[],[347,348],"SAMWater 水体提取","Sentinel-1 Sentinel-2 水面制图","SAMWater水体提取-4686","10.1080\u002F15481603.2026.2745243",{"doi":350,"openalex_id":352,"authors":353,"venue":312,"cited_by_count":77,"oa_url":382,"card":383,"direction":83,"ingested_from":85},"W7221158478",[354,357,360,363,366,368,370,372,375,377,379],{"name":355,"orcid":356},"Yuchen Liu","https:\u002F\u002Forcid.org\u002F0000-0002-6517-0022",{"name":358,"orcid":359},"Kang Zheng","https:\u002F\u002Forcid.org\u002F0000-0002-4811-0001",{"name":361,"orcid":362},"Yongze Song","https:\u002F\u002Forcid.org\u002F0000-0003-3420-9622",{"name":364,"orcid":365},"Lai Lai","https:\u002F\u002Forcid.org\u002F0000-0002-0744-6756",{"name":367,"orcid":9},"Ming Shen",{"name":369,"orcid":9},"Yanzhu Dong",{"name":371,"orcid":9},"Yuxuan Wang",{"name":373,"orcid":374},"Xiaoxiao Guo","https:\u002F\u002Forcid.org\u002F0000-0002-0068-8074",{"name":376,"orcid":9},"Dong Sheng",{"name":378,"orcid":9},"Zunyi Xie",{"name":380,"orcid":381},"Richard Iestyn Woolway","https:\u002F\u002Forcid.org\u002F0000-0003-0498-7968","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2745243?needAccess=true",{"tldr":384,"method":385,"finding":386,"direction":83,"opportunity":387},"提出SAMWater，用提示引导多模态基础模型实现可迁移地表水制图。","融合Sentinel-1\u002F2与SAM\u002FSAM 3，构建多提示与Water-IoU","在多地类气候区Kappa达85%以上，优于传统与深度学习及全球产品。","可探索将物理先验提示框架迁移至农田灌溉水体与洪涝监测的弱标签生成。","2026-10-11T23:30:31.848575Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":396,"score_detail":397,"sources":401,"tags":403,"primary_topic":406,"secondary_topics":411,"classification":412,"search_phrases":448,"slug":451,"view_count":69,"doi":452,"paper":453,"created_at":484},4602,"Phenology-driven multisensor unsupervised crop mapping in data-scarce irrigated agriculture: a case of Adea District in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwpt.2026.492","ABSTRACT Accurate irrigation planning, water allocation, and agricultural productivity assessment are frequently constrained in data-scare regions where reliable ground-based crop information and spatial coverage data are limited. This study addresses these gaps using a phenology-driven, multisensor unsupervised crop-mapping framework implemented within Google Earth Engine (GEE). The approach integrates monthly normalized difference vegetation index (NDVI) profiles from Sentinel-2, Sentinel-1VV backscatter, and digital elevation model–derived terrain slope into unsupervised K-means clustering algorithm. Adea District, Ethiopia, served as a demonstration site to evaluate and interpret the phenological signatures of key irrigated crops. The identified crop types were mapped to estimate spatial coverage and compared with the ground truth observations of irrigated wheat and tomato fields. The approach successfully distinguished irrigated wheat and tomato fields with an overall classification accuracy of 91%, a Kappa coefficient of 0.82, and estimated cultivated areas of 666.93 and 812.35 ha, respectively. Combining phenological profiles with multisensor satellite observations significantly enhance unsupervised classification performance in heterogeneous, smallholder-dominated agricultural landscapes. The cloud-based framework offers a computationally scalable and cost-effective solution for crop mapping, and water productivity monitoring in data-scarce agricultural regions.","在数据稀缺地区，可靠的基于地面作物信息和空间覆盖数据往往有限，精准灌溉规划、水资源分配和农业生产力评估因此常受制约。本研究针对这些不足，采用了一种由物候驱动、多传感器协同的无监督作物制图框架，并在Google Earth Engine（GEE）平台上实现。该方法将Sentinel-2的月度归一化植被指数（NDVI）时序、Sentinel-1 VV后向散射以及数字高程模型衍生的地形坡度整合到无监督K均值聚类算法中。以埃塞俄比亚Adea区为示范区，评估和解读主要灌溉作物的物候特征。对识别出的作物类型进行制图以估算空间覆盖范围，并与灌溉小麦和番茄田的地面实测数据进行对比。该方法成功区分了灌溉小麦和番茄田，总体分类精度达91%，Kappa系数为0.82，估算种植面积分别为666.93公顷和812.35公顷。将物候特征与多传感器卫星观测相结合，可显著提升异质性小农主导农业景观中的无监督分类性能。该基于云的框架为数据稀缺农业地区的作物制图和水分生产力监测提供了一种计算可扩展且成本效益高的解决方案。","Water Practice & Technology",67,{"impact":98,"substance":398,"depth":399,"authority":116,"freshness":157,"relevant":21,"comment":400},20,17,"方法新颖、精度可靠，但属区域案例研究，产业影响有限，可作为技术参考入选。",[402],{"name":395,"url":392},[248,27,404,28,405],"作物识别","灌溉农业",{"code":53,"slug":407,"name":408,"path":409,"parent_code":410},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":112,"locked":14,"version":34,"method":35,"evidence":413},{"candidates":414,"agricultural_evidence":40},[415,420,427,434,440,444],{"code":53,"score":13,"supported":40,"title":416,"abstract":417,"tags":419},[],[418,57,407],"灌溉",[405],{"code":421,"score":20,"supported":40,"title":422,"abstract":423,"tags":426},"A2",[],[424,425],"小麦","wheat",[],{"code":428,"score":20,"supported":40,"title":429,"abstract":430,"tags":433},"B1",[],[431,432],"番茄","tomato",[],{"code":435,"score":196,"supported":40,"title":436,"abstract":437,"tags":439},"G2",[],[438],"smallholder",[],{"code":39,"score":69,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[28,27],{"code":60,"score":21,"supported":14,"title":445,"abstract":446,"tags":447},[],[],[248],[449,450],"埃塞俄比亚 Adea 灌溉作物 遥感","Sentinel-2 NDVI 作物分类","埃塞俄比亚Adea灌溉作物遥感-4602","10.2166\u002Fwpt.2026.492",{"doi":452,"openalex_id":454,"authors":455,"venue":395,"cited_by_count":77,"oa_url":392,"card":479,"direction":145,"ingested_from":85},"W7221136167",[456,459,462,465,468,471,474,476],{"name":457,"orcid":458},"Belete Berhanu Kidanewold","https:\u002F\u002Forcid.org\u002F0000-0002-9876-8549",{"name":460,"orcid":461},"Jemal Mohammed Hassen","https:\u002F\u002Forcid.org\u002F0000-0002-7417-173X",{"name":463,"orcid":464},"Rahel Sintayehu Tessema","https:\u002F\u002Forcid.org\u002F0009-0006-2798-1752",{"name":466,"orcid":467},"Saba Kidane","https:\u002F\u002Forcid.org\u002F0009-0002-7403-9939",{"name":469,"orcid":470},"Mulugeta Melese","https:\u002F\u002Forcid.org\u002F0009-0008-3539-910X",{"name":472,"orcid":473},"Tadesse Shimels","https:\u002F\u002Forcid.org\u002F0009-0009-3351-5952",{"name":475,"orcid":9},"Mussie Alemayehu",{"name":477,"orcid":478},"Daneal Fekersillassie","https:\u002F\u002Forcid.org\u002F0009-0006-6194-0903",{"tldr":480,"method":481,"finding":482,"direction":83,"opportunity":483},"基于物候与多传感器数据，在埃塞俄比亚缺数据灌区实现无监督作物制图。","GEE平台融合Sentinel-2 NDVI、Sentinel-1 VV及DEM","成功区分灌溉小麦和番茄，总体精度91%，Kappa 0.82，面积分别为666.93和812.35公","可探索物候特征与多源遥感在缺数据小农灌区的迁移学习及作物水生产力监测。","2026-10-10T23:30:36.165502Z",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":490,"content":9,"source_name":491,"source_url":488,"published_at":492,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":493,"score_detail":494,"sources":496,"tags":498,"primary_topic":9,"secondary_topics":501,"classification":502,"search_phrases":525,"slug":528,"view_count":21,"doi":529,"paper":530,"created_at":557},4515,"A hybrid DeepGR4J and regionalization framework for lake water balance assessment in data-scarce regions: Application to Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejrh.2026.104046","Study region Lake Tana, the largest freshwater lake in Ethiopia and the headwater of the Blue Nile River, is located in the northwestern Ethiopian Highlands and is vital for water supply, hydropower, irrigation, and ecosystem services. The basin has strong rainfall seasonality, complex topography, and many ungauged catchments, making hydrological assessment difficult due to limited observations. Study focus This study develops a hybrid hydrological framework integrating the DeepGR4J rainfall-runoff model with regression-based regionalization to improve lake water balance estimation under data-scarce conditions. DeepGR4J combines the conceptual GR4J model with LSTM-based error correction to enhance runoff simulation, while regionalization transfers parameters from gauged to ungauged watersheds using physiographic and remote sensing descriptors. The framework is applied for 2000–2020 using CHIRPS precipitation, ERA5 temperature, and Hargreaves evapotranspiration estimates. New hydrological insights for the region Results show a mean annual inflow of 12.61 km³ yr⁻¹ and a combined water-balance outflow term of 12.67 km³ yr⁻¹, resulting in a net storage decline of −0.20 km³ yr⁻¹ and a residual imbalance of 0.06 km³ yr⁻¹. The model reproduces observed lake levels with high accuracy (R² = 0.94, NSE = 0.94, RMSE = 0.18 m). Findings highlight the strong contribution of ungauged catchments and demonstrate that hybrid conceptual-deep learning models with regionalization improve water balance estimation in data-scarce lake basins.","研究区域：塔纳湖（Lake Tana）是埃塞俄比亚最大的淡水湖，也是青尼罗河（Blue Nile River）的发源地，位于埃塞俄比亚高原西北部，对供水、水电、灌溉和生态系统服务至关重要。该流域降雨季节性显著、地形复杂，且存在大量无测站集水区，加之观测资料有限，使得水文评估十分困难。研究重点：本研究构建了一个混合水文框架，将DeepGR4J降雨-径流模型与基于回归的区域化方法相结合，以改进资料稀缺条件下的湖泊水量平衡估算。DeepGR4J将概念性GR4J模型与基于LSTM的误差校正相结合，以增强径流模拟；区域化方法则利用地貌和遥感描述因子将参数从有测站流域迁移至无测站流域。该框架应用于2000—2020年，采用CHIRPS降水、ERA5气温和Hargreaves蒸散发估算数据。该区域的新水文认识：结果表明，年均入湖水量为12.61 km³ yr⁻¹，水量平衡出流项合计为12.67 km³ yr⁻¹，导致净蓄水量下降−0.20 km³ yr⁻¹，残差失衡为0.06 km³ yr⁻¹。该模型以较高精度再现了观测湖水位（R² = 0.94，NSE = 0.94，RMSE = 0.18 m）。研究结果凸显了无测站集水区的重要贡献，并表明结合区域化的混合概念性-深度学习模型能够改进资料稀缺湖泊流域的水量平衡估算。","Journal of Hydrology Regional Studies","2026-10-07T00:00:00Z",73,{"impact":17,"substance":316,"depth":18,"authority":116,"freshness":98,"relevant":21,"comment":495},"将概念水文模型与LSTM误差校正及区域化方法结合，在缺资料流域实现高精度水量平衡评估，方法新颖且结论可靠，对农业水资源管理有参考价值。",[497],{"name":491,"url":488},[334,27,499,29,500],"水资源管理","数据稀缺",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":503},{"candidates":504,"agricultural_evidence":14,"reason":64},[505,512,516,520],{"code":53,"score":506,"supported":40,"title":507,"abstract":508,"tags":511},11,[],[418,509,407,510],"蒸散发","evapotranspiration",[29,499],{"code":270,"score":260,"supported":40,"title":513,"abstract":514,"tags":515},[],[334,335],[334],{"code":39,"score":260,"supported":40,"title":517,"abstract":518,"tags":519},[],[27,42],[27],{"code":191,"score":196,"supported":40,"title":521,"abstract":522,"tags":524},[],[523],"生态系统服务",[],[526,527],"Lake Tana 水量平衡","DeepGR4J 径流模拟","LakeTana水量平衡-4515","10.1016\u002Fj.ejrh.2026.104046",{"doi":529,"openalex_id":531,"authors":532,"venue":491,"cited_by_count":77,"oa_url":551,"card":552,"direction":83,"ingested_from":85},"W7220864762",[533,536,538,541,544,546,548],{"name":534,"orcid":535},"Temesgen T. Mihret","https:\u002F\u002Forcid.org\u002F0000-0001-9793-9070",{"name":537,"orcid":9},"Fasikaw A. Zemale",{"name":539,"orcid":540},"Abeyou Wale Worqlul","https:\u002F\u002Forcid.org\u002F0000-0002-7990-8446",{"name":542,"orcid":543},"Ayenew Desalegn Ayalew","https:\u002F\u002Forcid.org\u002F0000-0002-7331-8170",{"name":545,"orcid":9},"Margaret Chen",{"name":547,"orcid":9},"Seifu A. Tilahun",{"name":549,"orcid":550},"Nicola Fohrer","https:\u002F\u002Forcid.org\u002F0000-0002-7456-6301","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2214581826009444\u002Fpdf",{"tldr":553,"method":554,"finding":555,"direction":83,"opportunity":556},"提出DeepGR4J与区域化混合框架，评估数据稀缺的埃塞俄比亚塔纳湖水量平衡。","GR4J概念模型结合LSTM误差校正，回归区域化，CHIRPS与ERA5数据。","年均入流12.61 km³，储量净减0.20 km³，湖水位模拟R²=0.94。","可将该混合框架迁移至数据稀缺农业流域，结合遥感蒸散与作物需水评估灌溉可持续性。","2026-10-09T23:30:28.784309Z"]