[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-647":3,"related-647":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":50,"slug":53,"view_count":54,"doi":8,"paper":8,"created_at":55},647,"八部门联合印发通知:进一步加强工业等污水污染风险防范保障农业灌溉用水安全","https:\u002F\u002Fwww.ctdsb.net\u002Fc1747_202608\u002F2833244.html","生态环境部会同工信部、公安部、自然资源部、水利部、农业农村部、最高法、最高检联合印发《关于进一步加强工业等污水污染风险防范保障农业灌溉用水安全的通知》。从空间布局、排污管控、水质保障、监管执法4方面明确举措:严防化工园区转移带来的环境风险;推进农田周边工业园区污水集中处理设施升级改造;出台农田灌溉用水水质监测技术指南;重点整治利用溶洞、渗坑、裂隙等非法排放行为,加强行政执法与刑事司法衔接。",null,"## 生态环境部、工信部、公安部、自然资源部、水利部、农业农村部、最高法、最高检等，联合印发通知\n\n生态环境部 2026-08-18 07:25:17\n\n近日，生态环境部会同工业和信息化部、公安部、自然资源部、水利部、农业农村部、最高人民法院、最高人民检察院等联合印发了《关于进一步加强工业等污水污染风险防范保障农业灌溉用水安全的通知》。\n\n生态环境部水生态环境司有关负责人17日介绍，近年来，一些工业企业及园区、畜禽养殖场等与农田相邻，污水排放管控不到位，农业灌溉用水安全隐患不容忽视。此次印发的通知以保障农业灌溉用水安全为核心，以源头预防、过程把控、末端监管为主线，推动构建跨部门、全周期的风险防控体系。\n\n通知从空间布局、排污管控、水质保障、监管执法等四个方面明确相关举措。\n\n通知明确，在国土空间规划管控、生态环境分区管控、建设项目环境影响评价等环节，进一步规范农田周边、喀斯特地貌等风险较高地区，以及排放全盐量、重金属、有毒有害污染物等特征污染物的建设项目准入要求，严防化工园区转移带来的环境风险。\n\n通知要求，推进农田周边工业园区污水集中处理设施升级改造，开展工业、畜禽养殖涉水问题排查整治，对工业企业排放特征污染物可能影响下游农业灌溉用水安全的，开展地方水污染物排放标准制修订，加强排放管控要求。\n\n通知提出，进一步压实企业自行监测主体责任，出台农田灌溉用水水质监测技术指南，指导地方因地制宜制定农业灌溉用水水质监测计划，加强农业灌溉用水水质监测协作，明确信息通报、数据共享、应急处置等要求。\n\n通知要求，加大现场执法力度，重点整治利用溶洞、渗坑、裂隙等非法排放工业废水、畜禽粪污等行为。加强执法司法联动和信息共享，完善行政执法与刑事司法衔接，坚决打击污染环境犯罪。加强政策解读和宣传培训，提升企业守法自觉和基层履职能力。\n\n责任编辑:王念\n\n点赞 0\n\n更多精彩报道，请下载[极目新闻](http:\u002F\u002Fjmwap.ctdsb.net\u002Fweb\u002Fdownload.html)\n\n本文为极目号作者或合作机构在极目新闻发布内容，仅代表该作者或机构观点，不代表极目新闻的观点或立场。\n\n报料渠道：拨打24小时新闻热线027-86777777；登录极目新闻“报料”；私信“楚天都市报极目新闻”新浪微博，或关注“楚天都市报极目新闻”官方微信私信报料。","楚天都市报(生态环境部) 2026-08-17","2026-08-17T00:00:00Z","政策",10,true,93,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},28,23,18,14,1,"八部门联合发文，聚焦工业污水风险防范保障农业灌溉用水安全，政策层级高、内容具体，对农业信息化领域有重要指导意义。",[24],{"name":10,"url":6},[26,27,28,29],"生态环境部","农业灌溉","污水","八部门",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":38,"locked":39,"version":40,"method":41,"evidence":42},"classified",false,"2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":14},[44],{"code":31,"score":45,"supported":14,"title":46,"abstract":48,"tags":49},11,[47],"灌溉",[47],[],[51,52],"生态环境部 农业灌溉 八部门 污水","生态环境部 农业灌溉","生态环境部农业灌溉八部门污水-647",7,"2026-08-17T23:54:53.050394Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,148,211,278,344,456],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":8,"source_name":65,"source_url":62,"published_at":66,"category":67,"cover_url":8,"hotness":13,"is_selected":39,"score":68,"score_detail":69,"sources":74,"tags":76,"primary_topic":80,"secondary_topics":81,"classification":82,"search_phrases":116,"slug":119,"view_count":120,"doi":121,"paper":122,"created_at":147},2007,"AIDspat: Introduction of a model framework to estimate spatio-temporal agricultural irrigation demands – A study area in Germany","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110730","In the face of ongoing climate change, information about agricultural water usage is crucial for policymakers and planners. This study introduces the AIDspat model, a new framework for estimating agricultural irrigation demand (AID). The model combines an existing soil water balance model with the principle of irrigation scheduling. In addition to location-based climate, soil and land use data, the model takes into account general crop-specific parameters, irrigation techniques and farmers’ irrigation management decisions. This enables an objective determination of AID. The model enables scenario-based calculations with low computational cost across various scales, ranging from the field to the national level and spanning both past and future time periods. Integrating crop-specific differences through intra-annual dynamic plant development and considering irrigation techniques enhances the model’s accuracy and practical applicability. The model’s ability to capture the spatial and temporal dynamics of AID provides valuable insights for the sustainable management of agricultural water. Calibrated and validated for a study area in southern Germany, the model demonstrates a high degree of alignment between modeled values and literature benchmarks for a variety of agricultural crops. The modeled results show a good agreement with measured irrigation volumes, with yearly percentage differences ranging from -17.2% to +47% and a mean absolute percentage error (MAPE) of approximately 16.9% over the period from 2017 to 2023. Thus, the model provides planners and policymakers with an irrigation decision support system to analyze the impact of agricultural systems, climatic changes and agricultural decision-making processes, thereby facilitating sustainable agricultural irrigation management.","面对持续的气候变化，农业用水信息对政策制定者和规划者至关重要。本研究引入了AIDspat模型，这是一个估算农业灌溉需求（AID）的新框架。该模型将现有的土壤水平衡模型与灌溉调度原理相结合。除了基于位置的气候、土壤和土地利用数据外，模型还考虑了通用的作物特定参数、灌溉技术以及农民的灌溉管理决策。这使得AID能够被客观地确定。该模型支持基于情景的计算，计算成本低，可适用于从田间到国家层面的各种尺度，并涵盖过去和未来的时间段。通过年内动态植物发育整合作物特异性差异，并考虑灌溉技术，增强了模型的准确性和实际应用性。模型捕捉AID时空动态的能力为农业水资源的可持续管理提供了宝贵的见解。该模型在德国南部的一个研究区域进行了校准和验证，显示出模拟值与多种农作物的文献基准高度一致。模拟结果与实测灌溉量吻合良好，2017年至2023年间年度百分比差异范围为-17.2%至+47%，平均绝对百分比误差（MAPE）约为16.9%。因此，该模型为规划者和政策制定者提供了一个灌溉决策支持系统，用于分析农业系统、气候变化和农业决策过程的影响，从而促进可持续的农业灌溉管理。","Agricultural Water Management","2026-09-08T00:00:00Z","论文",75,{"impact":19,"substance":70,"depth":71,"authority":20,"freshness":72,"relevant":21,"comment":73},22,19,2,"提出AIDspat模型，结合土壤水平衡与灌溉调度，实现时空尺度农业灌溉需水估算，对可持续水资源管理有参考价值。",[75],{"name":65,"url":62},[77,27,78,79],"智慧农业","水资源管理","模型模拟",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":83},{"candidates":84,"agricultural_evidence":14},[85,90,97,104,111],{"code":31,"score":19,"supported":14,"title":86,"abstract":87,"tags":89},[32],[47,88,32],"水资源",[78],{"code":91,"score":57,"supported":14,"title":92,"abstract":93,"tags":96},"H2",[],[94,95],"土壤","soil",[],{"code":98,"score":57,"supported":14,"title":99,"abstract":100,"tags":103},"H7",[],[101,102],"气候变化","climate change",[],{"code":105,"score":106,"supported":14,"title":107,"abstract":108,"tags":110},"I2",4,[],[109],"decision support",[79],{"code":112,"score":21,"supported":39,"title":113,"abstract":114,"tags":115},"I1",[],[],[77],[117,118],"水资源管理 农业灌溉 智慧农业 模型模拟","水资源管理 农业灌溉","水资源管理农业灌溉智慧农业模型模拟-2007",5,"10.1016\u002Fj.agwat.2026.110730",{"doi":121,"openalex_id":123,"authors":124,"venue":65,"cited_by_count":139,"oa_url":62,"card":140,"direction":144,"ingested_from":146},"W4412168598",[125,127,129,131,133,136],{"name":126,"orcid":8},"Jacob Jeff Bernhardt",{"name":128,"orcid":8},"Frank Potts",{"name":130,"orcid":8},"Jan Bug",{"name":132,"orcid":8},"Maximilian Zinnbauer",{"name":134,"orcid":135},"Max Eysholdt","https:\u002F\u002Forcid.org\u002F0000-0001-9761-2991",{"name":137,"orcid":138},"Jochen Hack","https:\u002F\u002Forcid.org\u002F0000-0002-8060-7990",0,{"tldr":141,"method":142,"finding":143,"direction":144,"opportunity":145},"提出AIDspat模型，结合土壤水平衡与灌溉调度，估算时空农业灌溉需求。","结合土壤水平衡模型与灌溉调度原理，考虑气候、土壤、作物参数及灌溉管理决策。","模型在德国南部验证良好，MAPE约16.9%，能捕捉灌溉需求的时空动态。","农业人工智能与决策模型","可扩展至不同气候区，集成遥感数据或优化灌溉管理决策，提升模型适用性。","openalex","2026-09-09T23:30:46.078172Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":8,"source_name":65,"source_url":151,"published_at":154,"category":67,"cover_url":8,"hotness":13,"is_selected":39,"score":68,"score_detail":155,"sources":158,"tags":160,"primary_topic":163,"secondary_topics":164,"classification":165,"search_phrases":183,"slug":185,"view_count":156,"doi":186,"paper":187,"created_at":210},1831,"Water-yield supply–demand relationships in Northwest China three water lines region: An InVEST–glacier assessment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110752","Water scarcity poses a major challenge to sustainable development in arid regions, particularly where agricultural irrigation dominates water consumption. In glacier-influenced regions, conventional water yield assessments may underestimate water availability by neglecting glacier meltwater contributions. This study developed an InVEST–Glacier framework to quantify water yield service supply and demand in Northwest China Three Water Lines region (TWL region) and identify their dominant driving factors. The results showed that low water-yield areas (0–25 mm) accounted for 60.06%–70.19% of the region, while high water-yield areas were concentrated in mountainous regions. Glacier meltwater contributed more than 70% of water yield in most inland basins of the Tarim Basin and exceeded 90% in some years, but contributed less than 10% in the Yellow River and Datong River. Water demand was highly concentrated in irrigated plains and urbanized areas. The supply–demand relationship exhibited a pattern of “widespread surplus with localized stress”, with areas of ESDR \u003C 0.2 increasing from 67.59% to 79.50%, while areas with ESDR > 0.8 accounted for 14.68%–23.90%. Natural factors dominated the spatial differentiation of ESDR, with elevation, precipitation, and NDVI showing the highest integrated driving scores.These findings highlight the stage-dependent buffering role of glacier meltwater and the need for differentiated water-resource management in glacier-dependent and water-stressed regions.","水资源短缺对干旱地区的可持续发展构成重大挑战，尤其是在农业灌溉占用水主导地位的地区。在受冰川影响的区域，传统的水资源评估方法可能因忽略冰川融水贡献而低估水资源可用性。本研究构建了InVEST–冰川（InVEST–Glacier）框架，用以量化中国西北“三水线”区域（TWL区域）的水资源供给服务与需求，并识别其主导驱动因素。结果表明，低产水区（0–25毫米）占区域面积的60.06%–70.19%，而高产水区集中于山区。在塔里木盆地大部分内陆流域，冰川融水对产水量的贡献超过70%，部分年份甚至超过90%，但在黄河及大通河流域其贡献不足10%。水资源需求高度集中于灌溉平原和城市化区域。供需关系呈现“整体盈余、局部紧张”的格局，其中生态系统服务供需比（ESDR）小于0.2的区域面积从67.59%增加至79.50%，而ESDR大于0.8的区域占14.68%–23.90%。自然因素主导了ESDR的空间分异，其中海拔、降水和归一化植被指数（NDVI）的综合驱动得分最高。这些发现凸显了冰川融水在阶段性上的缓冲作用，以及在依赖冰川和缺水地区实施差异化水资源管理的必要性。","2026-09-05T00:00:00Z",{"impact":19,"substance":70,"depth":19,"authority":20,"freshness":156,"relevant":21,"comment":157},3,"研究提出InVEST-冰川框架量化水资源供需，对干旱区农业水资源管理有重要参考价值。",[159],{"name":65,"url":151},[27,161,78,162],"遥感评估","西北地区",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":166},{"candidates":167,"agricultural_evidence":14},[168,172,178],{"code":31,"score":13,"supported":14,"title":169,"abstract":170,"tags":171},[],[47,88,32],[78],{"code":173,"score":156,"supported":14,"title":174,"abstract":175,"tags":177},"H6",[],[176],"生态系统服务",[],{"code":98,"score":156,"supported":14,"title":179,"abstract":180,"tags":182},[],[181],"干旱",[],[184,118],"水资源管理 农业灌溉 西北地区 遥感评估","水资源管理农业灌溉西北地区遥感评估-1831","10.1016\u002Fj.agwat.2026.110752",{"doi":186,"openalex_id":188,"authors":189,"venue":65,"cited_by_count":139,"oa_url":202,"card":203,"direction":209,"ingested_from":146},"W7208829734",[190,192,194,196,198,200],{"name":191,"orcid":8},"Yingying Gai",{"name":193,"orcid":8},"Shnhong Mo",{"name":195,"orcid":8},"Peng Li",{"name":197,"orcid":8},"Mingjiang Deng",{"name":199,"orcid":8},"Hui Sun",{"name":201,"orcid":8},"Tao Zhou","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426006335\u002Fpdf",{"tldr":204,"method":205,"finding":206,"direction":207,"opportunity":208},"开发InVEST-冰川框架评估西北三线区域水资源供需，发现冰川融水贡献显著但区域差异大。","InVEST-冰川框架，结合冰川融水估算，分析供需及驱动因素。","冰川融水在塔里木盆地贡献超70%，供需总体盈余但局部紧张，自然因素主导空间分异。","农业绿色发展与碳","可研究冰川融水对农业灌溉的缓冲作用及气候变化下的水资源管理策略，结合农业需水预测。","农业遥感与作物表型","2026-09-07T23:30:23.347697Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":8,"source_name":217,"source_url":214,"published_at":218,"category":67,"cover_url":8,"hotness":13,"is_selected":39,"score":219,"score_detail":220,"sources":225,"tags":227,"primary_topic":232,"secondary_topics":233,"classification":239,"search_phrases":260,"slug":263,"view_count":139,"doi":264,"paper":265,"created_at":277},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":221,"substance":70,"depth":19,"authority":222,"freshness":223,"relevant":21,"comment":224},8,12,9,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[226],{"name":217,"url":214},[77,228,229,230,231],"农业人工智能","灌溉决策","安全保证","试验设计",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[234],{"code":105,"slug":235,"name":236,"path":237,"parent_code":238},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":39,"version":40,"method":41,"evidence":240},{"candidates":241,"agricultural_evidence":14},[242,247,252,256],{"code":31,"score":243,"supported":14,"title":244,"abstract":245,"tags":246},15,[32],[47,32],[229],{"code":105,"score":222,"supported":14,"title":248,"abstract":250,"tags":251},[249],"artificial intelligence",[109],[228],{"code":91,"score":57,"supported":14,"title":253,"abstract":254,"tags":255},[],[94,95],[],{"code":112,"score":21,"supported":39,"title":257,"abstract":258,"tags":259},[],[],[77],[261,262],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":264,"openalex_id":266,"authors":267,"venue":217,"cited_by_count":139,"oa_url":214,"card":272,"direction":144,"ingested_from":146},"W7223131205",[268,270],{"name":269,"orcid":8},"Nassima Benkhelifa",{"name":271,"orcid":8},"Yacine Aït Ahmed",{"tldr":273,"method":274,"finding":275,"direction":144,"opportunity":276},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":8,"source_name":284,"source_url":281,"published_at":285,"category":67,"cover_url":8,"hotness":13,"is_selected":39,"score":286,"score_detail":287,"sources":291,"tags":293,"primary_topic":299,"secondary_topics":300,"classification":301,"search_phrases":323,"slug":326,"view_count":139,"doi":327,"paper":328,"created_at":343},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment","2026-10-20T00:00:00Z",65,{"impact":221,"substance":288,"depth":289,"authority":222,"freshness":223,"relevant":21,"comment":290},20,16,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[292],{"name":284,"url":281},[294,295,296,297,298],"NDVI","埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":39,"version":40,"method":41,"evidence":302},{"candidates":303,"agricultural_evidence":14},[304,310,314,318],{"code":31,"score":57,"supported":14,"title":305,"abstract":306,"tags":309},[],[307,308],"蒸散发","evapotranspiration",[],{"code":98,"score":156,"supported":14,"title":311,"abstract":312,"tags":313},[],[101],[],{"code":173,"score":21,"supported":39,"title":315,"abstract":316,"tags":317},[],[],[296],{"code":319,"score":21,"supported":39,"title":320,"abstract":321,"tags":322},"I3",[],[],[294],[324,325],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":327,"openalex_id":329,"authors":330,"venue":284,"cited_by_count":139,"oa_url":337,"card":338,"direction":209,"ingested_from":146},"W7223064056",[331,334],{"name":332,"orcid":333},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":335,"orcid":336},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":339,"method":340,"finding":341,"direction":209,"opportunity":342},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":8,"source_name":350,"source_url":347,"published_at":218,"category":67,"cover_url":8,"hotness":13,"is_selected":39,"score":351,"score_detail":352,"sources":354,"tags":356,"primary_topic":360,"secondary_topics":361,"classification":368,"search_phrases":426,"slug":429,"view_count":139,"doi":430,"paper":431,"created_at":455},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":19,"substance":70,"depth":19,"authority":222,"freshness":223,"relevant":21,"comment":353},"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。",[355],{"name":350,"url":347},[77,228,357,358,359],"遥感","作物水分胁迫","精准灌溉",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[362,363],{"code":105,"slug":235,"name":236,"path":237,"parent_code":238},{"code":364,"slug":365,"name":366,"path":367,"parent_code":238},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":38,"locked":39,"version":40,"method":41,"evidence":369},{"candidates":370,"agricultural_evidence":14},[371,375,381,388,394,399,406,413,420],{"code":31,"score":243,"supported":14,"title":372,"abstract":373,"tags":374},[32],[47,32],[359],{"code":105,"score":13,"supported":14,"title":376,"abstract":377,"tags":380},[],[378,379,249],"机器学习","machine learning",[228],{"code":364,"score":223,"supported":14,"title":382,"abstract":383,"tags":387},[],[384,385,386],"物联网","internet of things","IoT",[],{"code":112,"score":54,"supported":14,"title":389,"abstract":390,"tags":393},[],[391,392],"数字孪生","digital twin",[77],{"code":319,"score":54,"supported":14,"title":395,"abstract":396,"tags":398},[],[357,397],"remote sensing",[357],{"code":400,"score":57,"supported":14,"title":401,"abstract":402,"tags":405},"A2",[],[403,404],"小麦","wheat",[],{"code":407,"score":57,"supported":14,"title":408,"abstract":409,"tags":412},"A3",[],[410,411],"玉米","maize",[],{"code":414,"score":57,"supported":14,"title":415,"abstract":416,"tags":419},"A5",[],[417,418],"马铃薯","potato",[],{"code":421,"score":156,"supported":14,"title":422,"abstract":423,"tags":425},"B2",[],[424],"葡萄",[],[427,428],"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":430,"openalex_id":432,"authors":433,"venue":350,"cited_by_count":139,"oa_url":347,"card":449,"direction":454,"ingested_from":146},"W7223088921",[434,437,440,443,446],{"name":435,"orcid":436},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":438,"orcid":439},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":441,"orcid":442},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":444,"orcid":445},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":447,"orcid":448},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":450,"method":451,"finding":452,"direction":209,"opportunity":453},"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":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":8,"source_name":462,"source_url":459,"published_at":218,"category":67,"cover_url":8,"hotness":13,"is_selected":39,"score":463,"score_detail":464,"sources":467,"tags":469,"primary_topic":472,"secondary_topics":473,"classification":476,"search_phrases":522,"slug":525,"view_count":139,"doi":526,"paper":527,"created_at":545},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":19,"substance":70,"depth":19,"authority":465,"freshness":223,"relevant":21,"comment":466},13,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[468],{"name":462,"url":459},[77,228,384,470,471],"精准农业","气候韧性",{"code":105,"slug":235,"name":236,"path":237,"parent_code":238},[474,475],{"code":364,"slug":365,"name":366,"path":367,"parent_code":238},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":39,"version":40,"method":41,"evidence":477},{"candidates":478,"agricultural_evidence":14},[479,487,492,498,502,506,510,516],{"code":105,"score":19,"supported":14,"title":480,"abstract":481,"tags":486},[249],[482,378,483,379,484,485],"深度学习","计算机视觉","deep learning","computer vision",[228],{"code":112,"score":289,"supported":14,"title":488,"abstract":490,"tags":491},[489],"precision agriculture",[470,489],[77,470],{"code":364,"score":13,"supported":14,"title":493,"abstract":494,"tags":497},[],[384,495,386,496],"边缘计算","edge computing",[384],{"code":31,"score":223,"supported":14,"title":499,"abstract":500,"tags":501},[],[47,88,32],[],{"code":98,"score":54,"supported":14,"title":503,"abstract":504,"tags":505},[],[101,102],[471],{"code":91,"score":57,"supported":14,"title":507,"abstract":508,"tags":509},[],[94,95],[],{"code":511,"score":156,"supported":14,"title":512,"abstract":513,"tags":515},"A7",[],[514],"间作",[],{"code":517,"score":156,"supported":14,"title":518,"abstract":519,"tags":521},"F3",[],[520],"养分管理",[],[523,524],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":526,"openalex_id":528,"authors":529,"venue":462,"cited_by_count":139,"oa_url":459,"card":540,"direction":454,"ingested_from":146},"W7223121456",[530,533,535,537],{"name":531,"orcid":532},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":534,"orcid":8},"Oluwadare OYEBODE",{"name":536,"orcid":8},"Iretiayo Adelaiye",{"name":538,"orcid":539},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":541,"method":542,"finding":543,"direction":144,"opportunity":544},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z"]