[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1767":3,"related-1767":99},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":98},1767,"Assessment of water use efficiency in arid ecosystems of the coastal land of Egypt using MODIS dataset","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67232-3","Abstract In drylands, the limited availability of reliable measurements makes it more difficult to understand how climatic and biogeochemical factors shape ecosystem processes across spatial gradients compared to mesic or humid regions. Water use efficiency (WUE) is a key physiological indicator for assessing interactions between carbon and water cycles in any ecosystem. Yet, in dryland ecosystems with sparse vegetation where evaporation represents a large fraction of total evapotranspiration (ET), the response of WUE to land use changes needs more investigation. To address this gap, the present study examines the spatial and temporal patterns of WUE in coastal dryland ecosystems of Egypt to better understand how land use practices affect water dynamics. Using an integrated approach that combines remote sensing products, WUE trends were assessed across representative sites. The results show high spatial variation in the WUE pattern over the 25-year period, with absolute values ranging from below 1.0 × 10 −3 g C mm −1 in sparsely vegetated zones up to 2.9 × 10 −3 g C mm −1 in reclaimed lands. The highest values of WUE are for the vegetated areas of croplands and reed vegetation, where the biomass reflects the growth of developed vegetation. The natural land covers are subjected to various changes due to human activities and expansion in the reclaimed agricultural areas where the urbanization activities increase ET and reduce WUE. These insights can contribute to a better understanding of dryland ecosystem functioning and can inform sustainable water and land management strategies in arid regions facing increasing water scarcity.","在干旱地区，可靠测量数据的有限性使得相较于湿润或半湿润地区，理解气候和生物地球化学因素如何塑造生态系统过程的空间梯度变得更加困难。水分利用效率（WUE）是评估任何生态系统中碳与水循环相互作用的关键生理指标。然而，在植被稀疏、蒸发占实际蒸散发（ET）很大比例的干旱生态系统中，水分利用效率对土地利用变化的响应仍需进一步研究。为弥补这一空白，本研究考察了埃及沿海干旱生态系统水分利用效率的时空格局，以更好地理解土地利用实践如何影响水分动态。通过结合遥感产品的综合方法，评估了代表性样点的水分利用效率趋势。结果显示，25年间水分利用效率格局存在高度空间变异性，绝对值范围从稀疏植被区的低于1.0×10⁻³ g C mm⁻¹到开垦土地的2.9×10⁻³ g C mm⁻¹不等。水分利用效率最高值出现在农田和芦苇植被的植被覆盖区域，其生物量反映了发育植被的生长状况。由于人类活动和开垦农业区的扩张，自然土地覆盖类型经历了各种变化，城市化活动增加了蒸散发并降低了水分利用效率。这些见解有助于加深对干旱生态系统功能的理解，并可为面临日益严重水资源短缺的干旱地区提供可持续水资源和土地管理策略的参考。",null,"Scientific Reports","2026-09-05T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,13,5,1,"利用MODIS数据评估埃及干旱区水分利用效率，对农业水资源管理有参考价值，但地域较远，影响有限。",[25],{"name":10,"url":6},[27,28,29,30],"农业水资源","遥感","干旱区","生态监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,52,59,66],{"code":32,"score":13,"supported":45,"title":46,"abstract":47,"tags":51},true,[],[48,49,50],"水资源","蒸散发","evapotranspiration",[27],{"code":53,"score":54,"supported":45,"title":55,"abstract":56,"tags":58},"I3",7,[],[28,57],"remote sensing",[28],{"code":60,"score":61,"supported":45,"title":62,"abstract":63,"tags":65},"H7",4,[],[64],"干旱",[29],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"H6",[],[],[30],[72,73],"农业水资源 生态监测 干旱区 遥感","农业水资源 生态监测","农业水资源生态监测干旱区遥感-1767",2,"10.1038\u002Fs41598-026-67232-3",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":89,"oa_url":90,"card":91,"direction":95,"ingested_from":97},"W7208807648",[80,83,86],{"name":81,"orcid":82},"Amira M. Hotaiba","https:\u002F\u002Forcid.org\u002F0000-0001-5305-1555",{"name":84,"orcid":85},"Boshra Salem","https:\u002F\u002Forcid.org\u002F0000-0001-9715-6324",{"name":87,"orcid":88},"Marwa Waseem A. Halmy","https:\u002F\u002Forcid.org\u002F0000-0002-4183-973X",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67232-3.pdf",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"利用MODIS数据评估埃及沿海干旱生态系统水分利用效率的时空格局及土地利用影响。","集成遥感产品，分析25年WUE趋势，对比不同土地覆盖类型。","WUE空间变异大，耕地和芦苇植被最高，城市化增加蒸散降低WUE。","农业遥感与作物表型","可研究干旱区土地利用变化对WUE的驱动机制，结合高分辨率数据优化水资源管理。","openalex","2026-09-06T23:30:27.242542Z",{"total":100,"page":22,"page_size":100,"items":101},6,[102,200,295,371,442,546],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":122,"classification":123,"search_phrases":148,"slug":151,"view_count":89,"doi":152,"paper":153,"created_at":199},4597,"Evaluating OpenET evapotranspiration estimates in California vineyards: Insights from the GRAPEX experiment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2026.111505","California agriculture faces escalating water-management challenges from drought and heat while adapting to groundwater-sustainability policies. Accurate field-scale evapotranspiration (ET) values are essential for irrigation decisions and water conservation. OpenET integrates six satellite-based ET models in Google Earth Engine to provide near–real-time actual ET (ET a ) at 30 m and daily resolution. We evaluated OpenET against eddy-covariance ET a from 11 wine-grape vineyards monitored by the Grape Remote sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX). We compared individual-model and ensemble ET a to tower ET a and conducted site-focused analyses to diagnose performance under key microclimate and management conditions. The OpenET ensemble ET a value closely matched tower ET a (daily R 2 = 0.82; MAE = 0.63 mm d -1 ; monthly R 2 = 0.89; MAE = 14.8 mm month -1 ) with negligible bias and generally outperformed individual models. Performance improved with temporal aggregation, consistent with reduced sensitivity to Landsat overpass timing and reference ET-based interpolation at the daily timestep. Skill was highest at monthly and seasonal scales, while daily errors increased during advective conditions and short-lived irrigation deficits, and tower energy-balance closure influenced inferred bias at advection-prone sites. Controlled experiments showed that thermal-based models detected imposed vine water-stress responses directionally (but were dampened in the ensemble) and that winter cover crops, and vine-row orientation produced consistent ET a shifts that propagated into thermal-based OpenET values. These results quantify when OpenET is most reliable for precision viticulture and provide guidance for using satellite ET a to support irrigation scheduling and water accounting in practice.","加利福尼亚农业在适应地下水可持续性政策的同时，面临着干旱和高温带来的日益加剧的水资源管理挑战。准确的田块尺度蒸散发（ET）值对于灌溉决策和节水至关重要。OpenET在Google Earth Engine中集成了六个基于卫星的ET模型，以30米空间分辨率和日时间分辨率提供近实时的实际蒸散发（ETa）。我们利用葡萄遥感大气廓线与蒸散发实验（GRAPEX）监测的11个酿酒葡萄园的通量观测ETa对OpenET进行了评估。我们将单个模型和集合ETa与塔站ETa进行了比较，并开展了站点聚焦分析，以诊断其在关键微气候和管理条件下的表现。OpenET集合ETa值与塔站ETa高度吻合（日尺度R²=0.82；MAE=0.63 mm d⁻¹；月尺度R²=0.89；MAE=14.8 mm month⁻¹），偏差可忽略不计，且总体优于单个模型。性能随时间聚合而提升，这与日时间步长下对Landsat过境时间和基于参考ET插值的敏感性降低相一致。在月和季节尺度上表现最佳，而日尺度误差在平流条件和短期灌溉亏缺期间增大，塔站能量平衡闭合影响了易发生平流站点推断偏差。控制实验表明，基于热红外的模型能够方向性地检测到施加的葡萄藤水分胁迫响应（但在集合中被削弱），且冬季覆盖作物和葡萄行向产生了持续的ETa变化，并传递至基于热红外的OpenET值中。这些结果量化了OpenET在精准葡萄栽培中最可靠的时机，并为在实践中利用卫星ETa支持灌溉调度和水核算提供了指导。","Agricultural and Forest Meteorology","2026-10-08T00:00:00Z",80,{"impact":18,"substance":112,"depth":18,"authority":113,"freshness":114,"relevant":22,"comment":115},22,14,8,"基于GRAPEX试验11个葡萄园涡度相关数据系统评估OpenET蒸散发产品精度，为卫星遥感支撑精准灌溉与农业水核算提供实证依据，方法扎实、结论可靠，对智慧农业水资源管理有参考价值。",[117],{"name":108,"url":105},[27,28,119,49,120],"葡萄园","智慧灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":124},{"candidates":125,"agricultural_evidence":45},[126,132,139,143],{"code":32,"score":127,"supported":45,"title":128,"abstract":129,"tags":131},20,[50],[130,48,49,33,50],"灌溉",[27,120,49],{"code":133,"score":54,"supported":45,"title":134,"abstract":135,"tags":138},"B2",[],[136,137],"葡萄","grape",[119],{"code":53,"score":54,"supported":45,"title":140,"abstract":141,"tags":142},[],[28,57],[28],{"code":60,"score":100,"supported":45,"title":144,"abstract":145,"tags":147},[],[64,146],"drought",[],[149,150],"OpenET 蒸散发 葡萄园","GRAPEX 加州 葡萄园","OpenET蒸散发葡萄园-4597","10.1016\u002Fj.agrformet.2026.111505",{"doi":152,"openalex_id":154,"authors":155,"venue":108,"cited_by_count":89,"oa_url":105,"card":194,"direction":95,"ingested_from":97},"W7221033125",[156,159,161,163,165,167,169,171,173,175,177,180,182,184,186,188,190,192],{"name":157,"orcid":158},"Kyle  R. Knipper","https:\u002F\u002Forcid.org\u002F0000-0003-0889-8129",{"name":160,"orcid":9},"N. Bambach",{"name":162,"orcid":9},"M. Anderson",{"name":164,"orcid":9},"W. Kustas",{"name":166,"orcid":9},"A. J. McElrone",{"name":168,"orcid":9},"M. Swintek",{"name":170,"orcid":9},"F. Melton",{"name":172,"orcid":9},"D. Salazar-Martinez",{"name":174,"orcid":9},"L. Hipps",{"name":176,"orcid":9},"J. Alfieri",{"name":178,"orcid":179},"John H. Prueger","https:\u002F\u002Forcid.org\u002F0000-0002-7768-5760",{"name":181,"orcid":9},"L. McKee",{"name":183,"orcid":9},"S. Castro",{"name":185,"orcid":9},"P. Tolentino",{"name":187,"orcid":9},"I. Wright",{"name":189,"orcid":9},"A. Gal",{"name":191,"orcid":9},"N. Dokoozlian",{"name":193,"orcid":9},"M.M. Alsina",{"tldr":195,"method":196,"finding":197,"direction":95,"opportunity":198},"评估OpenET在加州葡萄园的蒸散发估算精度，并与涡度协方差观测对比。","利用GRAPEX实验11个葡萄园的涡度协方差数据，对比六种卫星模型及集合的ET。","OpenET集合在月\u002F季尺度精度高，日尺度在平流和灌溉亏缺时误差增大。","可研究平流条件下卫星ET校正方法，或融合地面微气象数据提升日尺度精度。","2026-10-10T23:30:31.071040Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":9,"source_name":206,"source_url":203,"published_at":207,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":213,"tags":215,"primary_topic":219,"secondary_topics":224,"classification":231,"search_phrases":260,"slug":263,"view_count":21,"doi":264,"paper":265,"created_at":294},4085,"Seasonal Snow Water Resources and Spring Runoff Harvesting Potential in the North Kazakhstan Region: An Integrated Remote Sensing and GIS-Based Multi-Criteria Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fearth7050163","Seasonal snow is a critical water resource in Northern Kazakhstan, where spring snowmelt dominates surface runoff and agricultural water supply. However, traditional rainwater harvesting frameworks often neglect snow water equivalent (SWE), limiting their applicability in cold, snow-dominated steppe environments. This study develops an integrated remote sensing and GIS-based approach to identify potential sites for spring runoff harvesting. Using Sentinel-2 imagery, snow cover fraction (SCF) was derived and combined with a digital elevation model (DEM) to estimate snow depth through a quadratic regression model, validated against in situ data from 23 meteorological stations. Snow water equivalent was then calculated by incorporating field-measured snow density. Alongside SWE, six additional parameters—slope, drainage density, land use\u002Fland cover (LULC), and soil texture (sand, silt, clay)—were integrated within an Analytical Hierarchy Process (AHP) multi-criteria decision analysis. Results show that SWE, drainage density, and LULC collectively contributed 75% of the weight in site suitability, underscoring their dominant role in snowmelt-driven hydrology. The final suitability map indicates that 28.03% of the region falls into the “Very High” class, concentrated in central and northwestern districts. Developing just 30% of these potential zones could yield approximately 4.2 × 108 m3 of usable meltwater, supporting up to 105,000 ha under full irrigation or 280,000 ha under deficit irrigation. The study demonstrates that integrating high-resolution SWE with MCDA provides a scalable framework for enhancing agricultural resilience, mitigating floods, and strengthening water security in cold, semi-arid landscapes.","季节性积雪是哈萨克斯坦北部的重要水资源，春季融雪主导着地表径流和农业供水。然而，传统的雨水收集框架往往忽视雪水当量（SWE），限制了其在寒冷、以积雪为主的草原环境中的适用性。本研究开发了一种集成遥感和GIS的方法，用于识别春季径流收集的潜在场址。利用Sentinel-2影像提取积雪覆盖率（SCF），并结合数字高程模型（DEM）通过二次回归模型估算雪深，利用23个气象站的实地数据进行验证。随后，结合实地测量的雪密度计算雪水当量。除SWE外，还将坡度、河网密度、土地利用\u002F土地覆盖（LULC）以及土壤质地（砂、粉砂、黏土）等六个附加参数纳入层次分析法（AHP）多准则决策分析。结果表明，SWE、河网密度和LULC在场地适宜性评价中合计贡献了75%的权重，凸显了它们在融雪驱动水文中的主导作用。最终适宜性图显示，该区域28.03%的面积属于“极高”等级，集中在中部和西北部地区。仅开发这些潜在区域的30%，即可产生约4.2 × 10⁸ m³的可用融水，可支持多达105,000公顷的充分灌溉或280,000公顷的亏缺灌溉。本研究表明，将高分辨率SWE与多准则决策分析相结合，为增强农业韧性、减轻洪水和加强寒冷半干旱地区的水安全提供了一个可扩展的框架。","Earth","2026-10-02T00:00:00Z",77,{"impact":19,"substance":210,"depth":18,"authority":20,"freshness":211,"relevant":22,"comment":212},21,9,"该研究将遥感与GIS多准则评估结合，量化了北哈萨克斯坦融雪水资源与灌溉潜力，方法新颖、数据扎实，对寒区农业水资源管理有参考价值，但地域性较强，属细分领域进展。",[214],{"name":206,"url":203},[27,216,28,217,218],"哈萨克斯坦","GIS","融雪灌溉",{"code":53,"slug":220,"name":221,"path":222,"parent_code":223},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[225,230],{"code":226,"slug":227,"name":228,"path":229,"parent_code":223},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":232},{"candidates":233,"agricultural_evidence":45},[234,239,243,248,255],{"code":53,"score":235,"supported":45,"title":236,"abstract":237,"tags":238},15,[57],[28,57],[28],{"code":226,"score":17,"supported":45,"title":240,"abstract":241,"tags":242},[217],[217],[217],{"code":32,"score":13,"supported":45,"title":244,"abstract":245,"tags":247},[],[130,48,33,246],"water resource",[27],{"code":249,"score":100,"supported":45,"title":250,"abstract":251,"tags":254},"H2",[],[252,253],"土壤","soil",[],{"code":60,"score":256,"supported":45,"title":257,"abstract":258,"tags":259},3,[],[64],[],[261,262],"北哈萨克斯坦 融雪 灌溉潜力","Sentinel-2 雪水当量 遥感","北哈萨克斯坦融雪灌溉潜力-4085","10.3390\u002Fearth7050163",{"doi":264,"openalex_id":266,"authors":267,"venue":206,"cited_by_count":89,"oa_url":203,"card":289,"direction":95,"ingested_from":97},"W7216430083",[268,271,274,276,278,280,283,286],{"name":269,"orcid":270},"Zhanassyl Teleubay","https:\u002F\u002Forcid.org\u002F0000-0003-4783-5489",{"name":272,"orcid":273},"Erkin Ussalinov","https:\u002F\u002Forcid.org\u002F0000-0003-1907-9532",{"name":275,"orcid":9},"Laura Ryskulbekova",{"name":277,"orcid":9},"Asset Arystanov",{"name":279,"orcid":9},"Ibragim Seitassanov",{"name":281,"orcid":282},"Улжан Онласын","https:\u002F\u002Forcid.org\u002F0000-0001-5789-5464",{"name":284,"orcid":285},"Nuray Kutymova","https:\u002F\u002Forcid.org\u002F0009-0000-7214-1708",{"name":287,"orcid":288},"Farabi Yermekov","https:\u002F\u002Forcid.org\u002F0000-0002-0290-3866",{"tldr":290,"method":291,"finding":292,"direction":95,"opportunity":293},"结合遥感与GIS评估哈萨克斯坦北部季节性雪水资源及春季径流收集潜力。","Sentinel-2与DEM估算雪深和SWE，结合AHP多准则决策分析选址。","SWE、河网密度和土地利用贡献75%权重，28%区域为极高适宜区。","可将SWE驱动的选址框架推广至其他寒旱区，并耦合气候情景与灌溉效率优化。","2026-10-03T23:30:32.427699Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":300,"content":9,"source_name":301,"source_url":298,"published_at":302,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":303,"score_detail":304,"sources":306,"tags":308,"primary_topic":312,"secondary_topics":313,"classification":318,"search_phrases":342,"slug":345,"view_count":256,"doi":346,"paper":347,"created_at":370},3790,"Evaluation of the accuracy and response to recorded irrigation events of remote sensing and reanalysis soil moisture products in a typical irrigation district","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110822","Soil moisture (SM) plays a critical role in the land–atmosphere water cycle and agricultural water resource management. However, the applicability of prevalent SM products in typical irrigation districts remains insufficiently assessed. In this study, two in situ SM observation networks—an automatic meteorological station network (AMS) and an agro-meteorological experimental station network (AES)—were used to comprehensively evaluate the accuracy and irrigation-associated response characteristics of seven microwave and reanalysis SM products in the North China Plain. The results indicated that: (1) AES-based evaluations generally showed a higher overall correlation (R = 0.58) and smaller errors with the SM products than AMS-based evaluations (R = 0.54). However, when restricted to common observation dates, the correlation coefficients decreased while the biases were close to zero in the AES-based evaluation. (2) Significant differences were observed among the evaluated products under different monitoring objectives. The ERA5-Land reanalysis product performed best for continuous SM monitoring, while the European Space Agency Climate Change Initiative (ESA CCI) Passive product achieved the highest correlation coefficient with in situ observations (R = 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.043 m 3 m −3 ) on recorded irrigation days and exhibited a comparatively pronounced positive SM response under precipitation-controlled conditions, indicating greater sensitivity to short-term irrigation-associated wetting. (3) Strict precipitation control substantially affected the interpretation of irrigation-associated responses. ERA5-Land and GLDAS-Noah showed little systematic positive change when precipitation was controlled using both station observations and model precipitation forcing, while threshold-based probability of detection (POD) varied substantially with the selected SM response threshold. These findings demonstrate that product suitability depends on the monitoring objective and that precipitation attribution and threshold sensitivity should be explicitly considered when using SM products for irrigation-related applications.","土壤水分（SM）在陆气水循环和农业水资源管理中起着至关重要的作用。然而，主流SM产品在典型灌区的适用性仍缺乏充分评估。本研究利用两套原位SM观测网络——自动气象站网络（AMS）和农业气象实验站网络（AES），综合评估了七种微波和再分析SM产品在华北平原的精度及与灌溉相关的响应特征。结果表明：（1）基于AES的评估总体上显示出比基于AMS的评估更高的整体相关性（R = 0.58）和更小的误差（R = 0.54）。然而，当限定于共同观测日期时，基于AES的评估中相关系数下降，而偏差接近于零。（2）在不同监测目标下，所评估产品之间存在显著差异。ERA5-Land再分析产品在连续SM监测中表现最佳，而欧洲空间局气候变化倡议（ESA CCI）被动产品在记录灌溉日与原位观测的相关系数最高（R = 0.69），无偏均方根误差最低（ubRMSE = 0.043 m³ m⁻³），并在降水控制条件下表现出相对明显的SM正响应，表明其对短期灌溉相关增湿具有更高的敏感性。（3）严格的降水控制显著影响了对灌溉相关响应的解释。当利用站点观测和模型降水强迫控制降水时，ERA5-Land和GLDAS-Noah几乎未表现出系统性正变化，而基于阈值的检测概率（POD）随所选SM响应阈值的变化而显著变化。这些发现表明，产品适用性取决于监测目标，且在将SM产品用于灌溉相关应用时，应明确考虑降水归因和阈值敏感性。","Agricultural Water Management","2026-09-28T00:00:00Z",74,{"impact":17,"substance":112,"depth":18,"authority":113,"freshness":114,"relevant":22,"comment":305},"基于华北平原实测网络的七种土壤水分产品评估，为灌溉监测产品选型提供实证依据，专业价值较高但属细分领域研究。",[307],{"name":301,"url":298},[27,28,309,310,311],"土壤墒情","灌溉管理","华北平原",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[314,317],{"code":249,"slug":253,"name":315,"path":316,"parent_code":36},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":53,"slug":220,"name":221,"path":222,"parent_code":223},{"status":39,"locked":14,"version":40,"method":41,"evidence":319},{"candidates":320,"agricultural_evidence":45},[321,326,330,335],{"code":32,"score":322,"supported":45,"title":323,"abstract":324,"tags":325},19,[33],[130,48,33,246],[27,310],{"code":249,"score":235,"supported":45,"title":327,"abstract":328,"tags":329},[253],[252,253],[309],{"code":53,"score":17,"supported":45,"title":331,"abstract":332,"tags":334},[57],[333],"估产",[28],{"code":60,"score":211,"supported":45,"title":336,"abstract":337,"tags":341},[],[338,339,340],"气候变化","农业气象","climate change",[],[343,344],"ESA CCI 土壤水分 灌溉","华北平原 土壤水分 遥感","ESACCI土壤水分灌溉-3790","10.1016\u002Fj.agwat.2026.110822",{"doi":346,"openalex_id":348,"authors":349,"venue":301,"cited_by_count":89,"oa_url":364,"card":365,"direction":95,"ingested_from":97},"W7214549221",[350,353,355,357,359,361],{"name":351,"orcid":352},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":354,"orcid":9},"Yu Li",{"name":356,"orcid":9},"Jiawei Lv",{"name":358,"orcid":9},"Lei Wang",{"name":360,"orcid":9},"Zhiqing Cheng",{"name":362,"orcid":363},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":366,"method":367,"finding":368,"direction":95,"opportunity":369},"评估七种遥感与再分析土壤湿度产品在典型灌区的精度及对灌溉事件的响应。","用自动气象站与农业气象站两套实测网络，对比七种微波\u002F再分析产品。","ESA CCI Passive对灌溉日响应最灵敏，ERA5-Land适合连续监测，降水控制影响响应判","可针对灌溉事件构建降水控制下的阈值自适应土壤湿度响应检测方法，提升灌区遥感监测可靠性。","2026-09-29T23:30:28.424080Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":9,"source_name":377,"source_url":374,"published_at":378,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":303,"score_detail":379,"sources":382,"tags":384,"primary_topic":387,"secondary_topics":388,"classification":390,"search_phrases":425,"slug":428,"view_count":75,"doi":429,"paper":430,"created_at":441},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",{"impact":19,"substance":127,"depth":380,"authority":20,"freshness":114,"relevant":22,"comment":381},17,"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[383],{"name":377,"url":374},[385,27,28,310,386],"智慧农业","农业干旱",{"code":53,"slug":220,"name":221,"path":222,"parent_code":223},[389],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":391},{"candidates":392,"agricultural_evidence":45},[393,397,402,406,413,420],{"code":53,"score":235,"supported":45,"title":394,"abstract":395,"tags":396},[57],[28,57],[28],{"code":32,"score":398,"supported":45,"title":399,"abstract":400,"tags":401},11,[],[130,48,49,33,50],[27,310],{"code":60,"score":100,"supported":45,"title":403,"abstract":404,"tags":405},[],[64,146],[],{"code":407,"score":100,"supported":45,"title":408,"abstract":409,"tags":412},"H8",[],[410,411],"可持续农业","sustainable agriculture",[],{"code":414,"score":100,"supported":45,"title":415,"abstract":416,"tags":419},"I7",[],[417,418],"文献计量","bibliometric",[],{"code":421,"score":22,"supported":14,"title":422,"abstract":423,"tags":424},"I1",[],[],[385],[426,427],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667","10.1007\u002Fs43621-026-04728-y",{"doi":429,"openalex_id":431,"authors":432,"venue":377,"cited_by_count":89,"oa_url":374,"card":436,"direction":95,"ingested_from":97},"W7213266434",[433],{"name":434,"orcid":435},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":437,"method":438,"finding":439,"direction":95,"opportunity":440},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":458,"secondary_topics":459,"classification":460,"search_phrases":517,"slug":520,"view_count":75,"doi":521,"paper":522,"created_at":545},2167,"Crop Simulation Model: A Digital Tool for Sustainable Water Management in Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95667","Efficient management of agricultural water resources is increasingly important under climate variability and freshwater scarcity. Accurate estimation of crop water requirements (CWR) supports crop productivity, irrigation scheduling and water-use efficiency. Conventional approaches, including crop-coefficient, Penman–Monteith and soil-water-balance methods, remain widely used but can require extensive field observations and may have limited capacity to represent dynamic soil–crop–atmosphere interactions. Crop simulation models provide a process-based digital framework that integrates weather, soil, crop and management information to simulate crop growth, evapotranspiration, yield and water productivity under different environmental and management conditions. This review synthesises the roles of widely used models, including DSSAT, APSIM, AquaCrop, CROPWAT and CropSyst, in estimating crop water requirements and supporting irrigation management. Applications across maize, wheat, rice, cotton and other crops show that these models can evaluate irrigation schedules, deficit-irrigation strategies, water-saving practices and potential responses to future climate conditions. Model performance, however, depends on appropriate input data, local calibration and validation. The review also identifies opportunities to strengthen crop simulation through integration with hydrological information, remote sensing, artificial intelligence and modules for biotic stresses. Overall, crop simulation models provide useful decision-support tools for evaluating water-management options and improving the evidence base for sustainable agricultural water-resource planning.","在气候变率和淡水稀缺背景下，农业水资源的高效管理日益重要。准确估算作物需水量（CWR）有助于支撑作物生产力、灌溉调度和水分利用效率。传统方法，包括作物系数法、Penman–Monteith法和土壤水量平衡法，仍被广泛使用，但可能需要大量田间观测，且在表征动态的土壤–作物–大气相互作用方面能力有限。作物模拟模型提供了一种基于过程的数字化框架，可整合天气、土壤、作物和管理信息，以模拟不同环境和管理条件下的作物生长、蒸散发、产量和水分生产力。本文综述了广泛使用的模型，包括DSSAT、APSIM、AquaCrop、CROPWAT和CropSyst，在估算作物需水量和支持灌溉管理中的作用。在玉米、小麦、水稻、棉花及其他作物上的应用表明，这些模型能够评估灌溉制度、亏缺灌溉策略、节水措施以及对未来气候条件的潜在响应。然而，模型性能取决于适当的输入数据、本地校准和验证。本综述还指出了通过与水文信息、遥感、人工智能及生物胁迫模块集成来加强作物模拟的机遇。总体而言，作物模拟模型为评估水资源管理方案和改善可持续农业水资源规划的证据基础提供了有用的决策支持工具。","International Journal of Environment and Climate Change","2026-09-10T00:00:00Z",76,{"impact":18,"substance":127,"depth":380,"authority":20,"freshness":114,"relevant":22,"comment":452},"系统综述DSSAT、APSIM、AquaCrop等作物模拟模型在作物需水估算与灌溉决策中的应用，并提出与遥感、AI融合方向，对农业水资源数字化管理有参考价值。",[454],{"name":448,"url":445},[385,456,27,28,310,457],"农业人工智能","作物模拟模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":461},{"candidates":462,"agricultural_evidence":45},[463,467,471,478,485,492,499,503,509,513],{"code":32,"score":398,"supported":45,"title":464,"abstract":465,"tags":466},[],[130,48,49,33,50],[27,310],{"code":53,"score":54,"supported":45,"title":468,"abstract":469,"tags":470},[],[28,57],[28],{"code":472,"score":100,"supported":45,"title":473,"abstract":474,"tags":477},"A1",[],[475,476],"水稻","rice",[],{"code":479,"score":100,"supported":45,"title":480,"abstract":481,"tags":484},"A2",[],[482,483],"小麦","wheat",[],{"code":486,"score":100,"supported":45,"title":487,"abstract":488,"tags":491},"A3",[],[489,490],"玉米","maize",[],{"code":493,"score":100,"supported":45,"title":494,"abstract":495,"tags":498},"A6",[],[496,497],"棉花","cotton",[],{"code":249,"score":100,"supported":45,"title":500,"abstract":501,"tags":502},[],[252,253],[],{"code":504,"score":61,"supported":45,"title":505,"abstract":506,"tags":508},"I2",[],[507],"artificial intelligence",[456],{"code":407,"score":256,"supported":45,"title":510,"abstract":511,"tags":512},[],[410],[],{"code":421,"score":22,"supported":14,"title":514,"abstract":515,"tags":516},[],[],[385],[518,519],"作物模拟模型 农业人工智能 农业水资源 智慧农业","作物模拟模型 农业人工智能","作物模拟模型农业人工智能农业水资源智慧农业-2167","10.9734\u002Fijecc\u002F2026\u002Fv16i95667",{"doi":521,"openalex_id":523,"authors":524,"venue":448,"cited_by_count":89,"oa_url":445,"card":539,"direction":95,"ingested_from":97},"W7212194041",[525,527,529,531,533,535,537],{"name":526,"orcid":9},"P. S. Manju",{"name":528,"orcid":9},"N. Manikandan",{"name":530,"orcid":9},"A.P. Ramaraj",{"name":532,"orcid":9},"V. S. Jinsy",{"name":534,"orcid":9},"K. V. Sumesh",{"name":536,"orcid":9},"V. Dhanalakshmi",{"name":538,"orcid":9},"N. Gopika",{"tldr":540,"method":541,"finding":542,"direction":543,"opportunity":544},"综述作物模拟模型在估算作物需水与灌溉管理中的应用及改进方向。","综述DSSAT、APSIM、AquaCrop、CROPWAT、CropSyst等","模型可评估灌溉策略与节水实践，但精度依赖本地校准与输入数据。","农业人工智能与决策模型","可探索作物模型与遥感、AI及水文信息耦合，提升需水估算与灌溉决策精度。","2026-09-11T23:30:30.176174Z",{"id":547,"title":548,"url":549,"summary":550,"summary_zh":551,"content":9,"source_name":552,"source_url":549,"published_at":553,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":554,"score_detail":555,"sources":557,"tags":559,"primary_topic":562,"secondary_topics":563,"classification":573,"search_phrases":622,"slug":625,"view_count":89,"doi":626,"paper":627,"created_at":651},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","2026-10-21T00:00:00Z",79,{"impact":18,"substance":112,"depth":18,"authority":17,"freshness":211,"relevant":22,"comment":556},"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。",[558],{"name":552,"url":549},[385,456,28,560,561],"作物水分胁迫","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[564,568],{"code":504,"slug":565,"name":566,"path":567,"parent_code":223},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":569,"slug":570,"name":571,"path":572,"parent_code":223},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":574},{"candidates":575,"agricultural_evidence":45},[576,580,586,593,599,603,607,611,618],{"code":32,"score":235,"supported":45,"title":577,"abstract":578,"tags":579},[33],[130,33],[561],{"code":504,"score":13,"supported":45,"title":581,"abstract":582,"tags":585},[],[583,584,507],"机器学习","machine learning",[456],{"code":569,"score":211,"supported":45,"title":587,"abstract":588,"tags":592},[],[589,590,591],"物联网","internet of things","IoT",[],{"code":421,"score":54,"supported":45,"title":594,"abstract":595,"tags":598},[],[596,597],"数字孪生","digital twin",[385],{"code":53,"score":54,"supported":45,"title":600,"abstract":601,"tags":602},[],[28,57],[28],{"code":479,"score":100,"supported":45,"title":604,"abstract":605,"tags":606},[],[482,483],[],{"code":486,"score":100,"supported":45,"title":608,"abstract":609,"tags":610},[],[489,490],[],{"code":612,"score":100,"supported":45,"title":613,"abstract":614,"tags":617},"A5",[],[615,616],"马铃薯","potato",[],{"code":133,"score":256,"supported":45,"title":619,"abstract":620,"tags":621},[],[136],[],[623,624],"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":626,"openalex_id":628,"authors":629,"venue":552,"cited_by_count":89,"oa_url":549,"card":645,"direction":650,"ingested_from":97},"W7223088921",[630,633,636,639,642],{"name":631,"orcid":632},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":634,"orcid":635},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":637,"orcid":638},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":640,"orcid":641},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":643,"orcid":644},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":646,"method":647,"finding":648,"direction":95,"opportunity":649},"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"]