[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2667":3,"related-2667":115},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":114},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。",null,"Discover Sustainability","2026-09-15T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业水资源","遥感","灌溉管理","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,67,75,82,89],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":57},15,true,[55],"remote sensing",[29,55],[29],{"code":40,"score":59,"supported":53,"title":60,"abstract":61,"tags":66},11,[],[62,63,64,41,65],"灌溉","水资源","蒸散发","evapotranspiration",[28,30],{"code":68,"score":69,"supported":53,"title":70,"abstract":71,"tags":74},"H7",6,[],[72,73],"干旱","drought",[],{"code":76,"score":69,"supported":53,"title":77,"abstract":78,"tags":81},"H8",[],[79,80],"可持续农业","sustainable agriculture",[],{"code":83,"score":69,"supported":53,"title":84,"abstract":85,"tags":88},"I7",[],[86,87],"文献计量","bibliometric",[],{"code":90,"score":22,"supported":14,"title":91,"abstract":92,"tags":93},"I1",[],[],[27],[95,96],"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",2,"10.1007\u002Fs43621-026-04728-y",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":111,"ingested_from":113},"W7213266434",[103],{"name":104,"orcid":105},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"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。","农业遥感与作物表型","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。","openalex","2026-09-16T23:30:29.241938Z",{"total":69,"page":22,"page_size":69,"items":116},[117,201,286,369,455,554],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":125,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":137,"classification":144,"search_phrases":171,"slug":174,"view_count":175,"doi":176,"paper":177,"created_at":200},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",{"impact":126,"substance":127,"depth":128,"authority":129,"freshness":21,"relevant":22,"comment":130},12,22,18,14,"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。",[132],{"name":123,"url":120},[28,29,134,30,135],"土壤墒情","华北平原",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[138,143],{"code":139,"slug":140,"name":141,"path":142,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":145},{"candidates":146,"agricultural_evidence":53},[147,153,158,163],{"code":40,"score":148,"supported":53,"title":149,"abstract":150,"tags":152},19,[41],[62,63,41,151],"water resource",[28,30],{"code":139,"score":52,"supported":53,"title":154,"abstract":155,"tags":157},[140],[156,140],"土壤",[134],{"code":33,"score":126,"supported":53,"title":159,"abstract":160,"tags":162},[55],[161],"估产",[29],{"code":68,"score":164,"supported":53,"title":165,"abstract":166,"tags":170},9,[],[167,168,169],"气候变化","农业气象","climate change",[],[172,173],"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",3,"10.1016\u002Fj.agwat.2026.110822",{"doi":176,"openalex_id":178,"authors":179,"venue":123,"cited_by_count":106,"oa_url":194,"card":195,"direction":111,"ingested_from":113},"W7214549221",[180,183,185,187,189,191],{"name":181,"orcid":182},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":184,"orcid":9},"Yu Li",{"name":186,"orcid":9},"Jiawei Lv",{"name":188,"orcid":9},"Lei Wang",{"name":190,"orcid":9},"Zhiqing Cheng",{"name":192,"orcid":193},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":196,"method":197,"finding":198,"direction":111,"opportunity":199},"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":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":213,"tags":215,"primary_topic":219,"secondary_topics":220,"classification":227,"search_phrases":250,"slug":253,"view_count":254,"doi":255,"paper":256,"created_at":285},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":17,"substance":211,"depth":128,"authority":20,"freshness":164,"relevant":22,"comment":212},21,"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":207,"url":204},[28,216,29,217,218],"哈萨克斯坦","GIS","融雪灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[221,226],{"code":222,"slug":223,"name":224,"path":225,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":228},{"candidates":229,"agricultural_evidence":53},[230,234,238,242,246],{"code":33,"score":52,"supported":53,"title":231,"abstract":232,"tags":233},[55],[29,55],[29],{"code":222,"score":126,"supported":53,"title":235,"abstract":236,"tags":237},[217],[217],[217],{"code":40,"score":13,"supported":53,"title":239,"abstract":240,"tags":241},[],[62,63,41,151],[28],{"code":139,"score":69,"supported":53,"title":243,"abstract":244,"tags":245},[],[156,140],[],{"code":68,"score":175,"supported":53,"title":247,"abstract":248,"tags":249},[],[72],[],[251,252],"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",5,"10.3390\u002Fearth7050163",{"doi":255,"openalex_id":257,"authors":258,"venue":207,"cited_by_count":106,"oa_url":204,"card":280,"direction":111,"ingested_from":113},"W7216430083",[259,262,265,267,269,271,274,277],{"name":260,"orcid":261},"Zhanassyl Teleubay","https:\u002F\u002Forcid.org\u002F0000-0003-4783-5489",{"name":263,"orcid":264},"Erkin Ussalinov","https:\u002F\u002Forcid.org\u002F0000-0003-1907-9532",{"name":266,"orcid":9},"Laura Ryskulbekova",{"name":268,"orcid":9},"Asset Arystanov",{"name":270,"orcid":9},"Ibragim Seitassanov",{"name":272,"orcid":273},"Улжан Онласын","https:\u002F\u002Forcid.org\u002F0000-0001-5789-5464",{"name":275,"orcid":276},"Nuray Kutymova","https:\u002F\u002Forcid.org\u002F0009-0000-7214-1708",{"name":278,"orcid":279},"Farabi Yermekov","https:\u002F\u002Forcid.org\u002F0000-0002-0290-3866",{"tldr":281,"method":282,"finding":283,"direction":111,"opportunity":284},"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":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":297,"tags":299,"primary_topic":303,"secondary_topics":308,"classification":311,"search_phrases":345,"slug":348,"view_count":106,"doi":349,"paper":350,"created_at":368},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。","Journal of Experimental Agriculture International","2026-09-22T00:00:00Z",72,{"impact":126,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":296},"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[298],{"name":292,"url":289},[300,301,29,302,30],"水稻","产量预测","作物模型",{"code":304,"slug":305,"name":300,"path":306,"parent_code":307},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[309,310],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":312},{"candidates":313,"agricultural_evidence":53},[314,318,322,326,330,334,340],{"code":304,"score":52,"supported":53,"title":315,"abstract":316,"tags":317},[305],[300,305],[300],{"code":40,"score":13,"supported":53,"title":319,"abstract":320,"tags":321},[],[62,63,41,151],[30],{"code":33,"score":21,"supported":53,"title":323,"abstract":324,"tags":325},[],[29,55],[301,29],{"code":139,"score":69,"supported":53,"title":327,"abstract":328,"tags":329},[],[156,140],[],{"code":222,"score":175,"supported":53,"title":331,"abstract":332,"tags":333},[],[217],[],{"code":335,"score":175,"supported":53,"title":336,"abstract":337,"tags":339},"J1",[],[338],"农业规划",[],{"code":341,"score":22,"supported":14,"title":342,"abstract":343,"tags":344},"I2",[],[],[302],[346,347],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168","10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":349,"openalex_id":351,"authors":352,"venue":292,"cited_by_count":106,"oa_url":289,"card":363,"direction":111,"ingested_from":113},"W7213966772",[353,355,357,359,361],{"name":354,"orcid":9},"Rana Prathap",{"name":356,"orcid":9},"G. Ravi Babu",{"name":358,"orcid":9},"V. Muthayya Chowdary",{"name":360,"orcid":9},"K.Krupavathi",{"name":362,"orcid":9},"K. Chandrasekhar",{"tldr":364,"method":365,"finding":366,"direction":111,"opportunity":367},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","2026-09-22T23:30:22.711640Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":385,"secondary_topics":386,"classification":393,"search_phrases":423,"slug":426,"view_count":175,"doi":427,"paper":428,"created_at":454},2318,"Estimation of field-scale crop evapotranspiration from the mechanistic SIF-ET model using the UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128332","Accurate estimation of evapotranspiration (ET) is critical for agricultural water management and understanding land-atmosphere interactions. Conventional thermal and optical remote sensing is a powerful tool for ET estimation, but existing methods remain constrained by empirical parameterization, calibration complexity, and limited applicability. As a direct indicator of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) enables consistent and accurate ET estimation through a mechanistically grounded framework that does not require site-specific empirical calibration of the SIF-GPP relationship. However, current SIF-based ET estimation mainly rely on coarse-resolution satellite data and its capacity to resolve high-resolution ET dynamics over farmland remains unexplored. To address this, the mechanistic light response model and water-carbon relationship was integrated to construct a SIF-ET model at field-scale using UAV narrow-band hyperspectra imagery. The principal conclusions are: (1) High-resolution crop (canola,soybean and wheat) canopy SIF 740 at 1-nm SR was retrieved from UAV hyperspectra imagery using the Photochemistry and Energy Fluxes (SCOPE) model; (2)The daily ET estimates from SIF-ET model were validated against the in-situ measurements across multiple growth stages and water-nitrogen treatments, with wheat exhibiting the highest precision (R²=0.72–0.94, RMSE = 0.04–0.11 mm\u002Fd, MAPE = 1.70–5.89%), followed by canola and soybean; (3) Cumulative ET errors across repeated UAV observations within each growth stage remained within acceptable bounds, indicating the stability of SIF-ET model in capturing long-term ET dynamics. Overall, the SIF-ET model from UAV hyperspectra imagery revealed fine-scale cropland ET spatial-temporal heterogeneity. These findings provide support for precision irrigation, rational water-nitrogen management, and stress diagnostics under changing environmental conditions.","准确估算蒸散发（ET）对农业水资源管理和理解陆气相互作用至关重要。传统的热红外与光学遥感是估算ET的有力工具，但现有方法仍受限于经验参数化、校准复杂性和适用性有限等问题。作为植被光合作用的直接指示因子，日光诱导叶绿素荧光（SIF）能够通过机理明确的框架实现一致且准确的ET估算，且无需对SIF-GPP关系进行站点特定的经验校准。然而，当前基于SIF的ET估算主要依赖粗分辨率卫星数据，其解析农田高分辨率ET动态的能力尚未得到探索。为此，本研究整合机理光响应模型与水碳关系，利用无人机窄波段高光谱影像构建了田块尺度的SIF-ET模型。主要结论如下：（1）利用光化学与能量通量（SCOPE）模型，从无人机高光谱影像中反演了1 nm光谱分辨率下油菜、大豆和小麦的高分辨率冠层SIF₇₄₀；（2）基于SIF-ET模型的日ET估算值在多个生育期和水氮处理下与原位观测进行了验证，其中小麦精度最高（R²=0.72–0.94，RMSE=0.04–0.11 mm\u002Fd，MAPE=1.70–5.89%），油菜和大豆次之；（3）各生育期内重复无人机观测的累积ET误差均在可接受范围内，表明SIF-ET模型在捕捉长期ET动态方面具有稳定性。总体而言，基于无人机高光谱影像的SIF-ET模型揭示了精细尺度的农田ET时空异质性。这些发现为精准灌溉、合理水氮管理以及变化环境条件下的胁迫诊断提供了支持。","European Journal of Agronomy","2026-09-12T00:00:00Z",81,{"impact":128,"substance":127,"depth":128,"authority":129,"freshness":164,"relevant":22,"comment":379},"该研究基于无人机高光谱与SIF-ET机理模型实现田块尺度作物蒸散发高精度估算，方法新颖、验证充分，对精准灌溉与水氮管理有实质参考价值，值得进入每日精选。",[381],{"name":375,"url":372},[27,383,29,384,64],"无人机","精准灌溉",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[387,388],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":389,"slug":390,"name":391,"path":392,"parent_code":307},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",{"status":46,"locked":14,"version":47,"method":48,"evidence":394},{"candidates":395,"agricultural_evidence":53},[396,400,405,412,419],{"code":40,"score":148,"supported":53,"title":397,"abstract":398,"tags":399},[65],[62,63,64,41,65],[384,64],{"code":33,"score":52,"supported":53,"title":401,"abstract":403,"tags":404},[402],"hyperspectral",[29,55],[29],{"code":389,"score":164,"supported":53,"title":406,"abstract":407,"tags":411},[],[408,409,410],"大豆","油菜","soybean",[],{"code":413,"score":69,"supported":53,"title":414,"abstract":415,"tags":418},"A2",[],[416,417],"小麦","wheat",[],{"code":90,"score":22,"supported":14,"title":420,"abstract":421,"tags":422},[],[],[27],[424,425],"智慧农业 精准灌溉 无人机 蒸散发","智慧农业 精准灌溉","智慧农业精准灌溉无人机蒸散发-2318","10.1016\u002Fj.eja.2026.128332",{"doi":427,"openalex_id":429,"authors":430,"venue":375,"cited_by_count":106,"oa_url":372,"card":449,"direction":111,"ingested_from":113},"W7212242584",[431,433,435,438,441,443,446],{"name":432,"orcid":9},"Ruiqi Du",{"name":434,"orcid":9},"Yonghong Zhang",{"name":436,"orcid":437},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":439,"orcid":440},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":442,"orcid":9},"Jian Gao",{"name":444,"orcid":445},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":447,"orcid":448},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":450,"method":451,"finding":452,"direction":111,"opportunity":453},"利用无人机高光谱影像构建田间尺度SIF-ET机理模型，实现作物蒸散发高精度估算。","无人机窄波段高光谱结合SCOPE模型反演SIF，耦合光响应与水碳关系构建SIF-","小麦估算精度最高（R²=0.72–0.94），模型能稳定捕捉多生育期蒸散发动态与空间异质性。","可探索多作物多环境下的SIF-ET普适性，并融合热红外与机器学习提升胁迫诊断能力。","2026-09-13T23:30:22.677041Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":9,"source_name":461,"source_url":458,"published_at":462,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":463,"score_detail":464,"sources":466,"tags":468,"primary_topic":471,"secondary_topics":472,"classification":478,"search_phrases":517,"slug":520,"view_count":175,"doi":521,"paper":522,"created_at":553},1895,"Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.03.745400","Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.","水资源短缺和日益不规律的降雨威胁着地中海地区鳄梨的生产，然而，长期亏缺灌溉下成熟树木的生理响应仍知之甚少。我们开展了一项为期两年的田间研究，在成熟鳄梨园中设置了三种灌溉制度，整合了土壤-植物-大气连续体的连续监测、无人机多光谱成像、冠层结构分析以及果实表型鉴定。两个研究年份的降雨量差异显著，为评估环境条件如何调节树木对水分限制的响应提供了独特机会。严重亏缺灌溉下的树木表现出深层土壤水分耗竭和生理特征趋于平缓的现象，叶片厚度和树干水势的昼夜变化接近于零，表明蒸腾作用极低，树木水分状态与环境需求脱钩。通过NDVI进行的无人机遥测在果实生长和成熟期检测到胁迫，但在开花期或夏季新梢生长期未检测到，揭示了后期成熟阶段对干旱的敏感性更高。尽管各灌溉处理间冠层面积无显著差异，但亏缺灌溉下冠层表面粗糙度显著增加，从而识别出一种新的干旱胁迫结构指标。尽管各处理间生理差异显著，果实数量保持稳定，而严重亏缺灌溉下果实重量显著下降，尤其在较湿润的年份更为明显，表明年降雨量调节着果实保留与果实生长之间的权衡。本研究首次提供了地中海条件下持续亏缺灌溉下鳄梨表现的多尺度连续表征。通过整合基于植物的传感器、遥感技术和人工智能，我们揭示了此前未被描述的胁迫动态，并为果树精准灌溉管理识别了新指标。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-07T00:00:00Z",73,{"impact":128,"substance":127,"depth":128,"authority":126,"freshness":175,"relevant":22,"comment":465},"研究利用传感器网络与机器学习持续监测牛油果缺水胁迫，揭示新结构指标，对精准灌溉有重要参考价值。",[467],{"name":461,"url":458},[27,469,29,384,470],"农业人工智能","牛油果",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[473,477],{"code":341,"slug":474,"name":475,"path":476,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":479},{"candidates":480,"agricultural_evidence":53},[481,485,491,497,505,509,513],{"code":40,"score":128,"supported":53,"title":482,"abstract":483,"tags":484},[41],[62,63,41],[384],{"code":341,"score":126,"supported":53,"title":486,"abstract":488,"tags":490},[487],"machine learning",[489],"artificial intelligence",[469],{"code":33,"score":13,"supported":53,"title":492,"abstract":493,"tags":496},[],[29,494,55,495],"无人机多光谱","multispectral",[29],{"code":498,"score":499,"supported":53,"title":500,"abstract":501,"tags":504},"B2",7,[],[502,503],"果树","orchard",[470],{"code":139,"score":69,"supported":53,"title":506,"abstract":507,"tags":508},[],[156,140],[],{"code":68,"score":69,"supported":53,"title":510,"abstract":511,"tags":512},[],[72,73],[],{"code":90,"score":22,"supported":14,"title":514,"abstract":515,"tags":516},[],[],[27],[518,519],"农业人工智能 智慧农业 精准灌溉 牛油果","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉牛油果-1895","10.64898\u002F2026.09.03.745400",{"doi":521,"openalex_id":523,"authors":524,"venue":461,"cited_by_count":106,"oa_url":546,"card":547,"direction":111,"ingested_from":113},"W7211887459",[525,528,531,534,537,540,543],{"name":526,"orcid":527},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":529,"orcid":530},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":532,"orcid":533},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":535,"orcid":536},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":538,"orcid":539},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":541,"orcid":542},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":544,"orcid":545},"Juan M. Losada","https:\u002F\u002Forcid.org\u002F0000-0002-7966-5018","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F07\u002F2026.09.03.745400.full.pdf",{"tldr":548,"method":549,"finding":550,"direction":551,"opportunity":552},"通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。","集成土壤-植物-大气连续体监测、无人机多光谱、冠层分析及机器学习。","严重亏缺灌溉导致深层土壤水分耗尽、生理活动减弱，果实重量下降，冠层表面粗糙度增加。","智慧农业 \u002F 农业物联网","可研究不同降雨年份下亏缺灌溉对果实品质的影响，或开发基于冠层粗糙度的干旱胁迫预警模型。","2026-09-08T23:30:14.886372Z",{"id":555,"title":556,"url":557,"summary":558,"summary_zh":559,"content":9,"source_name":560,"source_url":557,"published_at":561,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":562,"score_detail":563,"sources":565,"tags":567,"primary_topic":571,"secondary_topics":572,"classification":579,"search_phrases":605,"slug":608,"view_count":106,"doi":609,"paper":610,"created_at":620},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":128,"substance":18,"depth":17,"authority":126,"freshness":164,"relevant":22,"comment":564},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[566],{"name":560,"url":557},[469,568,29,569,570],"农业物联网","智慧灌溉","节水农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[573,578],{"code":574,"slug":575,"name":576,"path":577,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":580},{"candidates":581,"agricultural_evidence":53},[582,586,593,597,601],{"code":40,"score":148,"supported":53,"title":583,"abstract":584,"tags":585},[41],[62,63,41],[569,570],{"code":574,"score":128,"supported":53,"title":587,"abstract":589,"tags":592},[588],"IoT",[590,591,588],"物联网","internet of things",[568],{"code":33,"score":52,"supported":53,"title":594,"abstract":595,"tags":596},[55],[29,55],[29],{"code":341,"score":126,"supported":53,"title":598,"abstract":599,"tags":600},[489],[489],[469],{"code":76,"score":69,"supported":53,"title":602,"abstract":603,"tags":604},[],[79,80],[],[606,607],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":609,"openalex_id":611,"authors":612,"venue":560,"cited_by_count":106,"oa_url":557,"card":615,"direction":551,"ingested_from":113},"W7223090389",[613],{"name":614,"orcid":9},"Patil",{"tldr":616,"method":617,"finding":618,"direction":551,"opportunity":619},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]