[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1313":3,"related-1313":113},{"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":83,"slug":86,"view_count":21,"doi":87,"paper":88,"created_at":112},1313,"Agricultural water balance and variability in Utah irrigation canal companies with OpenET products and spatial data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00271-026-01172-8","Abstract Agricultural water optimization is critical for improving water security for the arid Western USA and other water-scarce regions. Optimization includes assessment of agricultural water diversions within conveyance systems managed by irrigation canal companies (ICC), yet few water balance approaches exist at this level. Satellite data provides insight into historical agricultural water use and the potential for optimization. In this study, historical remote sensing data from OpenET and gridMET were coupled with Geographic Information Systems data (fields\u002Fcanals distribution and agricultural diversion records) to implement a water balance model for three ICCs in Northern Utah. The monthly water balance model determined the change of water depletion at the root zone (∆Dr) with precipitation, irrigation and capillary rise from groundwater (CR) as inflows; and actual crop evapotranspiration (ET c act ), runoff and deep percolation (DP) as outflows. The water balance residual (WBR) was calculated as the unresolved portion of the water balance and reflect runoff, DP and other unquantified hydrologic exchanges (CR was considered only where supported). The spatial distribution of ∆Dr was captured by a Random Forest model using local observed data (ET c act and soil water content). Across the study area, WBRs varied spatially and temporally (2020–2023) from 0.01 to 2.7 cubic hectometers (12 − 2,210 acre-feet) per month, with the highest values occurring during peak irrigation season (July-August). Seasonally (Apr-Oct), WBR represented 5–29% of the sum of all water balance components across the studied ICCs and should be interpreted as an illustrative screening range. Overall, this study demonstrates the feasibility of approximating an ICC-scale water balance under data-limited conditions, and highlights the need for improved monitoring, data collection, and research.","农业用水优化对于改善美国西部干旱地区及其他水资源短缺地区的水安全至关重要。优化工作包括对灌溉渠公司（ICC）管理的输水系统内农业引水进行评估，然而在此层面上可用的水量平衡方法寥寥无几。卫星数据为历史农业用水情况及优化潜力提供了洞察。本研究将来自OpenET和gridMET的历史遥感数据与地理信息系统数据（田地\u002F渠道分布及农业引水记录）相结合，为犹他州北部的三个灌溉渠公司实施了水量平衡模型。月水量平衡模型确定了根区耗水变化量（∆Dr），其中降水、灌溉和地下水毛细上升（CR）为流入项；实际作物蒸散发（ET c act ）、径流和深层渗漏（DP）为流出项。水量平衡残差（WBR）被计算为水量平衡中未解决的部分，反映了径流、深层渗漏及其他未量化的水文交换（仅在数据支持的情况下考虑毛细上升）。∆Dr的空间分布通过使用当地观测数据（实际作物蒸散发和土壤含水量）的随机森林模型进行捕捉。在整个研究区域内，水量平衡残差在空间和时间上（2020—2023年）变化范围为每月0.01至2.7立方百米（12至2,210英亩-英尺），最高值出现在灌溉高峰期（7月至8月）。在季节尺度上（4月至10月），水量平衡残差占所研究灌溉渠公司所有水量平衡分量总和的5%至29%，应将其视为一个说明性的筛选范围。总体而言，本研究证明了在数据有限条件下近似估算灌溉渠公司尺度水量平衡的可行性，并强调了改进监测、数据收集和研究的必要性。",null,"Irrigation Science","2026-08-31T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,12,2,1,"研究利用OpenET和空间数据构建灌溉公司尺度水量平衡模型，方法新颖，数据详实，对干旱区农业水资源优化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"农业遥感","水资源管理","灌溉优化","OpenET",{"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},[44,53,61,69,76],{"code":32,"score":19,"supported":45,"title":46,"abstract":47,"tags":52},true,[33],[48,49,50,33,51],"灌溉","水资源","蒸散发","evapotranspiration",[28],{"code":54,"score":55,"supported":45,"title":56,"abstract":57,"tags":60},"I3",7,[],[58,59],"遥感","remote sensing",[27],{"code":62,"score":63,"supported":45,"title":64,"abstract":65,"tags":68},"H2",6,[],[66,67],"土壤","soil",[],{"code":70,"score":63,"supported":45,"title":71,"abstract":72,"tags":75},"I4",[],[73,74],"地理信息","geographic information",[],{"code":77,"score":78,"supported":45,"title":79,"abstract":80,"tags":82},"H7",3,[],[81],"干旱",[],[84,85],"水资源管理 农业遥感 灌溉优化 OpenET","水资源管理 农业遥感","水资源管理农业遥感灌溉优化OpenET-1313","10.1007\u002Fs00271-026-01172-8",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":103,"oa_url":104,"card":105,"direction":109,"ingested_from":111},"W7204819722",[91,94,97,100],{"name":92,"orcid":93},"Katherine Osorio-Diaz","https:\u002F\u002Forcid.org\u002F0000-0001-5301-6389",{"name":95,"orcid":96},"Alfonso F. Torres‐Rua","https:\u002F\u002Forcid.org\u002F0000-0002-2238-9550",{"name":98,"orcid":99},"Matt Yost","https:\u002F\u002Forcid.org\u002F0000-0001-5012-8481",{"name":101,"orcid":102},"Lawrence E. Hipps","https:\u002F\u002Forcid.org\u002F0000-0002-7658-8571",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs00271-026-01172-8.pdf",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"利用OpenET和空间数据构建犹他州灌溉公司水均衡模型，评估农业用水优化潜力。","耦合OpenET、gridMET遥感数据与GIS数据，建立月水均衡模型，并用随机","水均衡残差时空变化大，峰值在灌溉旺季，占组分5-29%，表明数据有限下可近似估算。","农业遥感与作物表型","可延伸研究灌溉公司尺度水均衡的不确定性量化，结合更高分辨率遥感与实地监测，提升模型精度。","openalex","2026-09-01T23:30:20.143754Z",{"total":63,"page":22,"page_size":63,"items":114},[115,185,259,369,425,491],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":127,"tags":129,"primary_topic":131,"secondary_topics":132,"classification":138,"search_phrases":166,"slug":168,"view_count":22,"doi":169,"paper":170,"created_at":184},1431,"S-SEBI based Evapotranspiration Estimation Using Landsat 8 Data in Kendrapara District of Odisha, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95656","Background: Accurate estimation of evapotranspiration (ET) is essential for agricultural water management, irrigation planning, and regional water-resource assessment, particularly in areas where ground-based meteorological observations are limited. Aim: This study aimed to assess the spatial and temporal variability of actual evapotranspiration (ETa) and reference evapotranspiration (ETo) over Kendrapara district, Odisha, India, during 2016 and 2021 using the Simplified Surface Energy Balance Index (S-SEBI) model, and to compare satellite-derived ETa with FAO Penman–Monteith and Hargreaves–Samani ETo estimates. Study Design: A remote sensing and GIS-based comparative study was conducted using the S-SEBI surface energy balance model to estimate ETa from Landsat 8 imagery and evaluate its performance against conventional ETo methods. Place and Duration of Study: The study was carried out in Kendrapara district, Odisha, India, using satellite and climate data for the years 2016 and 2021. Two dates were selected primarily based on the availability of suitable Landsat 8 images with adequate quality, spatial coverage, and minimal cloud contamination over the study area. Methodology: Landsat 8 (WRS Path\u002FRow 139\u002F46) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data were obtained from the USGS Earth Explorer platform to derive surface reflectance and land surface temperature. ETa was estimated using the S-SEBI model, while ETo values based on the FAO Penman–Monteith and Hargreaves–Samani methods were obtained from the Climate Engine platform. Statistical error analysis was performed to assess the agreement between satellite-derived ETa and reference ETo estimates. Results: ETa exhibited significant spatial variability associated with vegetation condition and surface moisture. Mean ETa increased from 3.27 mm day⁻¹ in 2016 to 3.87 mm day⁻¹ in 2021, corresponding to higher net radiation and soil heat flux. S-SEBI-derived ETa closely matched FAO Penman–Monteith ETo, whereas the Hargreaves–Samani method consistently overestimated evapotranspiration. Error analysis indicated lower bias and uncertainty for the S-SEBI estimates. Conclusion: The S-SEBI model reliably estimated spatially explicit ETa with minimal ground data, demonstrating strong potential for drought monitoring, crop water requirement assessment, irrigation planning, and sustainable water resource management in data-scarce regions.","背景：准确估算蒸散量（ET）对于农业用水管理、灌溉规划及区域水资源评估至关重要，尤其是在地面气象观测资料有限的地区。目的：本研究旨在利用简化地表能量平衡指数（S-SEBI）模型，评估2016年和2021年印度奥里萨邦肯德拉帕拉县实际蒸散量（ETa）和参考蒸散量（ETo）的时空变化，并将卫星反演的ETa与FAO Penman–Monteith和Hargreaves–Samani方法估算的ETo进行比较。研究设计：采用基于遥感和GIS的对比研究，利用S-SEBI地表能量平衡模型从Landsat 8影像估算ETa，并评估其与传统ETo方法的性能差异。研究地点与时间：研究在印度奥里萨邦肯德拉帕拉县开展，使用2016年和2021年的卫星及气候数据。主要根据研究区域内可用Landsat 8影像的质量、空间覆盖范围和云污染程度，选取了两个日期。方法：从USGS Earth Explorer平台获取Landsat 8（WRS Path\u002FRow 139\u002F46）操作陆地成像仪（OLI）和热红外传感器（TIRS）数据，用于反演地表反射率和地表温度。采用S-SEBI模型估算ETa，而基于FAO Penman–Monteith和Hargreaves–Samani方法的ETo值则从Climate Engine平台获取。通过统计误差分析评估卫星反演ETa与参考ETo估算值之间的一致性。结果：ETa表现出与植被状况和地表湿度相关的显著空间变异性。平均ETa从2016年的3.27毫米\u002F天增加到2021年的3.87毫米\u002F天，这与较高的净辐射和土壤热通量相对应。S-SEBI反演的ETa与FAO Penman–Monteith ETo高度吻合，而Hargreaves–Samani方法则持续高估蒸散量。误差分析表明，S-SEBI估算的偏差和不确定性较低。结论：S-SEBI模型能够在极少地面数据条件下可靠地估算具有空间显式特征的ETa，显示出在数据稀缺地区进行干旱监测、作物需水量评估、灌溉规划和可持续水资源管理的巨大潜力。","International Journal of Environment and Climate Change","2026-09-02T00:00:00Z",58,{"impact":13,"substance":19,"depth":125,"authority":20,"freshness":21,"relevant":22,"comment":126},16,"论文利用Landsat 8数据评估印度奥里萨邦蒸散发，方法可靠，对数据稀缺地区农业水资源管理有参考价值，但地域性强，时效性低。",[128],{"name":121,"url":118},[27,28,50,130],"Landsat",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[133],{"code":54,"slug":134,"name":135,"path":136,"parent_code":137},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":139},{"candidates":140,"agricultural_evidence":45},[141,147,152,156,161],{"code":32,"score":142,"supported":45,"title":143,"abstract":144,"tags":146},19,[51],[48,49,33,145,51],"water resource",[28,50],{"code":54,"score":148,"supported":45,"title":149,"abstract":150,"tags":151},8,[],[58,59],[130,27],{"code":62,"score":63,"supported":45,"title":153,"abstract":154,"tags":155},[],[66,67],[],{"code":77,"score":63,"supported":45,"title":157,"abstract":158,"tags":160},[],[81,159],"drought",[],{"code":70,"score":78,"supported":45,"title":162,"abstract":163,"tags":165},[],[164],"GIS",[],[167,85],"水资源管理 农业遥感 蒸散发 Landsat","水资源管理农业遥感蒸散发Landsat-1431","10.9734\u002Fijecc\u002F2026\u002Fv16i95656",{"doi":169,"openalex_id":171,"authors":172,"venue":121,"cited_by_count":103,"oa_url":118,"card":179,"direction":109,"ingested_from":111},"W7205007124",[173,175,177],{"name":174,"orcid":9},"Ritoban Pandit",{"name":176,"orcid":9},"Bama Shankar Rath",{"name":178,"orcid":9},"Dwarika Mohan Das",{"tldr":180,"method":181,"finding":182,"direction":109,"opportunity":183},"利用Landsat 8和S-SEBI模型估算印度奥里萨邦Kendrapara地区的蒸散发，验证其精度","S-SEBI模型、Landsat 8遥感数据、FAO Penman-Montei","S-SEBI估算的ETa与FAO PM方法接近，Hargreaves-Samani高估；ETa从20","可探索S-SEBI模型在不同气候区及高分辨率数据下的适用性，结合机器学习改进地表参数反演，提升数据稀缺区蒸散发估算精度。","2026-09-02T23:30:51.113113Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":198,"tags":200,"primary_topic":205,"secondary_topics":206,"classification":207,"search_phrases":223,"slug":226,"view_count":103,"doi":227,"paper":228,"created_at":258},5399,"Spatial Heterogeneity and Nonlinear Environmental Associations of Positive Climatic Moisture Deficit with Implications for Sustainable Water Resource Assessment: A Case Study of Anyang","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010595","Positive climatic moisture deficit (CMD+) characterizes the seasonal imbalance between precipitation supply and atmospheric water demand, yet its spatial heterogeneity and associations with environmental and human-related factors remain insufficiently understood. Taking Anyang, China, as a case study, we calculated annual CMD+ for 2000–2020 using 1 km monthly precipitation and potential evapotranspiration data and applied an extreme gradient boosting (XGBoost)–SHapley Additive exPlanations (SHAP) framework to the corresponding long-term mean fields to examine climatological spatial heterogeneity. Five predictors were considered, including elevation, normalized difference vegetation index (NDVI), irrigation fraction, built-up fraction, and population density. Nested spatial-block cross-validation showed good model generalization. Long-term mean CMD+ was generally lower in the western mountainous area and higher across the central and eastern plains. The digital elevation model (DEM) accounted for 66.10% of the normalized mean absolute SHAP importance, followed by NDVI (13.47%) and irrigation fraction (9.72%). A DEM-excluded sensitivity model retained a spatial cross-validated R2 of 0.781, compared with 0.898 for the full model. SHAP dependence analysis revealed pronounced nonlinear responses, with primary local model response transition points at approximately 297.1 m for DEM, 0.812 for NDVI, and 23.1% for irrigation fraction. The strongest pairwise SHAP interaction occurred between NDVI and irrigation fraction, accounting for 21.87% of total pairwise interaction strength. These results show that the spatial heterogeneity of CMD+ in Anyang is closely associated with topographic background, while vegetation status and irrigated agriculture provide additional nonlinear and interactive explanatory information. The identified spatial patterns and model-response transitions provide spatial information for understanding regional climatic moisture deficits and for subsequent sustainable water resource assessment.","正向气候水分亏缺（CMD+）刻画了降水供给与大气水分需求之间的季节性失衡，但其空间异质性及其与环境因素和人为因素的关联仍缺乏充分认识。以中国安阳为案例区，本研究利用1 km月降水量与潜在蒸散量数据计算了2000—2020年的年CMD+，并将极端梯度提升（XGBoost）—SHapley加性解释（SHAP）框架应用于相应的长期均值场，以考察气候态空间异质性。研究考虑了5个预测因子，包括高程、归一化植被指数（NDVI）、灌溉面积比例、建成区比例和人口密度。嵌套空间分块交叉验证表明模型具有良好的泛化能力。长期平均CMD+总体表现为西部山区较低、中部和东部平原较高。数字高程模型（DEM）占归一化平均绝对SHAP重要性的66.10%，其次为NDVI（13.47%）和灌溉面积比例（9.72%）。剔除DEM的敏感性模型保持了0.781的空间交叉验证R²，而完整模型为0.898。SHAP依赖分析揭示了显著的非线性响应，主要局部模型响应转变点分别约为DEM 297.1 m、NDVI 0.812和灌溉面积比例23.1%。最强的成对SHAP交互作用出现在NDVI与灌溉面积比例之间，占总成对交互强度的21.87%。结果表明，安阳CMD+的空间异质性与地形背景密切相关，而植被状况和灌溉农业提供了额外的非线性和交互解释信息。所识别的空间格局和模型响应转变点为理解区域气候水分亏缺及后续可持续水资源评估提供了空间信息。","Sustainability","2026-10-19T00:00:00Z",64,{"impact":148,"substance":19,"depth":125,"authority":195,"freshness":196,"relevant":22,"comment":197},13,9,"以安阳为案例，用遥感与XGBoost-SHAP揭示气候水分亏缺的空间异质性，方法有新意、结论可靠，但属区域案例研究，公共影响有限。",[199],{"name":191,"url":188},[27,201,202,203,204],"机器学习","灌溉管理","水资源评估","气候干旱",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":208},{"candidates":209,"agricultural_evidence":45},[210,214,219],{"code":32,"score":19,"supported":45,"title":211,"abstract":212,"tags":213},[145],[48,49,33,145,51],[202],{"code":215,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},"I2",[],[],[201],{"code":54,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[27],[224,225],"安阳 气候水分亏缺 遥感","XGBoost SHAP 水资源评估","安阳气候水分亏缺遥感-5399","10.3390\u002Fsu182010595",{"doi":227,"openalex_id":229,"authors":230,"venue":191,"cited_by_count":103,"oa_url":188,"card":252,"direction":257,"ingested_from":111},"W7222973420",[231,233,236,239,242,244,246,248,250],{"name":232,"orcid":9},"Yan Li",{"name":234,"orcid":235},"Xiaohua Yang","https:\u002F\u002Forcid.org\u002F0000-0001-8387-5213",{"name":237,"orcid":238},"Dehui Bian","https:\u002F\u002Forcid.org\u002F0000-0001-6921-1875",{"name":240,"orcid":241},"Qi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3156-9246",{"name":243,"orcid":9},"Lequn Gu",{"name":245,"orcid":9},"Ailing Zhang",{"name":247,"orcid":9},"Wei Gao",{"name":249,"orcid":9},"Wenjie Qi",{"name":251,"orcid":9},"Yayun Ma",{"tldr":253,"method":254,"finding":255,"direction":109,"opportunity":256},"以安阳为例，用XGBoost-SHAP分析2000—2020年气候水分亏缺的空间异质性及其环境关联。","1 km月降水与潜在蒸散数据计算CMD+，结合XGBoost-SHAP与嵌套空间","CMD+西部山区低、中东部平原高；高程贡献66.1%，NDVI与灌溉比例呈显著非线性交互。","可引入多源遥感与作物种植结构，探究CMD+非线性阈值对灌溉需水与水资源配置的传导机制。","数字乡村与农业信息化","2026-10-20T23:30:23.927575Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":272,"tags":274,"primary_topic":279,"secondary_topics":280,"classification":281,"search_phrases":342,"slug":345,"view_count":103,"doi":346,"paper":347,"created_at":368},4987,"Smart Agriculture Research in Date Palm (Phoenix dactylifera L.) Production: A Comprehensive Bibliometric Study","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15203137","The date palm (Phoenix dactylifera L.) is fundamental to food security, livelihoods, and agro-ecosystem sustainability across the Arabian Peninsula, yet its production is increasingly constrained by water scarcity, salinity, climate change, and biotic stresses. Smart agriculture technologies offer opportunities to improve crop monitoring, resource-use efficiency, and decision-making; however, their application in date palm production remains fragmented and insufficiently validated. This study evaluates the development, geographic distribution, technological focus, research approaches, and thematic structure of smart agriculture research on date palms in the Arabian Peninsula and other major date-producing countries. A bibliometric analysis was conducted using peer-reviewed English-language articles and reviews indexed in Scopus and Web of Science from 2016 to 2026, with 2026 representing a partial year. Following screening and duplicate removal, 164 published studies were retained. Publication output increased gradually, although the trend was not statistically significant (β = 1.25 articles year−1, p = 0.068; Mann–Kendall τ = 0.489, p = 0.060). The literature was concentrated in the Gulf Cooperation Council (GCC) region, with Saudi Arabia contributing 54 publications (32.9%). General agronomy\u002Fother topics accounted for 46.3% of publications, while remote sensing\u002FUAVs (17.1%), GIS\u002Fgeospatial technologies (14.0%), IoT\u002Fsensors (13.4%), and AI\u002Fmachine learning (9.8%) represented the leading specific technology categories. Water and irrigation management dominated thematic applications (46.3%), whereas Smart Farming\u002FIoT accounted for only 11.0%. Observational and monitoring studies comprised 50.0% of publications, compared with only 6.1% involving field experiments, indicating limited field-scale validation. Internationally co-authored studies received significantly more citations than domestically authored studies (16.55 vs. 8.68; p = 0.0007). Thematic analysis identified four major clusters: geospatial monitoring, water management, pest control, and sustainability, with AI\u002Fmachine learning showing marked growth after 2022. Altogether, the field is expanding but remains fragmented, highlighting the need for integrated IoT and sensor networks, rigorous field trials, AI-driven decision-support systems, and stronger regional and international collaboration to enable scalable, context-specific smart agriculture for date palm production.","海枣（Phoenix dactylifera L.）对阿拉伯半岛的粮食安全、生计和农业生态系统可持续性至关重要，然而其生产日益受到水资源短缺、盐碱化、气候变化和生物胁迫的制约。智慧农业技术为改善作物监测、资源利用效率和决策提供了机遇；然而，其在海枣生产中的应用仍然零散且缺乏充分验证。本研究评估了阿拉伯半岛及其他主要海枣生产国海枣智慧农业研究的发展、地理分布、技术重点、研究方法和主题结构。采用文献计量学分析方法，基于Scopus和Web of Science数据库中2016年至2026年索引的同行评审英文论文和综述，其中2026年为部分年份。经过筛选和去重后，共保留164篇已发表研究。发文量逐渐增长，但该趋势未达到统计学显著性（β = 1.25篇\u002F年，p = 0.068；Mann–Kendall τ = 0.489，p = 0.060）。文献集中于海湾合作委员会（GCC）地区，其中沙特阿拉伯贡献了54篇论文（32.9%）。一般农学\u002F其他主题占论文总数的46.3%，而遥感\u002F无人机（17.1%）、GIS\u002F地理空间技术（14.0%）、物联网\u002F传感器（13.4%）和人工智能\u002F机器学习（9.8%）为主要的具体技术类别。水资源与灌溉管理在主题应用中占主导地位（46.3%），而智慧农业\u002F物联网仅占11.0%。观测与监测类研究占论文总数的50.0%，而涉及田间试验的仅占6.1%，表明田间尺度的验证有限。国际合著论文的引用次数显著高于国内独著论文（16.55 vs. 8.68；p = 0.0007）。主题分析识别出四大聚类：地理空间监测、水资源管理、病虫害防治和可持续性，其中人工智能\u002F机器学习在2022年后呈现显著增长。总体而言，该领域正在扩展但仍显零散，凸显了整合物联网与传感器网络、严格的田间试验、人工智能驱动的决策支持系统以及加强区域和国际合作的必要性，以推动可规模化、因地制宜的海枣生产智慧农业。","Plants","2026-10-14T00:00:00Z",80,{"impact":19,"substance":269,"depth":19,"authority":270,"freshness":148,"relevant":22,"comment":271},22,14,"基于Scopus与WoS的164篇文献计量分析，揭示枣椰树智慧农业研究碎片化、田间验证不足，对干旱区智慧农业布局有参考价值。",[273],{"name":265,"url":262},[275,276,27,277,278],"智慧农业","农业人工智能","农业物联网","枣椰树",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":282},{"candidates":283,"agricultural_evidence":45},[284,293,299,303,308,312,319,326,332,338],{"code":285,"score":286,"supported":45,"title":287,"abstract":289,"tags":292},"I7",17,[288],"bibliometric",[290,291,288],"文献计量","研究方法",[],{"code":294,"score":17,"supported":45,"title":295,"abstract":297,"tags":298},"I1",[296],"smart agriculture",[275,296],[275],{"code":32,"score":196,"supported":45,"title":300,"abstract":301,"tags":302},[],[48,49,33],[],{"code":215,"score":55,"supported":45,"title":304,"abstract":305,"tags":307},[],[201,306],"machine learning",[276],{"code":54,"score":55,"supported":45,"title":309,"abstract":310,"tags":311},[],[58,59],[27],{"code":313,"score":55,"supported":45,"title":314,"abstract":315,"tags":318},"I5",[],[316,317],"物联网","IoT",[277],{"code":320,"score":63,"supported":45,"title":321,"abstract":322,"tags":325},"A8",[],[323,324],"粮食安全","food security",[],{"code":77,"score":63,"supported":45,"title":327,"abstract":328,"tags":331},[],[329,330],"气候变化","climate change",[],{"code":333,"score":78,"supported":45,"title":334,"abstract":335,"tags":337},"H6",[],[336],"农业生态",[],{"code":70,"score":78,"supported":45,"title":339,"abstract":340,"tags":341},[],[164],[],[343,344],"枣椰树 智慧农业 文献计量","农业人工智能 农业物联网 农业遥感 智慧农业","枣椰树智慧农业文献计量-4987","10.3390\u002Fplants15203137",{"doi":346,"openalex_id":348,"authors":349,"venue":265,"cited_by_count":103,"oa_url":262,"card":362,"direction":366,"ingested_from":111},"W7221786893",[350,353,356,359],{"name":351,"orcid":352},"Sajid Safeer","https:\u002F\u002Forcid.org\u002F0000-0002-0120-5900",{"name":354,"orcid":355},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":357,"orcid":358},"Sajid Maqsood","https:\u002F\u002Forcid.org\u002F0000-0003-2099-8392",{"name":360,"orcid":361},"Cataldo Pulvento","https:\u002F\u002Forcid.org\u002F0000-0002-2650-1204",{"tldr":363,"method":364,"finding":365,"direction":366,"opportunity":367},"用文献计量分析2016-2026年枣椰树智慧农业研究，发现领域增长但碎片化、田间验证不足。","Scopus与Web of Science文献计量分析，164篇论文，含趋势检验","研究集中于海湾地区，遥感与灌溉为主，田间试验仅6.1%，国际合作论文引用更高。","智慧农业 \u002F 农业物联网","枣椰树智慧农业缺乏IoT传感器集成与田间验证，AI决策支持系统是待填补的研究空白。","2026-10-15T23:30:08.558630Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":376,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":378,"sources":380,"tags":382,"primary_topic":386,"secondary_topics":387,"classification":392,"search_phrases":405,"slug":408,"view_count":22,"doi":409,"paper":410,"created_at":424},4914,"Water Optimizer: A Tool for Decision Support and Policy Analysis","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413426","Water Optimizer is a tool for analyzing alternative water management strategies when the available water supply is limited. It can be used by producers to determine the profit maximizing crops to produce and the optimum amount of water to apply to each crop, given a particular water supply. It can also be used to evaluate the economic consequences of public policies which expand or reduce the amount of irrigation.","水资源优化器是一种在可用供水有限时分析替代水资源管理策略的工具。生产者可用其确定在特定供水量下能实现利润最大化的作物种类及每种作物的最优灌溉水量，也可用于评估扩大或减少灌溉面积的公共政策的经济后果。","Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43","2026-10-13T00:00:00Z",{"impact":20,"substance":19,"depth":125,"authority":195,"freshness":148,"relevant":22,"comment":379},"内布拉斯加大学开发的灌溉决策支持工具，方法实用但属细分领域工具类论文，公共影响有限。",[381],{"name":375,"url":376},[383,28,384,385],"决策支持系统","灌溉决策","农业政策分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[388],{"code":215,"slug":389,"name":390,"path":391,"parent_code":137},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":393},{"candidates":394,"agricultural_evidence":45},[395,400],{"code":32,"score":396,"supported":45,"title":397,"abstract":398,"tags":399},11,[],[48,49,33],[28,384],{"code":215,"score":196,"supported":45,"title":401,"abstract":403,"tags":404},[402],"decision support",[],[383],[406,407],"Water Optimizer 灌溉决策","内布拉斯加大学 水资源管理","WaterOptimizer灌溉决策-4914","10.22004\u002Fag.econ.413426",{"doi":409,"openalex_id":411,"authors":412,"venue":375,"cited_by_count":103,"oa_url":417,"card":418,"direction":422,"ingested_from":111},"W38113320",[413,415],{"name":414,"orcid":9},"Derrel L. Martin",{"name":416,"orcid":9},"Ray Supalla","http:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43",{"tldr":419,"method":420,"finding":421,"direction":422,"opportunity":423},"开发了Water Optimizer工具，在有限供水下优化作物种植与灌溉量以最大化利润。","基于优化模型与决策支持工具，分析不同供水策略及政策的经济影响。","该工具能帮助生产者选择利润最大化作物和最优灌溉量，并评估灌溉政策的经济后果。","农业人工智能与决策模型","可结合实时气象与土壤数据，开发动态灌溉决策模型，并拓展至多目标（经济-环境）优化。","2026-10-14T23:30:55.832240Z",{"id":426,"title":427,"url":428,"summary":429,"summary_zh":430,"content":9,"source_name":431,"source_url":428,"published_at":432,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":433,"sources":435,"tags":437,"primary_topic":440,"secondary_topics":444,"classification":447,"search_phrases":470,"slug":473,"view_count":103,"doi":474,"paper":475,"created_at":490},4753,"Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04943-7","Groundwater is a crucial source of freshwater in semi-arid regions, particularly in water-stressed areas, such as northern Uganda, where the surface water is scarce and unreliable precipitation patterns changes. This province lacks sufficient clean water for human consumption and only 20% of the water for agriculture extracted from the ground due to inaccessibility and insufficient information regarding the groundwater. With regard to these difficulties in water that are faced by the local communities, it has not been yet studied the distribution of groundwater in Kitgum and Pader districts. Therefore, the result of this research contributes preliminary groundwater investigation for contacting this indispensable source of water. The study employed a geospatial approach integrated with multi-criteria analysis to identify groundwater potential zones in Kitgum and Pader districts of Acholi subregion. The Utilization of Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) and six thematic layers; rainfall, drainage density, slope, soil type, geology, and land use\u002Fland cover were selected based on their hydrogeological influence and the accessibility of the data. After standardizing, and assigning normalized weights,a weighted overlay analysis in ArcGIS Pro version 3.1.0 was used to synthesize these layers into a comprehensive groundwater potential zonation map. The result categorized the study area into five zones; very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones of 67.24% when it is combined both, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District showed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 75.0% alignment between the model and actual well performance, confirming the model’s reliability at acceptable result. This study demonstrated the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.","地下水是半干旱地区重要的淡水来源，尤其是在乌干达北部等水资源紧张地区，那里地表水稀缺且降水模式变化不可靠。该地区缺乏足够的清洁饮用水供人类消费，由于地下水难以获取且信息不足，仅有20%的农业用水来自地下开采。鉴于当地社区面临这些水资源困难，基特古姆和帕德尔地区的地下水分布尚未得到研究。因此，本研究结果为接触这一不可或缺的水源提供了初步的地下水调查。本研究采用地理空间方法与多准则分析相结合，识别阿乔利次区域基特古姆和帕德尔地区的地下水潜力区。利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），基于其水文地质影响和数据可获取性，选取了六个主题图层：降雨量、河网密度、坡度、土壤类型、地质和土地利用\u002F土地覆盖。经过标准化和归一化权重赋值后，使用ArcGIS Pro 3.1.0版本中的加权叠加分析将这些图层综合成一张全面的地下水潜力分区图。结果将研究区划分为五个区域：极低、低、中等、高和极高潜力。分析表明，基特古姆区主要以极低至低地下水潜力区为主，两者合计占67.24%，受较高河网密度和较陡坡度等因素制约。相反，帕德尔区表现出有利条件，其85.5%的面积被归类为高至极高地下水潜力，这归因于平缓的坡度和较高的降雨量，增强了入渗能力。利用18口现有钻孔的出水量数据进行验证，模型与实际井表现之间的一致率达到75.0%，证实了模型在可接受结果范围内的可靠性。本研究证明了基于GIS和AHP的方法作为数据稀缺环境中初步地下水勘探的成本效益工具的有效性。所生成的地下水潜力图为可持续水资源管理提供了重要的科学基础，有助于有针对性地选址钻孔，为知情决策提供依据。","Discover Sustainability","2026-10-10T00:00:00Z",{"impact":148,"substance":18,"depth":286,"authority":195,"freshness":196,"relevant":22,"comment":434},"基于GIS、遥感与AHP的乌干达北部地下水潜力制图研究，方法成熟、验证可靠，对缺水地区农业水资源规划有参考价值，但属区域性初步研究，公共影响有限。",[436],{"name":431,"url":428},[58,28,164,438,439],"地下水潜力","乌干达农业",{"code":70,"slug":441,"name":442,"path":443,"parent_code":137},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[445,446],{"code":54,"slug":134,"name":135,"path":136,"parent_code":137},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":448},{"candidates":449,"agricultural_evidence":45},[450,454,458,462,466],{"code":70,"score":19,"supported":45,"title":451,"abstract":452,"tags":453},[164],[73,164,74],[164],{"code":54,"score":17,"supported":45,"title":455,"abstract":456,"tags":457},[59],[58,59],[58],{"code":32,"score":148,"supported":45,"title":459,"abstract":460,"tags":461},[],[49,145],[438,28],{"code":62,"score":63,"supported":45,"title":463,"abstract":464,"tags":465},[],[66,67],[],{"code":77,"score":78,"supported":45,"title":467,"abstract":468,"tags":469},[],[81],[],[471,472],"乌干达 Kitgum Pader 地下水","GIS AHP 地下水潜力","乌干达KitgumPader地下水-4753","10.1007\u002Fs43621-026-04943-7",{"doi":474,"openalex_id":476,"authors":477,"venue":431,"cited_by_count":103,"oa_url":428,"card":485,"direction":109,"ingested_from":111},"W7221339030",[478,481,483],{"name":479,"orcid":480},"Ahmed Abdirizak Dirie","https:\u002F\u002Forcid.org\u002F0009-0002-8109-0724",{"name":482,"orcid":9},"Abdulkadir Ahmed Mohamed",{"name":484,"orcid":9},"Ambiga Kannapiran",{"tldr":486,"method":487,"finding":488,"direction":109,"opportunity":489},"用GIS、遥感和AHP制图评估乌干达两区地下水潜力并验证。","GIS与遥感结合AHP，加权叠加6个主题图层，用18口井数据验证。","Kitgum区67.24%为低潜力，Pader区85.5%为高潜力，模型与井数据吻合75%。","可将该地下水潜力制图方法迁移到其他数据稀缺区，并结合农业灌溉需求优化井位。","2026-10-12T23:30:24.761508Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":496,"content":9,"source_name":497,"source_url":494,"published_at":498,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":499,"score_detail":500,"sources":502,"tags":504,"primary_topic":508,"secondary_topics":509,"classification":511,"search_phrases":537,"slug":540,"view_count":22,"doi":541,"paper":542,"created_at":559},4527,"Socio‐Economic Impact of Sustainable Development of Water Resources in the Semi‐Arid Regions of Village Raleras, Maharashtra: A Case Study Using Machine Learning Techniques","https:\u002F\u002Fdoi.org\u002F10.1111\u002F1477-8947.70100","ABSTRACT From a socio‐economic perspective, it is of interest to find out whether sustainable water resources can truly sustain rural livelihoods. Based on historical research, it is known that an improvement on resilient economic indicators such as ground water is often correlated with socio‐economic improvements and other aspects of rural livelihoods. The extent of factors affecting livelihood sustenance and improvement in the village of Raleras, Maharashtra, aids in creating a definite understanding of the facets correlating with groundwater restoration. Subsistence channels, arising as an outcome of the created impoundment structures, are tested for their impact on income gains and livelihood improvement. Examination on collected opinion was carried out to provide insights into the ‘before’ and ‘after’ (2015–2023) conditions. A five‐capitals framework has been employed for the interpretation of these results, along with a multivariate analysis to predict a causal framework for the improvement of livelihoods in agrarian economies through creation of check dams and watershed management. Time series study shows that there is greater variance in the NDVI progressively from 2021 to 2023 with values ranging from 0.25 to 0.50 in 2015, to 0.50–0.75 by 2021. Further increase in green patches in 2023 indicates enhanced vegetation health especially in the northern regions. Alternately, NDWI values elaborate initial severe water stress (−0.25 to 0.00) with progressive improvements in localized areas. The spatial variation map shows that eventually there is a notable improvement with more land supporting shrubbery along with more water bodies. The prominent increase in vegetation particularly in the later years can be attributed to water management initiatives undertaken in Raleras. The distributions of the annual average revenue from agricultural operations in 2015 (M = 146,907, SD = 95445.8) and 2023 (M = 706860.5, SD = 655748.3) showed a statistically significant improvement [ t (214) = 13.06, p \u003C 0.001]. The low p ‐value of 0.001205 ( p \u003C 0.05) of the educational facilities utilised indicates a statistically significant result, proving the association between educational quality and improved water facilities. All of the variables under evaluation, including the respondents' vehicle type, landholding type, and house type, have large and positive chi‐squared values, suggesting that the ‘before’ and ‘after’ distributions for each of these variables differ. Principal Component Analysis is used to summarise correlated livelihood indicators and reveals that income growth and livelihood improvement is multi‐dimensional. Multiple pathways to income change are identified through Partial Least Squares Regression, including agriculture and market‐linked channels. A Random Forest Regression Model is utilised to assess the relative importance of livelihood channels and baseline characteristics. Results indicate that improvement in irrigation access, crop diversification, housing quality, livestock ownership and female literacy are strongly associated with higher income gains. Hence, it may be said that the groundwater recharge as a result of construction of check dams must have improved agricultural activity or agricultural output in a material manner, and accordingly all capitals for Raleras have improved.","从社会经济视角来看，探究可持续水资源能否真正维持农村生计具有重要意义。基于历史研究可知，地下水等韧性经济指标的改善往往与社会经济进步及农村生计的其他方面相关联。马哈拉施特拉邦拉莱拉斯村影响生计维持与改善的因素程度，有助于明确理解与地下水恢复相关的各个维度。作为所建蓄水设施产物的生计渠道，其对面收入增长和生计改善的影响得到了检验。对收集到的意见进行了考察，以深入了解“之前”和“之后”（2015—2023年）的状况。研究采用了五资本框架来解读这些结果，并结合多变量分析，预测通过修建拦水坝和流域管理来改善农业经济生计的因果框架。时间序列研究表明，从2021年到2023年，NDVI的变异性逐渐增大，2015年数值范围为0.25至0.50，到2021年升至0.50至0.75。2023年绿色斑块进一步增加，表明植被健康状况改善，尤其是在北部地区。另一方面，NDWI值显示初期存在严重的水分胁迫（−0.25至0.00），后在局部地区逐步改善。空间变异图显示，最终出现了显著改善，更多土地能够支持灌木生长，同时水体也有所增加。植被的显著增加，尤其是在后期，可归因于拉莱拉斯村所实施的水资源管理举措。2015年（M = 146,907，SD = 95445.8）和2023年（M = 706860.5，SD = 655748.3）农业经营年均收入的分布显示出统计学上显著的改善[t(214) = 13.06，p \u003C 0.001]。所利用教育设施的p值为0.001205（p \u003C 0.05），表明结果具有统计学显著性，证明了教育质量与改善的供水设施之间的关联。所有受评估变量，包括受访者的车辆类型、土地持有类型和房屋类型，均具有较大且正向的卡方值，表明这些变量各自的“之前”和“之后”分布存在差异。","Natural Resources Forum","2026-10-07T00:00:00Z",73,{"impact":20,"substance":269,"depth":19,"authority":195,"freshness":148,"relevant":22,"comment":501},"以机器学习与遥感数据量化印度半干旱村庄水资源可持续性对生计的影响，方法扎实、结论具体，但属境外案例研究，对国内三农信息化仅有方法借鉴价值。",[503],{"name":497,"url":494},[505,201,28,506,507],"乡村振兴","遥感监测","灌溉农业",{"code":215,"slug":389,"name":390,"path":391,"parent_code":137},[510],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":512},{"candidates":513,"agricultural_evidence":45},[514,518,522,528,533],{"code":215,"score":196,"supported":45,"title":515,"abstract":516,"tags":517},[306],[],[201],{"code":32,"score":148,"supported":45,"title":519,"abstract":520,"tags":521},[],[49,33],[28,507],{"code":523,"score":78,"supported":45,"title":524,"abstract":525,"tags":527},"C",[],[526],"livestock",[],{"code":529,"score":22,"supported":14,"title":530,"abstract":531,"tags":532},"G",[],[],[505],{"code":54,"score":22,"supported":14,"title":534,"abstract":535,"tags":536},[],[],[506],[538,539],"Raleras 马哈拉施特拉 水资源","check dam 地下水 生计","Raleras马哈拉施特拉水资源-4527","10.1111\u002F1477-8947.70100",{"doi":541,"openalex_id":543,"authors":544,"venue":497,"cited_by_count":103,"oa_url":9,"card":554,"direction":422,"ingested_from":111},"W7220922038",[545,548,550,552],{"name":546,"orcid":547},"Papiya Deb","https:\u002F\u002Forcid.org\u002F0000-0002-6554-5111",{"name":549,"orcid":9},"Roopsha Deb",{"name":551,"orcid":9},"Udaysinh Bhosale",{"name":553,"orcid":9},"Vemula Sai Vikas",{"tldr":555,"method":556,"finding":557,"direction":257,"opportunity":558},"用机器学习评估印度半干旱村庄水资源可持续开发对农村生计的社会经济影响。","NDVI\u002FNDWI遥感、五资本框架、多元分析与卡方检验，2015–2023数据。","水管理举措显著提升植被覆盖与农业收入，教育设施改善与水资源改善显著相关。","可结合遥感时序与农户调查，量化水利工程对生计的因果效应并推广至其他半干旱区。","2026-10-09T23:30:50.079298Z"]