[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3190":3,"related-3190":108},{"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":44,"search_phrases":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":107},3190,"Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183223","With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop\u002Firrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.","在淡水资源有限且用水需求不断增长的背景下，识别和监测水资源需求势在必行。农业灌溉是全球最大的用水部门，占淡水取水量的45%–90%。识别和追踪灌溉土地的变化对于可持续水资源管理以及预测农业用水需求和模式至关重要；然而，现有遥感观测的低分辨率阻碍了田块尺度的分析。土壤湿度观测数据的降尺度被提出作为解决这一问题的方法。本研究比较了五种灌溉识别方法的性能，所用数据基于新开发的降尺度深层土壤湿度外推方法，用于估算土壤湿度主动被动（SMAP）卫星在400 m空间分辨率下5 cm、20 cm和50 cm深度的土壤湿度（SM）。利用该数据以及气候灾害组红外降水与站点数据（CHIRPS）降水和Landsat归一化植被指数（NDVI），我们结合科罗拉多决策支持系统提供的2015年至2024年灌溉地图，在科罗拉多州的农田上就作物类型、灌溉类型和观测深度对这些方法进行了评估。在没有任何作物\u002F灌溉类型–观测深度组合的F1分数超过0.149或MCC值超过0.163的情况下，我们发现无论作物类型、灌溉类型和观测深度如何，这些方法均无法准确识别灌溉土地。由于这些方法在早期小区域研究中取得了成功，且分类地图呈现为大的、空间均一的斑块，我们将此解释为大规模异质区域上田块尺度检测所特有的局限性，而非数据集本身的缺陷。这些发现凸显了该降尺度SM数据集的局限性，并提出其他降尺度土壤湿度产品是否也存在这一局限性的问题。",null,"Remote Sensing","2026-09-19T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"该研究通过大区域对比实验揭示降尺度SMAP土壤水分在田块尺度灌溉识别上的局限，方法严谨、结论明确，对农业遥感与水资源管理有参考价值，但属细分领域学术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","水资源管理","土壤水分","灌溉识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,57,63,70,77],{"code":33,"score":19,"supported":51,"title":52,"abstract":53,"tags":56},true,[34],[54,55,34],"灌溉","水资源",[29],{"code":40,"score":58,"supported":51,"title":59,"abstract":60,"tags":62},15,[41],[61,41],"土壤",[30],{"code":64,"score":65,"supported":51,"title":66,"abstract":67,"tags":69},"I3",7,[],[28,68],"remote sensing",[28],{"code":71,"score":72,"supported":51,"title":73,"abstract":74,"tags":76},"I2",3,[],[75],"decision support",[],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"I1",[],[],[27],[83,84],"SMAP 土壤水分 灌溉识别","科罗拉多 灌溉制图 遥感","SMAP土壤水分灌溉识别-3190",4,"10.3390\u002Frs18183223",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":99,"oa_url":6,"card":100,"direction":104,"ingested_from":106},"W7213934204",[91,94,96],{"name":92,"orcid":93},"Annelise M. Turman","https:\u002F\u002Forcid.org\u002F0009-0000-2392-3631",{"name":95,"orcid":9},"Bin Fang",{"name":97,"orcid":98},"V. Vijaya Lakshmi","https:\u002F\u002Forcid.org\u002F0000-0001-9522-7897",0,{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"比较五种灌溉识别方法，发现降尺度SMAP土壤湿度在科罗拉多田间尺度无法准确识别灌溉。","用400米降尺度SMAP土壤湿度、CHIRPS降水和Landsat NDVI，对","所有方法F1最高仅0.149，无法准确识别灌溉地，归因于大区域异质性而非数据缺陷。","农业遥感与作物表型","可检验其他降尺度土壤湿度产品是否同样受大区域异质性限制，并探索融合多源数据提升田间尺度灌溉识别。","openalex","2026-09-22T23:30:26.627420Z",{"total":109,"page":22,"page_size":109,"items":110},6,[111,193,272,338,412,474],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":124,"tags":126,"primary_topic":131,"secondary_topics":132,"classification":139,"search_phrases":172,"slug":175,"view_count":176,"doi":177,"paper":178,"created_at":192},3804,"Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100941","Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET0) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET0. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET0 was the dominant predictor, and RF and BT achieved the highest independent-test performance (R2 = 0.875; RMSE = 0.398–0.399 mm day−1). Excluding ET0 reduced predictive performance, with RF, LSBoost, and BT achieving R2 values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET0 was unavailable: R2 decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R2 values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches.","准确估算实际蒸散发（ETa）对于可持续水资源管理和农业规划至关重要。本研究探讨了参考蒸散发（ET0）和土壤水分（SM）的可用性如何影响ETa预测性能及预测因子重要性，采用了随机森林（RF）、Bagging Trees（BT）、最小二乘提升（LSBoost）、广义加性模型（GAM）和多元线性回归（MLR）方法。在全球太阳辐射（Rs）、地表温度（LST）、归一化差异植被指数（NDVI）和SM的条件下，评估了包含和不包含ET0两种主要情景。模型使用80%的数据进行开发，在训练子集内进行五折交叉验证，最终性能在独立的20%测试子集上评估。模型可解释性通过SHAP、排列特征重要性（PFI）、偏依赖图（PDP）和时间符号SHAP分析进行评估，同时通过额外的SM消融实验量化了土壤水分的增量预测贡献。在情景I中，ET0是主导预测因子，RF和BT取得了最高的独立测试性能（R2 = 0.875；RMSE = 0.398–0.399 mm day−1）。排除ET0降低了预测性能，在情景II中RF、LSBoost和BT的R2值约为0.80，同时预测因子重要性结构主要转向Rs，其次是SM、LST和NDVI。去除SM进一步降低了两种情景下的性能，且在ET0不可用时性能下降幅度更大：情景I中R2下降了0.025–0.044，情景II中下降了0.051–0.099。在排除SM的情景I配置下（该配置使预测因子信息更接近此前的ANN和SAFER研究），R2值范围为0.820至0.832，表明ETa预测性能的差异不能仅归因于模型结构，还强烈依赖于预测因子信息含量。总体而言，ET0为ETa预测提供了大量大气需求信息，而Rs承担了主导预测作用，SM在ET0不可用时提供了特别重要的补充信息。综合性能、消融和可解释性分析表明，在开发和比较ETa估算方法时，需将预测因子组成与模型结构一并考虑。","Atmosphere","2026-09-27T00:00:00Z",73,{"impact":17,"substance":18,"depth":19,"authority":121,"freshness":122,"relevant":22,"comment":123},13,8,"方法对比与可解释性分析扎实，对农业水资源管理和灌溉决策有参考价值，但属专业论文，公共影响有限。",[125],{"name":117,"url":114},[27,127,128,129,130],"农业遥感","机器学习","土壤墒情","蒸散发估算",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[133,134],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":71,"slug":135,"name":136,"path":137,"parent_code":138},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":140},{"candidates":141,"agricultural_evidence":51},[142,149,153,158,164,168],{"code":33,"score":143,"supported":51,"title":144,"abstract":146,"tags":148},17,[145],"evapotranspiration",[55,147,145],"蒸散发",[],{"code":40,"score":58,"supported":51,"title":150,"abstract":151,"tags":152},[41],[61,41],[129],{"code":71,"score":21,"supported":51,"title":154,"abstract":156,"tags":157},[155],"machine learning",[],[128],{"code":159,"score":72,"supported":51,"title":160,"abstract":161,"tags":163},"J1",[],[162],"农业规划",[],{"code":78,"score":22,"supported":14,"title":165,"abstract":166,"tags":167},[],[],[27],{"code":64,"score":22,"supported":14,"title":169,"abstract":170,"tags":171},[],[],[127],[173,174],"参考蒸散发 土壤水分 机器学习","ET0 实际蒸散发 随机森林","参考蒸散发土壤水分机器学习-3804",2,"10.3390\u002Fatmos17100941",{"doi":177,"openalex_id":179,"authors":180,"venue":117,"cited_by_count":99,"oa_url":114,"card":186,"direction":190,"ingested_from":106},"W7214588993",[181,184],{"name":182,"orcid":183},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":185,"orcid":9},"Devrim Alkaya",{"tldr":187,"method":188,"finding":189,"direction":190,"opportunity":191},"评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。","用RF、BT、LSBoost、GAM、MLR结合SHAP、PFI、PDP及SM消","ET0主导预测，缺失时Rs主导且SM补充价值显著增大。","农业人工智能与决策模型","可探索缺ET0条件下融合遥感与土壤湿度的可解释ETa估算框架。","2026-09-29T23:30:57.729880Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":204,"tags":206,"primary_topic":210,"secondary_topics":211,"classification":217,"search_phrases":243,"slug":246,"view_count":72,"doi":247,"paper":248,"created_at":271},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":18,"depth":19,"authority":20,"freshness":122,"relevant":22,"comment":203},"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。",[205],{"name":199,"url":196},[207,28,129,208,209],"农业水资源","灌溉管理","华北平原",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[212,213],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":64,"slug":214,"name":215,"path":216,"parent_code":138},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":218},{"candidates":219,"agricultural_evidence":51},[220,226,230,235],{"code":33,"score":221,"supported":51,"title":222,"abstract":223,"tags":225},19,[34],[54,55,34,224],"water resource",[207,208],{"code":40,"score":58,"supported":51,"title":227,"abstract":228,"tags":229},[41],[61,41],[129],{"code":64,"score":17,"supported":51,"title":231,"abstract":232,"tags":234},[68],[233],"估产",[28],{"code":236,"score":21,"supported":51,"title":237,"abstract":238,"tags":242},"H7",[],[239,240,241],"气候变化","农业气象","climate change",[],[244,245],"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":247,"openalex_id":249,"authors":250,"venue":199,"cited_by_count":99,"oa_url":265,"card":266,"direction":104,"ingested_from":106},"W7214549221",[251,254,256,258,260,262],{"name":252,"orcid":253},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":255,"orcid":9},"Yu Li",{"name":257,"orcid":9},"Jiawei Lv",{"name":259,"orcid":9},"Lei Wang",{"name":261,"orcid":9},"Zhiqing Cheng",{"name":263,"orcid":264},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":267,"method":268,"finding":269,"direction":104,"opportunity":270},"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":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":290,"secondary_topics":291,"classification":293,"search_phrases":317,"slug":320,"view_count":176,"doi":321,"paper":322,"created_at":337},3184,"Soil Compaction and Irrigation Management: Implications of Soil Hydraulic Changes for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181853","Soil compaction is one of the main forms of physical soil degradation affecting agricultural productivity. Although its effects on soil structure and crop growth have been extensively investigated, their implications for irrigation management have received little attention. This review synthesises current knowledge on how soil compaction modifies the hydraulic functioning of agricultural soils and discusses the consequences for irrigation scheduling and precision irrigation. The available evidence shows that soil compaction reduces water infiltration and saturated hydraulic conductivity while altering soil water retention and plant-available water. Surface sealing and crust formation can further restrict infiltration, particularly when bare soil is exposed to high-energy rainfall or sprinkler irrigation. These changes modify water movement, redistribution and storage within the soil profile, affecting root development, plant physiological responses and crop productivity. The magnitude of these effects depends on soil texture, soil water status, crop species and environmental conditions. Conventional irrigation scheduling generally assumes stable soil hydraulic properties and may not adequately represent compacted soils. Recent advances in soil moisture sensing, crop modelling, remote sensing and decision support systems provide new opportunities to incorporate soil structural variability into irrigation management. Combining precision irrigation with preventive traffic management and practices that enhance soil structural and biological resilience may improve irrigation efficiency, optimise water use and enhance the long-term sustainability of irrigated agricultural systems.","土壤压实是影响农业生产力的土壤物理退化的主要形式之一。尽管其对土壤结构和作物生长的影响已得到广泛研究，但其对灌溉管理的影响却鲜受关注。本文综述了关于土壤压实如何改变农业土壤水力功能的现有知识，并探讨了其对灌溉制度和精准灌溉的影响。现有证据表明，土壤压实降低了水分入渗和饱和导水率，同时改变了土壤持水性和植物有效水量。地表封闭和结皮的形成会进一步限制入渗，尤其是当裸露土壤暴露于高能量降雨或喷灌时。这些变化改变了土壤剖面中水分的运动、再分布和储存，进而影响根系发育、植物生理响应和作物生产力。这些影响的程度取决于土壤质地、土壤水分状况、作物种类和环境条件。传统灌溉制度通常假设土壤水力性质稳定，可能无法充分反映压实土壤的情况。土壤水分传感、作物建模、遥感和决策支持系统方面的最新进展，为将土壤结构变异性纳入灌溉管理提供了新的机遇。将精准灌溉与预防性机械作业管理及增强土壤结构和生物韧性的措施相结合，有望提高灌溉效率、优化水资源利用，并增强灌溉农业系统的长期可持续性。","Agronomy","2026-09-20T00:00:00Z",78,{"impact":19,"substance":282,"depth":143,"authority":20,"freshness":122,"relevant":22,"comment":283},21,"系统综述土壤压实对土壤水力特性与灌溉管理的影响，提出将土壤结构变异纳入精准灌溉决策，对智慧农业与节水灌溉有参考价值。",[285],{"name":278,"url":275},[27,127,287,288,289],"精准灌溉","土壤墒情监测","土壤压实",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[292],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":294},{"candidates":295,"agricultural_evidence":51},[296,300,304,309,313],{"code":33,"score":19,"supported":51,"title":297,"abstract":298,"tags":299},[34],[54,55,34],[287],{"code":40,"score":20,"supported":51,"title":301,"abstract":302,"tags":303},[41],[61,41],[],{"code":78,"score":21,"supported":51,"title":305,"abstract":307,"tags":308},[306],"precision agriculture",[],[27],{"code":64,"score":65,"supported":51,"title":310,"abstract":311,"tags":312},[],[28,68],[127],{"code":71,"score":72,"supported":51,"title":314,"abstract":315,"tags":316},[],[75],[],[318,319],"土壤压实 精准灌溉","土壤水力特性 灌溉管理","土壤压实精准灌溉-3184","10.3390\u002Fagronomy16181853",{"doi":321,"openalex_id":323,"authors":324,"venue":278,"cited_by_count":99,"oa_url":275,"card":331,"direction":104,"ingested_from":106},"W7213866178",[325,328],{"name":326,"orcid":327},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":329,"orcid":330},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":332,"method":333,"finding":334,"direction":335,"opportunity":336},"综述土壤压实改变水力特性对灌溉管理的影响，提出将结构变异纳入精准灌溉。","文献综述，整合土壤水力、传感、作物模型与遥感技术。","压实降低入渗与导水率，传统灌溉调度假设不成立，需纳入结构变异。","智慧农业 \u002F 农业物联网","可开发融合土壤压实空间变异与实时传感的精准灌溉决策模型，填补结构退化下的调度空白。","2026-09-22T23:30:25.358657Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":350,"tags":352,"primary_topic":356,"secondary_topics":357,"classification":364,"search_phrases":391,"slug":394,"view_count":395,"doi":396,"paper":397,"created_at":411},3158,"Internet of things-based smart irrigation system using soil moisture and weather data","https:\u002F\u002Fdoi.org\u002F10.12928\u002Ftelkomnika.v24i5.27889","Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.","农业中的水资源浪费仍然是一项重大挑战，因为灌溉实践往往依赖固定时间表，而非实际田间条件。本研究提出了一种基于物联网（IoT）的智能灌溉系统，旨在通过实时监测和自动灌溉控制来提高用水效率。该系统将电容式土壤湿度传感器与从在线应用程序编程接口（API）获取的天气信息相结合，同时所有数据处理均在Raspberry Pi边缘设备上本地完成。系统采用基于规则的决策机制，将土壤状况分为干燥、适宜和湿润三类，并根据土壤湿度水平和降雨预报确定适当的灌溉措施。该系统使用低成本和易于获取的组件实现，并在受控条件下进行了测试，土壤湿度水平范围约为0%至85%以上。实验结果表明，系统能够一致地分类临界干燥、适宜和临界湿润状况，从而在不同情景下实现适当的灌溉响应。此外，电子邮件通知仅在临界状况下生成，而在适宜湿度水平下未触发任何警报，表明系统运行稳定可靠。所提出的系统为支持高效灌溉管理和可持续农业实践提供了一种实用且具有成本效益的解决方案。","TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z",58,{"impact":122,"substance":348,"depth":20,"authority":17,"freshness":122,"relevant":22,"comment":349},16,"低成本物联网智能灌溉系统，方法清晰、结论可靠，对节水农业有实用参考价值，但属常规技术验证类论文，影响范围有限。",[351],{"name":344,"url":341},[27,353,354,129,355],"物联网","智能灌溉","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[358,363],{"code":359,"slug":360,"name":361,"path":362,"parent_code":138},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":365},{"candidates":366,"agricultural_evidence":51},[367,371,377,381,387],{"code":33,"score":221,"supported":51,"title":368,"abstract":369,"tags":370},[34],[54,55,34],[354,355],{"code":359,"score":19,"supported":51,"title":372,"abstract":374,"tags":376},[373],"internet of things",[353,373,375],"IoT",[353],{"code":40,"score":58,"supported":51,"title":378,"abstract":379,"tags":380},[41],[61,41],[129],{"code":382,"score":72,"supported":51,"title":383,"abstract":384,"tags":386},"H8",[],[385],"可持续农业",[],{"code":78,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[27],[392,393],"IoT 智能灌溉 土壤湿度","Raspberry Pi 边缘计算 灌溉","IoT智能灌溉土壤湿度-3158",5,"10.12928\u002Ftelkomnika.v24i5.27889",{"doi":396,"openalex_id":398,"authors":399,"venue":344,"cited_by_count":99,"oa_url":341,"card":406,"direction":335,"ingested_from":106},"W7213907872",[400,402,404],{"name":401,"orcid":9},"Zakarie Abdi Mohamud",{"name":403,"orcid":9},"Rozeha Binti A. Rashid",{"name":405,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":407,"method":408,"finding":409,"direction":335,"opportunity":410},"基于物联网与土壤湿度及天气数据，实现低成本自动灌溉决策系统。","电容式土壤湿度传感器、天气API、Raspberry Pi边缘计算与规则决策。","系统能准确分类干、适宜、湿状态，仅在临界条件触发灌溉与邮件通知。","可引入机器学习预测土壤湿度动态，优化规则阈值并扩展至多作物多区域验证。","2026-09-22T23:30:10.956765Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":420,"score_detail":421,"sources":423,"tags":425,"primary_topic":427,"secondary_topics":428,"classification":431,"search_phrases":450,"slug":453,"view_count":22,"doi":454,"paper":455,"created_at":473},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",72,{"impact":121,"substance":282,"depth":143,"authority":121,"freshness":122,"relevant":22,"comment":422},"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[424],{"name":418,"url":415},[27,426,128,287,129],"农业人工智能",{"code":71,"slug":135,"name":136,"path":137,"parent_code":138},[429,430],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":432},{"candidates":433,"agricultural_evidence":51},[434,438,442,446],{"code":71,"score":348,"supported":51,"title":435,"abstract":436,"tags":437},[155],[128,155],[426,128],{"code":40,"score":58,"supported":51,"title":439,"abstract":440,"tags":441},[41],[61,41],[129],{"code":33,"score":13,"supported":51,"title":443,"abstract":444,"tags":445},[],[54,147,34,145],[287],{"code":78,"score":21,"supported":51,"title":447,"abstract":448,"tags":449},[306],[],[27],[451,452],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":454,"openalex_id":456,"authors":457,"venue":418,"cited_by_count":99,"oa_url":9,"card":468,"direction":190,"ingested_from":106},"W7213181547",[458,461,464,466],{"name":459,"orcid":460},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":462,"orcid":463},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":465,"orcid":9},"Kinnoree R. Pasha",{"name":467,"orcid":9},"Dilruba Yeasmin",{"tldr":469,"method":470,"finding":471,"direction":190,"opportunity":472},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":9,"source_name":480,"source_url":477,"published_at":481,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":482,"sources":485,"tags":487,"primary_topic":490,"secondary_topics":491,"classification":494,"search_phrases":518,"slug":521,"view_count":395,"doi":522,"paper":523,"created_at":539},2140,"AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84727","Modern agriculture faces severe challenges due to climate volatility, accelerating groundwater depletion, and the global imperative to maximize crop production on diminishing arable land. Traditional irrigation frameworks rely predominantly on static schedules or reactive threshold switching, leading to substantial water waste, energy inefficiencies, and suboptimal crop yields. To overcome these limitations, this paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework designed for real-time multi-parameter soil tracking and predictive smart irrigation. The system architecture deploys low-power IoT field nodes driven by ESP32 microcontrollers, integrated with capacitive soil moisture sensors, environmental sensors, and soil pH probes that stream telemetry data over lightweight MQTT protocols. To transition from reactive monitoring to proactive resource allocation, a cloud-based predictive engine utilizes Long Short-Term Memory (LSTM) neural networks to forecast 24- to-48-hour soil moisture depletion dynamics based on historical moisture profiles and localized meteorological factors. Experimental validation across a 90-day testbed demonstrates that the proposed predictive framework achieves a to reduction in total water consumption while maintaining optimal volumetric soil water content. Furthermore, deep-sleep dynamic power profiling confirms node energy autonomy of up to 219 days on a single battery charge, presenting a scalable, sustainable, and economically viable solution for precision agriculture.","现代农业正面临气候波动、地下水加速枯竭以及全球在日益减少的耕地上最大化作物产量的迫切需求等严峻挑战。传统灌溉框架主要依赖静态调度或反应式阈值切换，导致大量水资源浪费、能源效率低下以及作物产量欠优。为克服这些局限，本文提出了一种端到端的人工智能物联网（AIoT）框架，专为实时多参数土壤监测与预测性智能灌溉而设计。该系统架构部署了由ESP32微控制器驱动的低功耗物联网田间节点，集成了电容式土壤水分传感器、环境传感器和土壤pH探头，通过轻量级MQTT协议传输遥测数据。为实现从反应式监测向主动式资源分配的转变，基于云的预测引擎利用长短期记忆（LSTM）神经网络，根据历史水分剖面和局部气象因素，预测24至48小时的土壤水分消耗动态。在为期90天的测试平台上进行的实验验证表明，所提出的预测框架在保持最优土壤体积含水量的同时，实现了总用水量的降低。此外，深度睡眠动态功耗分析证实，节点在单次电池充电下可实现长达219天的能量自主运行，为精准农业提供了一种可扩展、可持续且经济可行的解决方案。","International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z",{"impact":19,"substance":483,"depth":143,"authority":17,"freshness":122,"relevant":22,"comment":484},20,"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[486],{"name":480,"url":477},[27,426,353,354,488,489],"精准农业","土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[492,493],{"code":359,"slug":360,"name":361,"path":362,"parent_code":138},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":495},{"candidates":496,"agricultural_evidence":51},[497,501,505,509,513],{"code":33,"score":19,"supported":51,"title":498,"abstract":499,"tags":500},[34],[54,55,34],[354],{"code":359,"score":58,"supported":51,"title":502,"abstract":503,"tags":504},[375],[353,375],[353],{"code":40,"score":20,"supported":51,"title":506,"abstract":507,"tags":508},[41],[61,41],[],{"code":78,"score":122,"supported":51,"title":510,"abstract":511,"tags":512},[],[488,306],[27,488],{"code":71,"score":86,"supported":51,"title":514,"abstract":515,"tags":517},[],[516],"artificial intelligence",[426],[519,520],"农业人工智能 土壤监测 智慧农业 智能灌溉","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业智能灌溉-2140","10.22214\u002Fijraset.2026.84727",{"doi":522,"openalex_id":524,"authors":525,"venue":480,"cited_by_count":99,"oa_url":477,"card":534,"direction":335,"ingested_from":106},"W7212115545",[526,528,530,532],{"name":527,"orcid":9},"Gowri M.",{"name":529,"orcid":9},"Boomika M.",{"name":531,"orcid":9},"S. Rakshana",{"name":533,"orcid":9},"Rubali R.",{"tldr":535,"method":536,"finding":537,"direction":335,"opportunity":538},"提出AIoT框架，用LSTM预测土壤湿度实现智能灌溉，节水并延长节点续航。","ESP32节点+电容湿度\u002FpH传感器，MQTT上云，LSTM预测24-48小时湿","90天试验节水显著，土壤含水量保持最优，单次电池续航达219天。","可探索多作物多气候下LSTM泛化能力，及边缘端轻量预测模型降低云依赖。","2026-09-11T23:30:10.173013Z"]