[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3370":3,"related-3370":130},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":89,"slug":92,"view_count":85,"doi":93,"paper":94,"created_at":129},3370,"A National Depth-Resolved Soil-Moisture-to-Electromagnetic Proxy Database for Hydrogeophysical Monitoring","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-563","Abstract. Soil moisture is a key control on drought, recharge, crop water availability, agricultural water management, and land-atmosphere exchange, but its depth-resolved spatial variability remains difficult to monitor over large areas. Non-invasive methods such as ground-penetrating radar (GPR), electrical resistivity, and electromagnetic surveys can support soil-moisture estimation, yet their interpretation depends strongly on soil texture and hydraulic-retention properties. This study develops a national six-depth soil-moisture-to-electromagnetic proxy database to support future GPR- and resistivity-based root-zone monitoring and decision support. We used multi-year soil-moisture, porosity, saturation, texture, and hydraulic-retention data from 117 monitoring stations across Hungary at 10, 20, 30, 45, 60, and 75 cm depth. Dielectric permittivity, EM-wave velocity, electrical conductivity, attenuation, and apparent resistivity were derived as proxy variables from observed soil moisture using established petrophysical relationships, including the Topp equation and an Archie-type formulation. The proxy database identified a consistent 30-45 cm buffering zone, where soil moisture increased to 22.25%, dielectric permittivity peaked at 12.29, EM-wave velocity reached a minimum of 0.0935 m ns⁻¹, and apparent resistivity decreased to 545 Ω m. Texture strongly shaped the translated EM response: sandy soils were drier and more resistive, whereas clayey soils retained more water and showed higher dielectric response. Hydraulic-retention variables provided calibration-relevant prior information for explaining why similar EM or resistivity signals may represent different true soil-moisture values across soil types. The framework provides a transferable national calibration layer for drought-monitoring agencies, irrigation planners, precision-agriculture users, and future drone-based geophysical surveys in regions with comparable monitoring and soil databases.","摘要：土壤水分是干旱、补给、作物可用水量、农业水资源管理和陆气交换的关键控制因素，但其深度分辨的空间变异性在大范围区域仍难以监测。探地雷达（GPR）、电阻率和电磁勘探等非侵入式方法可支持土壤水分估算，但其解译在很大程度上取决于土壤质地和水力保持特性。本研究构建了一个全国尺度的六深度土壤水分—电磁代用指标数据库，以支持未来基于GPR和电阻率的根区监测与决策支持。我们使用了匈牙利全国117个监测站多年在10、20、30、45、60和75 cm深度的土壤水分、孔隙度、饱和度、质地和水力保持数据。基于观测土壤水分，利用成熟的地球物理关系（包括Topp方程和Archie型公式），推导出介电常数、电磁波速度、电导率、衰减和视电阻率作为代用变量。该代用指标数据库识别出一致的30–45 cm缓冲带，其中土壤水分增至22.25%，介电常数峰值达12.29，电磁波速度最低为0.0935 m ns⁻¹，视电阻率降至545 Ω m。质地对转换后的电磁响应具有强烈影响：砂质土壤更干、电阻率更高，而黏质土壤持水更多并表现出更高的介电响应。水力保持变量为解释为何相似的电磁或电阻率信号在不同土壤类型中可能代表不同真实土壤水分值提供了与校准相关的先验信息。该框架为干旱监测机构、灌溉规划者、精准农业用户以及未来在具有可比监测和土壤数据库区域开展的无人机地球物理调查提供了一个可迁移的全国校准层。",null,"Earth System Science Data Discussions","2026-09-23T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于匈牙利117个站点六层深度构建的土壤水分—电磁代理数据库，为根区墒情遥感与精准灌溉决策提供可迁移校准层，方法新颖、数据扎实，对农业信息化有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","精准农业","遥感监测","土壤墒情","智慧灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,62,70,76,83],{"code":32,"score":50,"supported":51,"title":52,"abstract":53,"tags":55},15,true,[33],[54,33],"土壤",[29],{"code":39,"score":13,"supported":51,"title":57,"abstract":58,"tags":61},[],[59,60,40],"灌溉","水资源",[30],{"code":63,"score":64,"supported":51,"title":65,"abstract":66,"tags":69},"H7",6,[],[67,68],"干旱","drought",[],{"code":71,"score":72,"supported":51,"title":73,"abstract":74,"tags":75},"I1",4,[],[27],[27],{"code":77,"score":78,"supported":51,"title":79,"abstract":80,"tags":82},"I2",3,[],[81],"decision support",[],{"code":84,"score":85,"supported":14,"title":86,"abstract":87,"tags":88},"I3",2,[],[],[26,28],[90,91],"匈牙利 土壤水分 电磁代理数据库","探地雷达 根区土壤水分 监测","匈牙利土壤水分电磁代理数据库-3370","10.5194\u002Fessd-2026-563",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":120,"oa_url":6,"card":121,"direction":127,"ingested_from":128},"W7214076150",[97,100,102,104,107,109,111,114,117],{"name":98,"orcid":99},"Diaa Sheishah","https:\u002F\u002Forcid.org\u002F0000-0003-2050-6474",{"name":101,"orcid":9},"Enas Abdelsamei",{"name":103,"orcid":9},"Viktoria Blanka-Vegi",{"name":105,"orcid":106},"Károly Barta","https:\u002F\u002Forcid.org\u002F0000-0001-9450-5277",{"name":108,"orcid":9},"Ahmed M. Ali",{"name":110,"orcid":9},"Khaldoun Abualhin",{"name":112,"orcid":113},"Djamil Al‐Halbouni","https:\u002F\u002Forcid.org\u002F0000-0003-2254-3914",{"name":115,"orcid":116},"Wouter Arnoud Dorigo","https:\u002F\u002Forcid.org\u002F0000-0001-8054-7572",{"name":118,"orcid":119},"György Sípos","https:\u002F\u002Forcid.org\u002F0000-0001-6224-2361",0,{"tldr":122,"method":123,"finding":124,"direction":125,"opportunity":126},"构建匈牙利全国六深度土壤水分-电磁代理数据库，支持根区水文地球物理监测。","利用117站多年土壤水分、质地与水力参数，经Topp和Archie模型推导电磁代","发现30-45 cm缓冲带水分与介电常数峰值，质地显著影响电磁响应解释。","农业遥感与作物表型","可扩展至无人机地球物理调查与多区域迁移校准，解决土壤质地导致的电磁信号歧义。","农业人工智能与决策模型","openalex","2026-09-24T23:30:36.864649Z",{"total":64,"page":21,"page_size":64,"items":131},[132,243,318,376,445,517],{"id":133,"title":134,"url":135,"summary":136,"summary_zh":137,"content":9,"source_name":138,"source_url":135,"published_at":139,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":140,"score_detail":141,"sources":145,"tags":147,"primary_topic":151,"secondary_topics":155,"classification":158,"search_phrases":214,"slug":217,"view_count":120,"doi":218,"paper":219,"created_at":242},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development","2026-10-20T00:00:00Z",66,{"impact":19,"substance":17,"depth":50,"authority":142,"freshness":143,"relevant":21,"comment":144},12,8,"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[146],{"name":138,"url":135},[148,149,28,30,150],"气候变化","盐碱地治理","土壤盐渍化",{"code":63,"slug":152,"name":153,"path":154,"parent_code":36},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[156,157],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":159},{"candidates":160,"agricultural_evidence":51},[161,167,171,176,183,190,195,202,208],{"code":63,"score":17,"supported":51,"title":162,"abstract":164,"tags":166},[163],"climate change",[148,165,67,163],"气候适应",[148],{"code":32,"score":50,"supported":51,"title":168,"abstract":169,"tags":170},[33],[54,33],[149],{"code":39,"score":13,"supported":51,"title":172,"abstract":173,"tags":175},[],[59,60,40,174],"evapotranspiration",[30],{"code":84,"score":177,"supported":51,"title":178,"abstract":179,"tags":182},7,[],[180,181],"遥感","remote sensing",[28],{"code":184,"score":64,"supported":51,"title":185,"abstract":186,"tags":189},"A8",[],[187,188],"粮食安全","food security",[],{"code":71,"score":64,"supported":51,"title":191,"abstract":192,"tags":194},[],[27,193],"precision agriculture",[],{"code":196,"score":64,"supported":51,"title":197,"abstract":198,"tags":201},"I4",[],[199,200],"地理信息","geographic information",[],{"code":203,"score":78,"supported":51,"title":204,"abstract":205,"tags":207},"F3",[],[206],"养分管理",[],{"code":209,"score":78,"supported":51,"title":210,"abstract":211,"tags":213},"J1",[],[212],"农业政策",[],[215,216],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":218,"openalex_id":220,"authors":221,"venue":138,"cited_by_count":120,"oa_url":135,"card":237,"direction":125,"ingested_from":128},"W7222999158",[222,224,226,228,230,232,235],{"name":223,"orcid":9},"Asifa Naz",{"name":225,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":227,"orcid":9},"Syed Asghar Hussain Shah",{"name":229,"orcid":9},"Shahrish Nawaz",{"name":231,"orcid":9},"Muhmmad Irfan",{"name":233,"orcid":234},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":236,"orcid":9},"Muhammad Nouman Khalid",{"tldr":238,"method":239,"finding":240,"direction":125,"opportunity":241},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":256,"primary_topic":260,"secondary_topics":261,"classification":268,"search_phrases":299,"slug":302,"view_count":85,"doi":303,"paper":304,"created_at":317},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":142,"substance":18,"depth":17,"authority":19,"freshness":143,"relevant":21,"comment":253},"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。",[255],{"name":249,"url":246},[257,26,258,29,259],"智慧农业","机器学习","蒸散发估算",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[262,263],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":77,"slug":264,"name":265,"path":266,"parent_code":267},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":44,"locked":14,"version":45,"method":46,"evidence":269},{"candidates":270,"agricultural_evidence":51},[271,277,281,286,291,295],{"code":39,"score":272,"supported":51,"title":273,"abstract":274,"tags":276},17,[174],[60,275,174],"蒸散发",[],{"code":32,"score":50,"supported":51,"title":278,"abstract":279,"tags":280},[33],[54,33],[29],{"code":77,"score":20,"supported":51,"title":282,"abstract":284,"tags":285},[283],"machine learning",[],[258],{"code":209,"score":78,"supported":51,"title":287,"abstract":288,"tags":290},[],[289],"农业规划",[],{"code":71,"score":21,"supported":14,"title":292,"abstract":293,"tags":294},[],[],[257],{"code":84,"score":21,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[26],[300,301],"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","10.3390\u002Fatmos17100941",{"doi":303,"openalex_id":305,"authors":306,"venue":249,"cited_by_count":120,"oa_url":246,"card":312,"direction":127,"ingested_from":128},"W7214588993",[307,310],{"name":308,"orcid":309},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":311,"orcid":9},"Devrim Alkaya",{"tldr":313,"method":314,"finding":315,"direction":127,"opportunity":316},"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。","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":319,"title":320,"url":321,"summary":322,"summary_zh":9,"content":9,"source_name":323,"source_url":9,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":329,"tags":331,"primary_topic":335,"secondary_topics":336,"classification":338,"search_phrases":362,"slug":365,"view_count":85,"doi":9,"paper":366,"created_at":375},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。","MDPI Agronomy 16(18):1853","2026-09-20T00:00:00Z",68,{"impact":142,"substance":17,"depth":327,"authority":19,"freshness":20,"relevant":21,"comment":328},16,"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[330],{"name":323,"url":321},[332,27,333,30,334],"决策支持系统","无人机遥感","土壤压实",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[337],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":339},{"candidates":340,"agricultural_evidence":51},[341,345,349,354,358],{"code":39,"score":142,"supported":51,"title":342,"abstract":343,"tags":344},[59],[59],[30],{"code":71,"score":142,"supported":51,"title":346,"abstract":347,"tags":348},[27],[27],[27],{"code":32,"score":350,"supported":51,"title":351,"abstract":352,"tags":353},11,[54],[54],[],{"code":84,"score":72,"supported":51,"title":355,"abstract":356,"tags":357},[],[180],[333],{"code":77,"score":21,"supported":14,"title":359,"abstract":360,"tags":361},[],[],[332],[363,364],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",{"doi":9,"openalex_id":9,"authors":367,"venue":9,"cited_by_count":120,"oa_url":9,"card":368,"direction":372,"ingested_from":374},[],{"tldr":369,"method":370,"finding":371,"direction":372,"opportunity":373},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","智慧农业 \u002F 农业物联网","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":388,"tags":390,"primary_topic":395,"secondary_topics":396,"classification":402,"search_phrases":422,"slug":425,"view_count":78,"doi":426,"paper":427,"created_at":444},3845,"Problems of soil degradation assessment using spectral indices from space images","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71433-1","This article presents an integrated method of spectral indices for assessing soil degradation using Sentinel-2 Earth Remote Sensing (ERS) data. Unlike approaches based on individual indices, the method combines indicators of vegetation, humidity, thermal state, and salinity, increasing the accuracy of mapping saline and degraded lands while minimizing errors caused by variations in natural conditions. The application of the method in the Tashkent region of Uzbekistan (41.1465°N, 69.3009°E) showed that the category of “extremely high” and “high” degradation accounts for 3.29% and 8.28% of pixels, “medium” and “low” for 9.4% and 9.52%, while the remaining 69.51% belong to other category (class) of land cover, confirming the concentration of degradation foci in lowland areas. Comparison with 2020 showed a notable increase in the share of critical degradation (growth from 10.46% to 11.57%), while the share of the “extremely high” category increased from 2.80% to 3.29%, indicating a shift toward more severe conditions that aligns with official regional salinization statistics. Analysis of meteorological data (2013–2025) revealed an inverse relationship between temperature and precipitation, confirming the climatic nature of salinization; four stages of salinization were identified—latent, progressive, severe, and complete degradation and the main components method (PCA) was applied to reduce the size of the data. These results correspond to the expansion of irrigated lands in Uzbekistan from 3.5 to 8.5 million hectares and the associated increase in groundwater levels. Consequently, this method is not the final means of delimiting degraded lands, but represents a quick, inexpensive preliminary check before field surveys: it marks potentially degraded areas for priority ground surveys, supplementing official surveys and government statistics rather than replacing them. The method is applicable in agriculture, land cadastre, and environmental monitoring as an auxiliary tool for climate adaptation and water resource management.","本文提出了一种利用Sentinel-2地球遥感（ERS）数据评估土壤退化的光谱指数集成方法。与基于单一指数的方法不同，该方法综合了植被、湿度、热状态和盐分指标，提高了盐碱化和退化土地制图的精度，同时最大限度地减少了自然条件变化引起的误差。该方法在乌兹别克斯坦塔什干地区（北纬41.1465°，东经69.3009°）的应用表明，“极高”和“高”退化类别分别占像元的3.29%和8.28%，“中等”和“低”分别占9.4%和9.52%，其余69.51%属于其他土地覆盖类别（等级），证实了退化热点集中在低地地区。与2020年相比，临界退化比例显著增加（从10.46%增至11.57%），而“极高”类别比例从2.80%增至3.29%，表明退化状况趋于严重，这与官方区域盐渍化统计数据一致。气象数据（2013—2025年）分析揭示了气温与降水之间的反比关系，证实了盐渍化的气候成因；识别出盐渍化的四个阶段——潜伏期、进展期、严重期和完全退化期，并应用主成分分析方法（PCA）对数据进行了降维处理。这些结果与乌兹别克斯坦灌溉面积从350万公顷扩展至850万公顷以及相关的地下水位上升相吻合。因此，该方法并非划定退化土地的最终手段，而是实地调查前一种快速、低成本的初步筛查：它标记出潜在退化区域以供优先开展地面调查，是对官方调查和政府统计的补充而非替代。该方法适用于农业、土地地籍和环境监测领域，可作为气候适应和水资源管理的辅助工具。","Scientific Reports","2026-09-29T00:00:00Z",79,{"impact":327,"substance":18,"depth":17,"authority":386,"freshness":20,"relevant":21,"comment":387},14,"基于Sentinel-2多光谱指数的土壤退化综合评估方法，方法新颖、数据翔实，对盐渍化遥感监测与耕地保护有参考价值。",[389],{"name":382,"url":379},[391,28,392,393,394],"Sentinel-2","土壤退化","盐渍化","土地退化评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[397,398],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":84,"slug":399,"name":400,"path":401,"parent_code":267},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":44,"locked":14,"version":45,"method":46,"evidence":403},{"candidates":404,"agricultural_evidence":51},[405,409,414,418],{"code":32,"score":50,"supported":51,"title":406,"abstract":407,"tags":408},[33],[54,33],[393],{"code":39,"score":20,"supported":51,"title":410,"abstract":411,"tags":413},[],[59,60,412],"water resource",[],{"code":84,"score":143,"supported":51,"title":415,"abstract":416,"tags":417},[],[180,181],[391,28],{"code":63,"score":78,"supported":51,"title":419,"abstract":420,"tags":421},[],[165],[],[423,424],"乌兹别克斯坦 塔什干 土壤盐渍化","Sentinel-2 光谱指数 土壤退化","乌兹别克斯坦塔什干土壤盐渍化-3845","10.1038\u002Fs41598-026-71433-1",{"doi":426,"openalex_id":428,"authors":429,"venue":382,"cited_by_count":120,"oa_url":438,"card":439,"direction":125,"ingested_from":128},"W7214801255",[430,433,436],{"name":431,"orcid":432},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":434,"orcid":435},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":437,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":440,"method":441,"finding":442,"direction":125,"opportunity":443},"提出融合多光谱指数的Sentinel-2土壤退化评估方法，在乌兹别克斯坦验证并识别盐渍化阶段。","Sentinel-2影像多指数融合（植被、湿度、热、盐分）结合PCA与气象数据分","退化集中于低地，2020年以来极重度退化占比由2.80%升至3.29%，与官方盐渍化统计一致。","可将该快速预筛方法与地面调查、机器学习结合，构建多区域可迁移的退化早期预警与制图框架。","2026-09-30T23:30:29.386707Z",{"id":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":9,"source_name":451,"source_url":448,"published_at":452,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":453,"score_detail":454,"sources":456,"tags":458,"primary_topic":462,"secondary_topics":463,"classification":466,"search_phrases":488,"slug":491,"view_count":78,"doi":492,"paper":493,"created_at":516},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":142,"substance":18,"depth":17,"authority":386,"freshness":143,"relevant":21,"comment":455},"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。",[457],{"name":451,"url":448},[459,180,29,460,461],"农业水资源","灌溉管理","华北平原",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[464,465],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":84,"slug":399,"name":400,"path":401,"parent_code":267},{"status":44,"locked":14,"version":45,"method":46,"evidence":467},{"candidates":468,"agricultural_evidence":51},[469,474,478,483],{"code":39,"score":470,"supported":51,"title":471,"abstract":472,"tags":473},19,[40],[59,60,40,412],[459,460],{"code":32,"score":50,"supported":51,"title":475,"abstract":476,"tags":477},[33],[54,33],[29],{"code":84,"score":142,"supported":51,"title":479,"abstract":480,"tags":482},[181],[481],"估产",[180],{"code":63,"score":20,"supported":51,"title":484,"abstract":485,"tags":487},[],[148,486,163],"农业气象",[],[489,490],"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":492,"openalex_id":494,"authors":495,"venue":451,"cited_by_count":120,"oa_url":510,"card":511,"direction":125,"ingested_from":128},"W7214549221",[496,499,501,503,505,507],{"name":497,"orcid":498},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":500,"orcid":9},"Yu Li",{"name":502,"orcid":9},"Jiawei Lv",{"name":504,"orcid":9},"Lei Wang",{"name":506,"orcid":9},"Zhiqing Cheng",{"name":508,"orcid":509},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":512,"method":513,"finding":514,"direction":125,"opportunity":515},"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":518,"title":519,"url":520,"summary":521,"summary_zh":522,"content":9,"source_name":523,"source_url":520,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":524,"score_detail":525,"sources":528,"tags":530,"primary_topic":533,"secondary_topics":534,"classification":536,"search_phrases":559,"slug":562,"view_count":85,"doi":563,"paper":564,"created_at":578},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",78,{"impact":17,"substance":526,"depth":272,"authority":386,"freshness":143,"relevant":21,"comment":527},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。",[529],{"name":523,"url":520},[257,26,531,532,334],"精准灌溉","土壤墒情监测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[535],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":537},{"candidates":538,"agricultural_evidence":51},[539,543,547,551,555],{"code":39,"score":17,"supported":51,"title":540,"abstract":541,"tags":542},[40],[59,60,40],[531],{"code":32,"score":386,"supported":51,"title":544,"abstract":545,"tags":546},[33],[54,33],[],{"code":71,"score":20,"supported":51,"title":548,"abstract":549,"tags":550},[193],[],[257],{"code":84,"score":177,"supported":51,"title":552,"abstract":553,"tags":554},[],[180,181],[26],{"code":77,"score":78,"supported":51,"title":556,"abstract":557,"tags":558},[],[81],[],[560,561],"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":563,"openalex_id":565,"authors":566,"venue":523,"cited_by_count":120,"oa_url":520,"card":573,"direction":125,"ingested_from":128},"W7213866178",[567,570],{"name":568,"orcid":569},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":571,"orcid":572},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":574,"method":575,"finding":576,"direction":372,"opportunity":577},"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。","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"]