[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5229":3,"related-5229":90},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":42,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":89},5229,"Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105017","Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation。Agricultural Systems",null,"Agricultural Systems","2026-10-17T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,19,14,9,1,"耦合HYDRUS-2D与AquaCrop模拟覆膜滴灌下水盐与作物生长，方法有创新、结论可靠，属农业信息化与节水灌溉领域值得精选的论文。",[24],{"name":9,"url":6},[26,27,28,29],"作物模型","节水灌溉","覆膜滴灌","水盐调控",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":43,"locked":13,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,54,59],{"code":31,"score":49,"supported":50,"title":51,"abstract":52,"tags":53},12,true,[32],[32],[27],{"code":38,"score":55,"supported":50,"title":56,"abstract":57,"tags":58},11,[39],[39],[],{"code":60,"score":21,"supported":13,"title":61,"abstract":62,"tags":63},"I2",[],[],[26],[65,66],"HYDRUS-2D AquaCrop 覆膜滴灌","土壤水盐 作物生长 模拟","HYDRUS-2DAquaCrop覆膜滴灌-5229",0,"10.1016\u002Fj.agsy.2026.105017",{"doi":69,"openalex_id":71,"authors":72,"venue":9,"cited_by_count":68,"oa_url":6,"card":8,"direction":8,"ingested_from":88},"W7222801463",[73,75,77,79,81,84,86],{"name":74,"orcid":8},"Yunfeng Li",{"name":76,"orcid":8},"Jun Wang",{"name":78,"orcid":8},"Hexiang Zheng",{"name":80,"orcid":8},"Delong Tian",{"name":82,"orcid":83},"Jie Ren","https:\u002F\u002Forcid.org\u002F0000-0001-8795-0372",{"name":85,"orcid":8},"Hao Liu",{"name":87,"orcid":8},"Jingsheng Sun","openalex","2026-10-18T23:30:04.248249Z",{"total":91,"page":21,"page_size":91,"items":92},6,[93,169,246,358,426,503],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":8,"source_name":9,"source_url":96,"published_at":99,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":110,"secondary_topics":111,"classification":113,"search_phrases":142,"slug":145,"view_count":68,"doi":146,"paper":147,"created_at":168},5541,"Year-round modeling of soil water-heat-salt transport and crop growth under film-mulched drip irrigation with freeze-thaw cycles","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105024","Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.","背景 季节性冻土区覆膜滴灌农田的土壤水热盐迁移与作物生长，受生育期内部分覆膜和滴灌以及休止期土壤冻融过程的影响。理解全年土壤水热盐动态及作物生长对于农业可持续用水至关重要。目的 然而，现有全年模型在生育期多采用一维（1D）方法，忽略了部分覆膜滴灌条件下的水平盐分迁移，导致对盐分淋洗效率的高估。这一局限可通过开发兼顾生育期与休止期不同动态特征的全年模型加以解决。方法 本研究将前期开发的基于HYDRUS-2D的作物与水分模型（CAWM-H2D）与考虑土壤冻融过程的SHAW模型相耦合，构建了全年耦合模型CAWM-S。CAWM-S的主要特点包括：详细刻画生育期二维土壤水热盐迁移、作物生长与产量形成以及覆膜土壤表面能量平衡，同时描述休止期冻融过程引起的土壤水盐再分布。模型采用南疆季节性冻土区覆膜滴灌棉田连续两个全年周期的监测数据，在不同灌溉水矿化度水平下进行率定与验证。该验证按处理进行，且仅限于同一站点和两年时段；仍需在其他站点、年份、作物及灌溉系统下开展独立验证。结果与结论 在两个不同矿化度水平的年份中，土壤水分、温度、盐分、作物生长指标及产量的模拟结果与实测数据总体吻合，处理平均R²为0.48~0.98，NSE为0.42~0.97。此外，分析了不同灌水量与矿化度组合对土壤脱盐率、蒸散及其组分、作物产量和水分生产率的影响，揭示了覆膜滴灌条件下最优灌水量与灌溉水矿化度之间的二次关系。验证结果表明，CAWM-S能够有效量化土壤水热盐耦合动态与作物生长，并预测","2026-10-21T00:00:00Z",79,{"impact":102,"substance":17,"depth":16,"authority":19,"freshness":20,"relevant":21,"comment":103},16,"构建了覆盖冻融期的全年水热盐与作物生长耦合模型，方法有新意、验证数据扎实，对盐碱地滴灌节水有实用价值，但属单点两年验证的学术成果，产业影响有限。",[105],{"name":9,"url":96},[107,108,27,28,109],"棉花","盐碱地治理","土壤水盐运移",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[112],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":114},{"candidates":115,"agricultural_evidence":50},[116,122,128,135],{"code":31,"score":16,"supported":50,"title":117,"abstract":118,"tags":121},[32],[119,32,120],"灌溉","evapotranspiration",[27],{"code":38,"score":123,"supported":50,"title":124,"abstract":125,"tags":127},15,[39],[126,39],"土壤",[108],{"code":129,"score":130,"supported":50,"title":131,"abstract":132,"tags":134},"A6",4,[],[133],"cotton",[107],{"code":136,"score":137,"supported":50,"title":138,"abstract":139,"tags":141},"H8",3,[],[140],"sustainable agriculture",[],[143,144],"南疆 覆膜滴灌 棉花","CAWM-S 模型 冻融","南疆覆膜滴灌棉花-5541","10.1016\u002Fj.agsy.2026.105024",{"doi":146,"openalex_id":148,"authors":149,"venue":9,"cited_by_count":68,"oa_url":96,"card":162,"direction":166,"ingested_from":88},"W7223120642",[150,153,155,158,160],{"name":151,"orcid":152},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":154,"orcid":8},"Tongkai Guo",{"name":156,"orcid":157},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":159,"orcid":8},"Wenping Bi",{"name":161,"orcid":8},"Xiaomin Mao",{"tldr":163,"method":164,"finding":165,"direction":166,"opportunity":167},"构建全年耦合模型CAWM-S，模拟覆膜滴灌棉田水热盐运移与作物生长。","将HYDRUS-2D的CAWM-H2D与冻融模型SHAW耦合，用南疆两年监测数据","模型精度较高（R²0.48-0.98），揭示最优灌溉量与灌水盐度呈二次关系。","农业遥感与作物表型","可扩展至多站点、多作物与不同灌溉制度的独立验证，并耦合遥感数据实现区域尺度模拟。","2026-10-22T11:20:12.238655Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":8,"source_name":175,"source_url":172,"published_at":176,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":177,"score_detail":178,"sources":183,"tags":185,"primary_topic":190,"secondary_topics":195,"classification":198,"search_phrases":227,"slug":230,"view_count":21,"doi":231,"paper":232,"created_at":245},2033,"Regenerative benefits of legume–wheat intercropping systems on soil quality and resource-use efficiency under deficit irrigation in Egypt","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1923296","Background Soil degradation and water scarcity are major constraints to sustainable cereal production in arid and semi-arid regions. Legume–wheat intercropping may simultaneously improve soil quality and resource-use efficiency, but its regenerative performance under deficit irrigation remains insufficiently quantified. Methods A two-year field experiment was conducted during 2021\u002F22 and 2022\u002F23 in El-Minia Governorate, Middle Egypt, using a split-plot design with three replications. Three irrigation regimes—100%, 85%, and 70% of crop evapotranspiration (ETc)—were evaluated in combination with sole wheat and wheat intercropped with faba bean, pea, or fahl clover. Soil organic matter, available N, P, and K, soil pH, Soil Quality Index (SQI), crop yield, land equivalent ratio (LER), water equivalent ratio (WER), and change in water use (DWU) were assessed. Results All legume–wheat intercropping systems significantly improved soil organic matter and available macronutrients relative to sole wheat. Faba bean–wheat generally produced the greatest improvements in soil properties and SQI. The beneficial effects of intercropping persisted under severe deficit irrigation (70% ETc). Pea–wheat achieved the highest total LER (1.39) and WER (1.26), together with the highest wheat grain yield and a DWU of -20.48%. Conclusion Legume–wheat intercropping enhanced soil quality, land-use efficiency, and water-use efficiency under deficit irrigation. The results demonstrate that integrating legumes with wheat can provide a practical regenerative strategy for improving productivity and resilience of wheat-based systems in water-limited environments.","背景 土壤退化与水资源短缺是干旱和半干旱地区可持续谷物生产的主要限制因素。豆科作物-小麦间作可能同时改善土壤质量和资源利用效率，但其在亏缺灌溉条件下的再生性能仍缺乏充分的定量评估。方法 于2021\u002F22和2022\u002F23年度在埃及中部明亚省开展了一项为期两年的田间试验，采用裂区设计，设三次重复。评估了三种灌溉制度——分别为作物蒸散量（ETc）的100%、85%和70%——与单作小麦及小麦分别与蚕豆、豌豆或埃及三叶草间作的组合。测定了土壤有机质、速效氮、磷、钾、土壤pH、土壤质量指数（SQI）、作物产量、土地当量比（LER）、水当量比（WER）以及水分利用变化量（DWU）。结果 与单作小麦相比，所有豆科作物-小麦间作系统均显著提高了土壤有机质和速效大量元素含量。蚕豆-小麦间作通常在改善土壤性质和SQI方面效果最佳。间作的有益效应在严重亏缺灌溉（70% ETc）条件下依然持续。豌豆-小麦间作实现了最高的总LER（1.39）和WER（1.26），同时小麦籽粒产量最高，DWU为-20.48%。结论 豆科作物-小麦间作在亏缺灌溉条件下提高了土壤质量、土地利用效率和水分利用效率。结果表明，将豆科作物与小麦整合可为水资源受限环境中提高小麦种植系统的生产力和韧性提供一种实用的再生策略。","Frontiers in Sustainable Food Systems","2026-09-09T00:00:00Z",76,{"impact":102,"substance":17,"depth":179,"authority":180,"freshness":181,"relevant":21,"comment":182},17,13,8,"两年田间试验量化了亏缺灌溉下豆科-小麦间作的土壤改良与资源利用效率提升，数据扎实、结论可靠，对干旱半干旱区小麦可持续生产有参考价值。",[184],{"name":175,"url":172},[186,187,27,188,189],"小麦","土壤健康","间作套种","豆科绿肥",{"code":191,"slug":192,"name":186,"path":193,"parent_code":194},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[196,197],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":199},{"candidates":200,"agricultural_evidence":50},[201,206,210,214,221],{"code":31,"score":16,"supported":50,"title":202,"abstract":203,"tags":205},[32],[119,204,32,120],"水资源",[27],{"code":191,"score":123,"supported":50,"title":207,"abstract":208,"tags":209},[192],[186,192],[186],{"code":38,"score":123,"supported":50,"title":211,"abstract":212,"tags":213},[39],[126,39],[187],{"code":215,"score":19,"supported":50,"title":216,"abstract":218,"tags":220},"A7",[217],"intercropping",[219,217],"间作",[],{"code":222,"score":137,"supported":50,"title":223,"abstract":224,"tags":226},"H7",[],[225],"干旱",[],[228,229],"土壤健康 节水灌溉 豆科绿肥 间作套种","土壤健康 节水灌溉","土壤健康节水灌溉豆科绿肥间作套种-2033","10.3389\u002Ffsufs.2026.1923296",{"doi":231,"openalex_id":233,"authors":234,"venue":175,"cited_by_count":68,"oa_url":172,"card":239,"direction":243,"ingested_from":88},"W7212074132",[235,237],{"name":236,"orcid":8},"S.A.H. Ouda",{"name":238,"orcid":8},"Abd El-Hafeez Zohry",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"埃及两年试验表明，豆科-小麦间作在亏缺灌溉下仍能提升土壤质量与资源利用效率。","裂区试验，3种灌溉水平与4种种植模式，测SQI、LER、WER等指标。","蚕豆-小麦改善土壤最佳，豌豆-小麦LER和WER最高，间作效益在70%ETc下仍存在。","农业绿色发展与碳","可探究间作系统在不同气候与土壤类型下的长期固碳减排潜力及微生物机制。","2026-09-10T23:30:07.672063Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":8,"source_name":252,"source_url":249,"published_at":253,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":254,"score_detail":255,"sources":257,"tags":259,"primary_topic":264,"secondary_topics":268,"classification":271,"search_phrases":329,"slug":332,"view_count":68,"doi":333,"paper":334,"created_at":357},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":180,"substance":16,"depth":123,"authority":49,"freshness":181,"relevant":21,"comment":256},"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。",[258],{"name":252,"url":249},[260,108,261,262,263],"气候变化","遥感监测","智慧灌溉","土壤盐渍化",{"code":222,"slug":265,"name":266,"path":267,"parent_code":35},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[269,270],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":43,"locked":13,"version":44,"method":45,"evidence":272},{"candidates":273,"agricultural_evidence":50},[274,280,284,288,296,303,310,317,323],{"code":222,"score":16,"supported":50,"title":275,"abstract":277,"tags":279},[276],"climate change",[260,278,225,276],"气候适应",[260],{"code":38,"score":123,"supported":50,"title":281,"abstract":282,"tags":283},[39],[126,39],[108],{"code":31,"score":12,"supported":50,"title":285,"abstract":286,"tags":287},[],[119,204,32,120],[262],{"code":289,"score":290,"supported":50,"title":291,"abstract":292,"tags":295},"I3",7,[],[293,294],"遥感","remote sensing",[261],{"code":297,"score":91,"supported":50,"title":298,"abstract":299,"tags":302},"A8",[],[300,301],"粮食安全","food security",[],{"code":304,"score":91,"supported":50,"title":305,"abstract":306,"tags":309},"I1",[],[307,308],"精准农业","precision agriculture",[],{"code":311,"score":91,"supported":50,"title":312,"abstract":313,"tags":316},"I4",[],[314,315],"地理信息","geographic information",[],{"code":318,"score":137,"supported":50,"title":319,"abstract":320,"tags":322},"F3",[],[321],"养分管理",[],{"code":324,"score":137,"supported":50,"title":325,"abstract":326,"tags":328},"J1",[],[327],"农业政策",[],[330,331],"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":333,"openalex_id":335,"authors":336,"venue":252,"cited_by_count":68,"oa_url":249,"card":352,"direction":166,"ingested_from":88},"W7222999158",[337,339,341,343,345,347,350],{"name":338,"orcid":8},"Asifa Naz",{"name":340,"orcid":8},"Muhammad Abu Baker Siddiq",{"name":342,"orcid":8},"Syed Asghar Hussain Shah",{"name":344,"orcid":8},"Shahrish Nawaz",{"name":346,"orcid":8},"Muhmmad Irfan",{"name":348,"orcid":349},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":351,"orcid":8},"Muhammad Nouman Khalid",{"tldr":353,"method":354,"finding":355,"direction":166,"opportunity":356},"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":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":8,"source_name":364,"source_url":361,"published_at":365,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":366,"sources":368,"tags":370,"primary_topic":375,"secondary_topics":376,"classification":383,"search_phrases":403,"slug":406,"view_count":137,"doi":407,"paper":408,"created_at":425},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",{"impact":102,"substance":17,"depth":16,"authority":19,"freshness":20,"relevant":21,"comment":367},"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。",[369],{"name":364,"url":361},[371,261,372,373,374],"Sentinel-2","土壤退化","盐渍化","土地退化评估",{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},[377,378],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":289,"slug":379,"name":380,"path":381,"parent_code":382},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":43,"locked":13,"version":44,"method":45,"evidence":384},{"candidates":385,"agricultural_evidence":50},[386,390,395,399],{"code":38,"score":123,"supported":50,"title":387,"abstract":388,"tags":389},[39],[126,39],[373],{"code":31,"score":20,"supported":50,"title":391,"abstract":392,"tags":394},[],[119,204,393],"water resource",[],{"code":289,"score":181,"supported":50,"title":396,"abstract":397,"tags":398},[],[293,294],[371,261],{"code":222,"score":137,"supported":50,"title":400,"abstract":401,"tags":402},[],[278],[],[404,405],"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":407,"openalex_id":409,"authors":410,"venue":364,"cited_by_count":68,"oa_url":419,"card":420,"direction":166,"ingested_from":88},"W7214801255",[411,414,417],{"name":412,"orcid":413},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":415,"orcid":416},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":418,"orcid":8},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":421,"method":422,"finding":423,"direction":166,"opportunity":424},"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":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":8,"source_name":432,"source_url":429,"published_at":433,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":434,"score_detail":435,"sources":437,"tags":439,"primary_topic":445,"secondary_topics":446,"classification":452,"search_phrases":482,"slug":485,"view_count":486,"doi":487,"paper":488,"created_at":502},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":49,"substance":17,"depth":16,"authority":180,"freshness":181,"relevant":21,"comment":436},"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。",[438],{"name":432,"url":429},[440,441,442,443,444],"智慧农业","农业遥感","机器学习","土壤墒情","蒸散发估算",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[447,448],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"code":60,"slug":449,"name":450,"path":451,"parent_code":382},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":43,"locked":13,"version":44,"method":45,"evidence":453},{"candidates":454,"agricultural_evidence":50},[455,460,464,469,474,478],{"code":31,"score":179,"supported":50,"title":456,"abstract":457,"tags":459},[120],[204,458,120],"蒸散发",[],{"code":38,"score":123,"supported":50,"title":461,"abstract":462,"tags":463},[39],[126,39],[443],{"code":60,"score":20,"supported":50,"title":465,"abstract":467,"tags":468},[466],"machine learning",[],[442],{"code":324,"score":137,"supported":50,"title":470,"abstract":471,"tags":473},[],[472],"农业规划",[],{"code":304,"score":21,"supported":13,"title":475,"abstract":476,"tags":477},[],[],[440],{"code":289,"score":21,"supported":13,"title":479,"abstract":480,"tags":481},[],[],[441],[483,484],"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":487,"openalex_id":489,"authors":490,"venue":432,"cited_by_count":68,"oa_url":429,"card":496,"direction":500,"ingested_from":88},"W7214588993",[491,494],{"name":492,"orcid":493},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":495,"orcid":8},"Devrim Alkaya",{"tldr":497,"method":498,"finding":499,"direction":500,"opportunity":501},"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":504,"title":505,"url":506,"summary":507,"summary_zh":508,"content":8,"source_name":509,"source_url":506,"published_at":510,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":511,"score_detail":512,"sources":514,"tags":516,"primary_topic":520,"secondary_topics":521,"classification":524,"search_phrases":545,"slug":548,"view_count":137,"doi":549,"paper":550,"created_at":573},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":49,"substance":17,"depth":16,"authority":19,"freshness":181,"relevant":21,"comment":513},"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。",[515],{"name":509,"url":506},[517,293,443,518,519],"农业水资源","灌溉管理","华北平原",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[522,523],{"code":38,"slug":39,"name":40,"path":41,"parent_code":35},{"code":289,"slug":379,"name":380,"path":381,"parent_code":382},{"status":43,"locked":13,"version":44,"method":45,"evidence":525},{"candidates":526,"agricultural_evidence":50},[527,531,535,540],{"code":31,"score":18,"supported":50,"title":528,"abstract":529,"tags":530},[32],[119,204,32,393],[517,518],{"code":38,"score":123,"supported":50,"title":532,"abstract":533,"tags":534},[39],[126,39],[443],{"code":289,"score":49,"supported":50,"title":536,"abstract":537,"tags":539},[294],[538],"估产",[293],{"code":222,"score":20,"supported":50,"title":541,"abstract":542,"tags":544},[],[260,543,276],"农业气象",[],[546,547],"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":549,"openalex_id":551,"authors":552,"venue":509,"cited_by_count":68,"oa_url":567,"card":568,"direction":166,"ingested_from":88},"W7214549221",[553,556,558,560,562,564],{"name":554,"orcid":555},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":557,"orcid":8},"Yu Li",{"name":559,"orcid":8},"Jiawei Lv",{"name":561,"orcid":8},"Lei Wang",{"name":563,"orcid":8},"Zhiqing Cheng",{"name":565,"orcid":566},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":569,"method":570,"finding":571,"direction":166,"opportunity":572},"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"]