[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4904":3,"related-4904":90},{"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":39,"search_phrases":71,"slug":74,"view_count":22,"doi":75,"paper":76,"created_at":89},4904,"FUZZY MODELING OF SOIL SALINITY BASED ON SPECTRAL INDICATORS DERIVED FROM DIGITAL IMAGERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23331360","This study addresses the assessment of soil salinity levels in irrigated agricultural lands using digital remote sensing data. The main objective of the research is to develop a mathematical model for the quantitative assessment of soil salinity by processing spectral information contained in digital imagery and remote-sensing indicators associated with salinity on the basis of fuzzy set theory. The proposed approach considers the use of membership functions to account for the natural and transitional nature of salinity boundaries, the fuzzy evaluation of different spectral indicators, and their aggregation into a single integral salinity indicator. Based on the model, an algorithm for spatial identification and digital mapping of salinity levels is envisaged. The research results are aimed at establishing a scientific and methodological basis for rapid and spatially differentiated assessment of the salinity status of irrigated agricultural lands, supplementing existing field studies with remote sensing data, and automating salinity monitoring.","本研究旨在利用数字遥感数据评估灌溉农田的土壤盐渍化程度。研究的主要目标是基于模糊集理论，通过处理数字影像中包含的光谱信息以及与盐渍化相关的遥感指标，建立土壤盐渍化定量评估的数学模型。所提出的方法考虑使用隶属度函数以反映盐渍化边界的自然性和过渡性，对不同光谱指标进行模糊评价，并将其聚合为单一的综合盐渍化指标。基于该模型，设计了盐渍化程度空间识别与数字制图的算法。研究成果旨在为灌溉农田盐渍化状况的快速、空间差异化评估奠定科学和方法论基础，以遥感数据补充现有田间研究，并实现盐渍化监测的自动化。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-12T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"基于数字影像光谱指标与模糊集理论构建土壤盐渍化定量评估模型，方法有创新但属细分领域研究，具备一定参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","遥感","盐碱地治理","土壤盐渍化","模糊模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,66],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},15,true,[34],[50,34],"土壤",[29],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"I3",7,[],[28,57],"remote sensing",[28],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":65},"H3",3,[],[64],"灌溉",[],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"I1",[],[],[27],[72,73],"土壤盐渍化 遥感 光谱指标","灌溉农田 盐分监测 数字制图","土壤盐渍化遥感光谱指标-4904","10.5281\u002Fzenodo.23331360",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7221692005",[79],{"name":80,"orcid":9},"Bunyod Abduvakilovich Abduvakilov",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"基于数字影像光谱指标，用模糊集理论构建土壤盐分定量评估与制图模型。","模糊隶属度函数、光谱指标模糊评价与聚合、遥感影像处理。","模糊模型可处理盐分边界过渡性，实现灌区盐分快速空间差异化评估与制图。","农业遥感与作物表型","可结合多时相遥感与机器学习，提升模糊盐分模型在不同区域和作物下的泛化能力。","openalex","2026-10-14T23:30:30.645521Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,207,318,398,472,549],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":105,"tags":107,"primary_topic":111,"secondary_topics":116,"classification":122,"search_phrases":178,"slug":181,"view_count":81,"doi":182,"paper":183,"created_at":206},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":20,"substance":103,"depth":46,"authority":17,"freshness":21,"relevant":22,"comment":104},18,"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。",[106],{"name":99,"url":96},[108,29,109,110,30],"气候变化","遥感监测","智慧灌溉",{"code":112,"slug":113,"name":114,"path":115,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[117,118],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":60,"slug":119,"name":120,"path":121,"parent_code":37},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":123},{"candidates":124,"agricultural_evidence":47},[125,132,136,142,146,153,159,166,172],{"code":112,"score":103,"supported":47,"title":126,"abstract":128,"tags":131},[127],"climate change",[108,129,130,127],"气候适应","干旱",[108],{"code":33,"score":46,"supported":47,"title":133,"abstract":134,"tags":135},[34],[50,34],[29],{"code":60,"score":13,"supported":47,"title":137,"abstract":138,"tags":141},[],[64,139,119,140],"水资源","evapotranspiration",[110],{"code":53,"score":54,"supported":47,"title":143,"abstract":144,"tags":145},[],[28,57],[109],{"code":147,"score":91,"supported":47,"title":148,"abstract":149,"tags":152},"A8",[],[150,151],"粮食安全","food security",[],{"code":67,"score":91,"supported":47,"title":154,"abstract":155,"tags":158},[],[156,157],"精准农业","precision agriculture",[],{"code":160,"score":91,"supported":47,"title":161,"abstract":162,"tags":165},"I4",[],[163,164],"地理信息","geographic information",[],{"code":167,"score":61,"supported":47,"title":168,"abstract":169,"tags":171},"F3",[],[170],"养分管理",[],{"code":173,"score":61,"supported":47,"title":174,"abstract":175,"tags":177},"J1",[],[176],"农业政策",[],[179,180],"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":182,"openalex_id":184,"authors":185,"venue":99,"cited_by_count":81,"oa_url":96,"card":201,"direction":86,"ingested_from":88},"W7222999158",[186,188,190,192,194,196,199],{"name":187,"orcid":9},"Asifa Naz",{"name":189,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":191,"orcid":9},"Syed Asghar Hussain Shah",{"name":193,"orcid":9},"Shahrish Nawaz",{"name":195,"orcid":9},"Muhmmad Irfan",{"name":197,"orcid":198},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":200,"orcid":9},"Muhammad Nouman Khalid",{"tldr":202,"method":203,"finding":204,"direction":86,"opportunity":205},"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":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":219,"tags":221,"primary_topic":225,"secondary_topics":226,"classification":232,"search_phrases":270,"slug":273,"view_count":81,"doi":274,"paper":275,"created_at":317},5102,"Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42729-026-03629-3","Abstract Soils are fundamental to support ecosystem functions. Therefore, understanding soil organic carbon stock (SCS) as a strategic environmental component for climate regulation, sustainable agricultural practices, and maximizing fertilizer application is essential. This study aimed to predict and map the SCS at Palmital Farm, Minas Gerais state, using land use, parent materials, soil classes, proximal and remote sensing data as predictor variables. A total of 76 soil samples were collected in the surface (A) and subsurface (B) horizons using a regular grid design (200 × 200 m). Carbon content was determined using the Walkley–Black method. Prediction models were created via Random Forest algorithm and validated through leave-one-out cross-validation. Environmental and anthropogenic influences on SCS were analyzed statistically and spatially. The highest mean SCS values were found at the surface horizon under native vegetation and pasture areas (~ 33 Mg ha⁻¹), due to continuous organic matter deposition and lack of disturbance. Charnockite-derived soils showed higher SCS (31.24 Mg ha⁻¹, surface). Among soil classes, Argissolos stood out reaching the highest SCS (35.86 Mg ha⁻¹). Remote sensing data and terrain attributes yielded the best SCS prediction (R²=0.43). The main factors influencing SCS were land use\u002Fmanagement and soil texture. Also, strong correlations were observed between vegetation indices, some terrain attributes and SCS. This approach, combining multiple variables, indicated a moderate potential to achieve accurate SCS predictions using these data, demonstrating the high variability and complexity of SCS spatial variation in detailed-scale mapping.","摘要 土壤是支撑生态系统功能的基础。因此，理解土壤有机碳储量（SCS）作为气候调节、可持续农业实践和最大化肥料施用的战略性环境组成部分至关重要。本研究旨在利用土地利用、成土母质、土壤类别、近端和遥感数据作为预测变量，对米纳斯吉拉斯州Palmital农场的SCS进行预测和制图。采用规则网格设计（200 × 200 m），在表层（A）和亚表层（B）共采集76个土壤样品。碳含量采用Walkley–Black法测定。通过随机森林算法建立预测模型，并采用留一交叉验证进行验证。对环境和人为因素对SCS的影响进行了统计和空间分析。表层中原生植被和牧草地利用方式下的SCS均值最高（约33 Mg ha⁻¹），这归因于有机质的持续沉积和缺乏扰动。源自紫苏花岗岩的土壤表现出较高的SCS（31.24 Mg ha⁻¹，表层）。在土壤类别中，Argissolos表现突出，达到最高SCS（35.86 Mg ha⁻¹）。遥感数据和地形属性获得了最佳的SCS预测效果（R²=0.43）。影响SCS的主要因素为土地利用\u002F管理和土壤质地。此外，植被指数、部分地形属性与SCS之间观察到较强的相关性。这种结合多变量的方法表明，利用这些数据实现准确SCS预测具有中等潜力，同时也揭示了详细尺度制图中SCS空间变异的高变异性和复杂性。","Journal of soil science and plant nutrition","2026-10-14T00:00:00Z",67,{"impact":21,"substance":18,"depth":19,"authority":20,"freshness":217,"relevant":22,"comment":218},9,"该研究结合近地与遥感数据及随机森林算法预测土壤有机碳储量，方法新颖、数据扎实，对农业信息化与精准农业有参考价值，但属区域性案例研究，影响范围有限。",[220],{"name":213,"url":210},[27,222,223,28,224],"随机森林","土壤有机碳","土壤制图",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[227],{"code":53,"slug":228,"name":229,"path":230,"parent_code":231},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":233},{"candidates":234,"agricultural_evidence":47},[235,240,244,250,256,261,265],{"code":33,"score":236,"supported":47,"title":237,"abstract":238,"tags":239},16,[34],[50,34],[224,223],{"code":53,"score":46,"supported":47,"title":241,"abstract":242,"tags":243},[57],[28,57],[28],{"code":167,"score":91,"supported":47,"title":245,"abstract":246,"tags":249},[],[247,248],"肥料","fertilizer",[],{"code":251,"score":61,"supported":47,"title":252,"abstract":253,"tags":255},"H8",[],[254],"可持续农业",[],{"code":160,"score":61,"supported":47,"title":257,"abstract":258,"tags":260},[],[259],"空间分析",[],{"code":67,"score":22,"supported":14,"title":262,"abstract":263,"tags":264},[],[],[27],{"code":266,"score":22,"supported":14,"title":267,"abstract":268,"tags":269},"I2",[],[],[222],[271,272],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102","10.1007\u002Fs42729-026-03629-3",{"doi":274,"openalex_id":276,"authors":277,"venue":213,"cited_by_count":81,"oa_url":311,"card":312,"direction":86,"ingested_from":88},"W7222335269",[278,281,284,287,290,293,296,299,302,305,308],{"name":279,"orcid":280},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":282,"orcid":283},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":285,"orcid":286},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":288,"orcid":289},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":291,"orcid":292},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":294,"orcid":295},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":297,"orcid":298},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":300,"orcid":301},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":303,"orcid":304},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":306,"orcid":307},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":309,"orcid":310},"Sérgio Henrique Godinho Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2750-5976","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs42729-026-03629-3.pdf",{"tldr":313,"method":314,"finding":315,"direction":86,"opportunity":316},"结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。","76个样点、Walkley-Black法测碳，随机森林建模，留一交叉验证。","遥感与地形因子预测最佳（R²=0.43），土地利用和土壤质地主导碳储量空间变异。","可探索多源遥感与深度学习方法提升小尺度土壤碳制图精度，并耦合管理措施情景。","2026-10-16T23:30:31.433840Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":9,"source_name":324,"source_url":321,"published_at":325,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":335,"classification":341,"search_phrases":382,"slug":385,"view_count":386,"doi":9,"paper":387,"created_at":397},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":236,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":328},"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[330],{"name":324,"url":321},[332,156,28,29,333],"智慧农业","土壤监测",{"code":53,"slug":228,"name":229,"path":230,"parent_code":231},[336,340],{"code":160,"slug":337,"name":338,"path":339,"parent_code":231},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":342},{"candidates":343,"agricultural_evidence":47},[344,349,354,358,362,368,372,376],{"code":53,"score":103,"supported":47,"title":345,"abstract":346,"tags":348},[57],[28,57,347],"multispectral",[28],{"code":160,"score":19,"supported":47,"title":350,"abstract":352,"tags":353},[351],"GIS",[163,259,351,164],[],{"code":33,"score":46,"supported":47,"title":355,"abstract":356,"tags":357},[34],[50,34],[29],{"code":67,"score":13,"supported":47,"title":359,"abstract":360,"tags":361},[157],[],[332,156],{"code":266,"score":91,"supported":47,"title":363,"abstract":364,"tags":367},[],[365,366],"机器学习","machine learning",[],{"code":60,"score":61,"supported":47,"title":369,"abstract":370,"tags":371},[],[64],[],{"code":112,"score":61,"supported":47,"title":373,"abstract":374,"tags":375},[],[130],[],{"code":377,"score":61,"supported":47,"title":378,"abstract":379,"tags":381},"I7",[],[380],"methodological framework",[],[383,384],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":388,"authors":389,"venue":324,"cited_by_count":81,"oa_url":321,"card":392,"direction":86,"ingested_from":88},"W7221843179",[390],{"name":391,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":393,"method":394,"finding":395,"direction":86,"opportunity":396},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":405,"score_detail":406,"sources":409,"tags":411,"primary_topic":414,"secondary_topics":415,"classification":421,"search_phrases":446,"slug":449,"view_count":81,"doi":450,"paper":451,"created_at":471},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land",72,{"impact":17,"substance":407,"depth":19,"authority":20,"freshness":217,"relevant":22,"comment":408},21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[410],{"name":404,"url":401},[332,412,28,30,413],"农业人工智能","新疆农业",{"code":53,"slug":228,"name":229,"path":230,"parent_code":231},[416,417],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":266,"slug":418,"name":419,"path":420,"parent_code":231},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":422},{"candidates":423,"agricultural_evidence":47},[424,428,433,438,442],{"code":53,"score":46,"supported":47,"title":425,"abstract":426,"tags":427},[57],[28,57],[28],{"code":33,"score":429,"supported":47,"title":430,"abstract":431,"tags":432},14,[34],[50,34],[],{"code":266,"score":217,"supported":47,"title":434,"abstract":436,"tags":437},[435],"deep learning",[],[412],{"code":112,"score":61,"supported":47,"title":439,"abstract":440,"tags":441},[],[130],[],{"code":67,"score":22,"supported":14,"title":443,"abstract":444,"tags":445},[],[],[332],[447,448],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":450,"openalex_id":452,"authors":453,"venue":404,"cited_by_count":81,"oa_url":401,"card":466,"direction":86,"ingested_from":88},"W7221816183",[454,456,459,461,463],{"name":455,"orcid":9},"Mingjie Qian",{"name":457,"orcid":458},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":460,"orcid":9},"Peng Wang",{"name":462,"orcid":9},"Hangyuan Liu",{"name":464,"orcid":465},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":467,"method":468,"finding":469,"direction":86,"opportunity":470},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":479,"score_detail":480,"sources":483,"tags":485,"primary_topic":489,"secondary_topics":490,"classification":491,"search_phrases":511,"slug":514,"view_count":81,"doi":515,"paper":516,"created_at":548},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":236,"substance":481,"depth":103,"authority":429,"freshness":217,"relevant":22,"comment":482},22,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[484],{"name":478,"url":475},[109,486,30,487,488],"莫桑比克","土地覆盖变化","盐分反演",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":492},{"candidates":493,"agricultural_evidence":47},[494,498,502,506],{"code":33,"score":429,"supported":47,"title":495,"abstract":496,"tags":497},[34],[50,34],[],{"code":53,"score":54,"supported":47,"title":499,"abstract":500,"tags":501},[],[28,57],[109],{"code":112,"score":91,"supported":47,"title":503,"abstract":504,"tags":505},[],[129,130],[],{"code":507,"score":22,"supported":14,"title":508,"abstract":509,"tags":510},"H6",[],[],[487],[512,513],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":515,"openalex_id":517,"authors":518,"venue":478,"cited_by_count":81,"oa_url":475,"card":543,"direction":86,"ingested_from":88},"W7223069171",[519,521,523,525,528,530,533,536,538,540],{"name":520,"orcid":9},"Yue Li",{"name":522,"orcid":9},"Mengfei Shi",{"name":524,"orcid":9},"Fei Zhang",{"name":526,"orcid":527},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":529,"orcid":9},"Gowhar Meraj",{"name":531,"orcid":532},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":534,"orcid":535},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":537,"orcid":9},"Yuanfang Chai",{"name":539,"orcid":9},"Hongtao Xu",{"name":541,"orcid":542},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":544,"method":545,"finding":546,"direction":86,"opportunity":547},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":550,"title":551,"url":552,"summary":553,"summary_zh":554,"content":9,"source_name":555,"source_url":552,"published_at":556,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":479,"score_detail":557,"sources":559,"tags":561,"primary_topic":566,"secondary_topics":567,"classification":569,"search_phrases":592,"slug":595,"view_count":81,"doi":596,"paper":597,"created_at":617},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","Agricultural Systems","2026-10-21T00:00:00Z",{"impact":236,"substance":481,"depth":103,"authority":429,"freshness":217,"relevant":22,"comment":558},"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。",[560],{"name":555,"url":552},[562,29,563,564,565],"棉花","节水灌溉","覆膜滴灌","土壤水盐运移",{"code":60,"slug":119,"name":120,"path":121,"parent_code":37},[568],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":570},{"candidates":571,"agricultural_evidence":47},[572,576,580,587],{"code":60,"score":103,"supported":47,"title":573,"abstract":574,"tags":575},[119],[64,119,140],[563],{"code":33,"score":46,"supported":47,"title":577,"abstract":578,"tags":579},[34],[50,34],[29],{"code":581,"score":582,"supported":47,"title":583,"abstract":584,"tags":586},"A6",4,[],[585],"cotton",[562],{"code":251,"score":61,"supported":47,"title":588,"abstract":589,"tags":591},[],[590],"sustainable agriculture",[],[593,594],"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":596,"openalex_id":598,"authors":599,"venue":555,"cited_by_count":81,"oa_url":552,"card":612,"direction":86,"ingested_from":88},"W7223120642",[600,603,605,608,610],{"name":601,"orcid":602},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":604,"orcid":9},"Tongkai Guo",{"name":606,"orcid":607},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":609,"orcid":9},"Wenping Bi",{"name":611,"orcid":9},"Xiaomin Mao",{"tldr":613,"method":614,"finding":615,"direction":86,"opportunity":616},"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。","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"]