[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3560":3,"related-3560":81},{"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":8,"secondary_topics":30,"classification":31,"search_phrases":59,"slug":62,"view_count":21,"doi":63,"paper":64,"created_at":80},3560,"Integrating spectroscopy and remote sensing to assess soil salinization under treated wastewater irrigation: a case study in northern Tunisia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11600-026-02020-1","Integrating spectroscopy and remote sensing to assess soil salinization under treated wastewater irrigation: a case study in northern Tunisia。Acta Geophysica",null,"Acta Geophysica","2026-09-24T00:00:00Z","论文",10,false,64,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,18,16,13,9,1,"核心期刊论文，将光谱与遥感结合用于再生水灌溉下土壤盐渍化评估，方法有参考价值但属区域案例，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29],"遥感","土壤盐渍化","再生水灌溉","光谱监测",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":58},[37,45,52],{"code":38,"score":39,"supported":40,"title":41,"abstract":43,"tags":44},"I3",12,true,[42],"remote sensing",[42],[26],{"code":46,"score":47,"supported":40,"title":48,"abstract":50,"tags":51},"H2",11,[49],"soil",[49],[],{"code":53,"score":47,"supported":40,"title":54,"abstract":56,"tags":57},"H3",[55],"irrigation",[55],[],"农业相关性需要复核",[60,61],"突尼斯 再生水灌溉 土壤盐渍化","光谱 遥感 土壤盐分监测","突尼斯再生水灌溉土壤盐渍化-3560","10.1007\u002Fs11600-026-02020-1",{"doi":63,"openalex_id":65,"authors":66,"venue":9,"cited_by_count":77,"oa_url":8,"card":8,"direction":78,"ingested_from":79},"W7214208065",[67,69,72,74],{"name":68,"orcid":8},"Ines Smaani",{"name":70,"orcid":71},"Sonia Gannouni","https:\u002F\u002Forcid.org\u002F0000-0002-9083-832X",{"name":73,"orcid":8},"Salma Boussen",{"name":75,"orcid":76},"Noamen Rebaï","https:\u002F\u002Forcid.org\u002F0000-0001-6168-853X",0,"农业遥感与作物表型","openalex","2026-09-26T23:30:31.596176Z",{"total":82,"page":21,"page_size":82,"items":83},6,[84,176,233,283,345,398],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":108,"classification":118,"search_phrases":150,"slug":153,"view_count":77,"doi":154,"paper":155,"created_at":175},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","2026-10-14T00:00:00Z",72,{"impact":39,"substance":94,"depth":95,"authority":19,"freshness":20,"relevant":21,"comment":96},21,17,"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。",[98],{"name":90,"url":87},[100,101,26,27,102],"智慧农业","农业人工智能","新疆农业",{"code":38,"slug":104,"name":105,"path":106,"parent_code":107},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[109,113],{"code":46,"slug":49,"name":110,"path":111,"parent_code":112},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":114,"slug":115,"name":116,"path":117,"parent_code":107},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":119,"locked":13,"version":33,"method":34,"evidence":120},"classified",{"candidates":121,"agricultural_evidence":40},[122,127,133,138,145],{"code":38,"score":123,"supported":40,"title":124,"abstract":125,"tags":126},15,[42],[26,42],[26],{"code":46,"score":128,"supported":40,"title":129,"abstract":130,"tags":132},14,[49],[131,49],"土壤",[],{"code":114,"score":20,"supported":40,"title":134,"abstract":136,"tags":137},[135],"deep learning",[],[101],{"code":139,"score":140,"supported":40,"title":141,"abstract":142,"tags":144},"H7",3,[],[143],"干旱",[],{"code":146,"score":21,"supported":13,"title":147,"abstract":148,"tags":149},"I1",[],[],[100],[151,152],"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":154,"openalex_id":156,"authors":157,"venue":90,"cited_by_count":77,"oa_url":87,"card":170,"direction":78,"ingested_from":79},"W7221816183",[158,160,163,165,167],{"name":159,"orcid":8},"Mingjie Qian",{"name":161,"orcid":162},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":164,"orcid":8},"Peng Wang",{"name":166,"orcid":8},"Hangyuan Liu",{"name":168,"orcid":169},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":171,"method":172,"finding":173,"direction":78,"opportunity":174},"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":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":8,"source_name":182,"source_url":179,"published_at":183,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":184,"score_detail":185,"sources":188,"tags":190,"primary_topic":194,"secondary_topics":195,"classification":196,"search_phrases":217,"slug":220,"view_count":21,"doi":221,"paper":222,"created_at":232},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-12T00:00:00Z",70,{"impact":39,"substance":186,"depth":95,"authority":19,"freshness":16,"relevant":21,"comment":187},20,"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。",[189],{"name":182,"url":179},[191,26,192,27,193],"数字农业","盐碱地治理","模糊模型",{"code":46,"slug":49,"name":110,"path":111,"parent_code":112},[],{"status":119,"locked":13,"version":33,"method":34,"evidence":197},{"candidates":198,"agricultural_evidence":40},[199,203,208,213],{"code":46,"score":123,"supported":40,"title":200,"abstract":201,"tags":202},[49],[131,49],[192],{"code":38,"score":204,"supported":40,"title":205,"abstract":206,"tags":207},7,[],[26,42],[26],{"code":53,"score":140,"supported":40,"title":209,"abstract":210,"tags":212},[],[211],"灌溉",[],{"code":146,"score":21,"supported":13,"title":214,"abstract":215,"tags":216},[],[],[191],[218,219],"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":221,"openalex_id":223,"authors":224,"venue":182,"cited_by_count":77,"oa_url":179,"card":227,"direction":78,"ingested_from":79},"W7221692005",[225],{"name":226,"orcid":8},"Bunyod Abduvakilovich Abduvakilov",{"tldr":228,"method":229,"finding":230,"direction":78,"opportunity":231},"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。","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。","2026-10-14T23:30:30.645521Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":8,"content":8,"source_name":238,"source_url":8,"published_at":239,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":240,"score_detail":241,"sources":243,"tags":245,"primary_topic":8,"secondary_topics":248,"classification":249,"search_phrases":269,"slug":272,"view_count":273,"doi":8,"paper":274,"created_at":282},2855,"UAV无人机高光谱图像土壤盐度制图(湿度校正)——MDPI Agronomy","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1812","研究评估了6种光谱变换方法(原始反射率Ref、一阶导数FDR、PDS、OSC、FDR+PDS、FDR+OSC),结合3种机器学习算法(KNN、SVR、MLP)。进一步开发了集成这些基础学习者的Stacking集成模型,以提高湿度干扰下土壤盐度反演的精度。结果表明,Stacking模型在评估模型中达到最高的精度和稳定性。FDR+OSC-Stacking组合实现最佳验证性能,R²p=0.87,RMSEP=0.67 mS·cm⁻¹,RPD=2.93。FDR+OSC-Stacking组合成功应用于UAV高光谱图像,用于EC1:5的空间制图。来自吉林大学。","MDPI Agronomy","2026-09-15T00:00:00Z",75,{"impact":18,"substance":94,"depth":95,"authority":19,"freshness":16,"relevant":21,"comment":242},"方法组合新颖、验证指标扎实的无人机高光谱盐分制图研究，属细分领域实质进展，值得精选。",[244],{"name":238,"url":236},[100,246,247,26,27],"无人机","机器学习",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":250},{"candidates":251,"agricultural_evidence":13,"reason":58},[252,256,261,265],{"code":46,"score":47,"supported":40,"title":253,"abstract":254,"tags":255},[131],[131],[],{"code":114,"score":257,"supported":40,"title":258,"abstract":259,"tags":260},4,[],[247],[247],{"code":146,"score":21,"supported":13,"title":262,"abstract":263,"tags":264},[],[],[100],{"code":38,"score":21,"supported":13,"title":266,"abstract":267,"tags":268},[],[],[26],[270,271],"土壤盐渍化 智慧农业 机器学习 无人机","土壤盐渍化 智慧农业","土壤盐渍化智慧农业机器学习无人机-2855",2,{"doi":8,"openalex_id":8,"authors":275,"venue":8,"cited_by_count":77,"oa_url":8,"card":276,"direction":78,"ingested_from":281},[],{"tldr":277,"method":278,"finding":279,"direction":78,"opportunity":280},"用无人机高光谱结合Stacking集成模型实现湿度干扰下的土壤盐度制图。","6种光谱变换与KNN、SVR、MLP及Stacking集成，基于UAV高光谱数据","FDR+OSC-Stacking最优，R²p=0.87、RMSEP=0.67 mS·cm⁻¹、RPD","可探索多时相\u002F多传感器融合与迁移学习，提升不同湿度与区域下盐度反演泛化性。","agent","2026-09-18T00:03:30.822732Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":8,"source_name":182,"source_url":286,"published_at":289,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":290,"score_detail":291,"sources":294,"tags":296,"primary_topic":299,"secondary_topics":300,"classification":301,"search_phrases":328,"slug":331,"view_count":77,"doi":332,"paper":333,"created_at":344},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",74,{"impact":39,"substance":292,"depth":17,"authority":39,"freshness":12,"relevant":21,"comment":293},22,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[295],{"name":182,"url":286},[100,101,297,26,298],"粮食安全","气候分析",{"code":114,"slug":115,"name":116,"path":117,"parent_code":107},[],{"status":119,"locked":13,"version":33,"method":34,"evidence":302},{"candidates":303,"agricultural_evidence":40},[304,309,315,320,324],{"code":114,"score":20,"supported":40,"title":305,"abstract":307,"tags":308},[306],"artificial intelligence",[],[101],{"code":310,"score":140,"supported":40,"title":311,"abstract":312,"tags":314},"I5",[],[313],"农业传感",[],{"code":316,"score":21,"supported":13,"title":317,"abstract":318,"tags":319},"A8",[],[],[297],{"code":146,"score":21,"supported":13,"title":321,"abstract":322,"tags":323},[],[],[100],{"code":38,"score":21,"supported":13,"title":325,"abstract":326,"tags":327},[],[],[26],[329,330],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":332,"openalex_id":334,"authors":335,"venue":182,"cited_by_count":77,"oa_url":286,"card":338,"direction":343,"ingested_from":79},"W7223111766",[336],{"name":337,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":339,"method":340,"finding":341,"direction":78,"opportunity":342},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":8,"content":8,"source_name":350,"source_url":348,"published_at":289,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":351,"score_detail":352,"sources":354,"tags":356,"primary_topic":359,"secondary_topics":363,"classification":364,"search_phrases":376,"slug":379,"view_count":77,"doi":380,"paper":381,"created_at":397},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square",38,{"impact":16,"substance":82,"depth":16,"authority":82,"freshness":12,"relevant":21,"comment":353},"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[355],{"name":350,"url":348},[191,357,26,358],"农业信息化","地图评估",{"code":146,"slug":360,"name":361,"path":362,"parent_code":107},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":119,"locked":13,"version":33,"method":34,"evidence":365},{"candidates":366,"agricultural_evidence":40},[367,372],{"code":146,"score":19,"supported":40,"title":368,"abstract":370,"tags":371},[369],"digital agriculture",[369],[357,191],{"code":38,"score":21,"supported":13,"title":373,"abstract":374,"tags":375},[],[],[26],[377,378],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":380,"openalex_id":382,"authors":383,"venue":350,"cited_by_count":77,"oa_url":348,"card":8,"direction":396,"ingested_from":79},"W7223099987",[384,387,390,393],{"name":385,"orcid":386},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":388,"orcid":389},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":391,"orcid":392},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":394,"orcid":395},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":8,"source_name":404,"source_url":401,"published_at":405,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":406,"score_detail":407,"sources":409,"tags":411,"primary_topic":416,"secondary_topics":417,"classification":418,"search_phrases":439,"slug":442,"view_count":77,"doi":443,"paper":444,"created_at":476},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","2026-10-20T00:00:00Z",79,{"impact":18,"substance":292,"depth":17,"authority":128,"freshness":20,"relevant":21,"comment":408},"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。",[410],{"name":404,"url":401},[412,413,27,414,415],"遥感监测","莫桑比克","土地覆盖变化","盐分反演",{"code":46,"slug":49,"name":110,"path":111,"parent_code":112},[],{"status":119,"locked":13,"version":33,"method":34,"evidence":419},{"candidates":420,"agricultural_evidence":40},[421,425,429,434],{"code":46,"score":128,"supported":40,"title":422,"abstract":423,"tags":424},[49],[131,49],[],{"code":38,"score":204,"supported":40,"title":426,"abstract":427,"tags":428},[],[26,42],[412],{"code":139,"score":82,"supported":40,"title":430,"abstract":431,"tags":433},[],[432,143],"气候适应",[],{"code":435,"score":21,"supported":13,"title":436,"abstract":437,"tags":438},"H6",[],[],[414],[440,441],"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":443,"openalex_id":445,"authors":446,"venue":404,"cited_by_count":77,"oa_url":401,"card":471,"direction":78,"ingested_from":79},"W7223069171",[447,449,451,453,456,458,461,464,466,468],{"name":448,"orcid":8},"Yue Li",{"name":450,"orcid":8},"Mengfei Shi",{"name":452,"orcid":8},"Fei Zhang",{"name":454,"orcid":455},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":457,"orcid":8},"Gowhar Meraj",{"name":459,"orcid":460},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":462,"orcid":463},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":465,"orcid":8},"Yuanfang Chai",{"name":467,"orcid":8},"Hongtao Xu",{"name":469,"orcid":470},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":472,"method":473,"finding":474,"direction":78,"opportunity":475},"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"]