[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1639":3,"related-1639":122},{"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":31,"secondary_topics":37,"classification":44,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":121},1639,"Remote Sensing of Surface Soil Moisture: A Comprehensive Review of Retrieval Methods and Satellite Products","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fw18172180","Soil moisture (SM) is a key parameter in surface water and energy cycles, playing a vital role in agriculture, hydrology, and atmospheric science. Remote sensing (RS) has been widely applied in SM retrieval, yet most existing reviews focus on individual data sources or specific methodologies, lacking a systematic pathway from retrieval principles to product applications, which poses a barrier for early-stage researchers. To address this gap, this study provides a comprehensive review of commonly used SM retrieval models based on different RS data sources, including optical, thermal infrared, active microwave, passive microwave, and multi-source approaches. Their underlying principles, assumptions, advantages, and limitations are critically examined. In parallel, key specifications of major satellite-based SM products, such as spatial-temporal resolution, coverage, and data accessibility, are summarized and compared. By integrating methodological explanations with product characterization, this review serves as a practical reference for researchers entering the field, facilitating informed decisions in SM retrieval applications.","土壤水分（SM）是地表水与能量循环中的关键参数，在农业、水文学及大气科学领域发挥着至关重要的作用。遥感（RS）技术已被广泛应用于土壤水分反演，然而现有综述大多聚焦于单一数据源或特定方法，缺乏从反演原理到产品应用的系统性梳理，这对初入该领域的研究者构成了一定障碍。为弥补这一空白，本研究基于不同遥感数据源，对常用的土壤水分反演模型进行了全面综述，涵盖光学、热红外、主动微波、被动微波及多源融合等方法，并对其基本原理、假设条件、优势及局限性进行了批判性审视。与此同时，本文还对主要卫星土壤水分产品的关键规格进行了归纳与比较，包括时空分辨率、覆盖范围及数据可获取性等。通过将方法论阐释与产品特征描述相结合，本综述旨在为该领域的新进研究者提供实用参考，助力其在土壤水分反演应用中做出明智决策。",null,"Water","2026-09-03T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,12,7,1,"系统综述遥感土壤水分反演方法与卫星产品，对农业遥感研究有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"土壤水分","遥感反演","卫星产品","农业应用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,56],{"code":32,"score":17,"supported":51,"title":52,"abstract":53,"tags":55},true,[33],[54,33],"土壤",[27],{"code":39,"score":57,"supported":51,"title":58,"abstract":60,"tags":62},14,[59],"remote sensing",[61,59],"遥感",[],[64,65],"农业应用 卫星产品 土壤水分 遥感反演","农业应用 卫星产品","农业应用卫星产品土壤水分遥感反演-1639",0,"10.3390\u002Fw18172180",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":67,"oa_url":6,"card":114,"direction":118,"ingested_from":120},"W7207735811",[72,75,77,78,80,83,86,88,89,92,95,98,101,103,105,107,110,112],{"name":73,"orcid":74},"Xingrui Zeng","https:\u002F\u002Forcid.org\u002F0009-0007-2208-9711",{"name":76,"orcid":9},"Xiaoman Qi",{"name":76,"orcid":9},{"name":79,"orcid":9},"Junhuan Peng",{"name":81,"orcid":82},"Yuebin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-6978-4558",{"name":84,"orcid":85},"Liqiang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-4175-7590",{"name":87,"orcid":9},"Xiaotong Qi",{"name":87,"orcid":9},{"name":90,"orcid":91},"Yongze Song","https:\u002F\u002Forcid.org\u002F0000-0003-3420-9622",{"name":93,"orcid":94},"Linlin Xu","https:\u002F\u002Forcid.org\u002F0000-0002-3488-5199",{"name":96,"orcid":97},"Zhen Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4318-9996",{"name":99,"orcid":100},"Zhouzheng Gao","https:\u002F\u002Forcid.org\u002F0000-0001-7997-7719",{"name":102,"orcid":9},"吴小龙",{"name":104,"orcid":9},"谢传高",{"name":106,"orcid":9},"Xu Li",{"name":108,"orcid":109},"Xinwei Jiang","https:\u002F\u002Forcid.org\u002F0000-0001-6783-2176",{"name":111,"orcid":9},"Pengcheng Hu",{"name":113,"orcid":9},"Jiazhi Tang",{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"综述了基于不同遥感数据源的土壤水分反演模型及卫星产品，为初学者提供系统参考。","系统梳理光学、热红外、主动\u002F被动微波及多源遥感反演模型，并比较主要卫星产品。","现有综述缺乏从原理到应用的系统路径，本研究填补空白，提供实用指南。","农业遥感与作物表型","可探索多源遥感数据融合的土壤水分高精度反演方法，或开发面向农业应用的实时监测产品。","openalex","2026-09-04T23:30:32.234696Z",{"total":123,"page":22,"page_size":123,"items":124},6,[125,217,327,384,465,535],{"id":126,"title":127,"url":128,"summary":129,"summary_zh":130,"content":9,"source_name":131,"source_url":128,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":139,"tags":141,"primary_topic":146,"secondary_topics":147,"classification":154,"search_phrases":171,"slug":174,"view_count":67,"doi":175,"paper":176,"created_at":216},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":20,"substance":18,"depth":135,"authority":136,"freshness":137,"relevant":22,"comment":138},16,13,8,"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[140],{"name":131,"url":128},[142,61,143,144,145],"机器学习","土壤碳","Landsat","数字土壤制图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[148,153],{"code":149,"slug":150,"name":151,"path":152,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":155},{"candidates":156,"agricultural_evidence":51},[157,161,167],{"code":32,"score":57,"supported":51,"title":158,"abstract":159,"tags":160},[33],[54,33],[],{"code":149,"score":162,"supported":51,"title":163,"abstract":165,"tags":166},9,[164],"machine learning",[],[142],{"code":39,"score":137,"supported":51,"title":168,"abstract":169,"tags":170},[],[61,59],[144,61],[172,173],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":175,"openalex_id":177,"authors":178,"venue":131,"cited_by_count":67,"oa_url":9,"card":211,"direction":118,"ingested_from":120},"W7222107492",[179,182,185,188,191,194,197,200,202,205,208],{"name":180,"orcid":181},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":183,"orcid":184},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":186,"orcid":187},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":189,"orcid":190},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":192,"orcid":193},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":195,"orcid":196},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":198,"orcid":199},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":201,"orcid":9},"A. E. Shatunov",{"name":203,"orcid":204},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":206,"orcid":207},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":209,"orcid":210},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":212,"method":213,"finding":214,"direction":118,"opportunity":215},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":225,"score_detail":226,"sources":229,"tags":231,"primary_topic":236,"secondary_topics":237,"classification":239,"search_phrases":279,"slug":282,"view_count":67,"doi":283,"paper":284,"created_at":326},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":137,"substance":18,"depth":227,"authority":136,"freshness":162,"relevant":22,"comment":228},17,"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。",[230],{"name":223,"url":220},[232,233,234,61,235],"数字农业","随机森林","土壤有机碳","土壤制图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[238],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":240},{"candidates":241,"agricultural_evidence":51},[242,246,250,257,264,270,275],{"code":32,"score":135,"supported":51,"title":243,"abstract":244,"tags":245},[33],[54,33],[235,234],{"code":39,"score":17,"supported":51,"title":247,"abstract":248,"tags":249},[59],[61,59],[61],{"code":251,"score":123,"supported":51,"title":252,"abstract":253,"tags":256},"F3",[],[254,255],"肥料","fertilizer",[],{"code":258,"score":259,"supported":51,"title":260,"abstract":261,"tags":263},"H8",3,[],[262],"可持续农业",[],{"code":265,"score":259,"supported":51,"title":266,"abstract":267,"tags":269},"I4",[],[268],"空间分析",[],{"code":271,"score":22,"supported":14,"title":272,"abstract":273,"tags":274},"I1",[],[],[232],{"code":149,"score":22,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[233],[280,281],"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":283,"openalex_id":285,"authors":286,"venue":223,"cited_by_count":67,"oa_url":320,"card":321,"direction":118,"ingested_from":120},"W7222335269",[287,290,293,296,299,302,305,308,311,314,317],{"name":288,"orcid":289},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":291,"orcid":292},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":294,"orcid":295},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":297,"orcid":298},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":300,"orcid":301},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":303,"orcid":304},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":306,"orcid":307},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":309,"orcid":310},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":312,"orcid":313},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":315,"orcid":316},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":318,"orcid":319},"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":322,"method":323,"finding":324,"direction":118,"opportunity":325},"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":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":9,"source_name":333,"source_url":330,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":334,"sources":336,"tags":338,"primary_topic":339,"secondary_topics":340,"classification":346,"search_phrases":364,"slug":365,"view_count":67,"doi":366,"paper":367,"created_at":383},5091,"Assessing the impacts of gold mining on vegetation and soil degradation using multi-temporal remote sensing in Kintinian, Siguiri prefecture, Guinea","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101570","Gold mining, while economically significant, has increasingly driven environmental degradation in many parts of West Africa, including Kintinian in the Siguiri Prefecture of the Republic of Guinea. This study evaluated the extent and environmental impacts of gold mining on soil and vegetation dynamics between 1990 and 2016, with an emphasis on landscape transformation and ecological degradation. The study integrated field observations and ground-truthing with multi-temporal satellite imagery (Landsat 1990, 2000, 2010, and 2016; and Sentinel-1) within the Geographical Pathways to Environmental Sustainability (GPES) framework. Geographic Information Systems (GIS) were employed for change detection analysis, while the Soil-Adjusted Vegetation Index (SAVI) quantified vegetation dynamics and Analytical Hierarchical Principles (AHP) weighed the vulnerability degradation threshold. Findings reveal substantial land-use dynamics associated with intensified mining activities. Agricultural land declined by 13%, while forest cover decreased by 23% over the study period. Vegetation condition deteriorated as SAVI values declined from 0.928 (1990) to 0.602 (2016), indicating reduced vegetative vigor and increasing soil exposure. Soil vulnerability was weighted using Land Use Land Cover (10%), Iron Oxide (35%), Enhanced Built-up and Bare Soil (15%), Ground Soil Index (15%), and Land Surface Temperature (25%), demonstrating significant degradation found at the abandoned industrial mining sites that were reconverted for artisanal mining purposes. The study concluded that gold mining has significantly accelerated landscape transformation, vegetation loss, and soil exposure, suggesting sustainable mining practices and targeted land restoration strategies in the mining-affected regions.","金矿开采虽具有重要经济意义，却在包括几内亚共和国锡吉里省金蒂尼安在内的西非许多地区日益加剧环境退化。本研究评估了1990年至2016年间金矿开采对土壤和植被动态的影响范围及环境效应，重点关注景观转变与生态退化。研究在“环境可持续性地理路径”（GPES）框架内，将实地观测和地面验证与多时相卫星影像（1990、2000、2010和2016年Landsat影像；以及Sentinel-1影像）相结合。采用地理信息系统（GIS）进行变化检测分析，利用土壤调节植被指数（SAVI）量化植被动态，并运用层次分析法（AHP）对脆弱性退化阈值进行加权。研究结果揭示了与采矿活动加剧相关的显著土地利用动态。研究期内，农用地减少13%，森林覆盖减少23%。植被状况恶化，SAVI值从0.928（1990年）降至0.602（2016年），表明植被活力下降、土壤裸露增加。土壤脆弱性采用土地利用\u002F土地覆盖（10%）、氧化铁（35%）、增强型建成区与裸土（15%）、地表土壤指数（15%）和地表温度（25%）进行加权，结果显示，在重新转为手工采矿用途的废弃工业采矿场址，土壤退化显著。研究结论认为，金矿开采显著加速了景观转变、植被丧失和土壤裸露，建议在受采矿影响地区推行可持续采矿实践并实施有针对性的土地恢复策略。","Environmental and Sustainability Indicators",{"impact":67,"substance":67,"depth":67,"authority":67,"freshness":67,"relevant":67,"comment":335},"该论文研究几内亚金矿开采对植被与土壤退化的遥感评估，与三农、农业信息化、智慧农业等主题无关，不建议进入每日精选。",[337],{"name":333,"url":330},[12],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[341,345],{"code":265,"slug":342,"name":343,"path":344,"parent_code":43},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":347},{"candidates":348,"agricultural_evidence":51},[349,353,360],{"code":32,"score":57,"supported":51,"title":350,"abstract":351,"tags":352},[33],[54,33],[],{"code":265,"score":162,"supported":51,"title":354,"abstract":355,"tags":359},[],[356,357,358],"地理信息","GIS","geographic information",[],{"code":39,"score":137,"supported":51,"title":361,"abstract":362,"tags":363},[59],[],[],[],"Assessingtheimpactsofgoldminingonvegetat-5091","10.1016\u002Fj.indic.2026.101570",{"doi":366,"openalex_id":368,"authors":369,"venue":333,"cited_by_count":67,"oa_url":330,"card":378,"direction":118,"ingested_from":120},"W7222305702",[370,372,375],{"name":371,"orcid":9},"Mohamed Traoré",{"name":373,"orcid":374},"Opeyemi Aniramu","https:\u002F\u002Forcid.org\u002F0000-0002-5111-0221",{"name":376,"orcid":377},"Oluwagbenga O. I. Orimoogunje","https:\u002F\u002Forcid.org\u002F0000-0001-8070-5040",{"tldr":379,"method":380,"finding":381,"direction":118,"opportunity":382},"评估几内亚金矿开采对植被与土壤退化的影响。","多时相Landsat\u002FSentinel-1影像、GIS变化检测、SAVI与AHP","1990-2016年农地减13%、林地减23%，SAVI由0.928降至0.602，土壤退化严重。","可结合时序遥感与机器学习，构建矿区复垦动态监测与生态恢复评估模型。","2026-10-16T23:30:27.217565Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":391,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":402,"secondary_topics":403,"classification":406,"search_phrases":449,"slug":452,"view_count":453,"doi":9,"paper":454,"created_at":464},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":135,"substance":18,"depth":227,"authority":136,"freshness":137,"relevant":22,"comment":394},"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。",[396],{"name":390,"url":387},[398,399,61,400,401],"智慧农业","精准农业","盐碱地治理","土壤监测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[404,405],{"code":265,"slug":342,"name":343,"path":344,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":407},{"candidates":408,"agricultural_evidence":51},[409,414,418,422,427,431,437,443],{"code":39,"score":19,"supported":51,"title":410,"abstract":411,"tags":413},[59],[61,59,412],"multispectral",[61],{"code":265,"score":227,"supported":51,"title":415,"abstract":416,"tags":417},[357],[356,268,357,358],[],{"code":32,"score":17,"supported":51,"title":419,"abstract":420,"tags":421},[33],[54,33],[400],{"code":271,"score":13,"supported":51,"title":423,"abstract":425,"tags":426},[424],"precision agriculture",[],[398,399],{"code":149,"score":123,"supported":51,"title":428,"abstract":429,"tags":430},[],[142,164],[],{"code":432,"score":259,"supported":51,"title":433,"abstract":434,"tags":436},"H3",[],[435],"灌溉",[],{"code":438,"score":259,"supported":51,"title":439,"abstract":440,"tags":442},"H7",[],[441],"干旱",[],{"code":444,"score":259,"supported":51,"title":445,"abstract":446,"tags":448},"I7",[],[447],"methodological framework",[],[450,451],"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":455,"authors":456,"venue":390,"cited_by_count":67,"oa_url":387,"card":459,"direction":118,"ingested_from":120},"W7221843179",[457],{"name":458,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":460,"method":461,"finding":462,"direction":118,"opportunity":463},"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":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":9,"source_name":471,"source_url":468,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":472,"sources":475,"tags":477,"primary_topic":481,"secondary_topics":482,"classification":485,"search_phrases":509,"slug":512,"view_count":67,"doi":513,"paper":514,"created_at":534},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",{"impact":20,"substance":473,"depth":227,"authority":136,"freshness":162,"relevant":22,"comment":474},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。",[476],{"name":471,"url":468},[398,478,61,479,480],"农业人工智能","土壤盐渍化","新疆农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[483,484],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":149,"slug":150,"name":151,"path":152,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":486},{"candidates":487,"agricultural_evidence":51},[488,492,496,501,505],{"code":39,"score":17,"supported":51,"title":489,"abstract":490,"tags":491},[59],[61,59],[61],{"code":32,"score":57,"supported":51,"title":493,"abstract":494,"tags":495},[33],[54,33],[],{"code":149,"score":162,"supported":51,"title":497,"abstract":499,"tags":500},[498],"deep learning",[],[478],{"code":438,"score":259,"supported":51,"title":502,"abstract":503,"tags":504},[],[441],[],{"code":271,"score":22,"supported":14,"title":506,"abstract":507,"tags":508},[],[],[398],[510,511],"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":513,"openalex_id":515,"authors":516,"venue":471,"cited_by_count":67,"oa_url":468,"card":529,"direction":118,"ingested_from":120},"W7221816183",[517,519,522,524,526],{"name":518,"orcid":9},"Mingjie Qian",{"name":520,"orcid":521},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":523,"orcid":9},"Peng Wang",{"name":525,"orcid":9},"Hangyuan Liu",{"name":527,"orcid":528},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":530,"method":531,"finding":532,"direction":118,"opportunity":533},"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":536,"title":537,"url":538,"summary":539,"summary_zh":540,"content":9,"source_name":541,"source_url":538,"published_at":391,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":542,"score_detail":543,"sources":545,"tags":547,"primary_topic":551,"secondary_topics":552,"classification":555,"search_phrases":578,"slug":581,"view_count":67,"doi":582,"paper":583,"created_at":606},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors",75,{"impact":135,"substance":473,"depth":227,"authority":136,"freshness":137,"relevant":22,"comment":544},"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[546],{"name":541,"url":538},[398,548,549,550,479],"农业遥感","可解释AI","深度学习",{"code":149,"slug":150,"name":151,"path":152,"parent_code":43},[553,554],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":556},{"candidates":557,"agricultural_evidence":51},[558,562,566,570,574],{"code":149,"score":135,"supported":51,"title":559,"abstract":560,"tags":561},[498],[550,498],[549,550],{"code":39,"score":17,"supported":51,"title":563,"abstract":564,"tags":565},[59],[61,59],[548],{"code":32,"score":57,"supported":51,"title":567,"abstract":568,"tags":569},[33],[54,33],[],{"code":438,"score":259,"supported":51,"title":571,"abstract":572,"tags":573},[],[441],[],{"code":271,"score":22,"supported":14,"title":575,"abstract":576,"tags":577},[],[],[398],[579,580],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":582,"openalex_id":584,"authors":585,"venue":541,"cited_by_count":67,"oa_url":538,"card":601,"direction":118,"ingested_from":120},"W7221715654",[586,589,592,594,597,599],{"name":587,"orcid":588},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":590,"orcid":591},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":593,"orcid":9},"Junchi Ma",{"name":595,"orcid":596},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":598,"orcid":9},"Jiahao Qi",{"name":600,"orcid":9},"Yuan Jing",{"tldr":602,"method":603,"finding":604,"direction":118,"opportunity":605},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z"]