[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3060":3,"related-3060":102},{"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":75,"slug":78,"view_count":61,"doi":79,"paper":80,"created_at":101},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。",null,"Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z","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,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业遥感","农业信息化","遥感","土地利用",{"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},[45,52,59,65,70],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},6,true,[],[50,34],"土壤",[],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"H3",3,[],[57],"水资源",[],{"code":60,"score":61,"supported":14,"title":62,"abstract":63,"tags":64},"I1",2,[],[],[29,27],{"code":66,"score":61,"supported":14,"title":67,"abstract":68,"tags":69},"I3",[],[],[28,30],{"code":71,"score":22,"supported":14,"title":72,"abstract":73,"tags":74},"H6",[],[],[31],[76,77],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":91,"oa_url":92,"card":93,"direction":99,"ingested_from":100},"W7213644047",[83,85,87,89],{"name":84,"orcid":9},"Sreerama Naik Naik S R",{"name":86,"orcid":9},"T K Prasad",{"name":88,"orcid":9},"Feba Jose Jasmine",{"name":90,"orcid":9},"Jayapal G",0,"https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","农业遥感与作物表型","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-21T23:30:09.448414Z",{"total":46,"page":22,"page_size":46,"items":103},[104,179,261,348,424,495],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":9,"content":9,"source_name":109,"source_url":107,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":127,"classification":134,"search_phrases":156,"slug":159,"view_count":61,"doi":160,"paper":161,"created_at":178},3500,"Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10333-026-01102-6","Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam。Paddy and Water Environment","Paddy and Water Environment","2026-09-24T00:00:00Z",67,{"impact":17,"substance":113,"depth":114,"authority":20,"freshness":21,"relevant":22,"comment":115},18,16,"核心期刊论文，遥感驱动土壤肥力建模与水稻适宜性制图，方法有参考价值但属区域案例，未达每日精选门槛。",[117],{"name":109,"url":107},[27,29,30,119,120],"土壤肥力","水稻种植",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[128,129],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":66,"slug":130,"name":131,"path":132,"parent_code":133},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":135},{"candidates":136,"agricultural_evidence":47},[137,143,147,152],{"code":122,"score":138,"supported":47,"title":139,"abstract":140,"tags":142},15,[123],[123,141],"paddy",[120],{"code":33,"score":17,"supported":47,"title":144,"abstract":145,"tags":146},[34],[34],[119],{"code":66,"score":17,"supported":47,"title":148,"abstract":150,"tags":151},[149],"remote sensing",[149],[30],{"code":60,"score":61,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[29,27],[157,158],"Assam 洪泛平原 水稻 遥感","土壤肥力 遥感 建模","Assam洪泛平原水稻遥感-3500","10.1007\u002Fs10333-026-01102-6",{"doi":160,"openalex_id":162,"authors":163,"venue":109,"cited_by_count":91,"oa_url":9,"card":9,"direction":97,"ingested_from":100},"W7214202229",[164,166,169,171,174,176],{"name":165,"orcid":9},"Pompy Deka",{"name":167,"orcid":168},"Jwngsar Moshahary","https:\u002F\u002Forcid.org\u002F0000-0002-3409-6691",{"name":170,"orcid":9},"Mrinal Choudhury",{"name":172,"orcid":173},"Perves Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2114-1043",{"name":175,"orcid":9},"Britan Rahman",{"name":177,"orcid":9},"Ranjit Sarma","2026-09-25T23:30:30.923946Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":224,"slug":227,"view_count":91,"doi":228,"paper":229,"created_at":260},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。","Discover Soil.","2026-10-20T00:00:00Z",69,{"impact":17,"substance":18,"depth":114,"authority":20,"freshness":21,"relevant":22,"comment":189},"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[191],{"name":185,"url":182},[30,31,193,194],"土壤侵蚀","水土保持",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":198},{"candidates":199,"agricultural_evidence":47},[200,205,210,214,220],{"code":33,"score":201,"supported":47,"title":202,"abstract":203,"tags":204},14,[34],[50,34],[],{"code":71,"score":206,"supported":47,"title":207,"abstract":208,"tags":209},5,[],[194],[31,194],{"code":53,"score":54,"supported":47,"title":211,"abstract":212,"tags":213},[],[57],[],{"code":215,"score":54,"supported":47,"title":216,"abstract":217,"tags":219},"I4",[],[218],"GIS",[],{"code":66,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[30],[225,226],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":228,"openalex_id":230,"authors":231,"venue":185,"cited_by_count":91,"oa_url":253,"card":254,"direction":97,"ingested_from":100},"W7223058039",[232,235,238,241,244,247,250],{"name":233,"orcid":234},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":236,"orcid":237},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":239,"orcid":240},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":242,"orcid":243},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":245,"orcid":246},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":248,"orcid":249},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":251,"orcid":252},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":255,"method":256,"finding":257,"direction":258,"opportunity":259},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","农业绿色发展与碳","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","2026-10-21T23:30:23.420269Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":9,"source_name":267,"source_url":264,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":274,"primary_topic":278,"secondary_topics":279,"classification":285,"search_phrases":333,"slug":336,"view_count":61,"doi":9,"paper":337,"created_at":347},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":114,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":271},"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。",[273],{"name":267,"url":264},[27,275,30,276,277],"精准农业","盐碱地治理","土壤监测",{"code":66,"slug":130,"name":131,"path":132,"parent_code":133},[280,284],{"code":215,"slug":281,"name":282,"path":283,"parent_code":133},"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":286},{"candidates":287,"agricultural_evidence":47},[288,293,300,304,309,316,321,327],{"code":66,"score":113,"supported":47,"title":289,"abstract":290,"tags":292},[149],[30,149,291],"multispectral",[30],{"code":215,"score":19,"supported":47,"title":294,"abstract":295,"tags":299},[218],[296,297,218,298],"地理信息","空间分析","geographic information",[],{"code":33,"score":138,"supported":47,"title":301,"abstract":302,"tags":303},[34],[50,34],[276],{"code":60,"score":13,"supported":47,"title":305,"abstract":307,"tags":308},[306],"precision agriculture",[],[27,275],{"code":310,"score":46,"supported":47,"title":311,"abstract":312,"tags":315},"I2",[],[313,314],"机器学习","machine learning",[],{"code":53,"score":54,"supported":47,"title":317,"abstract":318,"tags":320},[],[319],"灌溉",[],{"code":322,"score":54,"supported":47,"title":323,"abstract":324,"tags":326},"H7",[],[325],"干旱",[],{"code":328,"score":54,"supported":47,"title":329,"abstract":330,"tags":332},"I7",[],[331],"methodological framework",[],[334,335],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":338,"authors":339,"venue":267,"cited_by_count":91,"oa_url":264,"card":342,"direction":97,"ingested_from":100},"W7221843179",[340],{"name":341,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":343,"method":344,"finding":345,"direction":97,"opportunity":346},"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":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":9,"source_name":354,"source_url":351,"published_at":355,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":357,"sources":361,"tags":363,"primary_topic":367,"secondary_topics":368,"classification":374,"search_phrases":398,"slug":401,"view_count":91,"doi":402,"paper":403,"created_at":423},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":17,"substance":358,"depth":19,"authority":20,"freshness":359,"relevant":22,"comment":360},21,9,"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。",[362],{"name":354,"url":351},[27,364,30,365,366],"农业人工智能","土壤盐渍化","新疆农业",{"code":66,"slug":130,"name":131,"path":132,"parent_code":133},[369,370],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":310,"slug":371,"name":372,"path":373,"parent_code":133},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":375},{"candidates":376,"agricultural_evidence":47},[377,381,385,390,394],{"code":66,"score":138,"supported":47,"title":378,"abstract":379,"tags":380},[149],[30,149],[30],{"code":33,"score":201,"supported":47,"title":382,"abstract":383,"tags":384},[34],[50,34],[],{"code":310,"score":359,"supported":47,"title":386,"abstract":388,"tags":389},[387],"deep learning",[],[364],{"code":322,"score":54,"supported":47,"title":391,"abstract":392,"tags":393},[],[325],[],{"code":60,"score":22,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[27],[399,400],"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":402,"openalex_id":404,"authors":405,"venue":354,"cited_by_count":91,"oa_url":351,"card":418,"direction":97,"ingested_from":100},"W7221816183",[406,408,411,413,415],{"name":407,"orcid":9},"Mingjie Qian",{"name":409,"orcid":410},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":412,"orcid":9},"Peng Wang",{"name":414,"orcid":9},"Hangyuan Liu",{"name":416,"orcid":417},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":419,"method":420,"finding":421,"direction":97,"opportunity":422},"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":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":439,"secondary_topics":440,"classification":443,"search_phrases":466,"slug":469,"view_count":91,"doi":470,"paper":471,"created_at":494},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":114,"substance":358,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":433},"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。",[435],{"name":430,"url":427},[27,28,437,438,365],"可解释AI","深度学习",{"code":310,"slug":371,"name":372,"path":373,"parent_code":133},[441,442],{"code":66,"slug":130,"name":131,"path":132,"parent_code":133},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":444},{"candidates":445,"agricultural_evidence":47},[446,450,454,458,462],{"code":310,"score":114,"supported":47,"title":447,"abstract":448,"tags":449},[387],[438,387],[437,438],{"code":66,"score":138,"supported":47,"title":451,"abstract":452,"tags":453},[149],[30,149],[28],{"code":33,"score":201,"supported":47,"title":455,"abstract":456,"tags":457},[34],[50,34],[],{"code":322,"score":54,"supported":47,"title":459,"abstract":460,"tags":461},[],[325],[],{"code":60,"score":22,"supported":14,"title":463,"abstract":464,"tags":465},[],[],[27],[467,468],"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":470,"openalex_id":472,"authors":473,"venue":430,"cited_by_count":91,"oa_url":427,"card":489,"direction":97,"ingested_from":100},"W7221715654",[474,477,480,482,485,487],{"name":475,"orcid":476},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":478,"orcid":479},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":481,"orcid":9},"Junchi Ma",{"name":483,"orcid":484},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":486,"orcid":9},"Jiahao Qi",{"name":488,"orcid":9},"Yuan Jing",{"tldr":490,"method":491,"finding":492,"direction":97,"opportunity":493},"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",{"id":496,"title":497,"url":498,"summary":499,"summary_zh":500,"content":9,"source_name":501,"source_url":498,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":502,"sources":504,"tags":506,"primary_topic":509,"secondary_topics":510,"classification":512,"search_phrases":552,"slug":555,"view_count":22,"doi":556,"paper":557,"created_at":569},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",{"impact":17,"substance":358,"depth":19,"authority":20,"freshness":359,"relevant":22,"comment":503},"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[505],{"name":501,"url":498},[27,275,30,507,508],"土壤健康","覆盖作物",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[511],{"code":66,"slug":130,"name":131,"path":132,"parent_code":133},{"status":40,"locked":14,"version":41,"method":42,"evidence":513},{"candidates":514,"agricultural_evidence":47},[515,519,523,527,534,541,547],{"code":33,"score":138,"supported":47,"title":516,"abstract":517,"tags":518},[34],[50,34],[507],{"code":66,"score":17,"supported":47,"title":520,"abstract":521,"tags":522},[149],[30],[30],{"code":60,"score":21,"supported":47,"title":524,"abstract":525,"tags":526},[],[275,306],[27,275],{"code":528,"score":46,"supported":47,"title":529,"abstract":530,"tags":533},"A4",[],[531,532],"油菜","rapeseed",[],{"code":535,"score":46,"supported":47,"title":536,"abstract":537,"tags":540},"H8",[],[538,539],"可持续农业","sustainable agriculture",[],{"code":542,"score":54,"supported":47,"title":543,"abstract":544,"tags":546},"B1",[],[545],"萝卜",[],{"code":548,"score":22,"supported":14,"title":549,"abstract":550,"tags":551},"A7",[],[],[508],[553,554],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":556,"openalex_id":558,"authors":559,"venue":501,"cited_by_count":91,"oa_url":9,"card":564,"direction":97,"ingested_from":100},"W7221669942",[560,562],{"name":561,"orcid":9},"Ali Mirzakhani Nafchi",{"name":563,"orcid":9},"Salman Mirzaee",{"tldr":565,"method":566,"finding":567,"direction":97,"opportunity":568},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z"]