[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3500":3,"related-3500":104},{"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":31,"secondary_topics":37,"classification":50,"search_phrases":78,"slug":81,"view_count":74,"doi":82,"paper":83,"created_at":103},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",null,"Paddy and Water Environment","2026-09-24T00:00:00Z","论文",10,false,67,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,8,1,"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。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业信息化","遥感","土壤肥力","水稻种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":51,"locked":13,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,63,67,72],{"code":32,"score":57,"supported":58,"title":59,"abstract":60,"tags":62},15,true,[33],[33,61],"paddy",[30],{"code":39,"score":16,"supported":58,"title":64,"abstract":65,"tags":66},[40],[40],[29],{"code":45,"score":16,"supported":58,"title":68,"abstract":70,"tags":71},[69],"remote sensing",[69],[28],{"code":73,"score":74,"supported":13,"title":75,"abstract":76,"tags":77},"I1",2,[],[],[27,26],[79,80],"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":82,"openalex_id":84,"authors":85,"venue":9,"cited_by_count":100,"oa_url":8,"card":8,"direction":101,"ingested_from":102},"W7214202229",[86,88,91,93,96,98],{"name":87,"orcid":8},"Pompy Deka",{"name":89,"orcid":90},"Jwngsar Moshahary","https:\u002F\u002Forcid.org\u002F0000-0002-3409-6691",{"name":92,"orcid":8},"Mrinal Choudhury",{"name":94,"orcid":95},"Perves Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2114-1043",{"name":97,"orcid":8},"Britan Rahman",{"name":99,"orcid":8},"Ranjit Sarma",0,"农业遥感与作物表型","openalex","2026-09-25T23:30:30.923946Z",{"total":105,"page":21,"page_size":105,"items":106},6,[107,180,253,343,418,493],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":8,"source_name":113,"source_url":110,"published_at":114,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":115,"score_detail":116,"sources":119,"tags":121,"primary_topic":127,"secondary_topics":128,"classification":131,"search_phrases":152,"slug":155,"view_count":74,"doi":156,"paper":157,"created_at":179},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":16,"substance":117,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":118},20,"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[120],{"name":113,"url":110},[122,123,124,125,126],"数字农业","Sentinel-2","土壤有机碳","遥感监测","水稻休耕",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[129,130],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":132},{"candidates":133,"agricultural_evidence":58},[134,139,144,148],{"code":39,"score":57,"supported":58,"title":135,"abstract":136,"tags":138},[40],[137,40],"土壤",[124],{"code":32,"score":140,"supported":58,"title":141,"abstract":142,"tags":143},14,[33],[34,33],[],{"code":45,"score":20,"supported":58,"title":145,"abstract":146,"tags":147},[],[28,69],[123,125],{"code":73,"score":21,"supported":13,"title":149,"abstract":150,"tags":151},[],[],[122],[153,154],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":156,"openalex_id":158,"authors":159,"venue":113,"cited_by_count":100,"oa_url":110,"card":174,"direction":101,"ingested_from":102},"W7214617000",[160,163,165,168,171],{"name":161,"orcid":162},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":164,"orcid":8},"D Priya",{"name":166,"orcid":167},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":169,"orcid":170},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":172,"orcid":173},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":175,"method":176,"finding":177,"direction":101,"opportunity":178},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":8,"source_name":186,"source_url":183,"published_at":187,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":188,"score_detail":189,"sources":192,"tags":194,"primary_topic":197,"secondary_topics":198,"classification":200,"search_phrases":234,"slug":237,"view_count":100,"doi":238,"paper":239,"created_at":252},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":17,"substance":190,"depth":17,"authority":16,"freshness":20,"relevant":21,"comment":191},22,"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[193],{"name":186,"url":183},[26,195,34,196,28],"农业人工智能","产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[199],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":201},{"candidates":202,"agricultural_evidence":58},[203,207,211,218,225,229],{"code":32,"score":57,"supported":58,"title":204,"abstract":205,"tags":206},[33],[34,33],[34],{"code":45,"score":20,"supported":58,"title":208,"abstract":209,"tags":210},[],[28,69],[196,28],{"code":212,"score":105,"supported":58,"title":213,"abstract":214,"tags":217},"A8",[],[215,216],"粮食安全","food security",[],{"code":219,"score":220,"supported":58,"title":221,"abstract":222,"tags":224},"J1",3,[],[223],"农业规划",[],{"code":73,"score":21,"supported":13,"title":226,"abstract":227,"tags":228},[],[],[26],{"code":230,"score":21,"supported":13,"title":231,"abstract":232,"tags":233},"I2",[],[],[195],[235,236],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":238,"openalex_id":240,"authors":241,"venue":186,"cited_by_count":100,"oa_url":183,"card":247,"direction":101,"ingested_from":102},"W7222942064",[242,244],{"name":243,"orcid":8},"Nayana Venkatesh",{"name":245,"orcid":246},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":248,"method":249,"finding":250,"direction":101,"opportunity":251},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":8,"source_name":259,"source_url":256,"published_at":260,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":271,"secondary_topics":272,"classification":279,"search_phrases":328,"slug":331,"view_count":74,"doi":8,"paper":332,"created_at":342},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":18,"substance":117,"depth":263,"authority":19,"freshness":20,"relevant":21,"comment":264},17,"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。",[266],{"name":259,"url":256},[26,268,28,269,270],"精准农业","盐碱地治理","土壤监测",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[273,278],{"code":274,"slug":275,"name":276,"path":277,"parent_code":49},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":13,"version":52,"method":53,"evidence":280},{"candidates":281,"agricultural_evidence":58},[282,287,295,299,304,310,316,322],{"code":45,"score":17,"supported":58,"title":283,"abstract":284,"tags":286},[69],[28,69,285],"multispectral",[28],{"code":274,"score":263,"supported":58,"title":288,"abstract":290,"tags":294},[289],"GIS",[291,292,289,293],"地理信息","空间分析","geographic information",[],{"code":39,"score":57,"supported":58,"title":296,"abstract":297,"tags":298},[40],[137,40],[269],{"code":73,"score":12,"supported":58,"title":300,"abstract":302,"tags":303},[301],"precision agriculture",[],[26,268],{"code":230,"score":105,"supported":58,"title":305,"abstract":306,"tags":309},[],[307,308],"机器学习","machine learning",[],{"code":311,"score":220,"supported":58,"title":312,"abstract":313,"tags":315},"H3",[],[314],"灌溉",[],{"code":317,"score":220,"supported":58,"title":318,"abstract":319,"tags":321},"H7",[],[320],"干旱",[],{"code":323,"score":220,"supported":58,"title":324,"abstract":325,"tags":327},"I7",[],[326],"methodological framework",[],[329,330],"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":8,"openalex_id":333,"authors":334,"venue":259,"cited_by_count":100,"oa_url":256,"card":337,"direction":101,"ingested_from":102},"W7221843179",[335],{"name":336,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":338,"method":339,"finding":340,"direction":101,"opportunity":341},"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":344,"title":345,"url":346,"summary":347,"summary_zh":348,"content":8,"source_name":349,"source_url":346,"published_at":350,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":351,"score_detail":352,"sources":356,"tags":358,"primary_topic":361,"secondary_topics":362,"classification":368,"search_phrases":392,"slug":395,"view_count":100,"doi":396,"paper":397,"created_at":417},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":16,"substance":353,"depth":263,"authority":19,"freshness":354,"relevant":21,"comment":355},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。",[357],{"name":349,"url":346},[26,195,28,359,360],"土壤盐渍化","新疆农业",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[363,364],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":230,"slug":365,"name":366,"path":367,"parent_code":49},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":51,"locked":13,"version":52,"method":53,"evidence":369},{"candidates":370,"agricultural_evidence":58},[371,375,379,384,388],{"code":45,"score":57,"supported":58,"title":372,"abstract":373,"tags":374},[69],[28,69],[28],{"code":39,"score":140,"supported":58,"title":376,"abstract":377,"tags":378},[40],[137,40],[],{"code":230,"score":354,"supported":58,"title":380,"abstract":382,"tags":383},[381],"deep learning",[],[195],{"code":317,"score":220,"supported":58,"title":385,"abstract":386,"tags":387},[],[320],[],{"code":73,"score":21,"supported":13,"title":389,"abstract":390,"tags":391},[],[],[26],[393,394],"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":396,"openalex_id":398,"authors":399,"venue":349,"cited_by_count":100,"oa_url":346,"card":412,"direction":101,"ingested_from":102},"W7221816183",[400,402,405,407,409],{"name":401,"orcid":8},"Mingjie Qian",{"name":403,"orcid":404},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":406,"orcid":8},"Peng Wang",{"name":408,"orcid":8},"Hangyuan Liu",{"name":410,"orcid":411},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":413,"method":414,"finding":415,"direction":101,"opportunity":416},"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":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":8,"source_name":424,"source_url":421,"published_at":260,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":351,"score_detail":425,"sources":427,"tags":429,"primary_topic":432,"secondary_topics":433,"classification":435,"search_phrases":475,"slug":478,"view_count":21,"doi":479,"paper":480,"created_at":492},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":16,"substance":353,"depth":263,"authority":19,"freshness":354,"relevant":21,"comment":426},"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。",[428],{"name":424,"url":421},[26,268,28,430,431],"土壤健康","覆盖作物",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[434],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":436},{"candidates":437,"agricultural_evidence":58},[438,442,446,450,457,464,470],{"code":39,"score":57,"supported":58,"title":439,"abstract":440,"tags":441},[40],[137,40],[430],{"code":45,"score":16,"supported":58,"title":443,"abstract":444,"tags":445},[69],[28],[28],{"code":73,"score":20,"supported":58,"title":447,"abstract":448,"tags":449},[],[268,301],[26,268],{"code":451,"score":105,"supported":58,"title":452,"abstract":453,"tags":456},"A4",[],[454,455],"油菜","rapeseed",[],{"code":458,"score":105,"supported":58,"title":459,"abstract":460,"tags":463},"H8",[],[461,462],"可持续农业","sustainable agriculture",[],{"code":465,"score":220,"supported":58,"title":466,"abstract":467,"tags":469},"B1",[],[468],"萝卜",[],{"code":471,"score":21,"supported":13,"title":472,"abstract":473,"tags":474},"A7",[],[],[431],[476,477],"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":479,"openalex_id":481,"authors":482,"venue":424,"cited_by_count":100,"oa_url":8,"card":487,"direction":101,"ingested_from":102},"W7221669942",[483,485],{"name":484,"orcid":8},"Ali Mirzakhani Nafchi",{"name":486,"orcid":8},"Salman Mirzaee",{"tldr":488,"method":489,"finding":490,"direction":101,"opportunity":491},"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",{"id":494,"title":495,"url":496,"summary":497,"summary_zh":8,"content":8,"source_name":498,"source_url":496,"published_at":499,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":261,"score_detail":500,"sources":502,"tags":504,"primary_topic":506,"secondary_topics":507,"classification":510,"search_phrases":529,"slug":532,"view_count":21,"doi":533,"paper":534,"created_at":566},4500,"Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11163942\u002Fv1","Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square","Research Square","2026-10-08T00:00:00Z",{"impact":17,"substance":117,"depth":17,"authority":12,"freshness":20,"relevant":21,"comment":501},"将无人机遥感与深度学习结合用于水稻白叶枯病早期检测，方法有创新、结论具应用价值，但属预印本且为细分技术进展，适合进入主题聚合而非头条精选。",[503],{"name":498,"url":496},[26,195,34,28,505],"病害检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[508,509],{"code":230,"slug":365,"name":366,"path":367,"parent_code":49},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":511},{"candidates":512,"agricultural_evidence":58},[513,517,521,525],{"code":32,"score":16,"supported":58,"title":514,"abstract":515,"tags":516},[33],[33],[34],{"code":230,"score":16,"supported":58,"title":518,"abstract":519,"tags":520},[381],[381],[195],{"code":45,"score":16,"supported":58,"title":522,"abstract":523,"tags":524},[69],[69],[28],{"code":73,"score":21,"supported":13,"title":526,"abstract":527,"tags":528},[],[],[26],[530,531],"无人机遥感 水稻 白叶枯病","深度学习 水稻病害 早期检测","无人机遥感水稻白叶枯病-4500","10.21203\u002Frs.3.rs-11163942\u002Fv1",{"doi":533,"openalex_id":535,"authors":536,"venue":498,"cited_by_count":100,"oa_url":496,"card":8,"direction":101,"ingested_from":102},"W7221055917",[537,540,543,545,547,550,552,554,557,559,561,563],{"name":538,"orcid":539},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":541,"orcid":542},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":544,"orcid":8},"Ian  Carlo Perilla",{"name":546,"orcid":8},"Stephen Klassen",{"name":548,"orcid":549},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":551,"orcid":8},"Raphael  Gabriel Carvajal",{"name":553,"orcid":8},"Kendrix Ortega",{"name":555,"orcid":556},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":558,"orcid":8},"Nurmi Pangesti",{"name":560,"orcid":8},"Caesar Arloo R. Centeno",{"name":562,"orcid":8},"Arnel Rala",{"name":564,"orcid":565},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z"]