[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4824":3,"related-4824":112},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":87,"slug":90,"view_count":91,"doi":92,"paper":93,"created_at":111},4824,"Robust site-specific radiometric calibration of PlanetScope NDVI using UAS-based reference data for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3897\u002Fjbgs.e185817","Index images provide opportunities for monitoring phenotypic characteristics such as plant height and disease symptoms, as well as the detection of pest presence. This allows for targeted application of agrochemicals and the implementation of preventive measures, leading to increased yields and reduced environmental impact. The study focuses on the radiometric inconsistencies between satellite-based imagery and high-resolution unmanned aerial system (UAS) imagery. While PlanetScope provides high temporal resolution, its spectral response often deviates from ground-truth UAS flight missions due to varying atmospheric conditions and sensor specifications. A robust site-specific calibration workflow using Quantile-Based Piecewise Linear Normalization and residual-based outlier mitigation was used. The method was tested across three different crop types: barley, sunflower and maize. Initial results showed significant systematic offset. Following the application of the proposed robust normalization model, the root mean square error (RMSE) was reduced by up to 39%, while maintaining high coefficients of determination. The study demonstrates that site-specific non-linear calibration is essential for reliable crop health monitoring, particularly in heterogeneous fields where soil background and canopy shadows significantly affect the spectral signature satellite data, enabling accurate multi-temporal analysis and better-informed decisions for variable-rate fertilization and yield prediction.","指数影像为监测株高、病害症状等表型特征以及检测害虫存在提供了机会。这使得农药的精准施用和预防措施的实施成为可能，从而提高产量并减少环境影响。本研究聚焦于卫星影像与高分辨率无人机系统（UAS）影像之间的辐射不一致性问题。尽管PlanetScope具有高时间分辨率，但由于大气条件和传感器规格的差异，其光谱响应往往偏离地面实测的UAS飞行任务数据。本研究采用了一种稳健的站点特异性校准工作流程，使用基于分位数的分段线性归一化与基于残差的异常值抑制方法。该方法在三种不同作物类型上进行了测试：大麦、向日葵和玉米。初步结果显示出显著的系统性偏移。在应用所提出的稳健归一化模型后，均方根误差（RMSE）最多降低了39%，同时保持了较高的决定系数。研究表明，站点特异性非线性校准对于可靠的作物健康监测至关重要，尤其是在土壤背景和冠层阴影显著影响卫星数据光谱特征的异质田块中，该方法能够实现准确的多时相分析，并为变量施肥和产量预测提供更明智的决策依据。",null,"Journal of the Bulgarian Geographical Society","2026-10-12T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"该论文提出基于UAS参考数据的PlanetScope NDVI站点特异性辐射定标方法，RMSE降低39%，对精准农业多时相监测有实用价值，但属细分方法研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","遥感","NDVI","作物监测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":86},[39,46,54,61,68,75,81],{"code":40,"score":13,"supported":41,"title":42,"abstract":44,"tags":45},"I1",true,[43],"precision agriculture",[],[27,28],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":53},"A3",6,[],[51,52],"玉米","maize",[],{"code":55,"score":48,"supported":41,"title":56,"abstract":57,"tags":60},"F3",[],[58,59],"施肥","fertilization",[],{"code":62,"score":48,"supported":41,"title":63,"abstract":64,"tags":67},"H2",[],[65,66],"土壤","soil",[],{"code":69,"score":70,"supported":41,"title":71,"abstract":72,"tags":74},"A4",3,[],[73],"向日葵",[],{"code":76,"score":70,"supported":41,"title":77,"abstract":78,"tags":80},"F4",[],[79],"农药",[],{"code":82,"score":70,"supported":14,"title":83,"abstract":84,"tags":85},"I3",[],[],[30,31,29],"多个产业证据相当，需要确认主归属",[88,89],"PlanetScope NDVI 辐射定标","UAS 无人机 遥感 精准农业","PlanetScopeNDVI辐射定标-4824",0,"10.3897\u002Fjbgs.e185817",{"doi":92,"openalex_id":94,"authors":95,"venue":10,"cited_by_count":91,"oa_url":103,"card":104,"direction":108,"ingested_from":110},"W7221533152",[96,98,101],{"name":97,"orcid":9},"Yana Lipiyska",{"name":99,"orcid":100},"Tsvetelina Atansova-Evdenova","https:\u002F\u002Forcid.org\u002F0009-0000-5669-6439",{"name":102,"orcid":9},"A. S. Yordanov","https:\u002F\u002Fjbgs.arphahub.com\u002Farticle\u002F185817\u002Fdownload\u002Fpdf\u002F",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"提出基于无人机参考数据的站点特异性辐射校准方法，提升PlanetScope NDVI精度。","分位数分段线性归一化与残差异常值抑制，测试大麦、向日葵和玉米。","校准后RMSE降低达39%，保持高决定系数，证明站点非线性校准必要性。","农业遥感与作物表型","可探索多时相、多作物通用校准模型，结合土壤背景与冠层阴影动态校正。","openalex","2026-10-13T23:30:23.916508Z",{"total":48,"page":22,"page_size":48,"items":113},[114,181,273,321,412,486],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":137,"classification":138,"search_phrases":163,"slug":166,"view_count":91,"doi":167,"paper":168,"created_at":180},5404,"Validation of a Day-of-Year Standardized Canopy Indicator for Regional Crop Monitoring: Suitability and Decoupling Boundaries","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1707.v1","Translating satellite-derived crop canopy anomalies into regional production signals is critical for agricultural monitoring. However, standard anomaly indicators assume a stable, direct coupling between greenness and production, ignoring phenological shifts, sub-seasonal cropping dynamics, and harvested acreage fluctuations. I evaluate the validation boundaries and expected conditions of a pixel-level, day-of-year (DOY)-first Leaf Area Index (LAI) Z-score anomaly indicator with a leave- year-out baseline (2003–2022) across five contrasting global breadbaskets: Australia, Indo-Gangetic Plains (IGP), North China Plain (NCP), US Corn Belt, and Ukraine. The analysis shows that DOY- first standardization reduces temporal composite timing mismatches, though coupling with regional statistics varies systematically. Retrospective validation reveals that direct canopy-yield coupling remains robust exclusively in rainfed Australia (Pearson’s r = 0.90), where yield variability dominates production variance (explaining 90% of variance) and moisture stress limits canopy development. In contrast, three interpretation boundaries are identified: (1) greening trend-sensitivity in the IGP, where detrending reduces the association to r = 0.53; (2) temporal representativeness bias in the double-cropped NCP, requiring crop-calendar windowing to recover the wheat signal (r = 0.51); and (3) area-shock-sensitivity in Ukraine (where harvested area contractions explain 78% of production variance), decoupling canopy vigor from production (r = 0.40). Furthermore, the LAI Z-score provides a more interpretable canopy-response measure than the Vegetation Condition Index (VCI) in dense canopies where NDVI saturates. These validation levels are synthesized into a prototype diagnostic decision framework to support indicator configuration in operational monitoring systems.","将卫星反演的作物冠层异常转化为区域产量信号对农业监测至关重要。然而，标准异常指标假定绿度与产量之间存在稳定、直接的耦合关系，忽略了物候偏移、次季节作物动态以及收获面积波动。我评估了一种像元级、以年积日（DOY）为首要标准化维度的叶面积指数（LAI）Z分数异常指标在五个对比鲜明的全球粮仓——澳大利亚、印度河-恒河平原（IGP）、华北平原（NCP）、美国玉米带和乌克兰——的验证边界与预期适用条件，该指标采用留一年基线（2003—2022年）。分析表明，以DOY为首要标准化维度可减少时间合成时相不匹配，但其与区域统计数据的耦合关系存在系统性差异。回顾性验证显示，冠层与产量的直接耦合仅在雨养条件下的澳大利亚保持稳健（Pearson's r = 0.90），该地区产量变异性主导产量方差（解释90%的方差），且水分胁迫限制冠层发育。相比之下，识别出三个解释边界：（1）IGP的绿度趋势敏感性，去趋势后关联性降至r = 0.53；（2）双季种植的NCP的时间代表性偏差，需按作物日历开窗以恢复小麦信号（r = 0.51）；（3）乌克兰的面积冲击敏感性（收获面积缩减解释78%的产量方差），导致冠层活力与产量解耦（r = 0.40）。此外，在NDVI饱和的密集冠层中，LAI Z分数提供了比植被条件指数（VCI）更具可解释性的冠层响应度量。这些验证层级被综合为一个原型诊断决策框架，以支持业务化监测系统中的指标配置。","Preprints.org","2026-10-19T00:00:00Z",65,{"impact":17,"substance":18,"depth":124,"authority":48,"freshness":125,"relevant":22,"comment":126},18,8,"预印本提出按年积日标准化的LAI异常指标，并在五大农业区验证其适用边界，方法有新意但尚未经同行评审，属细分领域进展。",[128],{"name":120,"url":117},[27,29,31,130,131],"产量估测","叶面积指数",{"code":133,"slug":134,"name":135,"path":136,"parent_code":9},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":139,"locked":14,"version":35,"method":36,"evidence":140},"partial",{"candidates":141,"agricultural_evidence":41,"reason":162},[142,149,154,158],{"code":143,"score":48,"supported":41,"title":144,"abstract":145,"tags":148},"A2",[],[146,147],"小麦","wheat",[],{"code":47,"score":48,"supported":41,"title":150,"abstract":151,"tags":153},[],[51,152],"corn",[],{"code":82,"score":70,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[130,31,131,29],{"code":40,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[27],"一级领域明确，二级方向需要细分",[164,165],"LAI Z-score 作物监测","遥感 作物产量 估测","LAIZ-score作物监测-5404","10.20944\u002Fpreprints202610.1707.v1",{"doi":167,"openalex_id":169,"authors":170,"venue":120,"cited_by_count":91,"oa_url":117,"card":174,"direction":179,"ingested_from":110},"W7222967882",[171],{"name":172,"orcid":173},"Xiaolong Song","https:\u002F\u002Forcid.org\u002F0000-0002-2106-6629",{"tldr":175,"method":176,"finding":177,"direction":108,"opportunity":178},"验证了按年积日标准化的LAI Z-score冠层指标在五大农业区的适用边界。","用2003–2022年留一年基线，在五个全球粮仓区做LAI DOY标准化与产量相","仅雨养澳大利亚冠层-产量强耦合(r=0.90)，其余区域受趋势、物候和面积冲击影响而解耦。","可发展融合物候窗口、去趋势与收获面积因子的分区自适应冠层异常指标诊断框架。","农业人工智能与决策模型","2026-10-20T23:30:40.675672Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":188,"score_detail":189,"sources":195,"tags":197,"primary_topic":200,"secondary_topics":206,"classification":207,"search_phrases":240,"slug":243,"view_count":231,"doi":244,"paper":245,"created_at":272},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences",93,{"impact":190,"substance":191,"depth":192,"authority":193,"freshness":21,"relevant":22,"comment":194},26,24,19,15,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[196],{"name":187,"url":184},[27,198,28,29,199],"农业人工智能","全球评估",{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":208,"locked":14,"version":35,"method":36,"evidence":209},"classified",{"candidates":210,"agricultural_evidence":41},[211,216,223,230,235],{"code":40,"score":212,"supported":41,"title":213,"abstract":214,"tags":215},11,[],[27,28,43],[27,28],{"code":201,"score":217,"supported":41,"title":218,"abstract":219,"tags":222},7,[],[220,221],"机器学习","machine learning",[198],{"code":224,"score":48,"supported":41,"title":225,"abstract":226,"tags":229},"H3",[],[227,228],"灌溉","irrigation",[],{"code":82,"score":231,"supported":41,"title":232,"abstract":233,"tags":234},4,[],[29],[29],{"code":76,"score":70,"supported":41,"title":236,"abstract":237,"tags":239},[],[238],"除草",[],[241,242],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":244,"openalex_id":246,"authors":247,"venue":187,"cited_by_count":91,"oa_url":184,"card":266,"direction":270,"ingested_from":110},"W7222974188",[248,251,254,257,260,263],{"name":249,"orcid":250},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":252,"orcid":253},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":255,"orcid":256},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":258,"orcid":259},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":261,"orcid":262},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":264,"orcid":265},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":267,"method":268,"finding":269,"direction":270,"opportunity":271},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","智慧农业 \u002F 农业物联网","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":9,"content":9,"source_name":278,"source_url":276,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":280,"sources":283,"tags":285,"primary_topic":9,"secondary_topics":288,"classification":289,"search_phrases":302,"slug":305,"view_count":91,"doi":306,"paper":307,"created_at":320},5241,"Assessing Nutritional Status in Super-High-Density Olive Orchards: Integrating Diagnostic Models and NDVI-Based Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11156833\u002Fv1","Assessing Nutritional Status in Super-High-Density Olive Orchards: Integrating Diagnostic Models and NDVI-Based Remote Sensing。Research Square","Research Square","2026-10-16T00:00:00Z",{"impact":281,"substance":124,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":282},16,"该研究将诊断模型与NDVI遥感结合用于超密植油橄榄园营养评估，方法有创新、对智慧果园管理有参考价值，但属预印本且应用面偏窄，可入精选但非头条。",[284],{"name":278,"url":276},[27,29,30,286,287],"果园管理","油橄榄",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":290},{"candidates":291,"agricultural_evidence":14,"reason":301},[292,297],{"code":82,"score":20,"supported":41,"title":293,"abstract":295,"tags":296},[294],"remote sensing",[294],[30,29],{"code":40,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[27],"农业相关性需要复核",[303,304],"超密植 油橄榄 营养诊断","NDVI 遥感 果园","超密植油橄榄营养诊断-5241","10.21203\u002Frs.3.rs-11156833\u002Fv1",{"doi":306,"openalex_id":308,"authors":309,"venue":278,"cited_by_count":91,"oa_url":319,"card":9,"direction":270,"ingested_from":110},"W7222710807",[310,313,316],{"name":311,"orcid":312},"Mahrokh Sharifmand","https:\u002F\u002Forcid.org\u002F0009-0006-8010-5348",{"name":314,"orcid":315},"Mehdi Taheri","https:\u002F\u002Forcid.org\u002F0000-0002-9228-763X",{"name":317,"orcid":318},"Ebrahim Sepehr","https:\u002F\u002Forcid.org\u002F0000-0001-5843-0669","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11156833\u002Flatest.pdf","2026-10-18T23:30:11.167282Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":333,"tags":335,"primary_topic":338,"secondary_topics":342,"classification":352,"search_phrases":396,"slug":399,"view_count":400,"doi":9,"paper":401,"created_at":411},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":281,"substance":331,"depth":19,"authority":20,"freshness":125,"relevant":22,"comment":332},20,"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。",[334],{"name":327,"url":324},[27,28,29,336,337],"盐碱地治理","土壤监测",{"code":82,"slug":339,"name":340,"path":341,"parent_code":205},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[343,348],{"code":344,"slug":345,"name":346,"path":347,"parent_code":205},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":62,"slug":66,"name":349,"path":350,"parent_code":351},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":208,"locked":14,"version":35,"method":36,"evidence":353},{"candidates":354,"agricultural_evidence":41},[355,360,368,372,376,380,384,390],{"code":82,"score":124,"supported":41,"title":356,"abstract":357,"tags":359},[294],[29,294,358],"multispectral",[29],{"code":344,"score":19,"supported":41,"title":361,"abstract":363,"tags":367},[362],"GIS",[364,365,362,366],"地理信息","空间分析","geographic information",[],{"code":62,"score":193,"supported":41,"title":369,"abstract":370,"tags":371},[66],[65,66],[336],{"code":40,"score":13,"supported":41,"title":373,"abstract":374,"tags":375},[43],[],[27,28],{"code":201,"score":48,"supported":41,"title":377,"abstract":378,"tags":379},[],[220,221],[],{"code":224,"score":70,"supported":41,"title":381,"abstract":382,"tags":383},[],[227],[],{"code":385,"score":70,"supported":41,"title":386,"abstract":387,"tags":389},"H7",[],[388],"干旱",[],{"code":391,"score":70,"supported":41,"title":392,"abstract":393,"tags":395},"I7",[],[394],"methodological framework",[],[397,398],"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":402,"authors":403,"venue":327,"cited_by_count":91,"oa_url":324,"card":406,"direction":108,"ingested_from":110},"W7221843179",[404],{"name":405,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":407,"method":408,"finding":409,"direction":108,"opportunity":410},"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":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":419,"sources":421,"tags":423,"primary_topic":426,"secondary_topics":427,"classification":429,"search_phrases":468,"slug":471,"view_count":22,"doi":472,"paper":473,"created_at":485},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":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":420},"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。",[422],{"name":418,"url":415},[27,28,29,424,425],"土壤健康","覆盖作物",{"code":62,"slug":66,"name":349,"path":350,"parent_code":351},[428],{"code":82,"slug":339,"name":340,"path":341,"parent_code":205},{"status":208,"locked":14,"version":35,"method":36,"evidence":430},{"candidates":431,"agricultural_evidence":41},[432,436,440,444,450,457,463],{"code":62,"score":193,"supported":41,"title":433,"abstract":434,"tags":435},[66],[65,66],[424],{"code":82,"score":17,"supported":41,"title":437,"abstract":438,"tags":439},[294],[29],[29],{"code":40,"score":125,"supported":41,"title":441,"abstract":442,"tags":443},[],[28,43],[27,28],{"code":69,"score":48,"supported":41,"title":445,"abstract":446,"tags":449},[],[447,448],"油菜","rapeseed",[],{"code":451,"score":48,"supported":41,"title":452,"abstract":453,"tags":456},"H8",[],[454,455],"可持续农业","sustainable agriculture",[],{"code":458,"score":70,"supported":41,"title":459,"abstract":460,"tags":462},"B1",[],[461],"萝卜",[],{"code":464,"score":22,"supported":14,"title":465,"abstract":466,"tags":467},"A7",[],[],[425],[469,470],"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":472,"openalex_id":474,"authors":475,"venue":418,"cited_by_count":91,"oa_url":9,"card":480,"direction":108,"ingested_from":110},"W7221669942",[476,478],{"name":477,"orcid":9},"Ali Mirzakhani Nafchi",{"name":479,"orcid":9},"Salman Mirzaee",{"tldr":481,"method":482,"finding":483,"direction":108,"opportunity":484},"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":487,"title":488,"url":489,"summary":490,"summary_zh":491,"content":9,"source_name":492,"source_url":489,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":493,"score_detail":494,"sources":497,"tags":499,"primary_topic":501,"secondary_topics":505,"classification":506,"search_phrases":530,"slug":533,"view_count":22,"doi":534,"paper":535,"created_at":551},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors",78,{"impact":124,"substance":331,"depth":124,"authority":495,"freshness":125,"relevant":22,"comment":496},14,"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[498],{"name":492,"url":489},[27,198,28,29,500],"多源数据融合",{"code":205,"slug":502,"name":503,"path":504,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":139,"locked":14,"version":35,"method":36,"evidence":507},{"candidates":508,"agricultural_evidence":41,"reason":162},[509,513,519,523],{"code":40,"score":281,"supported":41,"title":510,"abstract":511,"tags":512},[43],[28,43],[27,28],{"code":201,"score":193,"supported":41,"title":514,"abstract":516,"tags":518},[515],"deep learning",[517,515],"深度学习",[198],{"code":82,"score":193,"supported":41,"title":520,"abstract":521,"tags":522},[294],[29,294],[29],{"code":524,"score":217,"supported":41,"title":525,"abstract":526,"tags":529},"I5",[],[527,528],"边缘计算","edge computing",[500],[531,532],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":534,"openalex_id":536,"authors":537,"venue":492,"cited_by_count":91,"oa_url":489,"card":546,"direction":108,"ingested_from":110},"W7221565940",[538,541,544],{"name":539,"orcid":540},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":542,"orcid":543},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":545,"orcid":9},"Cundeng Wang",{"tldr":547,"method":548,"finding":549,"direction":108,"opportunity":550},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z"]