[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3677":3,"related-3677":62},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":61},3677,"A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04697-2","Abstract Sustainable development has become a key priority for the agricultural sector, both globally and in Quebec. Digital agriculture, as an important component of sustainable agricultural systems, represents a promising pathway toward achieving sustainable development goals. However, despite the recognized benefits of digital agriculture, the opportunities, limitations, and research gaps associated with its contribution to sustainable development in Quebec remain insufficiently understood. This study applies a systematic tracking approach to identify applications of digital technologies in Quebec’s agricultural sector and evaluate their potential contributions to provincial sustainable development objectives while highlighting gaps and opportunities for their enhanced deployment. A total of 59 peer-reviewed publications and grey literature reports were identified and analyzed. Overall, 14 categories of digital technologies were documented within Quebec’s agricultural sector. Their representation in the scientific literature was primarily concentrated in three dominant categories: web\u002Fmobile applications and software (31%), machine learning and sensors (14%), and satellite technologies (13%). The identified digital technologies have significant potential to support sustainable development objectives in Quebec agriculture through improved pesticide monitoring, soil health assessment and conservation, fertilizer management optimization, water-use efficiency, and biodiversity conservation. Collectively, these technologies could be combined in 65 distinct ways to support sustainable development goals; however, only 54% of this potential is currently being exploited in Quebec. This highlights substantial opportunities for expanding the role of digital agriculture in addressing sustainability challenges. The study also reveals important knowledge gaps, particularly regarding the social dimensions influencing the diffusion and adoption of digital technologies in agriculture. A limited understanding of the factors affecting Quebec farmers’ adoption decisions remains a major barrier to maximizing the benefits of digital agriculture. Addressing these social and behavioral dimensions will be essential for accelerating the transition toward more sustainable, digitally enabled agricultural systems in Quebec.","摘要 可持续发展已成为全球及魁北克农业部门的重要优先事项。数字农业作为可持续农业系统的重要组成部分，是实现可持续发展目标的一条充满前景的路径。然而，尽管数字农业的益处已得到认可，但其对魁北克可持续发展所作贡献的机遇、局限及研究空白仍未被充分理解。本研究采用系统性追踪方法，识别数字技术在魁北克农业部门中的应用，评估其对省级可持续发展目标的潜在贡献，同时揭示其加强部署的空白与机遇。共识别并分析了59篇同行评审出版物和灰色文献报告。总体而言，魁北克农业部门中记录到14类数字技术。其在科学文献中的呈现主要集中在三个主导类别：网络\u002F移动应用程序与软件（31%）、机器学习与传感器（14%）以及卫星技术（13%）。所识别的数字技术通过改善农药监测、土壤健康评估与保护、肥料管理优化、水资源利用效率以及生物多样性保护，具有支持魁北克农业可持续发展目标的显著潜力。总体而言，这些技术可以65种不同方式组合以支持可持续发展目标；然而，魁北克目前仅开发利用了其中54%的潜力。这凸显了扩大数字农业在应对可持续性挑战方面作用的重大机遇。本研究还揭示了重要的知识空白，特别是影响数字技术在农业中扩散与采纳的社会维度方面。对影响魁北克农民采纳决策的因素了解有限，仍是最大化数字农业效益的主要障碍。解决这些社会与行为维度对于加速魁北克向更可持续、数字化赋能的农业系统转型至关重要。",null,"Discover Sustainability","2026-09-27T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"基于59篇文献的系统梳理，量化了魁北克数字农业技术类别与可持续目标支撑潜力，指出仅54%潜力被利用及农户采纳的社会维度研究缺口，对区域数字农业政策有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","农业遥感","机器学习","农业可持续发展",[33,34],"魁北克 数字农业","魁北克 农业技术 应用","魁北克数字农业-3677",0,"10.1007\u002Fs43621-026-04697-2",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7214562996",[41,43,46,49,51],{"name":42,"orcid":9},"Sambiani D. Y. Tindjiete",{"name":44,"orcid":45},"Terence Épule Épule","https:\u002F\u002Forcid.org\u002F0000-0002-5756-382X",{"name":47,"orcid":48},"Daniel Etongo","https:\u002F\u002Forcid.org\u002F0000-0002-8237-0843",{"name":50,"orcid":9},"Changhui Peng",{"name":52,"orcid":53},"Paul Célicourt","https:\u002F\u002Forcid.org\u002F0000-0001-9297-6593",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"系统盘点魁北克数字农业技术应用及其对可持续发展的贡献与知识空白。","系统追踪59篇同行评议与灰色文献，归类14类数字技术并评估组合潜力。","技术可65种组合支撑可持续发展目标，但仅54%潜力被利用，社会采纳因素研究不足。","数字乡村与农业信息化","可深入研究农户数字技术采纳的社会行为因素，填补魁北克及类似地区扩散机制的研究空白。","openalex","2026-09-28T23:30:36.987716Z",{"total":63,"page":22,"page_size":63,"items":64},6,[65,130,164,199,243,290],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":9,"source_name":71,"source_url":68,"published_at":72,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":74,"sources":77,"tags":79,"search_phrases":82,"slug":85,"view_count":36,"doi":86,"paper":87,"created_at":129},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment","2026-09-22T00:00:00Z",64,{"impact":75,"substance":19,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":76},8,"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[78],{"name":71,"url":68},[28,29,80,30,81],"水稻","高光谱遥感",[83,84],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":86,"openalex_id":88,"authors":89,"venue":71,"cited_by_count":36,"oa_url":9,"card":123,"direction":127,"ingested_from":60},"W7214027889",[90,93,96,99,102,105,107,109,112,115,117,119,121],{"name":91,"orcid":92},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":94,"orcid":95},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":97,"orcid":98},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":100,"orcid":101},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":103,"orcid":104},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":106,"orcid":9},"Ying Luo",{"name":108,"orcid":9},"Jiaqian Zhang",{"name":110,"orcid":111},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":113,"orcid":114},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":116,"orcid":9},"Chaoliang Peng",{"name":118,"orcid":9},"Duan Tian",{"name":120,"orcid":9},"Weihao Wang",{"name":122,"orcid":9},"Jiaxin Liu",{"tldr":124,"method":125,"finding":126,"direction":127,"opportunity":128},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","农业遥感与作物表型","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":9,"content":9,"source_name":135,"source_url":133,"published_at":72,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":140,"tags":142,"search_phrases":145,"slug":148,"view_count":36,"doi":149,"paper":150,"created_at":163},3135,"Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10453-3","Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework。Precision Agriculture","Precision Agriculture",82,{"impact":19,"substance":18,"depth":19,"authority":138,"freshness":13,"relevant":22,"comment":139},14,"方法新颖、数据可靠，对草地精准施肥有参考价值，但属细分领域研究，未达重大突破层级。",[141],{"name":135,"url":133},[28,143,29,30,144],"无人机","草地氮素",[146,147],"UAV 多光谱 草地 氮素","Precision Agriculture 氮素估算","UAV多光谱草地氮素-3135","10.1007\u002Fs11119-026-10453-3",{"doi":149,"openalex_id":151,"authors":152,"venue":135,"cited_by_count":36,"oa_url":9,"card":9,"direction":9,"ingested_from":60},"W7213942551",[153,155,158,161],{"name":154,"orcid":9},"Antônio de Oliveira Costa Neto",{"name":156,"orcid":157},"Yiannis Ampatzidis","https:\u002F\u002Forcid.org\u002F0000-0002-3660-3298",{"name":159,"orcid":160},"Andrea Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-1521-6942",{"name":162,"orcid":9},"Jim Fletcher","2026-09-22T23:30:03.351447Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":179,"search_phrases":182,"slug":185,"view_count":36,"doi":186,"paper":187,"created_at":198},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna","2026-09-07T00:00:00Z",72,{"impact":19,"substance":18,"depth":19,"authority":174,"freshness":175,"relevant":22,"comment":176},12,2,"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[178],{"name":170,"url":167},[28,180,29,30,181],"精准施肥","小农户",[183,184],"农业遥感 智慧农业 机器学习 精准施肥","农业遥感 智慧农业","农业遥感智慧农业机器学习精准施肥-1888","10.4038\u002Fjur.v14i1.8134",{"doi":186,"openalex_id":188,"authors":189,"venue":170,"cited_by_count":36,"oa_url":167,"card":192,"direction":196,"ingested_from":60},"W7211915396",[190],{"name":191,"orcid":9},"Anuga Liyanage",{"tldr":193,"method":194,"finding":195,"direction":196,"opportunity":197},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","智慧农业 \u002F 农业物联网","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z",{"id":200,"title":201,"url":202,"summary":203,"summary_zh":204,"content":9,"source_name":205,"source_url":202,"published_at":206,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":207,"score_detail":208,"sources":212,"tags":214,"search_phrases":217,"slug":220,"view_count":36,"doi":221,"paper":222,"created_at":242},1751,"Downscaling SMAP Soil Moisture to 1 km with Machine Learning and MODIS Data for Agricultural Drought Assessment in Békés County, Hungary","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8090373","Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R2 = 0.71, RMSE = 0.0295 m3\u002Fm3) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling.","土壤水分（SM）的精确制图对于有效监测农业干旱至关重要。然而，被动微波产品（包括9公里分辨率的土壤水分主动被动（SMAP）反演数据）空间分辨率较粗，限制了其在区域和地方尺度上的有效性。为解决这一局限，本研究比较了三种基于机器学习的降尺度框架，旨在将匈牙利贝凯什县的SMAP土壤水分分辨率从9公里提升至1公里。研究时段覆盖2020年至2023年的生长季（4月至10月）。研究中将一组多时相MODIS衍生变量——包括植被指数（NDVI、EVI）、白天和夜间地表温度、蒸散量——以及土地覆盖分类和地形高程作为辅助预测变量。三种机器学习算法，即随机森林（RF）、极限梯度提升（XGBoost）和梯度提升机（GBM），被训练和评估。结果表明：（1）RF模型在测试阶段（R² = 0.71，RMSE = 0.0295 m³\u002Fm³）精度最高，且经四个原位监测站验证，确认其在小尺度上具有可靠的土壤水分估算能力；（2）白天地表温度是所有模型中最重要的预测变量，凸显了控制地表土壤水分动态的强烈热-湿耦合关系；（3）经过验证的RF模型生成的1公里标准化土壤水分指数（SSI）图有效捕捉了年际干旱变率，识别出2022年7月的严重干旱事件。总体而言，本研究提出了一种适用于中欧农业环境的降尺度方法，用于生成高分辨率土壤水分数据。所得到的1公里土壤水分和SSI产品为决策者在干旱期加强规划、通过改进灌溉调度减少农业损失提供了有价值的工具。","AgriEngineering","2026-09-04T00:00:00Z",66,{"impact":174,"substance":209,"depth":19,"authority":174,"freshness":210,"relevant":22,"comment":211},20,4,"研究利用机器学习将SMAP土壤水分降尺度至1km，提升农业干旱监测精度，方法新颖，数据详实，对区域农业管理有参考价值。",[213],{"name":205,"url":202},[28,29,30,215,216],"干旱监测","土壤水分",[218,219],"农业遥感 土壤水分 干旱监测 智慧农业","农业遥感 土壤水分","农业遥感土壤水分干旱监测智慧农业-1751","10.3390\u002Fagriengineering8090373",{"doi":221,"openalex_id":223,"authors":224,"venue":205,"cited_by_count":36,"oa_url":202,"card":236,"direction":241,"ingested_from":60},"W7208719644",[225,227,230,233],{"name":226,"orcid":9},"Mahrokh Shafiei",{"name":228,"orcid":229},"István Waltner","https:\u002F\u002Forcid.org\u002F0000-0002-6704-8936",{"name":231,"orcid":232},"Z. Vekerdy","https:\u002F\u002Forcid.org\u002F0000-0002-5677-8298",{"name":234,"orcid":235},"Gábor Halupka","https:\u002F\u002Forcid.org\u002F0000-0003-4640-0061",{"tldr":237,"method":238,"finding":239,"direction":127,"opportunity":240},"用机器学习将SMAP土壤水分降尺度至1km，评估匈牙利农业干旱。","比较RF、XGBoost、GBM，结合MODIS植被指数、地表温度等预测变量。","RF精度最高，白天LST最重要，成功捕捉2022年7月严重干旱。","可探索将降尺度方法应用于其他区域或作物，结合更高分辨率遥感或气象数据提升精度。","农业人工智能与决策模型","2026-09-05T23:30:50.054057Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":255,"tags":257,"search_phrases":260,"slug":263,"view_count":22,"doi":264,"paper":265,"created_at":289},1532,"Analysis of crop residue cover in the North China Plain based on multispectral remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727176","Crop residue cover (CRC) is a critical parameter for evaluating conservation tillage practices, mitigating soil erosion, and developing models for global carbon cycle monitoring. Traditional optical remote sensing index-based methods are significantly affected by variations in soil and crop residue moisture content as well as interference from emerging subsequent crops, resulting in low estimation accuracy and poor stability of CRC. Due to the presence of subsequent crops, the non-photosynthetic vegetation fraction (fNPV) derived from linear spectral mixture analysis is typically lower than the actual field CRC. This study proposes a novel CRC estimation method based on linear spectral mixture analysis. The workflow consists of three main components: (1) development of a Crop Residue Random Forest (CRRF) index using a random forest regression model to simulate the narrowband SINDRI index – which is less sensitive to moisture – from broadband multispectral reflectance; (2) construction of a two-dimensional triangular feature space combining the CRRF and NDVI indices, followed by linear spectral mixture analysis to simultaneously estimate field fNPV, photosynthetic vegetation fraction (fPV), and bare soil fraction (fBS); and (3) retrieval of the true CRC fraction at the sowing stage using the formula CRC = fNPV \u002F (1 − fPV). The proposed method was validated and applied regionally using multi-temporal Sentinel-2 MSI and MODIS imagery. Results demonstrate that: (1) the method effectively mitigates moisture interference and achieves high-accuracy fNPV estimation (R2 = 0.84, RMSE = 0.10); (2) the integration of Sentinel-2 and MODIS sensors enables stable estimation and spatio-temporal dynamic analysis of CRC across the North China Plain, with high temporal consistency between images acquired approximately 16 days apart (the majority of agricultural pixels showing absolute differences within ±0.15). This approach provides an efficient and robust technical solution for remote sensing monitoring of CRC fraction in complex agricultural environments and offers a valuable methodological reference for the assessment of conservation tillage practices.","作物残茬覆盖度（CRC）是评估保护性耕作措施、减缓土壤侵蚀以及构建全球碳循环监测模型的关键参数。传统光学遥感指数方法受土壤和作物残茬含水量变化及新生后茬作物干扰的影响显著，导致CRC估算精度低、稳定性差。由于后茬作物的存在，基于线性光谱混合分析提取的非光合植被覆盖度（fNPV）通常低于田间实际CRC值。本研究提出了一种基于线性光谱混合分析的CRC估算新方法。该方法流程主要包括三个部分：（1）利用随机森林回归模型模拟对水分敏感性较低的窄带SINDRI指数，进而构建作物残茬随机森林（CRRF）指数，该指数可由宽带多光谱反射率计算得到；（2）结合CRRF与NDVI指数构建二维三角特征空间，通过线性光谱混合分析同时估算田间fNPV、光合植被覆盖度（fPV）和裸土覆盖度（fBS）；（3）利用公式CRC = fNPV \u002F (1 − fPV)反演播种期真实CRC值。该方法利用多时相Sentinel-2 MSI和MODIS影像进行了验证及区域应用。结果表明：（1）该方法能有效削弱水分干扰，实现高精度fNPV估算（R² = 0.84，RMSE = 0.10）；（2）Sentinel-2与MODIS传感器的联合应用可实现华北平原CRC的稳定估算及时空动态分析，且间隔约16天获取的影像间具有较高时间一致性（绝大多数农业像元的绝对差异在±0.15以内）。该方法为复杂农业环境下CRC覆盖度的遥感监测提供了高效、稳健的技术方案，也为保护性耕作措施评估提供了有价值的方法参考。","International Journal of Remote Sensing","2026-09-02T00:00:00Z",76,{"impact":19,"substance":209,"depth":19,"authority":20,"freshness":253,"relevant":22,"comment":254},7,"提出基于随机森林与线性光谱混合分析的秸秆覆盖度估算新方法，在华北平原多源遥感数据上验证有效，对保护性耕作监测有方法论参考价值。",[256],{"name":249,"url":246},[28,29,30,258,259],"保护性耕作","秸秆覆盖",[261,262],"保护性耕作 农业遥感 智慧农业 机器学习","保护性耕作 农业遥感","保护性耕作农业遥感智慧农业机器学习-1532","10.1080\u002F01431161.2026.2727176",{"doi":264,"openalex_id":266,"authors":267,"venue":249,"cited_by_count":36,"oa_url":9,"card":284,"direction":127,"ingested_from":60},"W7205007595",[268,271,274,276,279,282],{"name":269,"orcid":270},"Jibo Yue","https:\u002F\u002Forcid.org\u002F0000-0001-9766-5313",{"name":272,"orcid":273},"Ranran Yang","https:\u002F\u002Forcid.org\u002F0000-0003-1937-2042",{"name":275,"orcid":9},"Yinghao Lin",{"name":277,"orcid":278},"Nianxu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-0152-8750",{"name":280,"orcid":281},"Qingjiu Tian","https:\u002F\u002Forcid.org\u002F0000-0003-0986-6479",{"name":283,"orcid":9},"Jia Tian",{"tldr":285,"method":286,"finding":287,"direction":127,"opportunity":288},"提出基于线性光谱混合分析和随机森林的作物残茬覆盖度遥感估算方法，应用于华北平原。","随机森林模拟窄带SINDRI，结合NDVI构建三角特征空间，线性光谱混合分析估算","方法有效缓解水分干扰，fNPV估算R²=0.84，RMSE=0.10，多源影像时间一致性高。","可探索将方法扩展至其他作物类型或区域，或结合高光谱影像提高精度，并研究残茬覆盖度与土壤碳动态的关系。","2026-09-03T23:30:30.109393Z",{"id":291,"title":292,"url":293,"summary":294,"summary_zh":295,"content":9,"source_name":296,"source_url":293,"published_at":297,"category":12,"cover_url":9,"hotness":13,"is_selected":298,"score":299,"score_detail":300,"sources":303,"tags":305,"search_phrases":309,"slug":312,"view_count":36,"doi":313,"paper":314,"created_at":326},3672,"Hybrid Attention Transformers for Multi-Spectral Satellite Super-Resolution","https:\u002F\u002Fdoi.org\u002F10.31224\u002F8320","Spaceborne optical imaging missions, such as the European Space Agency's Copernicus Sentinel-2 constellation, provide vital multi-spectral observations worldwide, yet optical aperture diffraction limits native Ground Sampling Distance (GSD) to 10 m across visible and near-infrared (VNIR) bands. Traditional Single-Image Super-Resolution (SISR) algorithms designed for 8-bit photographic imagery introduce severe radiometric distortions that corrupt downstream biophysical canopy analyses. In this letter, we present HAT-Light, an edge-efficient continuous-scale Hybrid Attention Transformer engineered specifically for 4-channel (RGB+NIR), 16-bit Bottom-Of-Atmosphere (BOA) surface reflectance imagery. HAT-Light addresses the limitations of standard self-attention by integrating non-overlapping Window Multi-Head Self-Attention with Depthwise Convolutional Feed-Forward Networks (DW-FFN), effectively recovering translation-equivariant localized inductive biases essential for resolving fine agricultural field parcel boundaries and airport runway geometries. Arbitrary continuous magnification (s in [2.0, 4.0]) is enabled through harmonic sinusoidal Feature-wise Linear Modulation (FiLM) within a single checkpoint. Furthermore, we enforce physical radiance conservation through a composite multi-task loss suite that unites Smooth Charbonnier regression, 2D real Fourier transform (rFFT2) spectral alignment, directional Sobel edge penalties, and a numerically bounded Convex Cosine Spectral Angle Mapper (SAM) loss ensuring FP16 numerical stability. Evaluated across 600 curated Sentinel-2 test patches spanning five distinct biomes, HAT-Light establishes state-of-the-art accuracy (33.48 dB PSNR, 0.9048 SSIM, 1.37 deg SAM, and 1.89 ERGAS), outperforming Bicubic (+1.25 dB) and RCAN (+0.71 dB). Spatial edge transect profiling and agricultural NDVI correlation (R^2 = 0.898) confirm robust biophysical conservation. Under the Wald synthesis protocol on 100 authentic 2.5 m USGS NAIP aerial patches, HAT-Light achieves superior zero-shot transfer (0.8332 SSIM) at real-time edge throughput (78.7 FPS) on an edge GPU.","星载光学成像任务，如欧洲航天局哥白尼哨兵-2星座，在全球范围内提供了重要的多光谱观测数据，然而光学孔径衍射将可见光与近红外（VNIR）波段的原生地面采样距离（GSD）限制在10 m。专为8位摄影图像设计的传统单图像超分辨率（SISR）算法会引入严重的辐射畸变，从而破坏下游的植被生物物理分析。本文提出HAT-Light，一种面向边缘高效推理的连续尺度混合注意力Transformer，专为4通道（RGB+NIR）、16位大气底层（BOA）地表反射率影像设计。HAT-Light通过将非重叠窗口多头自注意力与深度可分离卷积前馈网络（DW-FFN）相结合，解决了标准自注意力的局限性，有效恢复了平移等变的局部归纳偏置，这对于分辨精细农田地块边界和机场跑道几何形态至关重要。通过谐波正弦特征级线性调制（FiLM），在单一检查点内实现了任意连续放大倍数（s∈[2.0, 4.0]）。此外，我们通过复合多任务损失套件强制物理辐射守恒，该套件融合了平滑Charbonnier回归、二维实傅里叶变换（rFFT2）频谱对齐、方向性Sobel边缘惩罚以及数值有界的凸余弦光谱角映射（SAM）损失，确保FP16数值稳定性。在涵盖五种不同生物群系的600个精选哨兵-2测试图像块上进行评估，HAT-Light确立了最先进的精度（33.48 dB PSNR、0.9048 SSIM、1.37° SAM和1.89 ERGAS），优于双三次插值（+1.25 dB）和RCAN（+0.71 dB）。空间边缘剖面分析和农业NDVI相关性（R²=0.898）证实了稳健的生物物理守恒性。在100个真实2.5 m USGS NAIP航空图像块上按照Wald合成协议进行测试，HAT-Light在边缘GPU上以实时边缘吞吐量（78.7 FPS）实现了优异的零样本迁移性能（0.8332 SSIM）。","OpenAlex","2026-09-26T00:00:00Z",true,81,{"impact":19,"substance":18,"depth":301,"authority":20,"freshness":21,"relevant":22,"comment":302},19,"面向Sentinel-2多光谱16位反射率影像的超分辨率新方法，兼顾辐射保真与NDVI生物物理一致性，对农业遥感监测有实质技术增量。",[304],{"name":296,"url":293},[28,306,29,307,308],"农业人工智能","卫星影像","作物监测",[310,311],"Sentinel-2 超分辨率 农业遥感","HAT-Light 多光谱 卫星影像","Sentinel-2超分辨率农业遥感-3672","10.31224\u002F8320",{"doi":313,"openalex_id":315,"authors":316,"venue":9,"cited_by_count":36,"oa_url":320,"card":321,"direction":127,"ingested_from":60},"W7214467740",[317],{"name":318,"orcid":319},"Naman Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-8443-0985","https:\u002F\u002Fengrxiv.org\u002Fpreprint\u002Fdownload\u002F8320\u002F13499\u002F11736",{"tldr":322,"method":323,"finding":324,"direction":127,"opportunity":325},"提出HAT-Light混合注意力Transformer，实现Sentinel-2多光谱16位影像连续","混合窗口自注意力与深度可分离卷积前馈网络，结合FiLM连续尺度调制和多任务物理约","在600个Sentinel-2测试块上达33.48dB PSNR，NDVI相关性R²=0.898，边","可探索超分后多光谱影像对作物长势、病虫害等下游农学任务的定量增益与不确定性传播。","2026-09-28T23:30:28.027834Z"]