[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4364":3,"related-4364":78},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":52,"slug":55,"view_count":21,"doi":56,"paper":57,"created_at":77},4364,"Multi-model comparison of satellite remote sensing-driven GPP models and products in a temperate deciduous forest","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecoinf.2026.104077","Accurate estimation of terrestrial gross primary productivity (GPP) is essential for understanding forest carbon dynamics, yet satellite remote sensing-driven GPP products often differ in their ability to capture seasonal changes in ecosystem productivity. This study evaluated five GPP estimates, including MODIS MOD17, a site-calibrated light use efficiency (LUE) model, the Vegetation Photosynthesis Model (VPM), the Temperature-Greenness (TG) model, and SMAP Level 4 Carbon, against six years of eddy covariance-derived GPP from a temperate deciduous forest in the Daniel Boone National Forest, Kentucky, USA. MODIS and SMAP were evaluated as operational products without site-specific recalibration, while LUE, VPM, and TG were evaluated as site-calibrated diagnostic models using leave-one-year-out validation. Under common-row evaluation, SMAP, VPM, and TG showed the strongest overall agreement with EC-derived GPP, with SMAP producing the lowest RMSE, highest R 2 , and highest NSE. However, seasonal diagnostics revealed important differences in model behavior. All five estimates underestimated summer GPP during peak canopy activity, while all models overestimated GPP during the fall transition. Winter performance was weak across models, but bias direction was mixed because EC-derived winter GPP was close to zero. Residual-driver diagnostics indicated that summer underestimation was associated with high canopy activity and atmospheric demand, especially EVI and VPD gradients, while fall overestimation was associated with radiation, canopy, temperature, and phenological gradients during senescence. A supplementary MODIS-only FLUXNET assessment supported the broader relevance of summer MODIS underestimation across deciduous broadleaf forest sites. These findings indicate that improving satellite GPP estimates in temperate deciduous forests requires season-specific attention to peak-season stress responses, senescence-period downregulation, and low-GPP winter conditions.","准确估算陆地总初级生产力（GPP）对于理解森林碳动态至关重要，然而卫星遥感驱动的GPP产品在捕捉生态系统生产力季节性变化方面的能力往往存在差异。本研究评估了五种GPP估算结果，包括MODIS MOD17、站点校准的光能利用率（LUE）模型、植被光合作用模型（VPM）、温度-绿度（TG）模型以及SMAP Level 4 Carbon，并以美国肯塔基州丹尼尔·布恩国家森林一片温带落叶林六年涡度协方差（EC）衍生的GPP为参考。MODIS和SMAP作为业务化产品进行评估，未进行站点特异性重校准；而LUE、VPM和TG则作为站点校准的诊断模型，采用留一年交叉验证进行评估。在共同行评估下，SMAP、VPM和TG与EC衍生GPP的总体一致性最强，其中SMAP的RMSE最低、R²最高、NSE最高。然而，季节性诊断揭示了模型行为的重要差异。所有五种估算在冠层活动高峰期均低估了夏季GPP，而所有模型在秋季过渡期均高估了GPP。冬季表现普遍较弱，但偏差方向不一致，因为EC衍生的冬季GPP接近于零。残差驱动因子诊断表明，夏季低估与高冠层活动和大气需求相关，尤其是EVI和VPD梯度；而秋季高估则与衰老期的辐射、冠层、温度和物候梯度相关。一项仅针对MODIS的FLUXNET补充评估支持了夏季MODIS低估在落叶阔叶林站点中具有更广泛的普遍性。这些发现表明，改进温带落叶林的卫星GPP估算需要针对特定季节关注旺季胁迫响应、衰老期下调以及低GPP冬季条件。",null,"Ecological Informatics","2026-10-05T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,22,18,14,1,"多模型对比评估卫星遥感GPP产品在温带落叶林的表现，方法严谨、结论具体，对农业碳汇遥感监测有参考价值，但属细分领域学术进展，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","碳汇","生态模型","GPP估算","落叶林",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":51},[38,46],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"I3",15,true,[43],"remote sensing",[26,43],[26],{"code":47,"score":21,"supported":14,"title":48,"abstract":49,"tags":50},"H4",[],[],[27],"农业相关性需要复核",[53,54],"Daniel Boone 落叶林 碳通量","生态模型 落叶林 GPP估算 碳汇","DanielBoone落叶林碳通量-4364","10.1016\u002Fj.ecoinf.2026.104077",{"doi":56,"openalex_id":58,"authors":59,"venue":10,"cited_by_count":68,"oa_url":69,"card":70,"direction":74,"ingested_from":76},"W7220461078",[60,62,64,66],{"name":61,"orcid":9},"Ife Familusi",{"name":63,"orcid":9},"Maheteme Gebremedhin",{"name":65,"orcid":9},"Buddhi Gyawali",{"name":67,"orcid":9},"Utkarsha Basnet",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1574954126004851\u002Fpdf",{"tldr":71,"method":72,"finding":73,"direction":74,"opportunity":75},"对比五种卫星遥感GPP模型在温带落叶林的表现，揭示季节偏差规律。","用6年涡度协方差GPP数据，评估MODIS、LUE、VPM、TG和SMAP模型。","所有模型低估夏季GPP、高估秋季GPP，SMAP整体表现最佳。","农业遥感与作物表型","可开发季节特异性GPP模型，重点改进峰值期胁迫响应和衰老期下调机制。","openalex","2026-10-07T23:30:28.996554Z",{"total":79,"page":21,"page_size":79,"items":80},6,[81,181,268,335,402,457],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":9,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":101,"secondary_topics":106,"classification":112,"search_phrases":156,"slug":159,"view_count":160,"doi":161,"paper":162,"created_at":180},3554,"Spatial Estimation of Carbon Sequestration Using Remote Sensing and Its Application in Carbon-credit Assessment: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125","Carbon sequestration is fundamental to climate-change mitigation because terrestrial and aquatic ecosystems store carbon in vegetation, biomass, soils, and the Earth’s crust. Reliable estimation of carbon stocks and their temporal and spatial changes is essential for the formulation of climate policy, ecosystem management, carbon accounting, and carbon-credit assessment. Conventional field measurements provide detailed information but are constrained by sampling requirements, cost, labour, spatial heterogeneity, and limitations in continuous monitoring. Remote sensing provides a complementary approach by enabling spatially continuous observations of vegetation characteristics, land-cover change, biomass, and other carbon-related variables across different spatial and temporal scales. Advances in multispectral, hyperspectral, synthetic aperture radar, LiDAR, thermal, and high-resolution satellite observations have extended the capability for carbon estimation. Their integration with geographic information systems, field observations, statistical techniques, and machine-learning algorithms enables precise spatial assessment of above-ground biomass, below-ground biomass, soil organic carbon, forest carbon, agricultural carbon, mangrove carbon, and blue-carbon resources. However, uncertainties associated with sensor characteristics, field-data quality, model selection, spatial variability, temporal dynamics, and scaling remain acknowledged limitations. Converting spatial carbon estimates into carbon credits requires consideration of baseline conditions, additionality, permanence, leakage, uncertainty, verification, and measurement, reporting, and verification requirements. This review critically examines remote sensing technologies, carbon-estimation models, spatial mapping approaches, machine-learning techniques, carbon-accounting frameworks, and their integration into carbon-credit assessment. It places particular emphasis on multisource data fusion, emerging Earth-observation technologies, artificial intelligence, uncertainty assessment, and digital MRV systems. Major methodological, technological, and policy gaps are identified, along with opportunities for developing transparent, spatially explicit, scientifically robust, and scalable carbon-credit assessment frameworks.","碳固存是减缓气候变化的基础，因为陆地和水生生态系统将碳储存在植被、生物量、土壤和地壳中。可靠估算碳储量及其时空变化，对于制定气候政策、生态系统管理、碳核算和碳信用评估至关重要。传统野外测量可提供详细信息，但受采样要求、成本、劳动力、空间异质性以及连续监测局限性等因素制约。遥感提供了一种互补方法，能够在不同时空尺度上对植被特征、土地覆盖变化、生物量及其他碳相关变量进行空间连续观测。多光谱、高光谱、合成孔径雷达、激光雷达、热红外和高分辨率卫星观测的进步，拓展了碳估算能力。这些技术与地理信息系统、野外观测、统计技术和机器学习算法的集成，使得对地上生物量、地下生物量、土壤有机碳、森林碳、农业碳、红树林碳和蓝碳资源进行精确空间评估成为可能。然而，与传感器特性、野外数据质量、模型选择、空间变异性、时间动态和尺度转换相关的不确定性仍是公认的局限。将空间碳估算转化为碳信用，需要考虑基线条件、额外性、永久性、泄漏、不确定性、核查以及测量、报告和核查要求。本综述批判性地审视了遥感技术、碳估算模型、空间制图方法、机器学习技术、碳核算框架及其在碳信用评估中的集成。特别强调了多源数据融合、新兴地球观测技术、人工智能、不确定性评估和数字化MRV系统。识别了主要的方法学、技术和政策差距，以及开发透明、空间明确、科学稳健且可扩展的碳信用评估框架的机遇。","Journal of Geography Environment and Earth Science International","2026-09-25T00:00:00Z",67,{"impact":91,"substance":19,"depth":92,"authority":91,"freshness":93,"relevant":21,"comment":94},12,16,9,"系统综述遥感碳汇估算与碳信用评估的方法、不确定性与数字MRV进展，对农业碳汇与碳交易信息化有参考价值，但属综述类论文，公共影响有限。",[96],{"name":87,"url":84},[98,26,27,99,100],"机器学习","碳信用","数字MRV",{"code":39,"slug":102,"name":103,"path":104,"parent_code":105},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[107],{"code":47,"slug":108,"name":109,"path":110,"parent_code":111},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":113,"locked":14,"version":34,"method":35,"evidence":114},"classified",{"candidates":115,"agricultural_evidence":41},[116,122,128,135,142,149],{"code":39,"score":19,"supported":41,"title":117,"abstract":118,"tags":121},[43],[26,43,119,120],"multispectral","hyperspectral",[26],{"code":47,"score":40,"supported":41,"title":123,"abstract":125,"tags":127},[124],"carbon sequestration",[126,124],"农业碳",[27],{"code":129,"score":130,"supported":41,"title":131,"abstract":132,"tags":134},"I2",7,[],[98,133],"artificial intelligence",[98],{"code":136,"score":79,"supported":41,"title":137,"abstract":138,"tags":141},"H2",[],[139,140],"土壤","soil",[],{"code":143,"score":79,"supported":41,"title":144,"abstract":145,"tags":148},"I4",[],[146,147],"地理信息","geographic information",[],{"code":150,"score":151,"supported":41,"title":152,"abstract":153,"tags":155},"H7",3,[],[154],"气候变化",[],[157,158],"遥感 碳汇 估算","碳信用 评估 遥感","遥感碳汇估算-3554",4,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125",{"doi":161,"openalex_id":163,"authors":164,"venue":87,"cited_by_count":68,"oa_url":84,"card":174,"direction":74,"ingested_from":76},"W7214392294",[165,167,170,172],{"name":166,"orcid":9},"Aishwarya Desai",{"name":168,"orcid":169},"Himalaya Ganachari","https:\u002F\u002Forcid.org\u002F0000-0002-6157-9242",{"name":171,"orcid":9},"Sangita Shinde",{"name":173,"orcid":9},"Sachinkumar Nandgude",{"tldr":175,"method":176,"finding":177,"direction":178,"opportunity":179},"综述遥感碳储量估算技术及其在碳信用评估中的应用与挑战。","综述多光谱、高光谱、SAR、LiDAR等遥感与GIS、机器学习融合方法。","遥感可空间连续估算多类碳库，但不确定性及MRV要求制约碳信用转化。","农业绿色发展与碳","可研究多源数据融合与AI驱动的数字MRV，降低碳信用评估不确定性。","2026-09-26T23:30:30.002197Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":192,"tags":194,"primary_topic":9,"secondary_topics":198,"classification":199,"search_phrases":222,"slug":225,"view_count":226,"doi":227,"paper":228,"created_at":267},3507,"OCO‐3 Meets ECOSTRESS: Insights Into Ecosystem Diurnal Water‐Use Efficiency From Co‐Located Solar‐Induced Fluorescence and Thermal Observations","https:\u002F\u002Fdoi.org\u002F10.1029\u002F2026gl124105","Abstract Terrestrial ecosystem regulation of carbon and water fluxes is critical for constraining climate–biosphere feedbacks but remains poorly quantified across space and time. Here, we evaluate whether co‐located observations from NASA's Orbiting Carbon Observatory‐3 (OCO‐3) and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can reproduce ecosystem water use efficiency (WUE) dynamics observed at FLUXNET sites across temporal scales, vegetation types, climates, and drought conditions. We created the ECOCO3 data set, which harmonizes OCO‐3 and ECOSTRESS observations in space and time. ECOCO3 captures broad seasonal and diurnal carbon and water flux patterns including midday drought responses. Sampling sensitivity analysis shows that ECOCO3 is primarily limited by available sample size for distinguishing vegetation and climate driven differences in WUE. Our findings highlight both the promise and limitations of remote sensing for resolving sub‐daily carbon–water coupling.","陆地生态系统对碳通量和水通量的调节对于约束气候–生物圈反馈至关重要，但在空间和时间尺度上仍缺乏充分的量化。在此，我们评估了来自NASA轨道碳观测站-3（OCO-3）和空间站生态系统星载热辐射计实验（ECOSTRESS）的同位观测能否在时间尺度、植被类型、气候条件和干旱状况下重现FLUXNET站点观测到的生态系统水分利用效率（WUE）动态。我们创建了ECOCO3数据集，该数据集在空间和时间上协调了OCO-3和ECOSTRESS的观测。ECOCO3能够捕捉广泛的季节性和日间碳通量与水通量模式，包括正午干旱响应。采样敏感性分析表明，ECOCO3主要受限于可用样本量，难以区分植被和气候驱动的WUE差异。我们的研究结果既凸显了遥感在解析亚日尺度碳–水耦合方面的前景，也揭示了其局限性。","Geophysical Research Letters","2026-09-23T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":40,"freshness":17,"relevant":21,"comment":191},"NASA两颗卫星协同观测提升生态系统碳水耦合监测能力，方法新颖、数据可靠，对农业遥感与水资源管理有参考价值。",[193],{"name":187,"url":184},[195,26,196,197,27],"农业遥感","水资源利用","生态监测",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":200},{"candidates":201,"agricultural_evidence":14,"reason":51},[202,206,212,217],{"code":39,"score":17,"supported":41,"title":203,"abstract":204,"tags":205},[],[26,43],[195,26],{"code":150,"score":79,"supported":41,"title":207,"abstract":208,"tags":211},[],[209,210],"干旱","drought",[],{"code":213,"score":21,"supported":14,"title":214,"abstract":215,"tags":216},"H3",[],[],[196],{"code":218,"score":21,"supported":14,"title":219,"abstract":220,"tags":221},"H6",[],[],[197],[223,224],"OCO-3 ECOSTRESS 水分利用效率","ECOCO3 数据集 碳水通量","OCO-3ECOSTRESS水分利用效率-3507",5,"10.1029\u002F2026gl124105",{"doi":227,"openalex_id":229,"authors":230,"venue":187,"cited_by_count":68,"oa_url":261,"card":262,"direction":74,"ingested_from":76},"W7214122316",[231,234,237,240,243,246,249,252,255,258],{"name":232,"orcid":233},"Zoe Pierrat","https:\u002F\u002Forcid.org\u002F0000-0002-6726-2406",{"name":235,"orcid":236},"Thomas P. Kurosu","https:\u002F\u002Forcid.org\u002F0000-0003-2555-7780",{"name":238,"orcid":239},"Abhishek Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0002-3680-0160",{"name":241,"orcid":242},"Joshua B. Fisher","https:\u002F\u002Forcid.org\u002F0000-0003-4734-9085",{"name":244,"orcid":245},"Margaret C. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1481-9706",{"name":247,"orcid":248},"Le Kuai","https:\u002F\u002Forcid.org\u002F0000-0001-6406-1150",{"name":250,"orcid":251},"Kaniska Mallick","https:\u002F\u002Forcid.org\u002F0000-0002-2735-930X",{"name":253,"orcid":254},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"name":256,"orcid":257},"Benjamin C. Wiebe","https:\u002F\u002Forcid.org\u002F0000-0002-9325-1540",{"name":259,"orcid":260},"Kerry A. Cawse-Nicholson","https:\u002F\u002Forcid.org\u002F0000-0002-0510-4066","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1029\u002F2026GL124105",{"tldr":263,"method":264,"finding":265,"direction":74,"opportunity":266},"融合OCO-3与ECOSTRESS观测评估生态系统日间水分利用效率动态。","构建ECOCO3数据集，结合FLUXNET站点验证与采样敏感性分析。","ECOCO3能捕捉季节与日间碳-水通量模式，但样本量限制其区分植被与气候差异。","可探索多源遥感融合提升亚日尺度碳水耦合估算精度，并扩展至农田生态系统。","2026-09-25T23:30:33.881463Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":276,"score_detail":277,"sources":280,"tags":282,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":314,"slug":317,"view_count":151,"doi":318,"paper":319,"created_at":334},2423,"Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71784-9","Abstract Against the backdrop of China’s “Dual Carbon” (carbon peak and carbon neutrality) strategic initiative, terrestrial ecosystem carbon sequestration has become a core pillar of climate mitigation and ecological governance. This study systematically quantifies the spatiotemporal distribution of carbon storage and and projects long-term carbon sink trajectories under divergent socioecological development pathways across the Fen River Basin—a representative ecologically fragile tributary catchment of the Yellow River. Four scenarios based on the basin’s ecological landscape planning were established: Natural Development, Economic Priority, Cropland Protection, and Ecological Protection. By coupling the PLUS and InVEST models, an analytical framework linking land use change with ecosystem carbon storage was constructed to simulate and predict the spatial distribution and dynamics of carbon storage under different scenarios. The Geodetector was used to identify the key driving factors. The results indicate that: (1) From 2005 to 2020, cropland remained the dominant land use type but showed a continuous decreasing trend alongside grassland degradation. Forest land area initially decreased before recovering slightly, while built-land expanded consistently. (2) The total carbon storage in the basin showed a declining trend, decreasing from 700.32 Tg in 2005 to 683.48 Tg in 2020, with a cumulative loss of 16.84 Tg. Soil organic carbon was the major carbon pool. Spatially, areas with high carbon storage were primarily distributed in the eastern and western parts of the basin, while the central-southern regions generally had lower carbon storage. Carbon storage exhibited significant positive spatial autocorrelation, with High-High clusters (hot spots) being more extensive than Low-Low clusters (cold spots). The area experiencing carbon loss was substantially larger than the area with carbon gain. (3) Multi-scenario simulations revealed that different development pathways significantly impact carbon storage. The Ecological Protection scenario yielded the highest carbon storage (683.57 Tg), highlighting the effectiveness of ecological protection in enhancing regional carbon sink capacity. (4) NDVI was the dominant factor influencing the spatial heterogeneity of carbon storage, possessing strong independent explanatory power. Its interactive effects with other factors further enhanced the ability to explain variations in carbon storage. This study elucidates the coupling mechanism between land use change and carbon storage, providing a scientific reference for ecological protection, land structure optimization, and carbon sink enhancement in the Fen River Basin.","在中国“双碳”（碳达峰与碳中和）战略倡议背景下，陆地生态系统碳汇已成为气候减缓与生态治理的核心支柱。本研究系统量化了汾河流域——黄河具有代表性的生态脆弱支流流域——碳储量的时空分布，并预测了不同社会生态发展路径下的长期碳汇轨迹。基于流域生态景观规划，设立了四种情景：自然发展、经济优先、耕地保护和生态保护。通过耦合PLUS与InVEST模型，构建了土地利用变化与生态系统碳储量关联的分析框架，以模拟和预测不同情景下碳储量的空间分布与动态变化。运用地理探测器识别关键驱动因子。结果表明：（1）2005年至2020年，耕地仍为主要土地利用类型，但呈持续下降趋势，草地亦退化。林地面积先减少后略有恢复，建设用地持续扩张。（2）流域碳储量总量呈下降趋势，从2005年的700.32 Tg降至2020年的683.48 Tg，累计损失16.84 Tg。土壤有机碳为主要碳库。空间上，高碳储量区主要分布于流域东西部，中南部地区碳储量普遍较低。碳储量表现出显著的正空间自相关，高-高集聚区（热点）范围大于低-低集聚区（冷点）。碳损失面积显著大于碳增益面积。（3）多情景模拟揭示，不同发展路径对碳储量影响显著。生态保护情景碳储量最高（683.57 Tg），凸显了生态保护在提升区域碳汇能力方面的有效性。（4）NDVI是影响碳储量空间分异的主导因子，具有较强独立解释力。其与其他因子的交互作用进一步增强了对碳储量变化的解释能力。本研究阐明了","Scientific Reports","2026-09-12T00:00:00Z",77,{"impact":92,"substance":278,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":279},21,"耦合PLUS-InVEST-GeoDetector量化汾河流域碳储时空演变并模拟多情景碳汇路径，方法新颖、数据扎实，对黄河流域生态保护与土地结构优化有参考价值。",[281],{"name":274,"url":271},[26,283,284,285,27],"生态保护","黄河流域","土地利用",{"code":47,"slug":108,"name":109,"path":110,"parent_code":111},[],{"status":113,"locked":14,"version":34,"method":35,"evidence":289},{"candidates":290,"agricultural_evidence":41},[291,295,299,305,310],{"code":47,"score":130,"supported":41,"title":292,"abstract":293,"tags":294},[],[27,124],[27],{"code":136,"score":79,"supported":41,"title":296,"abstract":297,"tags":298},[],[139,140],[],{"code":300,"score":151,"supported":41,"title":301,"abstract":302,"tags":304},"H1",[],[303],"耕地保护",[],{"code":218,"score":306,"supported":14,"title":307,"abstract":308,"tags":309},2,[],[],[285,283],{"code":39,"score":21,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[26],[315,316],"土地利用 生态保护 黄河流域 碳汇","土地利用 生态保护","土地利用生态保护黄河流域碳汇-2423","10.1038\u002Fs41598-026-71784-9",{"doi":318,"openalex_id":320,"authors":321,"venue":274,"cited_by_count":68,"oa_url":328,"card":329,"direction":74,"ingested_from":76},"W7212371101",[322,324,326],{"name":323,"orcid":9},"Jie Chen",{"name":325,"orcid":9},"Pengxiang Gao",{"name":327,"orcid":9},"Yi Hou","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71784-9_reference.pdf",{"tldr":330,"method":331,"finding":332,"direction":178,"opportunity":333},"耦合PLUS-InVEST-GeoDetector模拟汾河流域多情景碳储量演变并识别驱动因子。","PLUS与InVEST耦合模拟四情景土地利用，GeoDetector识别驱动因子","2005-2020年碳储量下降16.84 Tg，生态保护情景碳储量最高，NDVI为主导驱动因子。","可引入农业管理措施与政策情景，细化耕地保护对碳汇的贡献机制研究。","2026-09-14T23:30:24.574426Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":346,"tags":348,"primary_topic":353,"secondary_topics":357,"classification":358,"search_phrases":385,"slug":388,"view_count":68,"doi":389,"paper":390,"created_at":401},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":91,"substance":18,"depth":19,"authority":91,"freshness":13,"relevant":21,"comment":345},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[347],{"name":341,"url":338},[349,350,351,26,352],"智慧农业","农业人工智能","粮食安全","气候分析",{"code":129,"slug":354,"name":355,"path":356,"parent_code":105},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":113,"locked":14,"version":34,"method":35,"evidence":359},{"candidates":360,"agricultural_evidence":41},[361,365,371,376,381],{"code":129,"score":93,"supported":41,"title":362,"abstract":363,"tags":364},[133],[],[350],{"code":366,"score":151,"supported":41,"title":367,"abstract":368,"tags":370},"I5",[],[369],"农业传感",[],{"code":372,"score":21,"supported":14,"title":373,"abstract":374,"tags":375},"A8",[],[],[351],{"code":377,"score":21,"supported":14,"title":378,"abstract":379,"tags":380},"I1",[],[],[349],{"code":39,"score":21,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[26],[386,387],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":389,"openalex_id":391,"authors":392,"venue":341,"cited_by_count":68,"oa_url":338,"card":395,"direction":400,"ingested_from":76},"W7223111766",[393],{"name":394,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":396,"method":397,"finding":398,"direction":74,"opportunity":399},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":9,"content":9,"source_name":407,"source_url":405,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":417,"secondary_topics":421,"classification":422,"search_phrases":435,"slug":438,"view_count":68,"doi":439,"paper":440,"created_at":456},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square",38,{"impact":17,"substance":79,"depth":17,"authority":79,"freshness":13,"relevant":21,"comment":410},"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[412],{"name":407,"url":405},[414,415,26,416],"数字农业","农业信息化","地图评估",{"code":377,"slug":418,"name":419,"path":420,"parent_code":105},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":113,"locked":14,"version":34,"method":35,"evidence":423},{"candidates":424,"agricultural_evidence":41},[425,431],{"code":377,"score":426,"supported":41,"title":427,"abstract":429,"tags":430},13,[428],"digital agriculture",[428],[415,414],{"code":39,"score":21,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[26],[436,437],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":439,"openalex_id":441,"authors":442,"venue":407,"cited_by_count":68,"oa_url":405,"card":9,"direction":455,"ingested_from":76},"W7223099987",[443,446,449,452],{"name":444,"orcid":445},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":447,"orcid":448},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":450,"orcid":451},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":453,"orcid":454},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":465,"sources":467,"tags":469,"primary_topic":9,"secondary_topics":472,"classification":473,"search_phrases":480,"slug":483,"view_count":68,"doi":484,"paper":485,"created_at":500},5574,"Aggregation of results of automatic classification of remote sensing data of natural objects","https:\u002F\u002Fdoi.org\u002F10.31772\u002F2712-8970-2026-27-3-390-399","Methods for aggregating the results of automatic classification of multidimensional spectral remote sensing data are proposed. This task is accomplished using a modified «Forel» algorithm and a procedure for testing the hypothesis of the homogeneity of the distribution laws of random variables characterizing the detected classes. When automatic classification algorithms are designed to detect sets of random variable values corresponding to unimodal fragments of their probability density, it is reasonable to regroup classes within the space of their mean random variable values. This is implemented using a modification of the “Forel” algorithm, the parameters of which are determined based on the results of a computational experiment. If additional information about the properties of a natural object is available, it can be used to aggregate the results of automatic classification in the spectral feature space. Under these conditions, class aggregation is based on testing the hypothesis of the homogeneity of the distribution laws of the random variables that characterize the additional information. To solve this problem, a nonparametric pattern recognition algorithm that follows the maximum likelihood criterion is used. The optimal blur coefficients of the classification algorithm's kernel functions are selected based on the requirement to minimize the pattern recognition error estimate in the “sliding exam” mode. The obtained results are summarized in the construction of an information graph, the nodes of which correspond to classes. An edge is formed between two graph nodes if the pattern recognition error for a given pair of classes does not differ significantly from 0,5. A modification of the Kolmogorov–Smirnov test is used to test the hypotheses. A macroclass is formed by classes corresponding to a complete subgraph that do not reliably differ in the distribution patterns of features in the additional information. The sequential procedure for forming macroclasses begins with finding the complete subgraph with the maximum number of classes. The results obtained form the basis for developing a decision rule for assessing the membership of spectral feature values in the resulting macroclasses. The proposed method for aggregating the results of automatic classification is relevant for the system analysis of remote sensing data.","针对多维光谱遥感数据自动分类结果的聚合方法被提出。该任务通过改进的“Forel”算法以及用于检验表征所检测类别的随机变量分布律同质性假设的程序来完成。当自动分类算法旨在检测对应于其概率密度单峰片段的随机变量值集合时，在其均值随机变量值空间内重新分组类别是合理的。这通过“Forel”算法的改进版本实现，其参数基于计算实验结果确定。如果存在关于自然对象属性的附加信息，则可将其用于聚合光谱特征空间中的自动分类结果。在这些条件下，类别聚合基于检验表征附加信息的随机变量分布律同质性假设。为解决该问题，采用遵循最大似然准则的非参数模式识别算法。分类算法核函数的最优模糊系数基于在“滑动检验”模式下最小化模式识别误差估计的要求进行选择。所得结果汇总于信息图的构建中，其节点对应于类别。如果给定类别对的模式识别误差与0.5无显著差异，则在两个图节点之间形成边。使用Kolmogorov–Smirnov检验的改进版本来检验假设。宏类由对应于完全子图的类别形成，这些类别在附加信息中的特征分布模式上没有可靠差异。形成宏类的顺序过程从寻找具有最大类别数的完全子图开始。所得结果构成了开发决策规则的基础，该规则用于评估光谱特征值在所得宏类中的隶属关系。所提出的用于聚合自动分类结果的方法对于遥感数据的系统分析具有相关性。","Siberian Aerospace Journal","2026-10-20T00:00:00Z",{"impact":68,"substance":68,"depth":68,"authority":68,"freshness":68,"relevant":68,"comment":466},"该文为遥感数据自动分类结果聚合的算法论文，属通用遥感方法研究，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[468],{"name":463,"url":460},[26,470,471],"光谱数据","自动分类",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":474},{"candidates":475,"agricultural_evidence":14,"reason":51},[476],{"code":39,"score":40,"supported":41,"title":477,"abstract":478,"tags":479},[43],[26,43],[26],[481,482],"光谱数据 自动分类 遥感","光谱数据 自动分类","光谱数据自动分类遥感-5574","10.31772\u002F2712-8970-2026-27-3-390-399",{"doi":484,"openalex_id":486,"authors":487,"venue":463,"cited_by_count":68,"oa_url":494,"card":495,"direction":74,"ingested_from":76},"W7223058937",[488,491],{"name":489,"orcid":490},"Aleksandr V. Lapko","https:\u002F\u002Forcid.org\u002F0000-0002-0664-3870",{"name":492,"orcid":493},"Vasiliy A. Lapko","https:\u002F\u002Forcid.org\u002F0000-0001-6938-9323","https:\u002F\u002Fjournals.eco-vector.com\u002F2712-8970\u002Farticle\u002Fdownload\u002F719886\u002F228882",{"tldr":496,"method":497,"finding":498,"direction":74,"opportunity":499},"提出聚合多维光谱遥感自动分类结果的方法，合并相似类别为宏类。","改进Forel算法、非参数模式识别、Kolmogorov-Smirnov检验与信","基于分布同质性检验可有效聚合自动分类类别，形成稳定宏类。","可将该聚合框架用于作物遥感分类后处理，提升类别一致性与可解释性。","2026-10-22T11:20:42.307504Z"]