[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2150":3,"related-2150":127},{"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":55,"slug":58,"view_count":22,"doi":59,"paper":60,"created_at":126},2150,"From in situ to remote sensing: a framework for quantifying boundary-condition sensitivity in soil organic carbon models across diverse European sites","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-4735","Abstract. Process-based soil organic carbon (SOC) models are widely used for carbon accounting and assessments. For applications beyond experimental sites, remote sensing–derived inputs are often used to complement or replace in situ boundary conditions. This substitution introduces additional sensitivity that has rarely been explicitly quantified. Here, we propose a general framework to quantify boundary-condition sensitivity in SOC modelling, distinguishing between dynamic sensitivity, which reflects deviations in the temporal trajectory of SOC stocks, and endpoint sensitivity, which captures differences in estimated SOC changes over defined reporting periods. Sensitivity is quantified by comparing substitution simulations, in which boundary conditions are replaced one by one with remote sensing–derived spatial datasets, against a baseline simulation driven by in situ observations. The framework is applied to 16 long-term experimental sites across Europe, covering diverse land-use types and environmental conditions. Boundary-condition substitution affected both SOC trajectories and endpoint estimates, although the magnitude and form of these effects varied among inputs. Initial SOC, vegetation-derived carbon inputs, and climate data were the dominant drivers of dynamic sensitivity. Fixed endpoint sensitivity was generally small relative to measured SOC changes, supporting the use of remote sensing–derived spatial datasets as scalable boundary-condition proxies for SOC modelling. Rolling endpoint sensitivity further revealed that SOC change assessments depend not only on SOC dynamics but also on the selected reporting window. Short reporting periods were more affected by short-term variability, whereas longer periods were more susceptible to accumulated deviations. A reporting window of approximately 15–30 years provided a practical balance between these effects. Overall, the framework enables a systematic quantification of sensitivity arising from boundary-condition substitution, enabling evaluation of both trajectory and reporting robustness. These findings highlight the importance of jointly considering critical boundary conditions and reporting-window design when applying remote sensing–driven SOC modelling for regional carbon accounting.","摘要。基于过程的土壤有机碳（SOC）模型被广泛用于碳核算与评估。在实验站点之外的应用中，遥感衍生的输入常被用来补充或替代原位边界条件。这种替代引入了额外的敏感性，而该敏感性鲜有被明确量化。在此，我们提出一个通用框架，用于量化SOC建模中的边界条件敏感性，区分动态敏感性——反映SOC储量时间轨迹的偏差——和端点敏感性——捕捉在既定报告期内估算的SOC变化差异。敏感性通过将替代模拟（即边界条件逐一被遥感衍生的空间数据集替换）与由原位观测驱动的基线模拟进行比较来量化。该框架应用于欧洲16个长期实验站点，涵盖多种土地利用类型和环境条件。边界条件替代影响了SOC轨迹和端点估算，尽管这些影响的幅度和形式因输入而异。初始SOC、植被衍生的碳输入和气候数据是动态敏感性的主要驱动因素。固定端点敏感性相对于实测SOC变化通常较小，支持将遥感衍生的空间数据集作为SOC建模的可扩展边界条件代理。滚动端点敏感性进一步揭示，SOC变化评估不仅取决于SOC动态，还取决于所选的报告窗口。较短报告期更受短期变异影响，而较长时期更易受累积偏差影响。约15–30年的报告窗口在这些效应之间提供了实际平衡。总体而言，该框架能够系统量化由边界条件替代引起的敏感性，从而评估轨迹稳健性和报告稳健性。这些发现凸显了在应用遥感驱动的SOC建模进行区域碳核算时，联合考虑关键边界条件和报告窗口设计的重要性。",null,"OpenAlex","2026-09-09T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,8,1,"提出量化边界条件敏感性的通用框架，覆盖欧洲16个长期试验点，方法新颖、结论对遥感驱动碳核算具参考价值，但属细分领域学术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"土壤有机碳","遥感","碳核算","农业模型","欧盟",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":54},[39,48],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"H2",15,true,[44],"soil",[46,44],"土壤",[27],{"code":49,"score":41,"supported":42,"title":50,"abstract":52,"tags":53},"I3",[51],"remote sensing",[28,51],[28],"农业相关性需要复核",[56,57],"土壤有机碳 农业模型 碳核算 欧盟","土壤有机碳 农业模型","土壤有机碳农业模型碳核算欧盟-2150","10.5194\u002Fegusphere-2026-4735",{"doi":59,"openalex_id":61,"authors":62,"venue":9,"cited_by_count":116,"oa_url":117,"card":118,"direction":124,"ingested_from":125},"W7212029701",[63,66,68,71,73,76,79,81,84,86,89,92,95,97,100,103,106,108,111,113],{"name":64,"orcid":65},"Yue Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-0099-4098",{"name":67,"orcid":9},"Maria Costanza Andrenelli",{"name":69,"orcid":70},"Quentin Beauclaire","https:\u002F\u002Forcid.org\u002F0000-0001-8334-5570",{"name":72,"orcid":9},"Eyal Ben-Dor",{"name":74,"orcid":75},"Sara Bergante","https:\u002F\u002Forcid.org\u002F0000-0002-1311-3325",{"name":77,"orcid":78},"Javier Bravo-García","https:\u002F\u002Forcid.org\u002F0000-0001-6305-4346",{"name":80,"orcid":9},"David de la Fuente Blanco",{"name":82,"orcid":83},"Asa Gholizadeh","https:\u002F\u002Forcid.org\u002F0000-0003-4419-5463",{"name":85,"orcid":9},"David Čejka",{"name":87,"orcid":88},"Sabine Chabrillat","https:\u002F\u002Forcid.org\u002F0000-0001-8600-5168",{"name":90,"orcid":91},"Bernard Heinesch","https:\u002F\u002Forcid.org\u002F0000-0001-7594-6341",{"name":93,"orcid":94},"Laura Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1827-9623",{"name":96,"orcid":9},"Kevin Küehl",{"name":98,"orcid":99},"Bernard Longdoz","https:\u002F\u002Forcid.org\u002F0000-0002-7737-8226",{"name":101,"orcid":102},"Carlos Lozano Fondón","https:\u002F\u002Forcid.org\u002F0000-0001-7878-5387",{"name":104,"orcid":105},"Petr Máca","https:\u002F\u002Forcid.org\u002F0000-0002-4972-3993",{"name":107,"orcid":9},"Robert Milewski",{"name":109,"orcid":110},"Stefano Monaco","https:\u002F\u002Forcid.org\u002F0000-0003-0205-0936",{"name":112,"orcid":9},"Dimitra Palantza",{"name":114,"orcid":115},"Francesco Palazzi","https:\u002F\u002Forcid.org\u002F0000-0003-4196-0190",0,"https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-4735\u002Fegusphere-2026-4735.pdf",{"tldr":119,"method":120,"finding":121,"direction":122,"opportunity":123},"提出量化土壤有机碳模型边界条件敏感性的框架，比较遥感替代原位输入的影响。","基于欧洲16个长期试验点，逐一用遥感数据替换边界条件并对比基线模拟。","初始SOC、植被碳输入和气候主导动态敏感性；15-30年报告窗口较平衡。","农业遥感与作物表型","可探索不同区域和作物体系下遥感替代边界条件的敏感性阈值与最优报告窗口设计。","智慧农业 \u002F 农业物联网","openalex","2026-09-11T23:30:12.218295Z",{"total":128,"page":22,"page_size":128,"items":129},6,[130,250,318,371,415,488],{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":144,"tags":146,"primary_topic":150,"secondary_topics":154,"classification":160,"search_phrases":202,"slug":205,"view_count":116,"doi":206,"paper":207,"created_at":249},5102,"Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42729-026-03629-3","Abstract Soils are fundamental to support ecosystem functions. Therefore, understanding soil organic carbon stock (SCS) as a strategic environmental component for climate regulation, sustainable agricultural practices, and maximizing fertilizer application is essential. This study aimed to predict and map the SCS at Palmital Farm, Minas Gerais state, using land use, parent materials, soil classes, proximal and remote sensing data as predictor variables. A total of 76 soil samples were collected in the surface (A) and subsurface (B) horizons using a regular grid design (200 × 200 m). Carbon content was determined using the Walkley–Black method. Prediction models were created via Random Forest algorithm and validated through leave-one-out cross-validation. Environmental and anthropogenic influences on SCS were analyzed statistically and spatially. The highest mean SCS values were found at the surface horizon under native vegetation and pasture areas (~ 33 Mg ha⁻¹), due to continuous organic matter deposition and lack of disturbance. Charnockite-derived soils showed higher SCS (31.24 Mg ha⁻¹, surface). Among soil classes, Argissolos stood out reaching the highest SCS (35.86 Mg ha⁻¹). Remote sensing data and terrain attributes yielded the best SCS prediction (R²=0.43). The main factors influencing SCS were land use\u002Fmanagement and soil texture. Also, strong correlations were observed between vegetation indices, some terrain attributes and SCS. This approach, combining multiple variables, indicated a moderate potential to achieve accurate SCS predictions using these data, demonstrating the high variability and complexity of SCS spatial variation in detailed-scale mapping.","摘要 土壤是支撑生态系统功能的基础。因此，理解土壤有机碳储量（SCS）作为气候调节、可持续农业实践和最大化肥料施用的战略性环境组成部分至关重要。本研究旨在利用土地利用、成土母质、土壤类别、近端和遥感数据作为预测变量，对米纳斯吉拉斯州Palmital农场的SCS进行预测和制图。采用规则网格设计（200 × 200 m），在表层（A）和亚表层（B）共采集76个土壤样品。碳含量采用Walkley–Black法测定。通过随机森林算法建立预测模型，并采用留一交叉验证进行验证。对环境和人为因素对SCS的影响进行了统计和空间分析。表层中原生植被和牧草地利用方式下的SCS均值最高（约33 Mg ha⁻¹），这归因于有机质的持续沉积和缺乏扰动。源自紫苏花岗岩的土壤表现出较高的SCS（31.24 Mg ha⁻¹，表层）。在土壤类别中，Argissolos表现突出，达到最高SCS（35.86 Mg ha⁻¹）。遥感数据和地形属性获得了最佳的SCS预测效果（R²=0.43）。影响SCS的主要因素为土地利用\u002F管理和土壤质地。此外，植被指数、部分地形属性与SCS之间观察到较强的相关性。这种结合多变量的方法表明，利用这些数据实现准确SCS预测具有中等潜力，同时也揭示了详细尺度制图中SCS空间变异的高变异性和复杂性。","Journal of soil science and plant nutrition","2026-10-14T00:00:00Z",67,{"impact":21,"substance":140,"depth":141,"authority":20,"freshness":142,"relevant":22,"comment":143},20,17,9,"该研究结合近地与遥感数据及随机森林算法预测土壤有机碳储量，方法新颖、数据扎实，对农业信息化与精准农业有参考价值，但属区域性案例研究，影响范围有限。",[145],{"name":136,"url":133},[147,148,27,28,149],"数字农业","随机森林","土壤制图",{"code":40,"slug":44,"name":151,"path":152,"parent_code":153},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[155],{"code":49,"slug":156,"name":157,"path":158,"parent_code":159},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":161,"locked":14,"version":35,"method":36,"evidence":162},"classified",{"candidates":163,"agricultural_evidence":42},[164,168,172,179,186,192,197],{"code":40,"score":17,"supported":42,"title":165,"abstract":166,"tags":167},[44],[46,44],[149,27],{"code":49,"score":41,"supported":42,"title":169,"abstract":170,"tags":171},[51],[28,51],[28],{"code":173,"score":128,"supported":42,"title":174,"abstract":175,"tags":178},"F3",[],[176,177],"肥料","fertilizer",[],{"code":180,"score":181,"supported":42,"title":182,"abstract":183,"tags":185},"H8",3,[],[184],"可持续农业",[],{"code":187,"score":181,"supported":42,"title":188,"abstract":189,"tags":191},"I4",[],[190],"空间分析",[],{"code":193,"score":22,"supported":14,"title":194,"abstract":195,"tags":196},"I1",[],[],[147],{"code":198,"score":22,"supported":14,"title":199,"abstract":200,"tags":201},"I2",[],[],[148],[203,204],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102","10.1007\u002Fs42729-026-03629-3",{"doi":206,"openalex_id":208,"authors":209,"venue":136,"cited_by_count":116,"oa_url":243,"card":244,"direction":122,"ingested_from":125},"W7222335269",[210,213,216,219,222,225,228,231,234,237,240],{"name":211,"orcid":212},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":214,"orcid":215},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":217,"orcid":218},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":220,"orcid":221},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":223,"orcid":224},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":226,"orcid":227},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":229,"orcid":230},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":232,"orcid":233},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":235,"orcid":236},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":238,"orcid":239},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":241,"orcid":242},"Sérgio Henrique Godinho Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2750-5976","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs42729-026-03629-3.pdf",{"tldr":245,"method":246,"finding":247,"direction":122,"opportunity":248},"结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。","76个样点、Walkley-Black法测碳，随机森林建模，留一交叉验证。","遥感与地形因子预测最佳（R²=0.43），土地利用和土壤质地主导碳储量空间变异。","可探索多源遥感与深度学习方法提升小尺度土壤碳制图精度，并耦合管理措施情景。","2026-10-16T23:30:31.433840Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":262,"tags":264,"primary_topic":269,"secondary_topics":273,"classification":274,"search_phrases":301,"slug":304,"view_count":116,"doi":305,"paper":306,"created_at":317},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":260,"substance":18,"depth":19,"authority":260,"freshness":13,"relevant":22,"comment":261},12,"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。",[263],{"name":256,"url":253},[265,266,267,28,268],"智慧农业","农业人工智能","粮食安全","气候分析",{"code":198,"slug":270,"name":271,"path":272,"parent_code":159},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":161,"locked":14,"version":35,"method":36,"evidence":275},{"candidates":276,"agricultural_evidence":42},[277,282,288,293,297],{"code":198,"score":142,"supported":42,"title":278,"abstract":280,"tags":281},[279],"artificial intelligence",[],[266],{"code":283,"score":181,"supported":42,"title":284,"abstract":285,"tags":287},"I5",[],[286],"农业传感",[],{"code":289,"score":22,"supported":14,"title":290,"abstract":291,"tags":292},"A8",[],[],[267],{"code":193,"score":22,"supported":14,"title":294,"abstract":295,"tags":296},[],[],[265],{"code":49,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[28],[302,303],"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":305,"openalex_id":307,"authors":308,"venue":256,"cited_by_count":116,"oa_url":253,"card":311,"direction":316,"ingested_from":125},"W7223111766",[309],{"name":310,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":312,"method":313,"finding":314,"direction":122,"opportunity":315},"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":319,"title":320,"url":321,"summary":322,"summary_zh":9,"content":9,"source_name":323,"source_url":321,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":324,"score_detail":325,"sources":327,"tags":329,"primary_topic":332,"secondary_topics":336,"classification":337,"search_phrases":349,"slug":352,"view_count":116,"doi":353,"paper":354,"created_at":370},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":21,"substance":128,"depth":21,"authority":128,"freshness":13,"relevant":22,"comment":326},"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。",[328],{"name":323,"url":321},[147,330,28,331],"农业信息化","地图评估",{"code":193,"slug":333,"name":334,"path":335,"parent_code":159},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":161,"locked":14,"version":35,"method":36,"evidence":338},{"candidates":339,"agricultural_evidence":42},[340,345],{"code":193,"score":20,"supported":42,"title":341,"abstract":343,"tags":344},[342],"digital agriculture",[342],[330,147],{"code":49,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[28],[350,351],"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":353,"openalex_id":355,"authors":356,"venue":323,"cited_by_count":116,"oa_url":321,"card":9,"direction":369,"ingested_from":125},"W7223099987",[357,360,363,366],{"name":358,"orcid":359},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":361,"orcid":362},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":364,"orcid":365},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":367,"orcid":368},"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":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":9,"source_name":377,"source_url":374,"published_at":378,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":379,"sources":381,"tags":383,"primary_topic":9,"secondary_topics":386,"classification":387,"search_phrases":394,"slug":397,"view_count":116,"doi":398,"paper":399,"created_at":414},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":116,"substance":116,"depth":116,"authority":116,"freshness":116,"relevant":116,"comment":380},"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。",[382],{"name":377,"url":374},[28,384,385],"光谱数据","自动分类",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":388},{"candidates":389,"agricultural_evidence":14,"reason":54},[390],{"code":49,"score":41,"supported":42,"title":391,"abstract":392,"tags":393},[51],[28,51],[28],[395,396],"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":398,"openalex_id":400,"authors":401,"venue":377,"cited_by_count":116,"oa_url":408,"card":409,"direction":122,"ingested_from":125},"W7223058937",[402,405],{"name":403,"orcid":404},"Aleksandr V. Lapko","https:\u002F\u002Forcid.org\u002F0000-0002-0664-3870",{"name":406,"orcid":407},"Vasiliy A. Lapko","https:\u002F\u002Forcid.org\u002F0000-0001-6938-9323","https:\u002F\u002Fjournals.eco-vector.com\u002F2712-8970\u002Farticle\u002Fdownload\u002F719886\u002F228882",{"tldr":410,"method":411,"finding":412,"direction":122,"opportunity":413},"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",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":426,"tags":428,"primary_topic":433,"secondary_topics":437,"classification":438,"search_phrases":468,"slug":471,"view_count":116,"doi":472,"paper":473,"created_at":487},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies",61,{"impact":21,"substance":19,"depth":424,"authority":260,"freshness":142,"relevant":22,"comment":425},14,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[427],{"name":421,"url":418},[28,429,430,431,432],"土地利用","土壤侵蚀","GIS","水土保持",{"code":187,"slug":434,"name":435,"path":436,"parent_code":159},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":161,"locked":14,"version":35,"method":36,"evidence":439},{"candidates":440,"agricultural_evidence":42},[441,447,452,456,462],{"code":187,"score":13,"supported":42,"title":442,"abstract":443,"tags":446},[],[444,431,445],"地理信息","geographic information",[431],{"code":49,"score":448,"supported":42,"title":449,"abstract":450,"tags":451},7,[],[28,51],[28],{"code":40,"score":128,"supported":42,"title":453,"abstract":454,"tags":455},[],[46,44],[],{"code":457,"score":181,"supported":42,"title":458,"abstract":459,"tags":461},"H3",[],[460],"灌溉",[],{"code":463,"score":464,"supported":14,"title":465,"abstract":466,"tags":467},"H6",2,[],[],[429,432],[469,470],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":472,"openalex_id":474,"authors":475,"venue":421,"cited_by_count":116,"oa_url":9,"card":481,"direction":122,"ingested_from":125},"W7223117733",[476,478],{"name":477,"orcid":9},"MERİÇ KURT",{"name":479,"orcid":480},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":482,"method":483,"finding":484,"direction":485,"opportunity":486},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":489,"title":490,"url":491,"summary":492,"summary_zh":493,"content":9,"source_name":494,"source_url":491,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":495,"score_detail":496,"sources":498,"tags":500,"primary_topic":503,"secondary_topics":504,"classification":505,"search_phrases":522,"slug":525,"view_count":116,"doi":526,"paper":527,"created_at":546},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy",73,{"impact":260,"substance":140,"depth":141,"authority":424,"freshness":13,"relevant":22,"comment":497},"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[499],{"name":494,"url":491},[265,501,28,502],"作物制图","SAR",{"code":49,"slug":156,"name":157,"path":158,"parent_code":159},[],{"status":161,"locked":14,"version":35,"method":36,"evidence":506},{"candidates":507,"agricultural_evidence":42},[508,513,518],{"code":49,"score":13,"supported":42,"title":509,"abstract":510,"tags":512},[],[28,51,511],"multispectral",[28],{"code":289,"score":128,"supported":42,"title":514,"abstract":515,"tags":517},[],[267,516],"food security",[],{"code":193,"score":22,"supported":14,"title":519,"abstract":520,"tags":521},[],[],[265],[523,524],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":526,"openalex_id":528,"authors":529,"venue":494,"cited_by_count":116,"oa_url":491,"card":541,"direction":122,"ingested_from":125},"W7223117203",[530,533,535,538],{"name":531,"orcid":532},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":534,"orcid":9},"Wentao Lv",{"name":536,"orcid":537},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":539,"orcid":540},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":542,"method":543,"finding":544,"direction":122,"opportunity":545},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z"]