[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2150":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":99},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",0,"10.5194\u002Fegusphere-2026-4735",{"doi":33,"openalex_id":35,"authors":36,"venue":9,"cited_by_count":32,"oa_url":90,"card":91,"direction":97,"ingested_from":98},"W7212029701",[37,40,42,45,47,50,53,55,58,60,63,66,69,71,74,77,80,82,85,87],{"name":38,"orcid":39},"Yue Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-0099-4098",{"name":41,"orcid":9},"Maria Costanza Andrenelli",{"name":43,"orcid":44},"Quentin Beauclaire","https:\u002F\u002Forcid.org\u002F0000-0001-8334-5570",{"name":46,"orcid":9},"Eyal Ben-Dor",{"name":48,"orcid":49},"Sara Bergante","https:\u002F\u002Forcid.org\u002F0000-0002-1311-3325",{"name":51,"orcid":52},"Javier Bravo-García","https:\u002F\u002Forcid.org\u002F0000-0001-6305-4346",{"name":54,"orcid":9},"David de la Fuente Blanco",{"name":56,"orcid":57},"Asa Gholizadeh","https:\u002F\u002Forcid.org\u002F0000-0003-4419-5463",{"name":59,"orcid":9},"David Čejka",{"name":61,"orcid":62},"Sabine Chabrillat","https:\u002F\u002Forcid.org\u002F0000-0001-8600-5168",{"name":64,"orcid":65},"Bernard Heinesch","https:\u002F\u002Forcid.org\u002F0000-0001-7594-6341",{"name":67,"orcid":68},"Laura Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1827-9623",{"name":70,"orcid":9},"Kevin Küehl",{"name":72,"orcid":73},"Bernard Longdoz","https:\u002F\u002Forcid.org\u002F0000-0002-7737-8226",{"name":75,"orcid":76},"Carlos Lozano Fondón","https:\u002F\u002Forcid.org\u002F0000-0001-7878-5387",{"name":78,"orcid":79},"Petr Máca","https:\u002F\u002Forcid.org\u002F0000-0002-4972-3993",{"name":81,"orcid":9},"Robert Milewski",{"name":83,"orcid":84},"Stefano Monaco","https:\u002F\u002Forcid.org\u002F0000-0003-0205-0936",{"name":86,"orcid":9},"Dimitra Palantza",{"name":88,"orcid":89},"Francesco Palazzi","https:\u002F\u002Forcid.org\u002F0000-0003-4196-0190","https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-4735\u002Fegusphere-2026-4735.pdf",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"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"]