[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2173":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":57},2173,"Integrating Satellite Data into Near-Term, Iterative Water Temperature Forecasting Workflows Improves the Scalability of Freshwater Forecasts","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15008569\u002Fv1","Freshwater ecosystems are experiencing rapid changes and increased variability due to human activities. Near-term iterative forecasting has been shown to be a valuable tool to predict how these ecosystems will change in the future, and can be used to improve both water management and ecosystem understanding. Many freshwater forecasting systems require high-frequency in situ sensor data as input, which limits their scalability to only sites with existing sensor monitoring infrastructure. To scale these forecasting methods to waterbodies with little or no in situ data, new tools are needed. Here, we evaluated how the assimilation of satellite data into a process-based hydrodynamic model using the open-source Forecasting Lake And Reservoir Ecosystems (FLARE) system affected forecast performance for 10 lakes across the US. We forecasted water temperature across water column depths every week for 1- to 14-days ahead over two years with forecast model initial conditions and parameters updated from the assimilation of the Landsat-8\u002F9 Surface Temperature product. Comparisons of forecasts with satellite data assimilation vs. forecasts with no data assimilation reveal that satellite data can improve forecast performance across 1- to 14-day horizons. However, forecast accuracy gains were variable among lakes; at eight sites satellite data assimilation either improved or was equivalent to forecasts with no data assimilation, but at two sites satellite data worsened performance. Differences in forecast performance were most strongly correlated with water clarity and local meteorology. This study demonstrates the promise of satellite remote sensing as a key tool to improve scalability of near-term iterative freshwater forecasting.","淡水生态系统正因人类活动而经历快速变化和变异性增加。近期迭代预测已被证明是预测这些生态系统未来变化的有价值工具，并可用于改进水资源管理和生态系统理解。许多淡水预测系统需要高频原位传感器数据作为输入，这将其可扩展性限制在已有传感器监测基础设施的站点。为了将这些预测方法扩展到几乎没有或完全没有原位数据的水体，需要新的工具。在此，我们评估了利用开源“湖泊与水库生态系统预测”（FLARE）系统将卫星数据同化到基于过程的水动力模型中，如何影响美国10个湖泊的预测性能。我们在两年内每周预测各水柱深度的水温，预测时效为1至14天，并利用Landsat-8\u002F9地表温度产品的同化来更新预测模型的初始条件和参数。将有卫星数据同化的预测与无数据同化的预测进行比较，结果表明卫星数据可以在1至14天的预测时效内提高预测性能。然而，预测精度的提升在湖泊之间存在差异；在8个站点，卫星数据同化要么改善了预测，要么与无数据同化的预测相当，但在2个站点，卫星数据使预测性能变差。预测性能的差异与水体透明度和当地气象条件相关性最强。本研究表明，卫星遥感有望成为提高近期迭代淡水预测可扩展性的关键工具。",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,"将卫星遥感数据同化进FLARE模型，验证了提升淡水水温预报可扩展性的新方法，对水产养殖与水域生态管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧渔业","遥感","水质监测","数据同化","淡水养殖",0,"10.22541\u002Fessoar.15008569\u002Fv1",{"doi":33,"openalex_id":35,"authors":36,"venue":9,"cited_by_count":32,"oa_url":49,"card":50,"direction":54,"ingested_from":56},"W7212081241",[37,40,43,46],{"name":38,"orcid":39},"Molly Stroud","https:\u002F\u002Forcid.org\u002F0000-0001-7389-1586",{"name":41,"orcid":42},"Cayelan C. Carey","https:\u002F\u002Forcid.org\u002F0000-0001-8835-4476",{"name":44,"orcid":45},"George H. Allen","https:\u002F\u002Forcid.org\u002F0000-0001-8301-5301",{"name":47,"orcid":48},"R. Quinn Thomas","https:\u002F\u002Forcid.org\u002F0000-0003-1282-7825","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15008569\u002Fv1",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"将卫星数据同化进FLARE模型，提升无传感器湖泊水温预报的可扩展性。","用FLARE系统同化Landsat-8\u002F9地表温度，对10个湖泊做1-14天水温","卫星同化整体提升预报精度，但效果因湖而异，与水体透明度和气象相关。","农业遥感与作物表型","可探索卫星同化在无监测水体中的适用条件，结合水体光学与气象因子优化预报策略。","openalex","2026-09-11T23:30:32.851926Z"]