[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-Sentinel-1":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"Sentinel-1","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":29,"tags":31,"primary_topic":16,"secondary_topics":36,"classification":37,"search_phrases":61,"slug":64,"view_count":7,"doi":65,"paper":66,"created_at":136},4235,"Right on time: evaluating accuracy and timing of burn-severity assessments to support post-fire management","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs42408-026-00589-y","Abstract Effective wildfire impact assessment and post-fire management rely on burn-severity maps that accurately characterize fire effects on vegetation and soils. These maps inform time-sensitive activities such as post-fire flood and debris-flow modeling, risk mitigation for homes and communities, and ecosystem recovery planning, but the satellite observations used to generate them are often obstructed or delayed by smoke, clouds, and snow. Across 9,129 large fires (≥ 404 ha) in Canada and the United States from 2016–2024, we quantified post-fire data obstruction and evaluated observational latency – defined as the time required to acquire the first usable scene – for four freely available moderate-resolution remote sensing datasets: Landsat, Sentinel-1, Sentinel-2, and the Harmonized Landsat Sentinel-2 (HLS) product. For multispectral datasets (Landsat, Sentinel-2, and HLS), cloud cover was the primary source of data obstruction, particularly in mesic and coastal regions, whereas Sentinel-1 (synthetic aperture radar) can penetrate clouds. Consequently, Sentinel-1 had the shortest observational latency (median = 4 days), followed by HLS (7 days), Sentinel-2 (10 days), and Landsat (17 days). Likewise, one-month composite imagery from Sentinel-1 (89.6%) and HLS (82.8%) achieved near-complete coverage for most fires, substantially better than Sentinel-2 (70.5%) and Landsat (56.1%). Despite its widespread use in operational burn-severity mapping, Landsat provided the slowest and least complete early post-fire coverage. For 77 fires in forested ecosystems, we also evaluated relationships between early post-fire imagery and field-derived burn-severity measurements collected 0–3 years after fire. All multispectral datasets corresponded well with field data and reliably identified areas of near-total canopy loss using imagery acquired within two months after fire (> 80% balanced accuracy), performing nearly as well as extended assessments using imagery from a full post-fire growing season. Sentinel-1 produced less accurate burn-severity estimates but may remain valuable where persistent cloud cover limits observations, including eastern Canada and the Pacific Northwest. Overall, HLS provided the best combination of latency, spatial coverage, and burn-severity mapping performance, making it a strong candidate for operational use and ecological monitoring across diverse North American ecosystems.","有效的野火影响评估和火后管理依赖于能够准确刻画火灾对植被和土壤影响的火烧严重度（burn severity）制图。这些地图为时间敏感的活动提供信息支持，如火后洪水和泥石流建模、住宅和社区的风险缓解以及生态系统恢复规划，但用于生成这些地图的卫星观测往往受到烟雾、云层和积雪的遮挡或延迟。针对2016—2024年加拿大和美国的9，129场大型火灾（≥ 404公顷），我们量化了火后数据受阻情况，并评估了四种免费可获取的中分辨率遥感数据集的观测延迟——定义为获取首个可用影像所需的时间：Landsat、Sentinel-1、Sentinel-2以及Landsat与Sentinel-2协调产品（HLS）。对于多光谱数据集（Landsat、Sentinel-2和HLS），云覆盖是数据受阻的主要来源，尤其在湿润和沿海地区，而Sentinel-1（合成孔径雷达）能够穿透云层。因此，Sentinel-1的观测延迟最短（中位数=4天），其次是HLS（7天）、Sentinel-2（10天）和Landsat（17天）。同样，Sentinel-1（89.6%）和HLS（82.8%）的月度合成影像对大多数火灾实现了近乎完全的覆盖，显著优于Sentinel-2（70.5%）和Landsat（56.1%）。尽管Landsat在业务化火烧严重度制图中被广泛使用，但其提供的火后早期覆盖最慢且最不完整。针对森林生态系统中的77场火灾，我们还评估了火后早期影像与火后0—3年采集的野外火烧严重度测量值之间的关系。所有多光谱数据集均与野外数据吻合良好，并利用火后两个月内获取的影像可靠地识别了近全部冠层损失区域（平衡精度> 80%），其表现几乎与使用整个火后生长季影像的扩展评估相当。Sentinel-1产生的火烧严重度估算精度较低，但在持续云覆盖限制观测的地区（包括加拿大东部和太平洋西北地区）可能仍有价值。总体而言，HLS在延迟、空间覆盖和火烧严重度制图性能方面提供了最佳组合，使其成为跨北美多样化生态系统业务化应用和生态监测的有力候选方案。",null,"Fire Ecology","2026-10-02T00:00:00Z","论文",10,false,81,{"impact":24,"substance":25,"depth":24,"authority":26,"freshness":27,"relevant":7,"comment":28},18,23,14,8,"基于北美9129场大火的遥感时效性评估，数据规模大、结论明确，对灾后遥感监测与生态恢复有直接参考价值。",[30],{"name":17,"url":13},[32,33,34,4,35],"遥感监测","植被恢复","林火生态","灾后评估",[],{"status":38,"locked":21,"version":39,"method":40,"evidence":41},"needs_review","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":21,"reason":60},[43,53],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},"I3",11,true,[],[49,50,51],"遥感","remote sensing","multispectral",[33,32],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":59},"H2",3,[],[58],"土壤",[],"农业相关性需要复核",[62,63],"Sentinel-1 火烧严重度 遥感","HLS 林火 灾后管理","Sentinel-1火烧严重度遥感-4235","10.1186\u002Fs42408-026-00589-y",{"doi":65,"openalex_id":67,"authors":68,"venue":17,"cited_by_count":127,"oa_url":128,"card":129,"direction":133,"ingested_from":135},"W7215928159",[69,72,75,78,81,84,87,90,93,96,99,101,103,106,109,112,115,118,121,124],{"name":70,"orcid":71},"Kyle C. Rodman","https:\u002F\u002Forcid.org\u002F0000-0001-9538-8412",{"name":73,"orcid":74},"Ellen Whitman","https:\u002F\u002Forcid.org\u002F0000-0002-4562-3645",{"name":76,"orcid":77},"Alexander A. Howe","https:\u002F\u002Forcid.org\u002F0000-0002-6815-7536",{"name":79,"orcid":80},"A. Spannuth","https:\u002F\u002Forcid.org\u002F0009-0007-9401-5842",{"name":82,"orcid":83},"Kevin Barrett","https:\u002F\u002Forcid.org\u002F0000-0003-1014-6372",{"name":85,"orcid":86},"Corey Beinhart","https:\u002F\u002Forcid.org\u002F0009-0006-9282-4534",{"name":88,"orcid":89},"Yan Boucher","https:\u002F\u002Forcid.org\u002F0000-0003-0411-6927",{"name":91,"orcid":92},"Sebastian U. Busby","https:\u002F\u002Forcid.org\u002F0000-0003-2084-0106",{"name":94,"orcid":95},"Jorge Téllez Carrasco","https:\u002F\u002Forcid.org\u002F0009-0002-3326-1306",{"name":97,"orcid":98},"Kyra Clark‐Wolf","https:\u002F\u002Forcid.org\u002F0000-0003-4584-0348",{"name":100,"orcid":16},"M. J. Corbett",{"name":102,"orcid":16},"J. E. Crouse",{"name":104,"orcid":105},"Victor Danneyrolles","https:\u002F\u002Forcid.org\u002F0000-0002-4839-8164",{"name":107,"orcid":108},"Patricia Dappen","https:\u002F\u002Forcid.org\u002F0009-0004-9910-7023",{"name":110,"orcid":111},"Kimberley T. Davis","https:\u002F\u002Forcid.org\u002F0000-0001-9727-374X",{"name":113,"orcid":114},"Brian J. Harvey","https:\u002F\u002Forcid.org\u002F0000-0002-5902-4862",{"name":116,"orcid":117},"Zachary A. Holden","https:\u002F\u002Forcid.org\u002F0000-0002-4319-6491",{"name":119,"orcid":120},"Sharon M. Hood","https:\u002F\u002Forcid.org\u002F0000-0002-9544-8208",{"name":122,"orcid":123},"José M. Iniguez","https:\u002F\u002Forcid.org\u002F0000-0002-4566-1297",{"name":125,"orcid":126},"Patrick M. A. James","https:\u002F\u002Forcid.org\u002F0000-0001-7639-7217",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs42408-026-00589-y.pdf",{"tldr":130,"method":131,"finding":132,"direction":133,"opportunity":134},"评估加拿大和美国9129场大火后四种遥感数据的观测延迟与烧毁严重度制图精度。","利用Landsat、Sentinel-1\u002F2及HLS数据，结合77场火灾的野外烧","Sentinel-1延迟最短（中位4天），HLS综合表现最佳，Landsat最慢且覆盖最差。","农业遥感与作物表型","可探索多源遥感融合与近实时烧毁严重度制图，并迁移至农田火灾或作物灾损评估。","openalex","2026-10-05T23:30:26.576406Z"]