The availability of high-resolution reference datasets representing in space and time and with high accuracy areas affected by fires is strategic for the validation of remotely-sensed Burned Area (BA) products. This paper proposes a methodology designed to build a burned area reference dataset from Sentinel-2 (S2) images at continental scale by implementing a stratified random sampling scheme. Representative sample units are selected across biomes and regions with high/low fire activity; each unit covers the extent of a S2 tile (∼10 000 km²) where image time series are classified with a supervised Random Forest algorithm to extract fire perimeters by exploiting visible to near and short-wave infrared S2 wavebands at 10 to 20 m spatial resolution. Time series have to satisfy requirements on maximum cloud cover, maximum time interval between consecutive images and minimum length to be suitable for being selected and processed. The proposed methodology was applied to Sub-Saharan Africa for the year 2019 to select 50 S2 sample units where time series were processed to deliver fire reference perimeters for accuracy assessment of regional BA products. Average series length is 140 days with the longest series in the savanna biome (maximum length is 355 days, 29 consecutive S2 images) and a total of 695 S2 images were processed to build the 2019 reference dataset. This dataset was compared to burned areas derived from very-high resolution Planetscope images over five S2 tiles obtaining 15.5% omission and 11.6% commission errors. To exemplify the use of this reference dataset, S2 perimeters were used to validate the NASA MCD64A1 Collection 6 and the ESA FireCCI51 BA products. The reference dataset has been added to the Burned Area Reference Database (BARD) (Franquesa et al., 2020) and is publicly available at https://doi.org/10.21950/VKFLCH.

高分辨率参考数据集的可用性表示空间和时间以及受火灾影响的高精度区域对于验证遥感燃烧区域 (BA) 产品具有战略意义。本文提出了一种方法，旨在通过实施分层随机抽样方案，从大陆尺度的 Sentinel-2 (S2) 图像构建烧毁区域参考数据集。具有代表性的样本单元是在具有高/低火灾活动的生物群落和区域中选择的；每个单元覆盖 S2 瓦片的范围（~10 000 km ²) 其中图像时间序列使用有监督的随机森林算法进行分类，以通过利用 10 到 20 m 空间分辨率的近波和短波红外 S2 波段的可见光来提取火灾周界。时间序列必须满足最大云量、连续图像之间的最大时间间隔和最小长度的要求，才能适合被选择和处理。拟议的方法在 2019 年应用于撒哈拉以南非洲地区，以选择 50 个 S2 样本单元，在这些样本单元中处理时间序列以提供火灾参考周长，用于区域 BA 产品的准确性评估。平均系列长度为 140 天，其中热带草原生物群系中最长的系列（最大长度为 355 天，连续 29 张 S2 图像），总共处理了 695 张 S2 图像以构建 2019 年参考数据集。将该数据集与来自 5 个 S2 瓦片上的超高分辨率 Planetscope 图像的烧毁区域进行比较，获得 15.5% 的遗漏和 11.6% 的佣金错误。为了举例说明此参考数据集的使用，S2 周界用于验证 NASA MCD64A1 Collection 6 和 ESA FireCCI51 BA 产品。参考数据集已添加到烧毁区域参考数据库 (BARD)（Franquesa 等人，2020 年），并可在 https://doi.org/10.21950/VKFLCH 上公开获取。