Deriving burn severity from multispectral satellite data is a widely adopted approach to infer the degree of environmental change caused by fire. Burn severity maps obtained by thresholding bi-temporal indices based on pre- and post-fire Normalized Burn Ratio (NBR) can vary substantially depending on temporal constraints such as matched acquisition and optimal seasonal timing. Satisfying temporal requirements is crucial to effectively disentangle fire and non-fire induced spectral changes and can be particularly challenging when only a few cloud-free images are available. Our study focuses on 10 wildfires that occurred in mountainous areas of the Piedmont Region (Italy) during autumn 2017 following a severe and prolonged drought period. Our objectives were to: (i) generate reflectance composites using Sentinel-2 imagery that were optimised for seasonal timing by embedding spatial patterns of long-term land surface phenology (LSP); (ii) produce and validate burn severity maps based on the modelled relationship between bi-temporal indices and field data; (iii) compare burn severity maps obtained using either a pair of cloud-free Sentinel-2 images, i.e. paired images, or reflectance composites. We proposed a pixel-based compositing algorithm coupling the weighted geometric median and thematic spatial information, e.g. long-term LSP metrics derived from the MODIS Collection 6 Land Cover Dynamics Product, to rank all the clear observations available in the growing season. Composite Burn Index data and bi-temporal indices exhibited a strong nonlinear relationship (R² > 0.85) using paired images or reflectance composites. Burn severity maps attained overall classification accuracy ranging from 76.9% to 83.7% (Kappa between 0.61 and 0.72) and the Relative differenced NBR (RdNBR) achieved the best results compared to other bi-temporal indices (differenced NBR and Relativized Burn Ratio). Improvements in overall classification accuracy offered by the calibration of bi-temporal indices with the dNBR offset were limited to burn severity maps derived from paired images. Reflectance composites provided the highest overall classification accuracy and differences with paired images were significant using uncalibrated bi-temporal indices (4.4% to 5.2%) while they decreased (2.8% to 3.2%) when we calibrated bi-temporal indices derived from paired images. The extent of the high severity category increased by ~19% in burn severity maps derived from reflectance composites (uncalibrated RdNBR) compared to those from paired images (calibrated RdNBR). The reduced contrast between healthy and burnt conditions associated with suboptimal seasonal timing caused an underestimation of burnt areas. By embedding spatial patterns of long-term LSP metrics, our approach provided consistent reflectance composites targeted at a specific phenological stage and minimising non-fire induced inter-annual changes. Being independent from the multispectral dataset employed, the proposed pixel-based compositing approach offers new opportunities for operational change detection applications in geographic areas characterised by persistent cloud cover.

从多光谱卫星数据中推导出烧伤严重程度是一种广泛采用的方法，用于推断火灾引起的环境变化程度。通过基于火灾前和火灾后标准化燃烧率 (NBR) 对双时态指数进行阈值化获得的燃烧严重性图可能会因时间限制（例如匹配的采集和最佳季节时间）而有很大差异。满足时间要求对于有效解决火灾和非火灾引起的光谱变化至关重要，并且在只有少数无云图像可用时尤其具有挑战性。我们的研究重点关注 2017 年秋季在皮埃蒙特地区（意大利）山区发生的 10 场野火，这些山火在经历了严重而长期的干旱期之后。我们的目标是：(i) 使用 Sentinel-2 图像生成反射复合材料，这些图像通过嵌入长期地表物候 (LSP) 的空间模式针对季节性时间进行了优化；(ii) 根据双时指数和现场数据之间的模型关系生成和验证烧伤严重程度图；(iii) 比较使用一对无云 Sentinel-2 图像（即配对图像）或反射合成图获得的烧伤严重程度图。我们提出了一种基于像素的合成算法，将加权几何中位数和专题空间信息（例如，源自 MODIS Collection 6 土地覆盖动态产品的长期 LSP 指标）结合起来，对生长季节中所有可用的清晰观测进行排序。复合燃烧指数数据和双时态指数表现出很强的非线性关系（(ii) 根据双时指数和现场数据之间的模型关系生成和验证烧伤严重程度图；(iii) 比较使用一对无云 Sentinel-2 图像（即配对图像）或反射合成图获得的烧伤严重程度图。我们提出了一种基于像素的合成算法，将加权几何中位数和专题空间信息（例如，源自 MODIS Collection 6 土地覆盖动态产品的长期 LSP 指标）结合起来，对生长季节中所有可用的清晰观测进行排序。复合燃烧指数数据和双时态指数表现出很强的非线性关系（(ii) 根据双时指数和现场数据之间的模型关系生成和验证烧伤严重程度图；(iii) 比较使用一对无云 Sentinel-2 图像（即配对图像）或反射合成图获得的烧伤严重程度图。我们提出了一种基于像素的合成算法，将加权几何中位数和专题空间信息（例如，源自 MODIS Collection 6 土地覆盖动态产品的长期 LSP 指标）结合起来，对生长季节中所有可用的清晰观测进行排序。复合燃烧指数数据和双时态指数表现出很强的非线性关系（即成对的图像，或反射合成。我们提出了一种基于像素的合成算法，将加权几何中位数和专题空间信息（例如，源自 MODIS Collection 6 土地覆盖动态产品的长期 LSP 指标）结合起来，对生长季节中所有可用的清晰观测进行排序。复合燃烧指数数据和双时态指数表现出很强的非线性关系（即成对的图像，或反射合成。我们提出了一种基于像素的合成算法，将加权几何中位数和专题空间信息（例如，源自 MODIS Collection 6 土地覆盖动态产品的长期 LSP 指标）结合起来，对生长季节中所有可用的清晰观测进行排序。复合燃烧指数数据和双时态指数表现出很强的非线性关系（[R ²> 0.85) 使用成对图像或反射合成。烧伤严重度图实现了 76.9% 到 83.7% 的总体分类准确度（Kappa 介于 0.61 和 0.72 之间），并且相对差异 NBR (RdNBR) 与其他双时间指标（差异 NBR 和相对化烧伤比）相比取得了最佳结果。通过使用 dNBR 偏移量校准双时态指数提供的整体分类精度的改进仅限于从配对图像得出的烧伤严重程度图。反射复合材料提供了最高的整体分类准确度，使用未校准的双时态指数（4.4% 至 5.2%）时与配对图像的差异显着，而当我们校准源自配对图像的双时态指数时，它们下降（2.8% 至 3.2%）。与来自配对图像（校准的 RdNBR）的烧伤严重程度图相比，从反射复合材料（未校准的 RdNBR）得出的烧伤严重程度图的高严重性类别的范围增加了约 19%。与次优季节性时间相关的健康和烧毁条件之间的对比度降低导致对烧毁面积的低估。通过嵌入长期 LSP 指标的空间模式，我们的方法提供了针对特定物候阶段的一致反射复合材料，并最大限度地减少了非火灾引起的年际变化。独立于所采用的多光谱数据集，所提出的基于像素的合成方法为以持续云覆盖为特征的地理区域中的操作变化检测应用程序提供了新的机会。