Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical dataset needed for improved fire behavior modeling and fire management. This study aims to investigate the geospatial interpolation techniques in mapping the daily fire progression and assess the accuracy of the derived maps from multisensor AF products. We focused on 42 large wildfires greater than 5000 acres in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for the comparison. The standard AF products from the moderate resolution imaging spectroradiometer (MODIS), the visible infrared imaging radiometer suite (VIIRS), and the combined products were used as inputs. We found that the estimated fire progression areas generated by the natural neighbor method with the combined MODIS and VIIRS AF input layers performed the best, with R2 of 0.7 ± 0.31 and RMSE of 1.25 ± 1.21 (103 acres) at a daily time scale; the accuracy was higher when assessed at a two-day rolling window, e.g., R2 of 0.83 ± 0.20 and RMSE of 0.74 ± 0.94 (103 acres). A relatively higher spatial accuracy was found using the 375 m VIIRS AF product as inputs, with a kappa score of 0.55 and an overall accuracy score of 0.59, when interpolated with the natural neighbor method. Furthermore, the locational pixel-based comparison showed 61% matched to a single day and an additional 25% explained within ±1 day of the estimation, revealing greater confidence in fire progression estimation at a two-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.

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基于卫星的火灾进展图：北加州大火的综合评估

基于卫星的主动火灾 (AF) 产品为构建连续火灾进展图提供了机会，这是改进火灾行为建模和火灾管理所需的关键数据集。本研究旨在调查地理空间插值技术在绘制每日火灾进展情况并评估多传感器 AF 产品派生地图的准确性。从 2017 年到 2018 年，我们专注于北加利福尼亚州超过 5000 英亩的 42 场大型野火，其中美国农业部国家红外行动 (NIROPS) 的每日火灾周长可用于比较。来自中等分辨率成像光谱仪 (MODIS)、可见红外成像辐射仪套件 (VIIRS) 的标准 AF 产品以及组合产品用作输入。我们发现由自然邻域法与 MODIS 和 VIIRS AF 输入层组合生成的估计火灾进展区域表现最好，在每日时间尺度上，R2 为 0.7 ± 0.31，RMSE 为 1.25 ± 1.21（103 英亩）；在为期两天的滚动窗口评估时，准确度更高，例如，R2 为 0.83 ± 0.20，RMSE 为 0.74 ± 0.94（103 英亩）。使用 375 m VIIRS AF 产品作为输入发现相对较高的空间精度，当使用自然邻域法进行插值时，kappa 得分为 0.55，整体精度得分为 0.59。此外，基于位置像素的比较显示 61% 与一天匹配，另外 25% 在估计的 ±1 天内得到解释，揭示了在两天移动时间间隔内对火灾进展估计的更大信心。