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The longest archive makes DMSP-OLS nighttime light (NTL) images unparalleled in relevant time series studies. However, these studies have been constrained by the blooming effect. The self-adjusting model (SEAM) proposed in 2019 solves this problem to some extent. However, SEAM assumed all pixels in NTL images with a constant blooming distance 3.5 km. In fact, the blooming distance is related to the land covers and the brightness of artificial lights. This assumption leads to large errors in cities that have blooming distance different from 3.5 km. To address this problem, this study proposed an improved SEAM model (iSEAM) by considering spatial heterogeneity of blooming distance. Specifically, iSEAM segmented the DMSP-OLS image to obtain light objects and then employed the random forest method to estimate the effective blooming distance for each light object, and then corrected the blooming effect of all pixels in each light object by a modified pixel brightness interactive model. The test in China shows that the blooming distance ranges from 0 to 12.55 km in China, with an average 3.36 km. The correlation coefficient (R) between the images corrected by iSEAM and the NPP-VIIRS images reaches 0.70 that is higher than other blooming effect correction methods. Moreover, the corrected images by iSEAM have higher spatial heterogeneity than other methods. These results suggest that by considering the spatial heterogeneity of effective blooming distance, iSEAM can serve as a more accurate and effective method to correct the blooming effect of DMSP-OLS NTL images.

iSEAM: Improving the Blooming Effect Adjustment for DMSP-OLS Nighttime Light Images by Considering Spatial Heterogeneity of Blooming Distance