Robust reconstruction of seamless daily VIIRS nighttime light imagery with cloud mask refinement and multi-strategy spatiotemporal gap-filling

The full potential of daily VIIRS nighttime light (NTL) data in characterizing human activity changes has been largely constrained due to pervasive data gaps induced by cloud contamination and poor-quality observations. While several gap-filling methods are available, their effectiveness and applicability remain limited by under-detected cloud contamination and in challenging data gap conditions. In this study, we proposed a novel spatiotemporal gap-filling framework to generate seamless daily VNP46 VIIRS NTL data. First, we refined the original VNP46 cloud mask to mitigate under-detected cloud coverage in the NTL data for subsequent gap-filling. Second, we identified pixels sharing spatiotemporal similarity to each target data gap pixel via a set of stringent search criteria. Third, based on similar pixel search outcomes, we developed a multi-strategy gap-filling approach leveraging spatiotemporal information from not only valid observations but also pixels that were data gaps at the target date. Evaluated with daily NTL images from 30 global cities, our results revealed a 39% cloud under-detection rate among pixels labelled as valid observations in the VNP46 product. Our proposed gap-filling method demonstrated an effective and robust performance in reconstructing the NTL intensity across various simulated data gap conditions, including abrupt NTL changes, cloud underdetection, and dense spatiotemporal data gap coverage (R 2 = 0.80–0.85, RMSE = 2.13–10.84 nW/cm 2 /sr). We also found that our stringent similar pixel search criteria and multi-strategy gap-filling approach can better secure the quality of similar pixels, method applicability, and gap-filling accuracy compared to existing methods, particularly in challenging conditions. Furthermore, our method successfully reconstructed NTL images with dense data gaps during Hurricane Maria and provided seamless and consistent insights into the spatial heterogeneity of post-hurricane recovery. Our proposed gap-filling method and the resulting seamless daily NTL data will help to maximize its untapped potential by enhancing our understanding of the inherent characteristics of daily NTL data and unlocking new NTL-based applications.