Downscaling NPP–VIIRS Nighttime Light Data Using Vegetation Nighttime Condition Index

Abstract

Nighttime light (NTL) data, a cornerstone in the scientific community, are widely used across various disciplines. However, the spatial resolution of the commonly used NTL datasets often falls coarse for detailed urban-scale analyses. Current downscaling approaches for NTL data typically rely on extensive auxiliary datasets, limiting their applicability to large geographical regions. In response, we have developed a novel NTL downscaling method that directly uses the vegetation nighttime condition index (VNCI) as input to downscale the national polar-orbiting partnership–visible infrared imaging radiometer suite NTL product. To showcase the potential of this innovative approach, we downscaled the NTL data for mainland China from 2013 to 2021 using only normalized difference vegetation index (NDVI) data as input. Our results demonstrate that the downscaled NTL data not only preserve the accuracy of the original NTL data but also reveal more spatial details and is consistent with the Luojia 1-01 NTL data. Our experiments underscore the significant advantages of the proposed VNCI-based NTL downscaling approach, including its simplicity and minimal data entry requirements, as it only necessitates NDVI as input. This practical and straightforward approach holds great promise for NTL-based urban studies.