A Vegetation Nighttime Condition Index Derived From the Triangular Feature Space Between Nighttime Light Intensity and Vegetation Index.

Nighttime light (NTL) data have been commonly used as a proxy for characterizing socioeconomic activities. Vegetation coverage has been found to be closely and inversely correlated with NTL intensity (NTLI). Although the combination of NTLI and vegetation indices has been studied in various applications, the complex relationship between NTLI and vegetation indices is not yet clear. By analyzing the relationship between normalized difference vegetation index (NDVI) and NTLI for the mainland China from 2013 to 2021, we found that the scatterplot between NDVI and NTLI exhibits a triangular shape with a physical meaning, which we called the NTLI-NDVI triangular feature space. Using the triangular feature space, we proposed the vegetation nighttime condition index (VNCI), which is defined as the ratio of NTLI differences among pixels with a specific NDVI value. VNCI is closely associated with local urban characteristics and has the ability to increase variation in NTLI conditions within urban areas. To demonstrate the application potential of the NTLI-NDVI triangular feature space and the proposed VNCI, two applications (urban area extraction and socioeconomic parameters estimation) were conducted. In terms of extracting urban areas, VNCI shows a better detection ability (with an average overall accuracy of 85.01%) than the original Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and other two existing indices in extracting urban areas. Moreover, we further proposed a simple and novel NTL correcting approach to correct NTL using VNCI, which effectively eliminates the impact of vegetation and enhances the accuracy of estimating socioeconomic parameters. Our findings demonstrated that the VNCI-corrected NTL data show superior performance (with an average R-2 of 0.9) in estimating both the gross domestic product and electric power consumption at the provincial level. We believe the NTLI-NDVI triangular feature space and VNCI hold great potential for NTL-based urban studies.