Modeling the direction and magnitude of angular effects in nighttime light remote sensing

Remote sensing of nighttime light (NTL) offers a unique opportunity to monitor urban dynamics and human socioeconomic activities directly from space. However, angular observations lead to inconsistencies among observations over the same area on different days, introducing uncertainty into daily NTL time series. This study aims to investigate this angular effect and its drivers using the Visible Infrared Imaging Radiometer Suite/Suomi (VIIRS) Black Marble NTL dataset. First, we proposed a conceptual model of the angular effect and hypothesized the mechanism of how urban three-dimensional (3D) landscapes form the anisotropic characteristics of artificial light observations. Second, we quantified the spatial patterns of the angular effect within five representative cities, and identified three distinctive types of angular effects: negative, U-shaped, and positive. Subsequently, the contribution of landscape factors to the direction (i.e., the type) and magnitude (i.e., NTL change rate with angle) of the angular effect is quantified using multinomial logistic regression and mediation analysis, respectively. The results show that the direction of the angular effect is mainly controlled by building height which determines the blocked and visible parts of artificial light at different satellite viewing angles. The magnitude of the angular effect is determined by both NTL brightness and landscape factors. The mediation analysis shows that landscape factors can have a direct effect on the magnitude of the angular effect as well as an indirect effect on the magnitude by affecting NTL brightness. Among the landscape factors, both vegetation and buildings are indicated to be significantly influential factors with direct and indirect effects. The findings of this research deepen our understanding of the NTL angular effect, guide the development of technologies for reconstructing high-quality daily NTL time series by correcting the angular effect, and help us better monitor high-frequency socioeconomic activities.