Estimating City Emission Function With VIIRS/DNB Data and Vegetation Phenology

The City Emission Function (CEF) characterizing the angular distribution patterns of urban nighttime light emissions, plays a decisive role in modeling the night sky radiant intensity. However, seldom studies have improved the CEF incorporating the vegetation phenological characteristics. The multi-angle, high-frequency nighttime light (NTL) observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) provide vital data support for estimating CEF coefficients. In this study, we developed a satellite-derived NTL model, which is a function of viewing zenith angle (VZA) and Normalized Difference Vegetation Index (NDVI). Based on this model, we performed angular normalization on Black Marble time series (2016–2018) for 11 deciduous vegetation-dominated study areas across North America. The proposed approach yields two main outcomes: (1) high-frequency pixel-level CEF coefficients, and (2) angular-normalized NTL time series, which were evaluated against near-nadir monthly composite products using two metrics, the coefficient of determination (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) and the root mean square error (RMSE). The results demonstrated that (1) the magnitude of the normalized CEF tends to decrease with increasing VZA and increase with rising NDVI, (2) angular normalization reduced the impact of VZA on NTL temporal dynamics, and (3) the addition of vegetation characteristics improved the accuracy of angular normalization algorithms, yielding an average increase in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i>² of 0.028 and a decrease in RMSE of 0.166 nW/sr. This study provides an effective approach to estimating urban emitted lights using nighttime light remote sensing data, and is expected to provide insights into urban environmental monitoring and sustainable lighting management.