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An Analysis of Environmental Effect on VIIRS Nighttime Light Monthly Composite Data at Multiple Scales in China | IEEE Journals & Magazine | IEEE Xplore

An Analysis of Environmental Effect on VIIRS Nighttime Light Monthly Composite Data at Multiple Scales in China


Abstract:

Nighttime light (NTL) can provide valuable information about human activities. The temporal NTL variation has been previously explored, but the effect of environmental fa...Show More
Topic: Remote Sensing Retrieval

Abstract:

Nighttime light (NTL) can provide valuable information about human activities. The temporal NTL variation has been previously explored, but the effect of environmental factors has not been fully considered. Here, this article focused on the environmental effect on NTL time series in China, using the visible infrared imaging radiometer suite (VIIRS) monthly products, Earth Observations Group (EOG) product, and Black Marble product, from January 2014 to December 2020. It was found that the NTL variations were statistically correlated with aerosols, vegetation, and surface albedo. NTL variations were negatively correlated with aerosol and vegetation, but positively correlated with surface albedo. Aerosol optical depth was important to explain the NTL variation among environmental factors. In 79% of urban areas in China, the adjusted R-squared of NTL and the three factors surpassed that of NTL and the two factors (vegetation and surface albedo) based on EOG product. In 60% of urban areas in China, the adjusted R-squared of NTL and the three factors surpassed that of NTL and the two factors (vegetation and surface albedo), based on Black Marble product. Both EOG monthly product and Black Marble monthly product were affected by aerosols, surface albedo, and vegetation at multiple scales. However, Black Marble product was less affected by aerosols than EOG product. This article suggests that environmental effect is crucial in the NTL variation. Understanding NTL temporal variation can improve the accuracy of time series VIIRS imagery for socioeconomic applications.
Topic: Remote Sensing Retrieval
Page(s): 825 - 840
Date of Publication: 26 December 2022

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