Publication Date

2021

Journal or Book Title

Remote Sensing

Abstract

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

DOI

https://doi.org/10.3390/rs13050922

Volume

13

Special Issue

Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations

Issue

5

License

UMass Amherst Open Access Policy

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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