Inter-calibration of DMSP-OLS and SNPP-VIIRS-DNB annual nighttime light composites using machine learning

Abstract

ABSTRACT The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are extensively used for socio-economic studies. The improved NTL products from the Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB), on-board the Suomi National Polar-Orbiting Partnership spacecraft and National Oceanic and Atmospheric Administration – 20 (NOAA-20) spacecraft’s, are now available since April 2012. This study investigates the potential of machine-learning algorithms for inter-calibrating them (i.e., DMSP-OLS and VIIRS-DNB) to produce time-series annual VIIRS-DNB-like NTL datasets for the time when VIIRS-DNB data did not exist, for long-term studies. Uttar Pradesh, one of the most populous and largest States of India, is selected as the study area. Two machine-learning algorithms are utilized: (1) Multi-Layer Perceptron (MLP), having deep neural networks (DNN) architecture, and (2) Random Forest (RF), a widely used method. The DMSP-OLS and VIIRS-DNB data of 2013 (common year of data availability) and ancillary data pertaining to land cover, topography, and road network are used to train the models. The qualitative and quantitative analysis of annual VIIRS-DNB-like NTL images simulated from annual DMSP-OLS composites of 2004–2012 indicates that RF captures better spatial details at the local-scale and is able to efficiently handle the saturation problem at urban centers; while MLP is found to be superior at regional-scale. Both MLP and RF models significantly reduce the blooming effect around settlements, a common problem observed in DMSP-OLS data. It is inferred that depending on the research objectives, both RF and MLP algorithms can be appropriately utilized for producing VIIRS-DNB-like NTL images from DMSP-OLS annual NTL composites. The research can be further expanded by using other DNN architecture-based algorithms and improved spatio-temporal ancillary datasets over areas with different socio-economic, physiographic, and climatic settings.