Comparative analysis between VIIRS-DNB and DMSP-OLS night-time light data to estimate electric power consumption in Uttar Pradesh, India

The night-time light (NTL) data from Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) onboard Suomi National Polar-Orbiting Partnership (S-NPP) spacecraft available since April, 2012 have successfully addressed some of the limitations of Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), owing to improved radiometric and spatial resolutions and onboard calibration. Earlier, DMSP-OLS NTL images majorly contributed for understanding the spatio-temporal dynamics of socio-economic factors such as Gross Domestic Product (GDP) and Electric Power Consumption (EPC), but the problem of saturated pixels and blooming effect affected the correlation with sum of lights (SOL). In the present study, we evaluate different methods to generate the annual VIIRS-DNB NTL composites from available monthly products for estimating EPC at different scales, and compare their performance with DMSP-OLS annual composites. The VIIRS-DNB annual NTL composites are prepared using various statistical measures, seasonal decomposition and principal component analysis (PCA). The SOL derived from these composites were analysed with respect to EPC at Zonal and Electricity Distribution Company (DISCOM) levels in the Uttar Pradesh State of India to identify the most appropriate annual representation of NTL from VIIRS-DNB. It is found that the annual composites prepared using PCA method provide the best results at both Zonal and DISCOM levels. Further, VIIRS-DNB annual NTL composites perform better than the DMSP-OLS composites. At the Zonal level, some of the highly populated and fully electrified Zones exhibit a distinct SOL-EPC relationship, attributable to enhanced power consumption by indoor equipment and industrial activities, which is not captured by satellite. Finally, empirical EPC prediction models are developed based on time-series VIIRS-DNB data from 2013 to 2017, which will help understanding the EPC patterns at regional level in the State of Uttar Pradesh.