DEEP LEARNING-BASED METHOD TO EXTEND THE TIME SERIES OF GLOBAL ANNUAL VIIRS-LIKE NIGHTTIME LIGHT DATA

Abstract

The nighttime light (NTL) remote sensed imagery has been applied in monitoring human activities from many perspectives. As the two most widely used NTL satellites, the Defense Meteorological Satellite Program (DMSP) Operational Linescan System and the Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) have different spatial and radiometric resolutions. Thus, some long-time series analysis cannot be conducted without effective and accurate cross-calibration of these two datasets. In this study, we proposed a deep-learning based model to simulate VIIRS-liked DMSP NTL data by integrating the enhanced vegetation index (EVI) data product from MODIS. By evaluating the spatial pattern of the results, the modified Self-Supervised Sparse-to-Dense networks delivered satisfying results of spatial resolution downscaling. The quantitative analysing of the simulated VIIRS-liked DMSP NTL with original VIIRS NTL showed a good consistency at the pixel level of four selected sub datasets with R2 ranging from 0.64 to 0.76, and RMSE ranging from 3.96-9.55. Our method presents that the deep learning model can learn from relatively raw data instead of fine processed data based on expert knowledge to cross-sensor calibration and simulation NTL data.