Nighttime Reflectance Generation in the Visible Band of Satellites

Kimoon Kim,Yong-Jae Moon,Eunsu Park,Ji-Hye Kim,Sungwook Hong,Gyungin Shin,Taeyoung Kim,Yerin Kim

doi:10.3390/rs11182087

Abstract

Visible (VIS) bands, such as the 0.675 μm band in geostationary satellite remote sensing, have played an important role in monitoring and analyzing weather and climate change during the past few decades with coarse spatial and high temporal resolution. Recently, many deep learning techniques have been developed and applied in a variety of applications and research fields. In this study, we developed a deep-learning-based model to generate non-existent nighttime VIS satellite images using the Conditional Generative Adversarial Nets (CGAN) technique. For our CGAN-based model training and validation, we used the daytime image data sets of reflectance in the Communication, Ocean and Meteorological Satellite / Meteorological Imager (COMS/MI) VIS (0.675 μm) band and radiance in the longwave infrared (10.8 μm) band of the COMS/MI sensor over five years (2012 to 2017). Our results show high accuracy (bias = −2.41 and root mean square error (RMSE) = 36.85 during summer, bias = −0.21 and RMSE = 33.02 during winter) and correlation (correlation coefficient (CC) = 0.88 during summer, CC = 0.89 during winter) of values between the observed images and the CGAN-generated images for the COMS VIS band. Consequently, our CGAN-based model can be effectively used in a variety of meteorological applications, such as cloud, fog, and typhoon analyses during daytime and nighttime.

Highlights

The importance of weather and climate change information is increasing because of many human demands, including for leisure, business, natural disaster relief, and military operations
VIS and IR bands on many geostationary satellites have been crucial for weather analysis, nowcasting, and forecasting at high spatial and temporal resolutions during the past few decades
The VIS band observation is only available during the day because it primarily observes the reflectance of sunlight off the Earth, while IR bands observe the energy emitted from the Earth without depending on sunlight

Summary

Introduction

The importance of weather and climate change information is increasing because of many human demands, including for leisure, business, natural disaster relief, and military operations. The common bands used by geostationary meteorological satellites are the VIS band in the 0.55 to 0.90 μm wavelength and IR bands in the 3.5 to 4.0 μm, 10.5 to 11.5 μm, and 11.5 to 12.5 μm in wavelength [6] The former observes sunlight reflected from the earth’s surface. We used the Far-East Asia area level 1 (L1B) images data of COMS/MI 1024 × 1024 pixels in size during the winter (December to February) and summer (June to August) seasons for 5 years from January 1, 2012 to December 31, 2017, to establish the AI-generated COMS images for training, validation, and test data [9] These were obtained from the National Meteorological Satellite Center (NMSC) of the KMA. We used COMS/MI observational data such as the COMS IR1 and VIS images corresponding to x and y, respectively

Band Selection and Implementation

Summary and Conclusions