Deep Learning-Generated Nighttime Reflectance and Daytime Radiance of the Midwave Infrared Band of a Geostationary Satellite

Yerin Kim,Sungwook Hong

doi:10.3390/rs11222713

Abstract

Midwave infrared (MWIR) band of 3.75 μm is important in satellite remote sensing in many applications. This band observes daytime reflectance and nighttime radiance according to the Earth’s and the Sun’s effects. This study presents an algorithm to generate no-present nighttime reflectance and daytime radiance at MWIR band of satellite observation by adopting the conditional generative adversarial nets (CGAN) model. We used the daytime reflectance and nighttime radiance data in the MWIR band of the meteoritical imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), as well as in the longwave infrared (LWIR; 10.8 μm) band of the COMS/MI sensor, from 1 January to 31 December 2017. This model was trained in a size of 1024 × 1024 pixels in the digital number (DN) from 0 to 255 converted from reflectance and radiance with a dataset of 256 images, and validated with a dataset of 107 images. Our results show a high statistical accuracy (bias = 3.539, root-mean-square-error (RMSE) = 8.924, and correlation coefficient (CC) = 0.922 for daytime reflectance; bias = 0.006, RMSE = 5.842, and CC = 0.995 for nighttime radiance) between the COMS MWIR observation and artificial intelligence (AI)-generated MWIR outputs. Consequently, our findings from the real MWIR observations could be used for identification of fog/low cloud, fire/hot-spot, volcanic eruption/ash, snow and ice, low-level atmospheric vector winds, urban heat islands, and clouds.

Highlights

The global change in weather and climate has been impacting human social life, ecological environment, and natural disasters
This study focuses on the generation of the Midwave infrared (MWIR) band using the conditional generative adversarial nets (CGAN) technique based on the physical characteristics of the MWIR and longwave infrared (LWIR) bands
A COMS LWIR band was chosen as a counter band to the paired MWIR and IR1 bands used in our CGAN-based model

Summary

Introduction

The global change in weather and climate has been impacting human social life, ecological environment, and natural disasters. Satellites have been playing a crucial role as one of the most important observation tools in the short-term to long-term analysis and forecasting during the past decades. Geostationary meteorological satellites have important roles in nowcasting and short-term weather analysis of convective clouds [1], and providing information on natural disasters like typhoon, floods, and heavy rainfall [2,3,4]. Geostationary meteorological satellites use VIS and IR bands to observe the Earth’s surface and atmosphere within the atmospheric window, in which limited atmospheric absorption occurs, such as 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 wavelengths [9]. The VIS band observes sunlight reflected from the Remote Sens. The VIS band observes sunlight reflected from the Remote Sens. 2016, 8, x FOR PEER REVIEW

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Conclusion