Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

Kyeong-Sang Lee,Kyung-Soo Han,Suyoung Sim,Daeseong Jung,Donghyun Jin,Noh-Hun Seong,Eunkyung Lee

doi:10.3390/rs14020360

Abstract

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

Highlights

Land surface reflectance (LSR) is defined as the ratio of upwelling solar radiation reflected by a land surface to downwelling solar radiation for specific solar and viewing geometries [1] and it is determined by the intrinsic property of the land surface as well as observation geometry [2]
Our results show comparable geographic distributions with Moderate Resolution Imaging Spectroradiometer (MODIS) at both red and NIR channels, there are differences between Geostationary Ocean Color Imager (GOCI) and MODIS LSRs
We developed an LSR retrieval algorithm for GOCI-II operation and describe the input data-induced uncertainty in estimated LSR

Summary

Introduction

Land surface reflectance (LSR) is defined as the ratio of upwelling solar radiation reflected by a land surface to downwelling solar radiation for specific solar and viewing geometries [1] and it is determined by the intrinsic property of the land surface as well as observation geometry [2]. Potapov et al [7] presented a consistent longterm LSR database based on Landsat satellite data for detecting changes in land cover both globally and locally. Accurate LSR data are necessary to understand changes in the land surface and to analyze land surface properties. Atmospheric correction to remove atmospheric effects from satellite observations is essential for terrestrial surface analysis [8,9,10]. It is divided into relative and absolute atmospheric corrections [11,12,13].

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