Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

Mingjun He,Xiaodong Zhang,Feng Zhou,Peiliang Li,Shuangyan He

doi:10.3390/rs13091640

Abstract

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.

Highlights

The normalized water-leaving radiance (nLw) data generated from the GOCI Data Processing System (GDPS) algorithm perform better than that from the SeaDAS algorithm at visible bands in this study area, while the SeaDAS
C50) in atmospheric correction [31], our results show that GDPS algorithm performs better in correcting atmospheric signal and extracting nLw signal at the research sites than SeaDAS algorithm
Results show that the performance of the GDPS algorithm and SeaDAS algorithm varies with measure time, seawater turbidity, and spectral bands

Summary

Introduction

Polar-orbiting ocean color sensors (e.g., MODIS, SeaWiFS, MERIS) are well suited for observing seasonal or annual variations of ocean phenomena on a global scale, their once-per-day-time coverage cannot resolve diel variability. 2021, 13, 1640 color sensors may provide an alternative when observing the ocean environments that vary on short temporal scales [2,3,4]. Geostationary ocean color imager (GOCI), the first geostationary ocean color mission, was designed to focus on an area of 2500 × 2500 km centered around the Korean Peninsula. It acquires multispectral images with a 500 m ground resolution eight times per day [4,5,6]. As with any ocean color sensors, the successful application of GOCI data depends on the quality of its data products, especially the normalized water-leaving radiance (nLw) or the remote sensing reflectance (Rrs) [16], which is mainly up to atmospheric correction

Methods

Results

Discussion

Conclusion