Publications

Liu, XY; Hu, JW; Tian, L; Yu, DF; Gao, H; Yang, L; An, DY (2024). Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 17, 2077-2089.

Abstract
Water transparency is one of the important parameters to describe the optical properties of seawater. It is a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. First, this article verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Second, the water transparency estimation results retrieved from Geostationary Ocean Color Imager (GOCI) using four atmospheric correction (AC) algorithms were compared and analyzed with in-situ data in the study area. The four AC algorithms are the standard AC algorithms of Korea Ocean Satellite Center (KOSC) GOCI GDPS1.3 and GDPS2.0, the standard near-infrared AC algorithm of NASA, and management unit of the North Sea mathematical models. At last, GOCI data on Mar 09, 2018, were used to analyze the differences in hourly changes of transparency caused by different AC algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R-2>0.8, MAPE = 16.4%). The difference in transparency value caused by the AC algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency is presented based on different AC algorithms, while the moment when the minimum value of transparency is presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different AC algorithms are not completely consistent.

DOI:
2151-1535

ISSN:
10.1109/JSTARS.2023.3343572