Remote Sensing Estimation of Sea Surface Salinity from GOCI Measurements in the Southern Yellow Sea

Deyong Sun,Shengqiang Wang,Yijun He,Xiaoping Su,Zhongfeng Qiu,Zhihua Mao

doi:10.3390/rs11070775

Abstract

Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak.

Highlights

Sea surface salinity (SSS), considered one of the primary elements characterizing the marine environment, plays a vital role in driving the ocean currents and further affects many phenomena and processes in the ocean
Our analysis focused on the salinity data at the water surface layer, which is consistent with what the optical satellite can observe
We analyzed the correlation between Rrs(λ) and SSS, together with log10SSS, at each chosen band (Figure 3a). These results clearly show that the relationship between Rrs(λ) and SSS is weak for all bands, with the maximum R value of only −0.18 at band 4 (555 nm)

Summary

Introduction

Sea surface salinity (SSS), considered one of the primary elements characterizing the marine environment, plays a vital role in driving the ocean currents and further affects many phenomena and processes in the ocean. Traditional methods of monitoring SSS are mostly based on measurements from vessels and buoys. These in situ measurements are often costly and time consuming; more importantly, they lead to a large limitation on spatiotemporal research studies on SSS [4]. Remote sensing can provide an alternative to SSS observation considering its advantages of large-coverage and real-time observations. Under these circumstances, more scientists and researchers have paid great attention to the application of satellite data for SSS estimation from space [5,6,7,8,9]

Objectives

Results

Discussion

Conclusion