Seasonal Variability of Diffuse Attenuation Coefficient in the Pearl River Estuary from Long-Term Remote Sensing Imagery

Chaoyu Yang,Haibin Ye,Shilin Tang

doi:10.3390/rs12142269

Abstract

We evaluated six empirical and semianalytical models of the diffuse attenuation coefficient at 490 nm (Kd(490)) using an in situ dataset collected in the Pearl River estuary (PRE). A combined model with the most accurate performance (correlation coefficient, R2 = 0.92) was selected and applied for long-term estimation from 2003 to 2017. Physical and biological processes in the PRE over the 14-year period were investigated by applying satellite observations (MODIS/Aqua data) and season-reliant empirical orthogonal function analysis (S-EOF). In winter, the average Kd(490) was significantly higher than in the other three seasons. A slight increasing trend was observed in spring and summer, whereas a decreasing trend was observed in winter. In summer, a tongue with a relatively high Kd(490) was found in southeastern Lingdingyang Bay. In Eastern Guangdong province (GDP), the relatively higher Kd(490) value was found in autumn and winter. Based on the second mode of S-EOF, we found that the higher values in the eastern GDP extended westward and formed a distinguishable tongue in winter. The grey relational analysis revealed that chlorophyll-a concentration (Cchla) and total suspended sediment concentration (Ctsm) were two dominant contributors determining the magnitude of Kd(490) values. The Ctsm-dominated waters were generally located in coastal and estuarine turbid waters; the Cchla-dominated waters were observed in open clear ocean. The distribution of constituents-dominated area was different in the four seasons, which was affected by physical forces, including wind field, river runoff, and sea surface temperature.

Highlights

The light diffuse attenuation coefficient (Kd(λ)) in aquatic systems is defined by the exponential decrease in the irradiance with depth [1,2]
The Morel and Mueller models underestimated the in situ values of the Pearl River estuary (PRE), they had little to no sensitivity along a broad gradient of in situ values
The results provided by both Mueller’s and Morel’s models constantly underestimated the Kd(490) compared with the in situ dataset for the PRE, with root mean square error (RMSE) higher than 1.1 m−1, mean absolute difference (MAD) close to 1.0 m−1, and mean absolute percentage difference (MAPD) up Rtoemot7e 0Se%ns..20T20,h12e, 22M69 orel and Mueller models underestimated the in situ values of the PRE, they had little to no semnosditeilvtoittyheaoltohenrgmoadbelrsoshaodwegdrathdatieWnant go’sfminodseilthuadvcaolnusiedse.rably lower RMSE and MAD values and outperformed the other models, especially at relatively higher Kd(490) levels

Summary

Introduction

The light diffuse attenuation coefficient (Kd(λ)) in aquatic systems is defined by the exponential decrease in the irradiance with depth [1,2]. Kd(λ) is an ecologically important water property that provides an estimate of the availability of light to underwater communities, which influences ecological processes and biogeochemical cycles in natural waters [3,4]. The estimation of Kd(λ) is critical for understanding physical processes such as sediment resuspension and heat transfer in the upper layer of the ocean [5,6,7]. The in situ Kd(λ) is traditionally measured by the ocean color scientific community at 490 nm, Kd(490), following the primary studies in the 1970s [8]. Traditional field measurement of Kd(λ) is costly and time consuming, but recent advances in satellite sensors have provided synoptic and frequent measurements of various bio-optical products on large scales, considerably improving spatial and temporal resolution compared to in situ data [9]. Several empirical and semianalytical models of Kd(490) are commonly used to derive the Kd(490) maps from satellite sensors such as the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) [10,11], Moderate Resolution Imaging Spectroradiometer (MODIS) [4,12], and the Medium Resolution Imaging Spectrometer (MERIS) [6,13]

Objectives

Methods

Results

Conclusion