Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea

Difeng Wang,Fang Gong,Qiyuan Cui,Xianqiang He,Yan Bai,Lifang Wang

doi:10.3390/rs10121896

Difeng Wang, Fang Gong + Show 4 more

Open Access

https://doi.org/10.3390/rs10121896

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Abstract

Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (Rrs) were used as inputs in our model. Compared with models that only use Rrs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R2) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved Rrs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.

Highlights

The East China Sea (ECS) is one of the largest shelf seas in the world
In situ Rrs_equi and sea surface salinity (SSS) were used as input parameters to the back-propagation neural network (BP-NN) models, and nitrate and phosphate were target parameters
The SSS data of Soil Moisture Active Passive (SMAP) were compared with measured salinity

Summary

Introduction

The East China Sea (ECS) is one of the largest shelf seas in the world. Affected by the nutrients and particles from the Changjiang River, the coastal waters of the ECS are characterized by low salinity, richness in nutrients, and high turbidity [1]. Several studies tried building empirical models to estimate the concentrations of these nutrients based on their relationships with chlorophyRlelm,otteoStenas.l20s1u8,s10p, xeFnOdR PeEdERmREaVItEtWer (TSM), and other optically sensitive mate roif a16ls in continental shelf and coastal waters [13,14,15]. Such relationships are usually unstable and less accurate because many factors can influa[5p–e8on]w.cIenerfputalhrtteoiocsuleflaorrr,eltalhraegtefii-orsscntaGlseehaonispdtasltoi.onnHga-rtoeyrwmOceoebavnseeCrrv,oalitonironIcmsowaagietshrt(ahGilgOhrCetIeg)m,ilpoauonrnasclh,aewnddbhsypicaStohiualtahrreKesoolirunetaifloinnuenced by large river run-off, nu20tr10ie, pnrtosviadreseeuigshut haolulyrlyciomnagseesrpvear tdiavyewaitnh da shpaatviael reqsuolautniotnitoaft5i0v0emr.eTlhaistihoignhsehr sippastiawl aintdh salinity [16,17]. Due to a combination oChfatnhgejiaCnghRaivnegr jcioanntrgibuRteisvaebrouitn6p6%utofatnhednnitreoagernshlooadreanadq8u4%acouf ltthue rpeh,osepuhotrruosplohaidcapetrion is one of the most widespreayeuedatrrowp[1ha9itc]e.atrDioquneuisatoolnietayocfpothmreobmibnoalsetitmownisdoeifsnpthrteehadeCwhEaaCntegrSjiqa. unaglitRyivperrobilnepmust and in the nearshore ECS

Hydrological and Water Quality Data

Satellite Data

Artificial Neural Network

Evaluation of Satellite-Measured Salinity

Conclusions