Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

Mengmeng Yang,Faisal Ahmed Khan,Qinping Liu,Hongzhen Tian

doi:10.3390/rs13040632

Mengmeng Yang, Faisal Ahmed Khan + Show 2 more

Open Access

https://doi.org/10.3390/rs13040632

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Abstract

Missing spatial data is one of the major concerns associated with the application of satellite data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has been proven to be an effective tool for filling spatial gaps in various satellite data products. The Ariake Sea, which is a turbid coastal sea, shows the large spatial and temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM). However, ocean color satellite data for this region usually have large gaps, which affects the accurate analysis of Chl-a and TSM variability. In this study, we applied the DINEOF method to fill the missing pixels from the regionally tuned Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua (hereafter, MODIS) Chl-a and MODIS-derived TSM datasets for the period 2002–2017. The validation results showed that the DINEOF reconstructed data were accurate and reliable. Furthermore, the Empirical Orthogonal Functions (EOF) analysis based on the reconstructed data was used to quantitatively analyze the spatial and temporal variability of Chl-a and TSM at both monthly and individual events of spring-neap tidal scales. The first three EOF modes of Chl-a showed seasonal variability mainly caused by precipitation, the sea surface temperature (SST), and river discharge for the first EOF mode and the sea level amplitude for the second. The first three EOF modes of TSM exhibited both seasonal and spring-neap tidal variability. The first and second EOF modes of TSM displayed spring-neap tidal variability caused by the sea level amplitude. The second EOF mode of TSM also showed seasonal variability caused by the sea level amplitude. In this study, we first applied the DINEOF method to reconstruct the satellite data and to capture the major spatial and temporal variability of Chl-a and TSM for the Ariake Sea. Our results demonstrate that the DINEOF method can reconstruct patchy oceanic color datasets and improve spatio-temporal variability analysis.

Highlights

The Data INterpolating Empirical Orthogonal Functions (DINEOF) method was successfully applied to fill the spatial gaps in all individual events of spring-neap tidal stages of the regionally-tuned Moderate Resolution Imaging Spectroradiometer (MODIS) Chl-a and MODISderived total suspended sediment (TSM) datasets from 2002 to 2017
The first three Empirical Orthogonal Functions (EOF) modes of Chl-a mainly revealed the seasonal variability of Chl-a
For the first EOF mode of Chl-a, it showed the general spatial distribution of Chl-a, and the variance was larger in the coastal areas

Summary

Introduction

2021, 13, 632 deposition and erosion patterns in estuaries and coastal zones and a necessary input for estimating the material fluxes from the land through rivers to the sea [1,2,3,4,5]. Satellite ocean color datasets are fraught with missing data caused by improper condisatellite ocean color datasets are fraught with missing data caused by improper conditions, tions, such as clouds and rain [3,6]. These missing data may cause bias in the analysis of such as clouds and rain [3,6]. It is essential that missing that are filled to better utilize the datasets

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