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Remote Sens. 2015, 7(5), 5373-5397; doi:10.3390/rs70505373

A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

1
School of Earth Science and Resources, Chang'an University, No.126 Yanta Road, Xi'an 710054, China
2
Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang'an University, Xi'an710054, China
3
China Institute of Hydrogeology and Engineering Geology Method, China Geological Survey, Baoding 071051, China
4
Department of Geography and Geoinformation Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
5
Department of Geography, University of Hong Kong, Pokfulam, Hong Kong
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Eurico J. D’Sa, Sachidananda Mishra and Prasad S. Thenkabail
Received: 9 October 2014 / Revised: 14 April 2015 / Accepted: 20 April 2015 / Published: 28 April 2015
(This article belongs to the Special Issue Remote Sensing of Water Resources)
View Full-Text   |   Download PDF [16994 KB, uploaded 28 April 2015]   |  

Abstract

Suspended sediment concentration (SSC) is one of the most critical parameters in ocean ecological environment evaluation and it can be determined using ocean color remote sensing (RS). The purpose of this study is to develop a model that provides a reliable and sensitive evaluation of SSC retrieval using RS data. Data were acquired for and gathered from the Gulf of Bohai where SSC levels are relatively low with an average value below 30 mg·L−1. The study indicates that the most sensitive band to SSC levels in the study area is the NIR band of Landsat5 TM images. A quadratic polynomial semi-analytical model appears to be the best retrieval model based on the relationship between the inherent optical properties (IOPs) and apparent optical properties (AOPs) of water as described by the quasi-analytical algorithm (QAA). The model has a higher precision and effectiveness for SSC retrieval than data-driven statistical models, especially when SSC level is relatively high. The average relative error and the root mean square error (RMSE) are 12.32% and 4.53 mg·L−1, respectively, while the correlation coefficient between observed and estimated SSC by the model is 0.95. Using the proposed retrieval model and TM data, SSC levels of the entire study region in the Gulf of Bohai were estimated. These estimates can serve as the baseline for efficient monitoring of the ocean environment in the future. View Full-Text
Keywords: suspended sediment concentration (SSC); remote sensing retrieval; semi-analytical model; quasi-analytical algorithm (QAA); the Gulf of Bohai suspended sediment concentration (SSC); remote sensing retrieval; semi-analytical model; quasi-analytical algorithm (QAA); the Gulf of Bohai
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kong, J.-L.; Sun, X.-M.; Wong, D.W.; Chen, Y.; Yang, J.; Yan, Y.; Wang, L.-X. A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China. Remote Sens. 2015, 7, 5373-5397.

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