Articles | Volume 10, issue 12
Geosci. Model Dev., 10, 4347–4365, 2017
Geosci. Model Dev., 10, 4347–4365, 2017

Model evaluation paper 30 Nov 2017

Model evaluation paper | 30 Nov 2017

A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

Chongyang Wang1,2,3, Shuisen Chen1, Dan Li1, Danni Wang4, Wei Liu1,2,3, and Ji Yang1,2,3 Chongyang Wang et al.
  • 1Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangdong Key Laboratory of Remote Sensing and GIS Technology Application, Guangzhou Institute of Geography, Guangzhou 510070, China
  • 2Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Department of Resources and the Urban Planning, Xin Hua College of Sun Yat-Sen University, Guangzhou 510520, China

Abstract. Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction between hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating TSS concentrations with wide range in estuaries and coasts by Landsat imagery, after reviewing a number of Landsat-based TSS retrieval models and improving a comparatively better one among them, this study developed a quadratic model using the ratio of logarithmic transformation of red band and near-infrared band and logarithmic transformation of TSS concentration (QRLTSS) based on 119 in situ samples collected in 2006–2013 from five regions of China. It was found that the QRLTSS model works well and shows a satisfactory performance. The QRLTSS model based on Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI (Operational Land Imager) sensors explained about 72 % of the TSS concentration variation (TSS: 4.3–577.2 mg L−1, N = 84, P value  < 0.001) and had an acceptable validation accuracy (TSS: 4.5–474 mg L−1, root mean squared error (RMSE)  ≤ 25 mg L−1, N = 35). In addition, a threshold method of red-band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proposed to solve the two-valued issue of the QRLTSS model and to retrieve TSS concentration from Landsat imagery. After a 6S model-based atmospheric correction of Landsat OLI and ETM+ imagery, the TSS concentrations of three regions (Moyangjiang River estuary, Pearl River estuary and Hanjiang River estuary) in Guangdong Province in China were mapped by the QRLTSS model. The results indicated that TSS concentrations in the three estuaries showed large variation ranging from 0.295 to 370.4 mg L−1. Meanwhile we found that TSS concentrations retrieved from Landsat imagery showed good validation accuracies with the synchronous water samples (TSS: 7–160 mg L−1, RMSE: 11.06 mg L−1, N = 22). The further validation from EO-1 Hyperion imagery also showed good performance (in situ synchronous measurement of TSS: 106–220.7 mg L−1, RMSE: 26.66 mg L−1, N = 13) of the QRLTSS model for the area of high TSS concentrations in the Lingding Bay of the Pearl River estuary. Evidently, the QRLTSS model is potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts by Landsat imagery, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. Furthermore, the QRLTSS model can be optimized to establish a regional or unified TSS retrieval model of estuaries and coasts in the world for different satellite sensors with medium- and high-resolution similar to Landsat TM, ETM+ and OLI sensors or with similar red bands and near-infrared bands, such as ALI, HJ-1 A and B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, and GF.

Short summary
Monitoring total suspended solids (TSS) concentration from satellites has unique advantages. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. We studied many previous different satellite sensors models and finally developed a new model.