A Landsat-based model for retrieving total suspended solids
concentration of estuaries and coasts
Authors: Wang Chongyang , Chen Shuisen , Li Dan , Liu Wei , Yang Ji , Wang Danni 
Published: April 2017

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 of 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 the TSS concentrations with wide range in estuaries and coasts by Landsat imageries, this study recalibrated and validated a number of regression-techniques-based TSS retrieval models using 129 in-situ samples collected from five regions of China during the period of 2006–2013. It was found that the optimized Quadratic model using the Ratio of Logarithmic transformation of red band and near infrared band and logarithmic transformation of TSS concentration (QRLTSS) works well and shows a relatively satisfactory performance. The adjusted QRLTSS model based on Landsat sensors explain about 72 % of the TSS concentration variation (TSS: 4.3–577.2 mg/L, N = 84) in the study and have an acceptable validation accuracy (TSS: 4.5–474 mg/L, RMSE: 21.5–25 mg/L, MRE: 27.2–32.5 %, N = 35). The QRLTSS model based on Landsat OLI is better than TM and ETM+ (R2: 0.7181 vs. 0.7079, 0.708) because of the optimization of OLI sensor's design. A threshold of red band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proved capable to help solve the QRLTSS model and retrieve TSS concentration from Landsat remote sensing imageries. After 6S model-based atmospheric correction of Landsat imageries, the TSS concentrations of three regions (Moyangjiang River Estuary, Pearl River Estuary and Hanjiang River Estuary) in Guangdong Province of China by OLI and ETM+ imageries were retrieved by the optimized QRLTSS model. As a result, the Landsat imagery inversed TSS concentrations showed good validation accuracies with the synchronous in-situ observation (TSS: 7–160 mg/L, RMSE: 11.06 mg/L, MRE: 24.1 %, N = 22). The TSS concentrations retrieved from Landsat imageries in the three estuaries showed large variation (0.295–370.4 mg/L). The further validation from EO-1 Hyperion imagery showed good performance of the model (In site synchronous measurement of TSS: 106–220.7 mg/L, RMSE: 26.66 mg/L, MRE: 12.6 %, N = 13) for the area of high TSS concentrations in Lingding Bay of Pearl River Estuaries as well. Evidently, the QRLTSS model can be potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts in the world by Landsat imageries, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. We believe that the optimized QRLTSS model can hopefully be further adjusted to establish a regional or unified TSS retrieval model of estuaries and coasts for different satellite sensors similar to Landsat OLI-ETM+-TM sensors or with similar red and near infrared bands, such as ALI, HJ-1 A/B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, GF, etc.