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

Abstract

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 Landsatbased 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 modelbased 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 similarPublished by Copernicus Publications on behalf of the European Geosciences Union.

Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China

“…Based on different remote sensing data, combined with analysis, semi-analysis/semi-experienced and empirical methods, many experts and scholars [10,14,18,22,[41][42][43] have calibrated a large number of remote sensing retrieval models for TSS in water bodies, and have obtained good retrieval accuracy with wide applications. In addition, there are many forms of TSS models, mainly including the single-band model, the band ratio model and the multi-band combination model.…”

“…Therefore, it is particularly important for real-time monitoring and management of the aquatic environment in the reservoir area to reveal the dynamic of TSS. Since the release of the guidance for the river chief system by the General Office of the State Council of China in 2016, the research of TSS in inland water including lakes and reservoirs has been paid more lasting attention, and the associated research has been conducted by a variety of scholars, governments and social communities [6][7][8], such as the Río Tercero reservoir (Córdoba, Argentina) [6], the Mekong and Bassac Rivers [9,10], the Amazon River [11], Taihu Lake [8,12], the Yangtze River [13][14][15] and the Pearl River [16][17][18][19].…”

“…With the continuous improvement in remote sensing data in spectral, spatial and temporal resolution, the use of different types of remote sensing data for TSS monitoring and evaluation is increasingly accepted and recognized. There are numerous remote sensing data for assessment of TSS including the series of Land Observation Satellite (Landsat) [18,[20][21][22], the Moderate Resolution Imaging Spectroradiometer (MODIS) [11,12,[23][24][25], the Geostationary Ocean Color Imager (GOCI) [8,26,27], Environment and Disaster Monitoring and Forecasting Small Satellite Constellation (HJ) [28], Medium Resolution Imaging Spectrometer (MERIS) [9] and so on. Among them, due to the high quality, high spatial resolution and inheritance, Landsat has become the most widely used remote sensing sensor in spatiotemporal dynamic analysis.…”

“…As far as the empirical model is concerned, it does not need to undergo complicated derivation and parameter initialization, and its form is simple with sufficient accuracy, which is highly efficient to use and will continue to be used for a long time in the future. Although many studies use empirical models for TSS estimation from different sensors, there are significant differences in the band selection of single-or multiple-band models [12,18,24,29,30]. First, the sensitivity of satellite sensors to different concentrations of TSS is different.…”

Assessment of For Euphrates River In Al- Najaf Governorate Water Pollution Using Remote Sensing Data

“…At the same time, band1-Ultra-blue is less correlated (r = 0.509). These notations were defined as follow [14]:…”