A Landsat-Based Model for Retrieving Total Suspended Solids Concentration of Estuaries and Coasts in China
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Geosci. Model Dev., 10, 4347–4365, 2017 https://doi.org/10.5194/gmd-10-4347-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. 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 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 Correspondence to: Shuisen Chen ([email protected]) Received: 6 December 2016 – Discussion started: 6 April 2017 Revised: 5 September 2017 – Accepted: 17 October 2017 – Published: 30 November 2017 Abstract. Retrieving total suspended solids (TSS) concen- two-valued issue of the QRLTSS model and to retrieve TSS tration accurately is essential for sustainable management concentration from Landsat imagery. After a 6S model- of estuaries and coasts, which plays a key role in the in- based atmospheric correction of Landsat OLI and ETMC im- teraction between hydrosphere, pedosphere and atmosphere. agery, the TSS concentrations of three regions (Moyangjiang Although many TSS retrieval models have been published, River estuary, Pearl River estuary and Hanjiang River es- the general inversion method that is applicable to different tuary) in Guangdong Province in China were mapped by field conditions is still under research. In order to obtain the QRLTSS model. The results indicated that TSS concen- a TSS remote sensing model that is suitable for estimating trations in the three estuaries showed large variation rang- TSS concentrations with wide range in estuaries and coasts ing from 0.295 to 370.4 mgL−1. Meanwhile we found that by Landsat imagery, after reviewing a number of Landsat- TSS concentrations retrieved from Landsat imagery showed based TSS retrieval models and improving a comparatively good validation accuracies with the synchronous water sam- better one among them, this study developed a quadratic ples (TSS: 7–160 mgL−1, RMSE: 11.06 mgL−1, N D 22). model using the ratio of logarithmic transformation of red The further validation from EO-1 Hyperion imagery also band and near-infrared band and logarithmic transformation showed good performance (in situ synchronous measurement of TSS concentration (QRLTSS) based on 119 in situ sam- of TSS: 106–220.7 mgL−1, RMSE: 26.66 mgL−1, N D 13) ples collected in 2006–2013 from five regions of China. It of the QRLTSS model for the area of high TSS concentra- was found that the QRLTSS model works well and shows tions in the Lingding Bay of the Pearl River estuary. Ev- a satisfactory performance. The QRLTSS model based on idently, the QRLTSS model is potentially applied to simu- Landsat TM (Thematic Mapper), ETMC (Enhanced The- late high-dynamic TSS concentrations of other estuaries and matic Mapper Plus) and OLI (Operational Land Imager) sen- coasts by Landsat imagery, improving the understanding of sors explained about 72 % of the TSS concentration variation the spatial and temporal variation of TSS concentrations on (TSS: 4.3–577.2 mgL−1, N D 84, P value < 0:001) and had regional and global scales. Furthermore, the QRLTSS model an acceptable validation accuracy (TSS: 4.5–474 mgL−1, can be optimized to establish a regional or unified TSS re- root mean squared error (RMSE) ≤ 25 mgL−1, N D 35). In trieval model of estuaries and coasts in the world for differ- addition, a threshold method of red-band reflectance (OLI: ent satellite sensors with medium- and high-resolution simi- 0.032, ETMC and TM: 0.031) was proposed to solve the lar to Landsat TM, ETMC and OLI sensors or with similar Published by Copernicus Publications on behalf of the European Geosciences Union. 4348 C. Wang et al.: A Landsat-based model for retrieving TSS concentration of estuaries and coasts in China red bands and near-infrared bands, such as ALI, HJ-1 A and alytical models are likely more applicable to different water B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, bodies than the empirical or semiempirical methods (Binding and GF. et al., 2012, 2010; Chen et al., 2015b; Giardino et al., 2007; Ma et al., 2010; Sipelgas et al., 2009). However, there are still limitations of the application due to the difficulties of re- trieval or inaccuracies on initialization parameters (Binding 1 Introduction et al., 2012; Chen et al., 2015b; Ma et al., 2010; Wu et al., 2013). Therefore, the empirical (especially semiempirical) The amount of total suspended solids (TSS) is a critical fac- methods are still used to estimate TSS concentration and will tor of the ecological environment of water bodies, which di- continue to be used for a long time due to their simplicity rectly and deeply affects their optical properties through ab- and sufficient accuracies. It should be noted that the applica- sorbing and scattering of the sunlight (Chen et al., 2015b; tions of empirical or semiempirical TSS models need to be Pozdnyakov et al., 2005; Wang et al., 2016; Wu et al., 2013), revalidated in different regions and periods because they are leading to impacts on the primary production of the water ar- largely region-, time- or environment-dependent (Wu et al., eas (May et al., 2003). Estuaries and coasts are the most im- 2013). We found that previous empirical or semiempirical portant intermediate zones that connect hydrosphere, pedo- Landsat-based TSS retrieval models vary greatly in their sphere and atmosphere, which then pass on a deep and wide main forms of a single band or multiple bands for different impact on many aspects of our societal and natural environ- estuaries and coasts (Ma et al., 2010; Wu et al., 2013). ment (Nechad et al., 2010; Pozdnyakov et al., 2005). The top- The TSS models of a single band include linear function ics of TSS concentration monitoring and spatial and temporal (Fraser, 1998; Islam et al., 2001; Nas et al., 2010; Rao et al., variation assessment have been paid great attention, and the 2009), exponential or logarithmic function (Keiner and Yan, associated research work has been conducted frequently by 1998; Wu et al., 2013; Zhang et al., 2014), and quadratic a variety of scholars, government branches and society com- function (Chen et al., 2014). Those models have been eas- munities (Caballero et al., 2014; Giardino et al., 2015; Liu ily applied to many regions because they not only have the et al., 2003; Lu et al., 2012; Montanher et al., 2014; Nechad simple forms but also more choice of remote sensing data. et al., 2010; Olmanson et al., 2013; Pozdnyakov et al., 2005; We know that the sensitivity of satellite sensor bands is dif- Rao et al., 2009; Shen et al., 2008; Tang et al., 2004b; ferent for different TSS concentrations. Many studies have Zhang et al., 2007). Many methods can be used to estimate proven that reflectance in the red band increases with increas- TSS concentrations of water bodies, including hydrological- ing TSS concentrations, but tends towards convergence or site monitoring, in situ investigation, physical models, nu- keeps stable due to saturation effect under high TSS concen- merical simulation, remote sensing and so on (Chen et al., trations (Ritchie and Zimba, 2006; Feng et al., 2014), while 2015a). Retrieving TSS concentrations from remote sensing the reflectance in the near-infrared band is more sensitive to data has unique advantages due to the wide spatial cover- high TSS concentrations compared to low TSS concentra- age and periodic revisit, such as the Land Observation Satel- tions (Chen et al., 2015b; Feng et al., 2014; Hu et al., 2004; lite (Landsat), the Earth-Observing One satellite (EO-1), the Wang et al., 2010). Thus, those models of a single band have Moderate Resolution Imaging Spectroradiometer (MODIS), limited applications in regions with a wide dynamic range of the Medium Resolution Imaging Spectrometer (MERIS), the TSS concentration. Geostationary Ocean Color Imager (GOCI), the Sea Viewing Models that combined multiple bands worked better than Wide Field of View Sensor (SeaWiFS), Systeme Probatoire those with a single band in avoiding the effect of saturation d’Observation dela Tarre (SPOT) and the Environment and for water bodies of high TSS concentrations and have been Disaster Monitoring and Forecasting Small Satellite Con- applied widely (Dekkera et al., 2001; Doxaran et al., 2003; stellation (HJ). Compared to other remote sensing data, the Feng et al., 2014; Montanher et al., 2014; Oyama et al., 2009; Landsat series of imagery has an advantage in spatiotemporal Wang et al., 2016). Although the band combination includes dynamics analysis of TSS concentrations (Wu et al., 2013) band ratio (Doxaran et al., 2003; Lathrop et al., 1991; Ritchie due to the additional good quality, high spatial resolution and and Cooper, 1991; Topliss et al., 1990; Wang et al., 2016) and inheritance, especially long-term historical data since 1972. other complex forms (Dekkera et al., 2001; Li et al., 2010; Many Landsat-based models have estimated the TSS con- Oyama et al., 2009; Song et al., 2011; Zhang et al., 2015), centration with empirical, semiempirical, semianalytical or these models of multiple-band combination can be also clas- analytical algorithms (Chen et al., 2014; Doxaran et al., sified into linear, exponential, logarithmic or quadratic func- 2003; Fraser, 1998; Islam et al., 2001; Li et al., 2010; Mon- tion.