Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM+: an example from the Yangtze River, China

Abstract

Suspended sediment concentration (SSC) is a critical parameter in the study of river sediment transport and water quality variation, but traditional measurement methods are costly and time-consuming. This paper is focused on presenting a methodology that may be useful in estimating SSC which is of key importance in process geomorphology and hydrology. In previous studies, remote sensing has been applied to estimate the SSC of sea waters as well as low turbid inland waters like lakes, reservoirs and short river reaches visible within a single Landsat satellite image coverage. Rivers, especially highly turbid large rivers, have largely been ignored. The dataset used in this paper includes measured SSC and multi-temporal Landsat ETM+ images covering most part of the Yangtze River. Using an effective easy-to-use atmospheric correction method that does not require in situ atmospheric conditions, retrieved water reflectance of Band 4 was found to be a good SSC indicator within the large SSC range 22-2610 mg l -1 . The newly developed regression relation between SSC and water reflectance of Band 4 appears to be able to provide a relatively accurate SSC estimate directly from Landsat ETM+ images for the Yangtze River from the upper, the middle to the lower reaches. With the relation it is possible to estimate or map out SSC dynamics of large rivers which lack SSC data due to constraints of conventional measurements. Figure 2. Variations of TOA reflectance (a and b) and retrieved water reflectance (c and d) at Bands 1-4 with increasing SSC. ρ TOA is TOA reflectance, and ρ w is water reflectance. Note the different scaling of the vertical axis in (a) and (b).Figure 4. Detailed results of regression between SSC and water reflectance at Band 4 within the SSC range 22-2610 mg l -1 in the Upper and Middle Yangtze River. (a) Raw data with bootstrapped calibration curve. (b) Validation results. (c) Residue of SSC in natural logarithm versus observed SSC in natural logarithm. (d) Relative error of SSC in percentage versus observed SSC.Note that 'residue of ln(SSC/mg l -1 )' refers to 'ln(Observed SSC/mg l -1 )' -'ln(Estimated SSC/mg l -1 )', and 'Relative error of SSC/%' refers to ('Observed SSC/mg l -1 ' -'Estimated SSC/mg l -1 ')/ 'Observed SSC/mg l -1 ' × 100%. Figure 5. Relations between SSC and water reflectance at Band 4 within the SSC range 22-2610 mg l -1 in the Upper Yangtze River (a) and the Middle Yangtze River (b), respectively. Note that the two regression relations are very similar to each other.

Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali

“…All these factors favor high turbidity and SPM concentrations, and possibly also high temporal dynamics. So far, few remote sensing studies [30,31] have addressed the case of extremely turbid inland water. Second, the dry season in the Sahel is marked by very high aerosol loadings (mostly mineral dust), whereas atmospheric water vapor is very low in the dry season and very high in the monsoon season.…”

Multitemporal analysis of algal blooms with MERIS images in a deep meromictic lake

“…The spectral bands used to estimate the algal grow can be affected by a significant amount of total suspended matter (TSM) usually influenced by heavy precipitations. For this reason, TSM presence was also evaluated, observing its albedo in the near-infrared bands (10-12-13-14), typically increasing with TSM quantities [Ruddick et al, 2006;Wang and Lu, 2009]. Furthermore, this analysis can also be useful to determine possible disturbances due to cirrus presence [Pepe et al, 2005].…”

Controls of channel morphology and sediment concentration on flow resistance in a large sand-bed river: A case study of the lower Yellow River

“…Remote sensing data from varying sources and sensors with high temporal resolution could significantly contribute to retrieve useful information of different landscape units. Many efforts have been devoted to develop efficient algorithms for acquiring hydraulic geometry variables and suspended sediment concentration from various available satellite images, which has been demonstrated to be practical and reliable (Smith et al, 1995, Smith and Pavelsky, 2008Bjerklie et al, 2003Bjerklie et al, , 2005bXu et al, 2004;LeFavour and Alsdorf, 2005;Leon et al, 2006;Wang et al, 2009a;Birkinshaw et al, 2010;Durand et al, 2010;Miller et al, 2014;Legleiter, 2015). For example, effective river channel width can be derived from different categories of remote sensing images, such as ASTER, LANDSAT, SPOT, ERS, QuickBird, and MODIS (Smith et al, 1995, Smith and Pavelsky, 2008Bjerklie et al, 2003Bjerklie et al, , 2005bXu et al, 2004;Birkinshaw et al, 2010), and water surface stage can be retrieved from the microwave remote sensing image data (SAR, LiDAR) and digital elevation model (DEM) (Bjerklie et al, 2003(Bjerklie et al, , 2005bLeFavour and Alsdorf, 2005).…”

“…For example, effective river channel width can be derived from different categories of remote sensing images, such as ASTER, LANDSAT, SPOT, ERS, QuickBird, and MODIS (Smith et al, 1995, Smith and Pavelsky, 2008Bjerklie et al, 2003Bjerklie et al, , 2005bXu et al, 2004;Birkinshaw et al, 2010), and water surface stage can be retrieved from the microwave remote sensing image data (SAR, LiDAR) and digital elevation model (DEM) (Bjerklie et al, 2003(Bjerklie et al, , 2005bLeFavour and Alsdorf, 2005). The SSC can be retrieved from different bands of MODIS, Landsat ETM+ and MSS, TM, and AVHRR based on the water body's spectral characteristics by relating spectral reflectance in the visible and near infrared bands to SSC (Ritchie and Cooper, 1988;Chen and Curran, 1992;Islam et al, 2001;Doxaran et al, 2002;Miller and Mckee, 2004;Wang et al, 2009a). In terms of mean flow depth, rational methods have been developed that are based on the observations of zero flows (Leon et al, 2006) and/or river channel geometry (Miller et al, 2014;Legleiter, 2015) from satellite altimeter data.…”