Liqin QuFollow

Date of Completion


Embargo Period



Remote Sensing, Yellow River, Suspended Sediment Concentration, Landsat 7, Spectroradiometer, Spectral Mixing Algorithm

Major Advisor

Xiusheng Yang

Associate Advisor

Daniel Civco

Associate Advisor

John Clausen

Associate Advisor

Thomas Meyer

Associate Advisor

Guiling Wang

Field of Study

Natural Resources: Land, Water, and Air


Doctor of Philosophy

Open Access

Open Access


This study aimed to develop an approach to use publicly accessible satellite imagery to quantify the suspended sediment concentration (SSC) in the Yellow River. The suspended sediment in the river affects to the hydrologic, geomorphologic, and ecologic functioning of river floodplains. Commonly used sampling methods are time consuming, labor intensive, and provide only point data. Current studies using re- mote sensing have focused mild waters (e.g. coastal, estuarine, lagoon, lakes and reservoirs) from where the method developed might not be appropriate for highly turbid inland waters. A laboratory spectrum experiment was conducted to investigate the reflective nature of sediment-laden water and the impact of sediment types on the reflectance. A spectral mixing algorithm based on a spectral linear mixture modeling approach was developed to estimate SSC from reflectance. We found that the models based on the spectral mixing algorithm were able to estimate SSC as high as 20 g/l. A field survey with on-site spectral and SSC measurements was conducted between the river channel and Sanmenxia reservoir on the Yellow River. The results confirmed an exponential relationship between SSC and reflectance. A single-band model was built to estimate SSC using band setting same as the band 4 of Landsat 7 image (760-900 nm)(R2 = 0.92, RMSE=0.241 g/l). The application of the spectral mixing approach to the on-site showed that the models based on the spectral mixing algorithm were performed as better as the single band exponential model (R2 = 0.86, RMSE=0.280 g/l) but the valid range for application was improved from 1.99 g/l to 347 g/l.

The spectral mixing algorithm was also applied to the Landsat-7 ETM+ data for remote sense the SSC in the Yellow River. For calibration and validation purpose, the daily SSC measured by the Yellow River Hydrological Monitoring System were associated with Landsat-7 ETM+ imagery from 1999 to 2010. The results showed that the model based on the spectral mixing algorithm could obtain a more promising relationship (R = 0.71) between SSC and reflectance than the conventional regression of a particular band (max R = 0.62). This study provides remote sensing method to estimate SSC with ready-to-use parameters for using Landsat-7 ETM+ imagery in the Yellow River. It could also provide spectral reference for remote sensing SSC in other highly turbid inland rivers.