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Proceedings Paper

Estimation of suspended sediment concentrations from remotely sensed spectral reflectance: a field calibration for the Yellow River
Author(s): Liqin Qu; Daniel Civco; Tingwu Lei; Xiusheng Yang
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Paper Abstract

The dynamic sediment distribution in large rivers with dams constructed has often been the focus of considerable attention because of their potential adverse environmental impacts. Sedimentation modeling and environmental assessment of man-made projects are often hindered by the lack of sediment measurements with spatial details. This study aimed to investigate the method used to estimate the suspended sediment concentrations (SSCs) from on-site spectral measurements. The study investigated the spectral signature of river water from the natural channel and Sanmenxia Reservoir on the Yellow River. A field spectral survey was conducted through on-site spectral measurements by using a spectroradimeter and SSC estimation by sampling. Reectance at 750 nm to 950 nm, with all correlation coefficient (r) between SSC and reectance > 0:7, seemed to be the appropriate range for SSC estimation. Simulated Landsat Enhanced Thematic Mapper Plus Band 4 (760 nm to 900 nm) was used to build the single band model for estimating SSC. The results confirmed that the exponential model based on the relationship between SSC and reectance (R2 = 0:92, root mean square error [RMSE]= 0:241 g=l) was better than the linear model between reectance and logarithm-transformed SSC (R2 = 0:90, RMSE = 0:310 g=l). We also applied the Spectral Mixing Algorithm (SMA) from the tank experiment to the on-site spectral measurements. The result showed that the SMA models performed as well as the single band exponential model (R2 = 0:86, RMSE = 0:280 g=l). However, the valid range for application was improved from 1:99 g=l to 347 g=l. This study could provide critical instructional assistance for estimating SSC directly from remote sensing data.

Paper Details

Date Published: 5 June 2014
PDF: 14 pages
Proc. SPIE 9112, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring IV, 91121A (5 June 2014); doi: 10.1117/12.2051357
Show Author Affiliations
Liqin Qu, Univ. of Connecticut (United States)
Daniel Civco, Univ. of Connecticut (United States)
Tingwu Lei, China Agricultural Univ. (China)
Xiusheng Yang, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 9112:
Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring IV
Šárka O. Southern; Mark A. Mentzer; Isaac Rodriguez-Chavez; Virginia E. Wotring, Editor(s)

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