Dr. Tingwu Lei Profile

Dr. Tingwu Lei

at China Agricultural Univ

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Publications (2)

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Proceedings Article | 5 June 2014 Paper

Estimation of suspended sediment concentrations from remotely sensed spectral reflectance: a field calibration for the Yellow River

Liqin Qu, Daniel Civco, Tingwu Lei, Xiusheng Yang

Proceedings Volume 9112, 91121A (2014) https://doi.org/10.1117/12.2051357

KEYWORDS: Landsat, Shape memory alloys, Remote sensing, Data modeling, Calibration, Statistical modeling, Near infrared, Solids, Environmental sensing, Earth observing sensors

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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.

Proceedings Article | 16 September 2011 Paper

Estimation of suspended sediment concentrations in the Yellow River by network monitoring records and satellite data

Liqin Qu, Xiusheng Yang, Tingwu Lei

Proceedings Volume 8156, 81560F (2011) https://doi.org/10.1117/12.894171

KEYWORDS: Reflectivity, Particles, Landsat, Satellites, Remote sensing, Atmospheric corrections, Data modeling, Scattering, Atmospheric particles, Earth observing sensors

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The sediment concentration in river flow is very important in monitoring of water quality, operation of the hydraulic facilities, and management of water resources. Commonly used sampling method is time consuming, labor intensive, and providing only point data at gauging station. This study is presenting a remote sensing approach to quantify suspended sediment concentration (SSC) of the high turbid flow in the Yellow River in China, where the high sediment transportation from severe soil erosion is a big environmental concern. The approach was based on public accessible satellite images and surface networking monitoring data. With the longest time series records, the Landsat EMT+ images were chosen to establish the remote sensing approach. Daily sediment records from 2 hydrological stations from 1999 to 2008 in the middle part Yellow River were associated with available satellite imaginary. The water reflectance was retrieved from the Landsat images by using an effective easy-to-use atmospheric correction method. Correlation among water reflectance at band 1 to 4, particle size of suspended sediment and SSC are analyzed to establish the SSC indices. According to the significance of relation between SSC and the water reflectance at different bands of Landsat data, regression models between SSC and water reflectance was developed. The model was calibrated by the daily sediment records from surface observation.

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