An optimal model for estimating suspended sediment concentration from Landsat TM images in the Caofeidian coastal waters

Suspended sediment concentration (SSC) is one of the most critical parameters in water quality and environmental evaluations. Remote sensing has the potential for monitoring the dynamics and spatial distribution of SSC efficiently. The primary objective of this study is to develop retrieval models that are reliable and sensitive to SSC levels in the Caofeidian area, a new seaport in northeast China, based on Landsat-5 Thematic Mapper (TM) images and a set of in situ data sets, including spectral reflectance data and water quality data. The study finds that the band reflectance ratio and binary combination factor (i.e. the ratio of the reflectance to the particle size) are more effective than single band reflectance, and a non-linear model is more potent than a linear model for predicting SSC in the Caofeidian waters. A quadratic polynomial regression model of the R _TM3/R _TM2 ratio is proposed as the optimal retrieval model after evaluating various models with respect to different sensitive factors. The accuracy of the model is acceptable with a relative error and a root mean square error of 25.35% and 7.22 mg l^{– 1}, respectively; the correlation coefficient between the observed and estimated SSCs is 0.986. This study also indicates that the band reflectance ratio and binary combination factor are effective in weakening and even partially eliminating the effects of the changes in the sediment type (i.e. particle size and refractive index). And the band reflectance ratio is more efficient. Using the proposed model and TM data, SSC levels for the entire region were estimated. Such results can serve as a baseline for future environmental monitoring efforts.