关键词: 曹妃甸, 悬浮泥沙, 垂向空间分布, 遥感模型

Abstract:

The suspended solids content in ocean water is a significant water quality parameter, and the use of remote sensing technology to monitor the suspended sediment concentration (SSC) is an important research direction in ocean color remote sensing. In this paper, based on a set of in situ data sets, which include the simultaneous spectral reflectance data and the SSC data of different depths obtained from 35 field sites distributed within the four profiles of Caofeidian waters, and combined with the Landsat-5 TM remote sensing data, an empirical statistical model between the SSC levels of water surface and the remote sensing reflectance was established. So the SSC levels of water surface in the study area were estimated. Subsequently, by respectively analyzing the correlation relationships of SSC levels between the surface and the middle layers, as well as the surface and the bottom layers, we deduced the SSC inversion results of the middle and bottom layers based on the results of surface SSC levels and we also studied the vertical spatial distribution patterns of SSC. The study results indicate that there is an obvious correlation between the SSC levels in the vertical direction. Based on the reflectance ratio (R_TM3/R_TM2) of Landsat-5 TM image, an optimized quadratic polynomial model for the SSC retrieval of water surface was established. Based on this model, the relevant models for the middle and bottom layers were also established. Then, the inversion precision of each model was validated using seven check points. The model's mean relative error of each layer was controlled to be less than 30%, the mean absolute errors and root mean square errors of the surface and middle layers were below 6 mg/L and 10 mg/L respectively. The precision of the bottom layer was slightly lower than the surface and middle layers. The results provide a foundation to further study the sediment transport rule of marine environment and the optimization of sediment transport model under hydrodynamic forces.

Key words: Caofeidian, suspended sediment, vertical spatial distribution, remote sensing models