Research on Inversion of Suspended Sediment Concentration in Estuary Surface Based on Remote Sensing and GIS

Abstract

The concentration of suspended sediments at the estuarine surface (SSC) is a crucial indicator for monitoring water bodies. Given the current situation in which SSC remote sensing inversion is primarily based on low-resolution satellites, this study first discusses remote sensing and GIS technologies before employing the Sentinel-2 satellite, whose resolution can exceed 10 m after resampling. Inversion and comparison of SSC content during wet and dry periods. In addition, based on the neural network’s ability to compensate for the inherent errors of traditional empirical techniques, this study designs and develops an artificial neural network-based neural network corrector to perform secondary correction on the empirical inversion results. In this study, the B2, B3, B4, and B8 inversion models are used to generate the sensitive bands, and the ratio of these bands is used to generate the inversion model. The results indicate that the model has a high level of precision, which can aid in reducing the model’s inherent error and ensuring inversion precision.