This paper presents empirical models for suspended sediment concentration (SSC) retrieval from Landsat 5 images in several Amazon Rivers. In view of great gaps in the time series of in situ SSC database in Amazon Rivers, estimates based on historical orbital data may be an option to open new research possibilities. The models are based on a database composed of 504 in situ samples and near-simultaneously Landsat images. Two approaches are tested: i) using the entire database and ii) regionalizing the data according to environmental features of the watersheds they belong. The results show that the use of the whole database does not provide accurate SSC estimates. The regional modeling provides better estimates by fragmenting the data into five clusters. All these models display p-values ≈ 1*10-6 , R² values ranging from 0,83 to 0,91. The cross validation LOOCV and relative error values also showed their robustness. The models are very accurate, mainly for low SSC levels, between 0 to 200 mg/l. As the concentration increases, the absolute error increases too, but relative errors remain low (up to 7%).Pages: 5848-585
