Remote sensing (RS) images can improve the knowledge on the exchanges of sediment between the main rivers and floodplains as it provides a synoptic view of water bodies, at local and regional scales. The monitoring of total suspended solids (TSS) is important because the proportion of organic to inorganic particles varies in time and space and is linked to biogeochemistry of floodplain environments. Moreover, this proportion maybe affected by climate change as well as land use and land cover change. In order to grasp the spatial distribution of suspended sediments in Amazon Floodplains lakes, we have applied Monte Carlo simulation for calibrating several empirical and semi-analytical algorithms to estimate TSS based on in-situ Rrs and TSS concentration measured between 2015-2017. Calibrated models were then applied to atmospheric corrected Landsat/8, Sentinel 2-A, and CBERS-4 scenes. The results showed that is possible to estimate TSS on the floodplains using these three satellites, with errors lower than 30%.