The Amazon floodplain represents one of the most important terrestrial ecosystems being a highly complex and dynamic environment, with a key role in the global carbon cycle. Therefore, the monitoring and management of their aquatic systems is vital to increase the knowledge on the biogeochemistry involving water components. Optically Active Components (OACs) as chlorophyll-a (chl-a) can be a proxy to environmental parameters such as water trophic status and primary productivity. Standard methods to determine chl-a are based on in situ measurements being expensive and time consuming, alternatively, remote sensing can be a viable option through the calibration of chl-a algorithms. Therefore, this work aims the assessment of empirical algorithms for chl-a retrieval in Amazon lakes with turbid waters using Remote Sensing reflectance (Rrs) from in situ data gathered in four campaigns between 2015 and 2017. In situ Rrs was then used to simulate Landsat 8/OLI and Sentinel 2/MSI images which were calibrated and validated by Monte Carlo simulation. The best algorithms were validated using images acquired almost concurrently to in situ data acquisition for both sensors. Preliminary results pointed out the ability to estimate chl-a with errors smaller than 30% for MAPE for simulated data.