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This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (R_rs) products over optically variable freshwater lake surfaces. MSI-R_rs products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for …