A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems

Abstract 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 clarityin the Google Earth Engine ( GEE) environment. Processing steps include (1) atmospheric correctionand masking of MSI and OLI imagery, and (2) generating scene-based water claritymaps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correctiontheory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEEto generate uniform remote sensing reflectances (R rs ) products over optically variable freshwater lake surfaces. MSI-R rs products derived from the atmospheric correctionwere used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SD MSI ) was then developed using a calibration dataset consisting of log-transformed SD in situ measurements (lnSD in situ ) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregionsin Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SD sOLI ) where cross-sensor performance can be evaluated during coincident overpass events. SD sOLI applied to both MSI and OLI (SD OLI ) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 ( n  = 18) and 13-Aug 2018 ( n  = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.