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 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 (Rrs) products over optically variable freshwater lake surfaces. MSI-Rrs 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 MSI (SDMSI) was then developed using a calibration dataset consisting of log-transformed SDin situ measurements (lnSDin 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 ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SDsOLI) where cross-sensor performance can be evaluated during coincident overpass events. SDsOLI applied to both MSI and OLI (SDOLI) 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.