Mapping water clarity in North American lakes and reservoirs using Landsat images on the GEE platform with the RGRB model

Abstract

Lakes and reservoirs provide important ecosystem services that support human welfare and socio-economic activities. However, in many world regions, the ecological integrity of lakes and reservoirs is threatened by human perturbations and climate change. The Secchi disk depth (SDD) is a widely-used proxy representing the trophic status of lakes and reservoirs and can be retrieved from remote sensing data. Despite their potential for large-scale (regional, global) and long-term lake and reservoir water clarity assessment, the transferability of remote sensing-based models has been a major limitation. In this study, we assembled in situ SDD in lakes and reservoirs across North America (NA) from five different sources. We identified a subset of 3235 samples collected within ±7 days of a Landsat satellite overpass. Relationships between various spectral index models calculated from Landsat top-of-atmosphere reflectance and in situ SDD were analyzed. A model based on Landsat blue/green plus blue/red ratios (denoted as RGRB) was selected to retrieve the SDD of all NA lakes and reservoirs. The RGRB model performed well during calibration (R2 = 0.81) and validation (R2 = 0.78, MAPE = 30.85 %). This model also exhibited stable and reliable performances regardless of the Landsat sensors (TM, ETM+, and OLI), despite spectral configuration differences among these sensors. RGRB was implemented to generate SDD maps for all lakes and reservoirs (water surface area ≥1 ha) across NA in 2019. More than 2.9 million lakes and reservoirs were mapped with Landsat OLI images, resulting in an average SDD of 3.84 ± 1.77 m. A strong positive relationship between average SDD and log-transformed water surface area (R2 = 0.80, p < 0.001) indicated that large lakes and reservoirs tend to be more transparent than small ones. Latitudinal variations were found in the water clarity gradient, with maximum SDD recorded at the 35°N–60°N latitude and lower SDD at the 10°N–30°N latitude. This model can be implemented using the Google Earth Engine platform to derive SDD for NA lakes and reservoirs at annual or even seasonal time steps to assess water eutrophication variation in both time and space at the continental scale.