Article
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Remote Sensing of Chlorophyll-A in Clear vs. Turbid Waters in Lakes
Version 1
: Received: 12 August 2024 / Approved: 13 August 2024 / Online: 14 August 2024 (07:23:03 CEST)
How to cite: Fendereski, F.; Creed, I. F.; Trick, C. G. Remote Sensing of Chlorophyll-A in Clear vs. Turbid Waters in Lakes. Preprints 2024, 2024080962. https://doi.org/10.20944/preprints202408.0962.v1 Fendereski, F.; Creed, I. F.; Trick, C. G. Remote Sensing of Chlorophyll-A in Clear vs. Turbid Waters in Lakes. Preprints 2024, 2024080962. https://doi.org/10.20944/preprints202408.0962.v1
Abstract
Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in-situ matchups from NB and SB (R² = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R² = 0.38, p < 0.05). In the deeper, clearer waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.
Keywords
phytoplankton blooms; chlorophyll-a; optical properties; Landsat; Google Earth Engine; Lake Winnipeg.
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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