Amir Masoud Chegoonian, Nima Pahlevan, Kiana Zolfaghari, Peter Leavitt, John-Mark Davies, Helen Baulch, Claude Duguay (2022). Quantification of chlorophyll-a concentration in small eutrophic lakes using sentinel-2 and landsat-8 imagery and locally tuned machine learning models: a case study in Buffalo Pound Lake, Canada. Proceedings of the GWF Annual Open Science Meeting, May 16-18, 2022.

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial in situ optical interferences of various water?constituents and uncertainties in the atmospheric correction process. Although various algorithms have been developed or adapted to estimate Chla from moderate-resolution terrestrial missions (~ 10 – 60 m), there remains a need for robust algorithms to retrieve Chla in small inland waters. Here, we train and test a support vector regression (SVR) model, which takes in satellite-derived remote-sensing reflectance spectra ( Rdrs Rrs

AOSM2022 FORMBLOOM First Author: Amir Masoud Chegoonian, University of Waterloo Additional Authors: Nima Pahlevan, NASA; Kiana Zolfaghari, University of Waterloo; Peter Leavitt, University of Regina; John-Mark Davies, Water Security Agency; Helen Baulch, University of Saskatchewan; Claude Duguay, University of Waterloo;