Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-a using Landsat TM/ETM+ over turbid waters

ABSTRACTRemote sensing of chlorophyll-a is challenging in water containing inorganic suspended sediments (i.e. non-volatile suspended solids, NVSS) and coloured dissolved organic matter (CDOM). The effects of NVSS and CDOM on empirical remote-sensing estimates of chlorophyll-a in inland waters have not been determined on a broad spatial and temporal scale. This study evaluated these effects using a long-term (1989–2012) data set that included chlorophyll-a, NVSS, and CDOM from 39 reservoirs across Missouri (USA). Model comparisons indicated that the machine-learning algorithm BRT (boosted regression trees, validation Nash–Sutcliffe coefficient = 0.350) was better than linear regression (validation Nash–Sutcliffe coefficient = 0.214) for chlorophyll-a estimate using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery. Only a small proportion of BRT model residuals could be explained by sediments or CDOM, and the observed trends in BRT residuals were different from the theoretical ...