A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters
The chlorophyll-a (Chl-a) concentration is one of the most important parameters for evaluating the state of water environments, which often vary markedly across both time and space. Here we propose a hybrid algorithm for retrieving the Chl-a values from in situ remote sensing data. This hybrid algorithm contains three individual Chl-a estimation algorithms that were previously developed for clear waters (a bluegreen algorithm), turbid waters (a two-band index-based rednear infrared algorithm), and highly turbid waters (a three-band index-based rednear infrared algorithm). The MCI value (maximum chlorophyll index) was used to switch the three algorithms. To evaluate the performance of the proposed hybrid algorithm, we used the in situ remote sensing reflectance and Chl-a values collected from five Asian lakes, the trophic status of which varied from oligotrophic to hypertrophic. The results showed that the hybrid algorithm performed well for a wide variety of optical properties, with the NMAE (normalized mean absolute error) of 13.3%. Our results indicate that the proposed hybrid algorithm has the potential for use as an operational tool for monitoring Chl-a in waters with widely varying trophic conditions without the requirement of reparameterization.
hybrid algorithm chlorophyll-a concentration bluegreen algorithm maximum chlorophyll index asian lakes rednir algorithm