Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10–187.40 mg·m−3 using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e., the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e., Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R2) in the range of 0.42–0.65. However, these processors underestimated the retrieved Chla > 100 mg·m−3, reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R2 = 0.65, Root mean square error (RMSE) = 22.18 mg·m−3, the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg·m−3, mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors—except for the BOL and FUB—were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e., band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime.