A Brand-New Algorithm for Mapping Algal Biomass in Lakes - Zhengyang Yu; Ronghua Ma; Minqi Hu; Kun Xue; Zhigang Cao; Junfeng Xiong (Journal of Remote Sensing)
Column-integrated algal biomass has been recognized as a more logical proxy for the evaluation of lake eutrophication. Here, an algorithm with a 3-step framework is put forward for algal biomass mapping in 3 lakes of China (Lake Hongze, Lake Taihu, and Lake Chaohu). It can be summarized in step 1: inversion of surface chlorophyll a (Chla), step 2: inversion of diffuse attenuation coefficient of the photosynthetic active radiation [Kd(PAR)], and step 3: estimation of algal biomass with a pretrained generalized additive model. The proposed algorithm outperforms the result-oriented and process-oriented methods in terms of accuracy in 3 lakes (the root mean square error [RMSE] values for datasets of Lake Hongze, Lake Taihu, and Lake Chaohu were 5.09, 8.21, and 3.90 mg/m2, respectively). Validated with match-up satellite data, the algorithm generates acceptable results (RMSE = 5.69 mg/m2, mean absolute percentage error = 30.9%, N = 16). Another important discovery is that the extremum of algal biomass of the entire lake (Btot) does not always coincide with that of total surface Chla. For example, the maximum total surface Chla was recorded in 2016, whereas the maximum Btot of Lake Hongze was observed in 2020. For Lake Taihu, 3 peaks of Btot appearing in 2017, 2019, and 2021, respectively, did not coincide with those of total surface Chla. For Lake Chaohu, the interannual Btot followed a bimodal pattern that differed from the pattern of interannual total surface Chla. The proposed algorithm plays an indispensable role in broadening the horizon for algal biomass inversion.