Mapping of Chl-a in Indonesia’s Five Priority Lakes uSing Linear Regression (Case Studies: Lake Singkarak, Toba, Maninjau, Matano, and Towuti)

Abstract

Abstract In Indonesia, 15 priority lakes need to be monitored regularly. Model algorithm development is the answer to accelerating lake water quality monitoring through chlorophyll-a concentration monitoring. This research aims to evaluate, develop, and find the most accurate global model algorithm for chlorophyll-a concentration mapping on Lake Singkarak, Toba, Maninjau, Matano, and Towuti. Algorithm development was made based on the existing 22-band ratio algorithm. Leave One Out Cross Validation (LOOCV) was used to evaluate the performance of all tested algorithms through the R2, NMAE, and RMSE accuracy results using 42 in-situ sample points of chlorophyll-a collected on five lakes. Lake Singkarak has an RMSE of 0.1 mg/m3, NMAE of 29.9%, R of 0.7, and R2 of 1.0. Lake Toba in the dry season has RMSE of 0.7 mg/m3, NMAE of 22.5%, R of 0.7 and R2 of 0.3. In the rainy season, Lake Toba has an RMSE of 0.1 mg/m3, NMAE of 7.1%, R of 0.5, and R2 of 0.3. Lake Maninjau has an RMSE of 0.6 mg/m3, NMAE of 32.5%, R of 0.5, and R2 of 0.3. Lake Matano has RMSE of 0.0 mg/m3, NMAE of 4.2%, R of 1.0 and R2 of 1.0. Lake Towuti has an RMSE of 0.0 mg/m3, NMAE of 3.4%, R of 1.0, and R2 of 1.0. The most accurate algorithm for Lake Singkarak, Toba (dry season), Toba (rainy season), Maninjau, Matano, and Towuti are C6, C6, C2, A2, C10, and B3, respectively