## 1 Introduction  
Lakes are land-surrounded water bodies that generally provide freshwater for human daily needs. For instance, water from Lake Biwa, Japan, is used as a water drinking resource for people in Osaka and Kyoto and has been maintained as a conservation ecosystem with good water quality [1]. In Indonesia, a freshwater treatment plant, namely, PDAM Kabupaten Kerinci, was built around Lake Kerinci in Jambi to take, store, filter, and distribute the water to people living nearby [2]. Meanwhile, the worldwide demand for fish products has steadily increased due to the growing need for protein and the shift in behavior towards the consumption of healthier food [3, 4]. The aquaculture industry often adds nutrient fertilizers, which are useful for commercial fish, to the water somewhere around the lake body. This procedure can fulfil the consumption demands; however, algal growth may be enhanced when nutrients are oversupplied. Consequently, the penetration of sunlight, which is required for respiration in fish, is limited and may lead to the extensive deterioration of water quality and the declining availability of freshwater, harming not only the fish, but also society. Therefore, the long-term monitoring of the water quality in lakes is necessary for the authorities to develop sustainable management initiatives to prevent water quality degradation and to maintain freshwater supplies in the future. Chlorophyll-a (Chla), a pigment found in every phytoplankton species, is considered a critical water quality parameter for many environmental issues [5, 6, 7]. The water quality and Chla concentration can be categorized into four classes based on the trophic state index: oligotrophic (less than 2.6 ug/L), mesotrophic (2.6-20 ug/L), eutrophic (20-56 ug/L), and hypertrophic (more than 56 ug/L) [8]. The water quality condition for each class is described in Table 1. Chla concentrations measured using field surveys are accurate and precise; however, the concentration data are only available at the sampling locations. Taking more measurements from the lake water body is hindered by the high labor and financial costs. Remote sensing technology enables researchers to empirically estimate the Chla concentration at the full spatial coverage of the lake water body by regressing the remote-sensing reflectance (\(R_{rs}\)) or the features with the in situ data obtained from field survey. [PERSON] and [PERSON] [9] utilized the features of band ratios, combining \(R_{rs}\) at wavelengths 443, 490, and 560 nm (denoted as \(\lambda_{443}\), \(\lambda_{490}\), and \(\lambda_{560}\)) in a three-band model, in which the in situ samples used in training ranged from 4.4 ug/L to 217.3 ug/L. [PERSON] et al. [10] exchanged the \(R_{rs}\) at wavelengths \(\lambda_{560}\) to \(\lambda_{645}\), which has been found to represent both water turbidity and algal absorption in a narrower range of in situ data (0.1-27.8 ug/L). [PERSON] et al. [11] performed local calibration in Chinese waters resulting in an \(R_{rs}\) feature at \(\lambda_{580}\), \(\lambda_{600}\), and \(\lambda_{692}\). [PERSON] et al. [12] and [PERSON] et al. [13] simplified the three-band model to a two-band model by removing the \(R_{rs}\) at \(\lambda_{443}\) due to the similar sensitivity to absorption as \(R_{rs}\) at \(\lambda_{490}\). Hence, [PERSON] and [PERSON] [14] proposed a differentiate index, called the normalized differentiate Chla index (NDCI), and demonstrated that the method outperforms the three-band and two-band models in cross validation. Many researchers [15, 16, 17, 18, 19, 20] searched for important features that are sensitive to Chla concentrations; however, the procedure is somewhat statistically exhaustive. Another promising procedure to estimate Chla concentrations is by means of an artificial neural network (ANN). [PERSON] et al. [21] proposed a fully connected neural network containing one hidden layer that revealed the capability of ANN for Chla concentration estimation. Similar work was also conducted by other researchers [22, 23, 24].