## 1 Introduction  
Clean water is an indispensable element for people's livelihood, industries and agriculture; thus, water resources are considered one of the most important assets for live creatures worldwide. Hence, it is necessary to manage and monitor water quality all the time. Many developing countries in the world where the ensuing industrialization and urbanization driven by rapid economic development have brought about tremendous pressure to the environment [1; 2; 3; 4; 5]. In many cities, pollution can be easily and rapidly spread from industrial areas to nearby residential areas and even reservoir catchment areas because the vicinity is very small. Although Taiwan is an area rich in water resources, its land area is so small that water storage is not as easy as imagined. In addition, due to global warming in recent years, each country on earth has been affected to varying degrees by extreme climate. Facing the problem of instantaneous high-intensity rainstorms or prolonged droughts, the water quality in various catchment areas will be unstable; hence, real-time water quality management is extremely important [6; 7; 8; 9]. Although the current commonly used water quality monitoring equipment has good reliability, some conventional monitors (e.g., pH meters, conductivity meters, etc.) must be manually calibratedand regularly cleaned, while traditional water quality analysis methods rely on on-site sampling and measurement and chemical analysis in the laboratory. All these require a lot of manpower, many material and financial resources and huge time cost. Moreover, due to the limited monitoring points, they cannot reflect immediate temporal and spatial changes in water quality. Based on the abovementioned fact, the macro real-time monitoring of water quality changes must be conducted, such that relevant departments can grasp the water environment and dynamically track and analyze water pollution and other events in time. Remote sensing technology, which can measure and monitor the water environment transition macroscopically and in real time, is introduced into the field of water quality monitoring to overcome the abovementioned shortcomings of traditional methods and meet the needs of water quality monitoring. Since NASA's launch of three satellites (i.e., Nimbus-7, TIROS-N and Seasat) related to ocean observation in 1978, more and more sensors have been used for water quality monitoring. The main data sources of water quality monitoring can be divided into multispectral imaging data, hyperspectral imaging data and non-imaging spectral data. Multispectral image data, such as Landsat Thematic Mapper (TM) images, are relatively easy to obtain; hence, they have been widely used in the quantitative remote sensing of water quality around the world. In addition, based on an in-depth understanding of the mechanism and principles of radiation propagation in water, many scholars have begun to devote time to improving the existing water quality remote sensing monitoring algorithms to further improve the inversion accuracy. [PERSON] and [PERSON] were the first researchers to study the trophic status of Alberta in Valencia Lake (Valencia, Spain) using Landsat-TM data [10]. They obtained empirical models of chlorophyll a andeston particles using TM band 1 (452-518 nm), but were unable to define a transparency model. [PERSON] et al. studied the nutritional status of lakes/ponds in the Southeast Regional Park, 20 km southeast of Madrid, Spain, and proposed a chlorophyll a and transparency model using TM band 2 (528-609 nm) and for the concentration of the total suspended particles using TM band 4 (776-904 nm) [11]. Further, with the continuous deepening of research on the spectral characteristics of the water quality parameters and the advancement of algorithmic research methods, water quality monitoring technology via remote sensing has been developed, from qualitative to quantitative modes.