## 3 Methods
### UAV Multispectral Surveys  
As shown in Figure 2, an octocopter (Tarot X8) with a pan-tilt system and a commercial multispectrometer (Micasence Altum) were utilized for data collection. The flight time was during 10:00 am-11:00 am, which was established with regard to the sunlight angle to minimize the sun-glint effects and the specular reflections. Six ground control points were evenly distributed in the flight area. The x, y and z coordinates were surveyed with a GPS with sub-metric precision. The camera had five multispectral bands, i.e., red (668 nm center, 14 nm bandwidth), green (560 nm center, 27 nm bandwidth), blue (475 nm center, 32 nm bandwidth), red edge (717 nm center, 12 nm bandwidth) and near infrared (NIR) (842 nm center, 57 nm bandwidth) and one thermal image sensor (Longwave infrared 8-14 \(\upmu\)m). The fields of view of the multispectral and thermal sensors were 48\({}^{\circ}\)\(\times\) 37\({}^{\circ}\) and 57\({}^{\circ}\)\(\times\) 44\({}^{\circ}\), respectively. The multispectral and thermal images from this camera can be acquired at 1 s intervals. In addition, the system had a calibrated reflective panel and a sunlight sensor, which can measure the instantaneous energy from the sun. The GPS system built into the irradiance sensor can be used to save geotagged images. The multispectrometer Micasence Altum mainly utilized the 'global shutter' technique to acquire all image pixels at a time rather than the 'rolling shutter' technique to line scan image pixels, which leads to no time interval for the same frame of image. The maximal sample point the camera can detect is 2064 \(\times\) 1544 pixels for five multispectral bands except the thermal image band. When  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sampling** & **Coordinate** & **Sampling** & **Chlorophyll a** & **Total Phosphorus** & **Transparency** & **Dissolved Oxygen** \\
**Point** & **Position** & **Date/Time** & **(\(\upmu\)g/L)** & **(\(\upmu\)g/L)** & **(\(\upmu\)m)** & **(\(\upmu\)g/L)** \\ \hline \hline S5 & 24\({}^{\circ}\)08\({}^{\prime}\)34\({}^{\prime\prime}\) N & 5/15 AM 10:00 & 61.9 & 65 &  
the UAV flight mission was planned at 15 m height, the spatial resolution of the captured spectral image was approximately 1 cm and the detected ground area of a captured image was estimated as 20.6 \(\times\) 15.4 m\({}^{2}\). Certainly, the detected area may vary depending on the flight altitude. Practical experience has shown that the quality of collected multispectral images is greatly dependent on the atmospheric conditions and geometric factors. Regarding the atmospheric conditions, the reflected light in terms of electromagnetic energy can be influenced by the angle of reflection, particles in the air and cloud shadow, etc., while geometric factors primarily concern the characteristics of specular, near-specular, near-diffuse or diffuse reflection. Strong mirror-like reflections on the surface of water should be avoided while UAV-based multispectral images are recorded. Further, stable control of the UAV flight is considered necessary and critical to improve the image quality. To eliminate these potential measurement errors, several essential procedures to calibrate the multispectral image are essentially required. Before the application of UAV imagery to multiple regression models, a series of image pre-processing steps needs to be completed. First, the raw image data, referred to as digit number (DN), are in the form of 16-bit and ranging from 0 to approximately 65,535 values, which cannot be directly employed to calculate corresponding reflectance. Instead, a calibrated reflectance panel (CRP) with accurate reflectance between 400 nm and 850 nm wavelength is introduced to adjust DN values to carry out the relative radiometric calibration.