# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Geospatial and Statistical Datasets
#### 2.2.2 Remote Sensing Data  
We used various remote sensing products (see Table 1) to quantify lake water dynamics and explore influencing factors. All of these remote sensing products [67] and the main data processing are completed on the Google Earth Engine platform. Google Earth Engine is a remote sensing archive with petabytes of data in one location, as well as a cloud-based geospatial processing platform for large-scale data analysis. It is unique in the field as an integrated platform due to the following advantages: (1) The public data catalog has vast amounts of publicly available data including imagery, geophysical, climate and weather, demographic, and vector data. (2) Unprecedented speed resulted from the distributed, cloud-based computing power which reduces processing times by orders of magnitude. (3) The application program interface (code editor) empowers traditional remote sensing scientists, and the graphical user interface (explorer) provides a friendly way to begin exploring and analyzing data for a much wider audience that lacks the technical capacity [67]. The European Commission's Joint Research Centre (JRC) Global Surface Water (GSW) was derived from three million Landsat satellite images (i.e., Landsat 5/7/8), recording the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence at a 30 m resolution from 1984 to 2019 [68]. Each pixel was individually classified as water/non-water. We derived multi-temporal water surface areas based on the JRC GSW images. Figure 4 shows examples of lake water areas extracted from Landsat images for the year 1998, 2003, 2008, and 2018, respectively. Each background image shown in Figure 4 is a false-color composite. Figure 3: An example of the lake bathymetric DEM of Turtle Lake in the state of Minnesota. The Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include evapotranspiration (ET) and gross primary product (GPP) at a 500 m spatial resolution with an 8 day temporal frequency from 2002 to 2017. Each PML_V2 imagery consists of five bands, including the gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), interception from vegetation canopy (Ei), waterbody, snow, and ice evaporation (ET_water). We derived the waterbody evaporation, a vital influencing factor, from the ET_water band in this study. The Daily Surface Weather and Climatological Summaries (Daymet V3) dataset provides gridded estimates of daily weather parameters for the United States. It was derived from selected meteorological station data and various supporting data sources during 1980-2020. Each Daymet V3 imagery has seven bands with a 1 km spatial resolution, including the duration of the daylight period (day1), daily total precipitation (prcp), incident shortwave radiation flux density (srad), Snow water equivalent (swe), Daily maximum 2 m air temperature (tmax), daily minimum 2 m air temperature (tmin), daily average partial pressure of water vapor (vp). As one of the influencing factors of water provisioning ecosystem services, the annual precipitation was derived from the 'prcp' band. The Terra Land Surface Temperature and Emissivity dataset from the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center includes an average 8 day land surface temperature (LST) in a 1 \(\times\) 1 km\({}^{2}\) grid with 12 bands since 2000. We used the 'day land surface temperature' band to extract the average daily temperature in 2018 for influencing factor analysis. Figure 4: Multi-temporal water areas of Lake Louisa derived from the JRC Global Surface Water (GSW) dataset. The National Land Cover Database (NLCD) is a 30 m Landsat-based land cover database. The 'landcover' band, which classified land use into 20 categories, was used to calculate the vegetation coverage rate and the artificial land coverage rate.