# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 2 Methods
### Data Analysis  
To classify the OFR surface area from PlanetScope, Basemap, and Planet Fusion, we clipped all available images using each OFR shapefile buffered to 100 m. Then, we calculated the normalized difference water index (NDWI) using the green and NIR bands [47], and we applied an adaptive Otsu threshold [48] for each image in the time series to separate water from non-water pixels. The Otsu threshold is a well-known algorithm used to classify surface water of inland water bodies [2; 4; 50; 51; 52; 53]. In addition, the Otsu threshold optimizes the separability of pixel values is contingent on the bimodal distribution of the pixel values (i.e., water and non-water pixels), which was ensured by clipping the satellite imagery using each OFR shapefile. After calculating the Otsu threshold and separating water from non-water pixels, we clipped the images one more time using the OFRs' shapefiles buffered at 20 m. This last step was done to minimize the impact (i.e., inflating surface area) of adjacent water bodies when estimating OFR surface area. All surface area image classification was done in Google Earth Engine [54].  
PlanetScope has a near-daily revisit time; however, the number of usable satellite images varies throughout the year due to the presence of clouds and sensor-related issues. To assess the number of different PlanetScope observations, we first counted the total number of observations for each OFR and for each month; then, we plotted the monthlydistribution of this number, including all OFRs (i.e., one boxplot for each month of the year that represents the variability in the number of monthly observations according to different OFRs). In addition, we evaluated the total number of different observations for each OFR along the year (i.e., a histogram that represents the distribution of the total number of observations for each OFR). A similar approach was used to assess the number of different Basemap observations and to count the number of Planet Fusion observations that were real, mixed (i.e., including real and synthetic pixels), and synthetic. Basemap images with the same PlanetScope scene composition were counted only once.  
For the different OFR surface area size classes (Figure 1), we assessed the uncertainties in the Basemap and Planet Fusion images by pairwise comparing them with PlanetScope and calculating the percent error (Equation (1)) monthly distribution and the monthly mean absolute percent error (MAPE; Equation (2)). In addition, for Planet Fusion, we divided the pairwise comparisons between real, mixed and synthetic surface area observations. We illustrated the surface area time series derived from PlanetScope, Basemap, and Planet Fusion for six OFRs of different sizes (Table 2). These OFRs were chosen to demonstrate the surface area time series variability from the different images and for OFRs located under different environmental conditions (e.g., presence of vegetation inside the OFR, close to adjacent water bodies, a multi-part OFR). In addition, we overlaid the OFRs' shapefile on high-resolution Google Maps satellite imagery to show the environmental conditions where the OFRs are located.  
\[\text{Percenterror}(\%)=\left(\left(y_{i}-x_{i}\right)/x_{i}\right)\ *100 \tag{1}\]  
\[\text{Mean absolute percent error}(\%)=\frac{1}{n}\Sigma\left|\frac{y_{i}-x_{i}}{x_{i}} \right|*100 \tag{2}\]  
where \(x_{i}\) is the SkySat or PlanetScope surface area and \(y_{i}\) is the Basemap or Planet Fusion surface area.