# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 4 Discussion  
The surface area validation carried out using multiple SkySat imagery showed that the methodology used to classify OFR surface area performed well for PlanetScope, Basemap, and Planet Fusion, with high agreement r\({}^{2}\geq\) 0.87 and MAPE between 7.05% and 10.08% for all image sources and all size classes (Table 4). Comparisons between Basemap and Planet Fusion with PlanetScope highlighted that most of the OFRs had good agreement with 61% of the OFRs with r\({}^{2}\geq\) 0.55, and small uncertainties with 70% of the OFRs with MAPE < 5% (Figure 6). Basemap and Planet Fusion presented similar monthly mean percent error (\(--\)-3-3%) and MAPE (~2.20-5.80%) throughout the year (Figure 7). In addition, percent error variability and MAPE decreased for the larger surface area observations (Figure 7). The highest monthly MAPE (5.80%) was found for Planet Fusion for observations between 0.1 and 5 ha, and the MAPE was \(\leq\)4.40% for Basemap and Planet Fusion for observations between 5 and 10 ha and between 10 and 30 ha. Furthermore, when analyzing the three Planet Fusion data categories (i.e., real, mixed, and synthetic), the greatest uncertainties were found for the synthetic images (MAPE ~ 5%), followed by mixed (MAPE ~ 4%) and real (MAPE ~ 3%) (Figure 5). These findings indicate that Basemap and Planet Fusion  
Figure 10: OFR E (see Table 2) PlanetScope, Basemap, and Planet Fusion false-color composites (blue: red, green: green, and red: NIR) and the surface water classification for 14 July 2020 and 16 October 2020.  
images can be employed to monitor OFRs with uncertainties \(<10\%\) when the sources are compared to the validation dataset and with uncertainties \(<5\%\) when compared to PlanetScope. However, time series obtained from Basemap and Planet Fusion can be highly variable (Figure 8E,F), as surface water classification can be impacted by the size of water bodies (Table 4, Figures 2, 4 and 6) and the environment in which OFRs are located (e.g., presence of vegetation within the OFRs; Figure 8, OFRs D-F). The number of cloud-free observations offered by Basemap and Planet Fusion enlightens the potential of these datasets to monitor OFR surface area changes (Figure 4). Both datasets pose advantages when compared to a single sensor approach--employing PlanetScope alone (Figure 4), or other sensors, for example, Landsat [23; 31; 56], Sentinel 1 [57], and Sentinel-2 [58; 59; 60]--or a multi-sensor approach [4; 33; 34]. Briefly, the use of a single sensor is limited to a few observations a month, and in some periods of the year in eastern Arkansas, there could be weeks without a cloud-free image [33]. Although the number of observations is improved when employing a multi-sensor approach, daily to sub-weekly monitoring is not attainable unless an assimilation algorithm [33] is implemented. In addition, when implementing a multi-sensor approach, it is necessary to acquire the data from different platforms (e.g., Planet Explore, Sentinel Hub, and Google Earth Engine), which can be time-consuming and a limiting factor if it is necessary to process, download, and move the satellite imagery across multiple platforms. In this study, we demonstrated that Basemap and Planet Fusion imagery processing can be done entirely in the cloud environment by leveraging the integration of Planet's Platform, Google Cloud Storage, and Google Earth Engine. This integration allows for swift analysis, and it can be used for other study regions without the need to acquire data from multiple platforms. Daily OFR surface area time series derived from Basemap and Planet Fusion revealed important differences between the two datasets. In general, Basemap had higher surface area variability, and it was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year (Figure 8).