# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 3 Results
### OFR Surface Area Time Series  
We selected six OFRs (Table 2) to illustrate the surface area time series derived from PlanetScope, Basemap, and Planet Fusion (Figure 8). The surface area time series show that different OFRs have different surface area change patterns. In general, the OFR surface area decreased between 20 July and 20 November (e.g., Figure 8, OFRs A-D), period of the year when farmers are irrigating their crops [23], and it increased between 21 January and 21 May, which are the months when the study region receives most of its annual precipitation [23]. When compared to PlanetScope and Basemap, Planet Fusion had a smoother surface area time series with less variability (e.g., Figure 8 OFRs A-D). In addition, the Planet Fusion time series was less affected by the presence of clouds and haze, which can increase or decrease OFR surface area. Even though we used a low cloud-cover threshold (<10%) for PlanetScope, there are several PlanetScope and Basemap images contaminated with cloud shadows and haze (e.g., Figure 8, OFR A, between 20 August and 20 September), indicating surface area ~20% larger than that of Planet Fusion. Other examples were observed between 20 July and 20 August and between 21 May and 21 June in Figure 8, OFR B, in which there were no PlanetScope images available and the Basemap shows abrupt changes in surface area--a drop of 20% and 15% for both dates--which were caused by the presence of cloud shadows and haze. In Figure 9, we highlighted the impact of clouds and haze for OFR A (16 August 2020) and OFR B (30 August 2020). For OFR A, PlanetScope and Basemap surface areas were ~20% larger than those of Planet Fusion, which is explained by the misclassification of water on the lower-right corner of the OFR. For OFR B, while the PlanetScope image had a surface area ~13% larger than that of Planet Fusion, the Basemap image indicated a surface area ~14% smaller. These discrepancies are caused by the presence of clouds in the PlanetScope image and haze in the Basemap image. Figure 7: Monthly percent error variability and MAPE calculated from the same-day pairwise comparisons between Basemap and Planet Fusion with PlanetScope for the three size classes (0.1–5 ha, 5–10 ha, and 10–30 ha). Figure 8: \(Cont.\)  
Figure 8: OFRs (see Table 2) surface-area time series derived from PlanetScope, Basemap, and Planet Fusion and OFR shapefiles overlaid on high-resolution Google Satellite imagery. Figure 9: OFRs A and B (see Table 2) PlanetScope, Basemap, and Planet Fusion false-color composites (blue: red, green: green, and red: NIR) and the surface-water classification for 16 August 2020 (OFR A) and 30 August 2020 (OFR B). OFR surface water classification is impacted by the OFRs' environmental conditions and their shape geometry. OFRs with complex geometries (e.g., not circular or square and shapes with a large number of edges) tend to have higher surface area classification uncertainties [33]. For example, Figure 8, OFR D, shows a multi-part OFR that may not have all parts inundated at the same time, which can explain part of the variability in the surface area time series for PlanetScope, Basemap, and Planet Fusion. The surface area time series from OFR E and OFR F (Figure 8) are influenced by the presence of vegetation within the OFRs. The presence of vegetation impacts surface water classification [5,33,55], leading to noisy surface area time series and abrupt changes (e.g., OFR E between 20 September and 21 January). In addition, the high variability in surface area for OFRs E and F is related to the presence of adjacent water bodies, which can inundate during flood events and contribute to changes on OFR boundary limits. We highlighted the impact of vegetation on the OFR E time series for two different occasions: 14 July 2020 and 16 October 2020 (Figure 10).