# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 2 Methods
### Satellite Imagery Datasets
#### 2.2.2 Normalized Surface-Reflectance Basemap  
We processed daily Basemap images corrected to surface reflectance using PlanetScope scenes, and a \"best scene on top\" algorithm [16, 18] that selects the highest quality imagery from the PlanetScope catalog. This algorithm ranks the PlanetScope scenes based on their quality by assessing the scenes' acutance (i.e., sharpness), the fraction of cloud cover, cloud shadow, haze, and presence of unusable pixels (e.g., no data). Briefly, this algorithm is based on a linear regression model approach that uses the clear pixels from the best-ranked scenes; we selected the best scenes first, then progressed successively until the images were filled or no scenes remained [18]. To obtain Basemap at a daily cadence, we employed a 30-day rolling window that may use PlanetScope scenes collected up to 15 days before or after the target date; however, if no usable pixels (i.e., cloud-free) are available in this time range, the image will contain no data. We did not observe any Basemap image with no-data in our study period. The rolling window approach weights on the image recency, for instance, a slightly hazy scene (e.g., \(\sim\)\(<\)10% hazy pixels) on the day of the Basemap image, will score higher than a very clear scene (i.e., no haze) from a few days before/after. In addition, due to the daily cadence, there may be Basemap images with the same PlanetScope scene composition, which leads to repetitive information when using the Basemap images to monitor OFRs.  
Basemap images were generated employing a two-step process: normalization and seamline removal. Normalization aims to radiometrically calibrate the Basemap images and to minimize the scene-to-scene variability when mosaicking PlanetScope scenes. For this step, the Framework for Operational Radiometric Correction for Environmental Monitoring (FORCE) [40] was used to generate a combined Landsat 8 and Sentinel-2 surface-reflectance product to be used as the \"gold\" radiometric reference during normalization. FORCE infers surface reflectance from Landsat 8 and Sentinel-2 by employing the 5S (simulation of the satellite signal in the solar spectrum) approach [41]. The aerosol optical depth is estimated using a dark-object-based approach where in water vapor content is derived from Landsat 8 (obtained from MODIS database) and Sentinel-2 (estimated on a pixel-specific basis) imagery. In addition, clouds and shadows are detected using a modified version of Fmask [42] for Sentinel-2 images [43] (see [16, 17, 44] for further details). An assessment of the FORCE atmospheric correction was performed as part of the atmospheric correction inter-comparison exercise [45], and the FORCE implementation uses the Landsat 8 and Sentinel-2 imagery mapped onto a common UTM grid to produce 30 m spatial-resolution imagery. Seamline removal enhances the visual appearance of the Basemap image edges. In this step, each PlanetScope scene used in the Basemap mosaic is set to match its neighbor--pixel values near a scene boundary change more than values away from the boundary; however, the pixel values are not modified. Specifically, we first calculated the Basemap mosaic pixel values gradient, then set the gradient values between 1 and 0 (scene boundary) and fixed the original pixel values along the Basemap mosaic edge. This process was applied independently for each band; therefore, it may alter band ratios near scene edges--this is most apparent when scenes do not match locally, for instance, for unmasked clouds. Lastly, the seamline removal may introduce artifacts (e.g., straight lines, distortions) at the Basemap mosaic boundary, which is most frequent over water when normalization cannot fully correct for differences between scenes due to waves and sun glint.