# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 1 Introduction  
Planet Labs currently operates more than 200 PlanetScope satellites in sun-synchronous orbits and frequently launches new satellites that are designed to have a short operational lifetime (<4 years). The PlanetScope satellite constellation enables near-daily monitoring with multi-spectral imagery at high spatial resolution (3 m) [1]. PlanetScope imagery has been applied to a variety of studies to monitor phenomena that require both high spatial and temporal resolution, for instance, to monitor small water bodies [2, 3, 4], estimate methane emissions from forested wetlands [5], assess river-ice and water velocity [6], improvecrop leaf-area-index estimation with sensor data fusion [7; 8; 9], and monitor near-real-time aboveground carbon emissions from tropical forests [10; 11; 12]. A recent global analysis of PlanetScope's temporal availability [13] showed that the annual and monthly number of PlanetScope observations does not vary uniformly across the globe. The authors attributed this finding to different PlanetScope orbits (i.e., altitude and inclinations), due to different numbers of sensors in orbit, which vary when PlanetScope satellites are decommissioned and replaced with new sensors, and due to images that cannot be geolocated [13]. In addition, it is well known that the number of observations from optical wavelength satellite imagery will vary according to dynamic and global cloud obscuration. While the PlanetScope cloud mask, Usable Data Mask 2 (UDM2), is available [1] and allows for discernment of classes like cloud, cloud shadow, and heavy, haze among others, its accuracy has not been thoroughly assessed [13; 14; 15] and it is not available for images prior to 2018 [1]. Aiming to overcome these limitations--irregular cadence and cloud obscuration--and to increase the applications of PlanetScope imagery, Planet Labs has focused on developing the next generation of tiled analysis ready datasets--Basemap [16] and Planet Fusion [17]--which are less affected by the presence of clouds and are set for a fixed temporal cadence. Basemap is generated by mosaicking the whole or part of the highest quality PlanetScope imagery, which is selected based on cloud cover and image acutance (i.e., sharpness). For example, for a given period of interest--Basemap can be processed using different image cadences, e.g., daily, weekly, biweekly--PlanetScope images are ranked based on these metrics such that cloud-free images have higher scores than cloudy images [16; 18]. Basemap is designed to monitor changes over time and for analytics-driven use cases, and it has been applied to several research projects, including monitoring of forest biomass [10; 11; 12], to assess carbon emissions from drainage canals [19], and to monitor coral reef map probabilities [20]. Planet Fusion, on the other hand, is based on the CubeSat-enabled spatiotemporal enhancement method [8], and it leverages the high spatial and temporal resolution provided by PlanetScope scenes with rigorously calibrated publicly available multispectral satellites (i.e., Sentinel-2, Landsat, MODIS, and VIIRS) to provide daily and radiometrically consistent and gap-filled surface-reflectance images that are free of clouds and shadows [17]. Planet Fusion is suitable to assess inter-day changes, for time-series analysis, and monitoring of disturbances of Earth's surface. Recently, Planet Fusion has been applied to monitor crop phenology, using the normalized difference vegetation index and leaf area index [21; 22]. Given that these datasets are cloud-free and processed to have daily cadence at high spatial resolution--both Basemap and Planet Fusion have 3 m pixel size--they provide an unprecedented opportunity to improve the monitoring of dynamic small water bodies, for instance, on-farm reservoirs (OFRs) that are used by farmers to store water during the wet season and for crop irrigation during the dry season.