# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion
### Future Applications of Planet Imagery
#### 5.3.2 Other Hydrological Applications  
With daily or near-daily acquisitions, Planet CubeSat images therefore hold potential to substantially impact hydrologic research, for example in data assimilation, flood monitoring and prediction, and floodplain connectivity. Data assimilation is critical for driving hydrologic models and typically involves interpolating ground-based or remotely sensed observations to improve model performance, providing complete estimates of a poorly-measured geophysical parameter or organizing/removing valuable/redundant information [60]. For example, retrievals of surface water fraction and snow cover obtained from Planet CubeSat imagery could be assimilated into regional hydrologic models. Merging time-series of CubeSat inundation maps with water surface heights from satellite altimeters would create large-scale observations of surface water storage [61]. Finally, the fine spatial resolution and anticipated near-daily availability of Planet CubeSat data should facilitate downscaling of coarser resolution satellite observations for regional and local hydrologic models [62]. High frequency Planet CubeSat imagery should also prove valuable for refining and calibrating flood prediction models [63], and with near-time capability assist in disaster relief efforts [64; 65]. Another clear application for Planet CubeSat imagery is remote estimation of river discharge from space. Deriving satellite retrievals of river discharge is an active research area within the hydrologic community, owing to the sparse network of in situ discharge gauges outside of the continental United States and Europe and the critical need for discharge measurements for global resource monitoring [4]. Figure 11: (**a**) Geometric revisit time and (**b**) average time in days between cloud-free revisits for PlanetScope imagery north of 55\({}^{\circ}\)N when satellite constellation is at full operational capacity. A number of different methods have been developed to estimate discharge from optical satellite imagery, often building statistical relationships between river width/extent both with and without in situ measurements [5; 66; 67; 68; 69; 70]. A major limitation of current remotely-sensed discharge estimates is the lack of sufficient temporal sampling of high resolution imagery due to revisit time or cloud cover [68]. While Planet imagery is still subject to cloud obscuration, its projected high imaging capabilities will enable more frequent cloud-free observations than traditional sensors and allow discharge estimation for more rivers than coarse-resolution MODIS. Planet CubeSat imagery is particularly valuable at high latitudes where orbits converge, surface water is both dynamic and abundant, and in situ observations are sparse. Northern rivers are critical not only for Arctic ecosystems but also for the global climate system, as increasing runoff to the Arctic ocean can impact ocean circulation patterns [71; 72]. However, rivers tend to be braided or anastomosing at these latitudes, making in situ monitoring especially difficult. Estimating discharge of large high latitude rivers such as the Yukon is therefore an obvious, critical application of Planet imagery. Planet imagery is also well-suited for mapping and tracking river ice breakup. Analyses of river ice breakup timing have typically relied on ground-based measurements [73; 74] which do not enable understanding of how ice breakup progresses downstream through space and time. While satellite-based methods of identifying river ice breakup across entire river lengths have been developed, they are limited by the coarse resolution of daily available imagery [75; 76]. Planet's 3-5 m resolution not only enables detection of river ice breakup at fine spatial scales, but the presence of minute-separated image pairs (due to orbital convergence at high latitudes) may allow ice tracking and therefore calculation of river velocity from space (Figure 12) [77; 78].