Comparing Discharge Estimates Made via the BAM Algorithm in High-Order Arctic Rivers Derived Solely From Optical CubeSat, Landsat, and Sentinel-2 Data

Conventional satellite platforms are limited in their ability to monitor rivers at fine spatial and temporal scales: suffering from unavoidable trade-offs between spatial and temporal resolutions. CubeSat constellations, however, can provide global data at high spatial and temporal resolutions, albeit with reduced spectral information. This study provides a first assessment of using CubeSat data for river discharge estimation in both gauged and ungauged settings. Discharge was estimated for 11 Arctic rivers with sizes ranging from 16 to >1,000 m wide using the Bayesian at-many-stations hydraulic geometry-Manning algorithm (BAM). BAM-at-many-stations hydraulic geometry solves for hydraulic geometry parameters to estimate flow and requires only river widths as input. Widths were retrieved from Landsat 8 and Sentinel-2 data sets and a CubeSat (the Planet company) data set, as well as their fusions. Results show satellite data fusion improves discharge estimation for both large (>100 m wide) and medium (40-100 m wide) rivers by increasing the number of days with a discharge estimation by a factor of 2-6 without reducing accuracy. Narrow rivers (<40 m wide) are too small for Landsat and Sentinel-2 data sets, and their discharge is also not well estimated using CubeSat data alone, likely because the four-band sensor cannot resolve water surfaces accurately enough. BAM technique outperforms space-based rating curves when gauge data are available, and its accuracy is acceptable when no gauge data are present (instead relying on global reanalysis for discharge priors). Ultimately, we conclude that the data fusion presented here is a viable approach toward improving discharge estimates in the Arctic, even in ungauged basins.