Comparing discharge estimates 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 16m to >1000 m wide using the Bayesian AMHG-Manning algorithm (BAM). BAM requires only river widths as input, and these were retrieved from government-sponsored satellite datasets (Landsat 8 and Sentinel-2) and a CubeSat (Planet Labs) dataset, 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) are too small for Landsat and Sentinel-2 datasets, and their discharge is also not well estimated using CubeSat data alone, likely because the 4-band sensor cannot resolve water surfaces accurately enough. We also find that the BAM technique outperforms space-based rating curves when gauge data are available, and BAM 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.