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

Abstract

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.

Extending the Ability of Near‐Infrared Images to Monitor Small River Discharge on the Northeastern Tibetan Plateau

“…The models based on width, depth, and slope generally have greater accuracy (Birkinshaw et al, 2014) and are a promising direction for ungauged river discharge estimations. In addition, estimating river discharge combine with multiple remotely sensed data sources and hydrological models has great potential (Bjerklie et al, 2003;Feng et al, 2019;Liu et al, 2015).…”

“…However, due to the current spatial and temporal resolutions of radar data, there is no application for monitoring the discharge of small rivers (Sichangi et al, 2016). The higher spatial resolution of images may facilitate the monitoring of smaller rive discharge, although the finer spatial resolution does not always yield higher accuracy (Feng et al, 2019). In addition, combining RS techniques with hydrological models may improve the monitoring accuracy (Brakenridge et al, 2012;Sun et al, 2015).…”

“…Multispectral images are used to obtain the water surface width of the main river channel (Smith & Pavelsky, 2008). Landsat MSS/TM/ETM/OLI (Feng et al, 2019;Jarihani et al, 2013;Ouma & Tateishi, 2006;Smith, 1997), MODIS (Smith & Pavelsky, 2008;Xiao et al, 2005), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (Sivanpillai & Miller, 2010) images are typically applied to extract river water surface information.…”

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

“…[70][71][72][73][74][75];Smith et al, 1996;Alsdorf, 2003;Frappart et al, 2005; LeFavour andAlsdorf, 2005), and Smith([76]; 1997) reviewed work to date at the time of writing. Kouraev et al ([77]; 2004) and similar studies continued to expand and refine the capabilities of traditional radar altimetry to compute discharge at locations where in situ data are available, and this work continues through today (e.g.,[78][79][80][81][82][83][84]; Pavelsky, 2014; Pavelsky and Smith, 2009; Schneider et al, 2017; Young et al, 2015; Paris et al, 2016; Nathanson et al, 2012; Feng et al, 2019)…”

“…[84];Feng et al, 2019). Research in this area also goes beyond the use of satellites.Ashmore and Sauks ([86]; 2006), Gleason et al ([87]; 2015), and Young et al ([81]; 2016) all used time lapse cameras to provide an RS signal for river effective width extraction for remote Arctic rivers coupled with in situ discharge measurements.…”