Combining Landsat 5 TM and UAV images to estimate river discharge with limited ground-based flow velocity and water level observations

Abstract

River discharge plays an indispensable role in maintaining the stability of the hydrosphere system and eco-environment. Previous methods that utilize satellite imagery to estimate discharge over poorly gauged basins are generally tailored for large rivers and heavily reliant on ground-based measurements. Consequently, uncertainties often escalate when these methods are applied to medium-sized rivers. Based on Landsat 5 Thematic Mapper (TM) and unmanned aerial vehicle (UAV) images, this study proposed a framework for estimating the discharge of large and medium rivers with limited ground observations. It comprises (1) a modified C/M method, which considers the spatial heterogeneity of rivers using single-site observation data, and (2) a newly developed method for estimating river bathymetry with zero discharge measurements (RIBA-zero). Results show that, utilizing the modified C/M method, rivers wider than three times the satellite resolution (i.e., 90 m) exhibit a relative root mean square error (rRMSE) of 0.23 in the velocity estimation. Narrower rivers display a slight increase in the rRMSE (0.41), which is still within an encouraging range. For both types of river widths, the accuracy of flow velocity estimation is higher during high-flow periods compared with the low-flow counterparts. In terms of the flow area estimation, the RIBA-zero method is much more suited for parabola-shaped cross-sections (rRMSE = 0.22) and flood seasons (rRMSE = 0.35). Additionally, when replacing 30-m Landsat 5 TM with 10 m-resolution Sentinel-2 imageries, the approaches make a significant improvement in velocity estimation for rivers narrower than 90 m across all periods, exhibiting great potential to estimate discharge in medium rivers with finer resolution satellite imageries. The framework requires a few ground observations for discharge estimates with the Nash–Sutcliffe efficiency coefficient (NSE) reaching ∼0.9, thereby greatly facilitating hydrology-related studies with profound implications for sustainable water resources management worldwide.