A new approach towards temporal densification of river discharge estimates by filling cloud- and stripe-gaps using Landsat and Sentinel-2 images

Abstract

ABSTRACT River discharge plays a vital role in the Earth’s water cycle. Recent studies have sought to estimate river discharge using optical sensors onboard satellites, typically deriving discharge from multispectral data after excluding many images that are contaminated by cloud or affected by the scan-line-corrector (SLC) failure on Landsat 7. As such, time series of estimated discharge generally include a high proportion of missing values. To overcome these issues, we propose an easy and feasible methodology to temporally densify the time series of discharge estimates by repairing cloud-contaminated and SLC-affected images on the Google Earth Engine platform. The middle Yangtze River (MYR) was chosen as the study area to demonstrate and evaluate the performance of the methodology. First, different water indices were compared to select the most appropriate indicator for water surface retrieval along the MYR. The Pekel method shows the best performance for extracting the water surface when turbid or under thin, semi-transparent clouds. Second, the performances of the Tourian method and the Manning equation for estimating the discharge were compared when using only good-quality images, yielding Nash-Sutcliffe efficiency (Nash) coefficients of 0.69 and 0.79, respectively. Images affected by the SLC and over 50% cloud cover accounted for 41% of 532 records used from 1986 to 2019. The repaired, temporally complete time series of discharge estimates by the Manning equation corresponds to a correlation coefficient of 0.86, a Nash coefficient of 0.73, and a root mean square error of 5697 m3/s from 2000 to 2019. These results show the strong potential of this method for estimating the discharge of rivers with known cross-sectional and the morphological conditions worldwide.