CoAuthors

Okeowo M.A. (University of Houston, USA); Hossain Faisal (University of Washington, USA); Getirana Augusto (NASA Goddard Space Flight Center, USA)

Limited access to water level data for lakes has been a major setback for regional and global studies of reservoirs, surface water storage changes and monitoring the hydrologic cycle. Until now, processing satellite radar altimetry data over inland water bodies on a large scale has been a cumbersome task primarily due to removal of contaminated measurements as a result of surrounding land. In this study, we proposed a new algorithm to automatically generate time series from raw satellite radar altimetry data without user intervention. With this method, users with little knowledge on the field can now independently process radar altimetry for diverse applications. The method is based on K-means clustering, Interquartile range (IQR) and statistical analysis of the dataset for outlier detection. Jason-2 and Envisat radar altimetry data were used to demonstrate the capability of this algorithm. A total of 37 satellite crossings over 30 lakes and reservoirs located in the U.S., Brazil and Nigeria were used due to the availability of in-situ data. We compared the results against in-situ data and RMSE values ranged from 0.09 m to 1.20 m. We were able to use the algorithm to generate water level time series for a reservoir length of 0.64 km to several km with low RMSE. The result of this algorithm is consistent and capable of processing water level of lakes and reservoirs at a regional and global scale with a high degree of reliability. The algorithm can also be extended generating water level time series with SARAL/AltiKa and recently launched Jason-3 and Sentinel-3A data. Finally, our automated time series generation algorithm will be a useful tool for the general user community increasing the use of altimetry time series data in studying the anthropogenic impacts on the water cycle, effects of climate change at a regional or global scale, as well as improving existing hydrologic models. Overall, the algorithm has increased efficiency in processing radar altimetry data and eliminated inconsistency in data processing.