The use of satellite radar altimetry has long been extended to areas other than the deep-ocean primarily because of the advances in radar waveform retracking methodologies. However, the retracking algorithms are limited to a handful shapes of return echoes over assumed known surfaces, while numerous unknown waveforms exist due to the complexity of real-world land cover and other surfaces. Measurements over a surface with seasonal or ephemeral patterns could thus degrade in accuracy due to varying characteristics from the corresponding radar backscatters. In this study, we demonstrate that the Qinghai Lake, an alpine water body with distinct seasonal variation between water and ice causes inaccurate surface-height estimates when using Envisat radar altimetry and conventional retracking techniques. Following the characterization of the lake surface using EO-1 and Landsat multispectral analysis, we hypothesize that the overestimation of the lake level during winter and early spring is not from the snow accumulation; rather it is due to an error of the onboard retracker (ICE-1) which is unable to properly model the quasi-specular waveforms. Hence, we first build a classification algorithm to identify the anomalous waveforms, and then use an empirical retracking gate correction to mitigate the ice contamination. The accuracy of the 20% threshold retracker (TR) after applying suggested gate correction has a significant improvement with a root-mean-square error (RMSE) of 6 ± 7 cm and a correlation of 0.98 compared with the in situ gauge data. The improvement in accuracy is 54% better than the ICE-1 and 85% than the OCEAN retrackers, respectively.
Abstract. The capabilities of radar altimetry to measure inland water bodies are well established and several river altimetry datasets are available. Here we produced a globally-distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level and applied it to all altimeter crossings of ocean draining rivers with widths > 900 m (> 34 % of global drainage area). We evaluated every VS, either quantitatively for VS where in-situ gages are available, or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1,478 VS. After quality control, the final product contained 810,403 measurements distributed over 932 VS located on 39 rivers. Available in-situ data allowed quantitative evaluation of 389 VS on 12 rivers. Median standard deviation of river elevation error is 0.93 m, Nash-Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at DOI 10.5067/PSGRA-SA2V1 (Durand et al., 2016).
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