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Evaluation and Validation of CryoSat-2-Derived Water Levels Using In Situ Lake Data from China

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 899;
Received: 7 April 2019 / Revised: 11 April 2019 / Accepted: 11 April 2019 / Published: 13 April 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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CryoSat-2 altimetry has become a valuable tool for monitoring the water level of lakes. In this study, a concentrated probability density function (PDF) method was proposed for preprocessing CryoSat-2 Geophysical Data Record (GDR) data. CryoSat-2 altimetry water levels were preprocessed and evaluated by in situ gauge data from 12 lakes in China. Results showed that the accuracy of the raw GDR data was limited due to outliers in most of the along-track segments. The outliers were generally significantly lower than the in situ values by several meters, and some by more than 30 m. Outlier detection, therefore, improves upon the accuracy of CryoSat-2 measurements. The concentrated PDF method was able to greatly improve the accuracy of CryoSat-2 measurements. The preprocessed CryoSat-2 measurements were able to observe lake levels with a high accuracy at nine of the twelve lakes, with an absolute mean difference of 0.09 m, an absolute standard deviation difference of 0.04 m, a mean root mean square error of 0.27 m, and a mean correlation coefficient of 0.84. Overall, the accuracy of CryoSat-2-derived lake levels was validated in China. In addition, the accuracy of Database for Hydrological Time Series of Inland Waters (DAHITI) and HYDROWEB water level products was also validated by in situ gauge data. View Full-Text
Keywords: altimetry; water level; CryoSat-2; outlier; lake; in situ gauge altimetry; water level; CryoSat-2; outlier; lake; in situ gauge

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Liu, Z.; Yao, Z.; Wang, R. Evaluation and Validation of CryoSat-2-Derived Water Levels Using In Situ Lake Data from China. Remote Sens. 2019, 11, 899.

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