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29 October 2019 Downscaling of GRACE datasets based on relevance vector machine using InSAR time series to generate maps of groundwater storage changes at local scale
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Abstract

Investigating groundwater storage (GWS) could greatly better our understanding of groundwater dynamics and the factors that influence them. To generate changes in ΔGWS at a finer scale, we developed a statistical downscaling model and applied it to the Gravity Recovery and Climate Experiment (GRACE). The model is based on the relevance vector machine (RVM), which needs the few parameters (kernel function and kernel width) for regression. In addition, considering the anthropogenic influence on the GWS, the interferometry of synthetic aperture radar (InSAR) time series, which could inverse the land subsidence resulting from groundwater extraction, was introduced into the model. The model was evaluated and compared with one constructed using support vector machine. We obtained a 0.1 deg  ×  0.1 deg ΔGWS from the model. It is shown that the study area suffered from a sustained groundwater reduction with an expansion of space during 2007 to 2010. Furthermore, RVM is suggested to construct the GRACE downscaling model instantly, and InSAR can be considered an important indicator.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Qi Shang, Xiangnan Liu, Xinyu Deng, and Biyao Zhang "Downscaling of GRACE datasets based on relevance vector machine using InSAR time series to generate maps of groundwater storage changes at local scale," Journal of Applied Remote Sensing 13(4), 048503 (29 October 2019). https://doi.org/10.1117/1.JRS.13.048503
Received: 12 July 2019; Accepted: 3 October 2019; Published: 29 October 2019
JOURNAL ARTICLE
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