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Assimilation of SBAS-InSAR Based Vertical Deformation Into Land Surface Model to Improve the Estimation of Terrestrial Water Storage

Authors :
Kun Chen
Guoxiang Liu
Wei Xiang
Tao Sun
Kun Qian
Jiaxin Cai
Saied Pirasteh
Xiao Chen
Source :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 2826-2835 (2022)
Publication Year :
2022
Publisher :
IEEE, 2022.

Abstract

The gravity recovery and climate experiment (GRACE) provides an unprecedented opportunity to detect the spatial and temporal variation of the terrestrial water storage (TWS) for regional to continental scales. However, the GRACE system's coarse temporal resolution (∼monthly) and data discontinuity missing perplexed the TWS research during the operation. In this article, the data assimilation (DA) method was employed to integrate the vertical deformation obtained from the small baseline subset (SBAS) InSAR processing into the NASA catchment land surface model (CLSM), which improved the estimation of the TWS. First, we used a one-dimensional ensemble Kalman filter for DA research to estimate the TWS in Dali Prefecture, southwestern China. Finally, we compared the estimated TWS with the GRACE-based TWS from December 2, 2018 to January 21, 2021. The unbiased root-mean-square of the open loop (OL; without DA) method and the SBAS-InSAR DA method are 61 mm and 30 mm in Dali Prefecture, respectively. Results revealed that the numerical difference between the estimated TWS and the GRACE TWS retrievals was significantly decreased by the SBAS-InSAR DA method than the OL method. In addition, the temporal resolution of the SBAS-InSAR DA-based TWS was improved to 12 days compared with GRACE-based TWS. Furthermore, we recovered the discontinuous deletion and blank of GRACE-based TWS from 2015 to 2018 by the SBAS-InSAR DA method.

Details

Language :
English
ISSN :
21511535
Volume :
15
Database :
Directory of Open Access Journals
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.b9019e815194d52ac423d12006b7aa8
Document Type :
article
Full Text :
https://doi.org/10.1109/JSTARS.2022.3162228