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Open AccessArticle

Mass Flux Solution in the Tibetan Plateau Using Mascon Modeling

1
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
2
Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Academic Editors: Cheinway Hwang, C.K. Shum, Richard Gloaguen and Prasad S. Thenkabail
Remote Sens. 2016, 8(5), 439; https://doi.org/10.3390/rs8050439
Received: 25 January 2016 / Revised: 16 May 2016 / Accepted: 17 May 2016 / Published: 23 May 2016
(This article belongs to the Special Issue Remote Sensing in Tibet and Siberia)
Mascon modeling is used in this paper to produce the mass flux solutions in the Tibetan Plateau. In the mascon modeling, the pseudo observations and their covariance matrices are derived from the GRACE monthly gravity field models. The sampling density of the pseudo observations is determined based on the eigenvalues of the covariance matrices. In the Tibetan Plateau, the sampling density of per 1.5° is the most appropriate among all choices. The mass flux variations from 2003 to 2014 are presented in this paper, which show large mass loss (about −15.5 Gt/year) in Tianshan, North India, and Eastern Himalaya, as well as strong positive signals (about 9 Gt/year) in the Inner Tibetan Plateau. After the glacier isostatic adjustment effects from Pau-5-AUT model are removed, the mass change rates in the Tibetan Plateau derived from CSR RL05, JPL RL05, GFZ RL05a, and Tongji-GRACE02 monthly models are −6.41 ± 4.74 Gt/year, −5.87 ± 4.88 Gt/year, −6.08 ± 4.65 Gt/year, and −11.50 ± 4.79 Gt/year, respectively, which indicate slight mass loss in this area. Our results confirm that mascon modeling is efficient in the recovery of time-variable gravity signals in the Tibetan Plateau. View Full-Text
Keywords: GRACE; mascon; Tibetan Plateau; mass flux solution GRACE; mascon; Tibetan Plateau; mass flux solution
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Chen, T.; Shen, Y.; Chen, Q. Mass Flux Solution in the Tibetan Plateau Using Mascon Modeling. Remote Sens. 2016, 8, 439.

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