Thu Apr 10, F. Flechtner
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Banerjee, C. and Nagesh Kumar, D., 2014. Identification of prominent spatio-temporal signals in GRACE derived terrestrial water storage for India. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL8:333–338, doi:10.5194/isprsarchives-XL-8-333-2014.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2014ISPAr.XL8..333B,
author = {{Banerjee}, C. and {Nagesh Kumar}, D.},
title = "{Identification of prominent spatio-temporal signals in GRACE derived terrestrial water storage for India}",
journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = 2014,
month = nov,
volume = {XL8},
pages = {333-338},
abstract = "{Fresh water is a necessity of the human civilization. But with the
increasing global population, the quantity and quality of
available fresh water is getting compromised. To mitigate this
subliminal problem, it is essential to enhance our level of
understanding about the dynamics of global and regional fresh
water resources which include surface and ground water reserves.
With development in remote sensing technology, traditional and
much localized in-situ observations are augmented with satellite
data to get a holistic picture of the terrestrial water
resources. For this reason, Gravity Recovery And Climate
Experiment (GRACE) satellite mission was jointly implemented by
NASA and German Aerospace Research Agency - DLR to map the
variation of gravitational potential, which after removing
atmospheric and oceanic effects is majorly caused by changes in
Terrestrial Water Storage (TWS). India also faces the challenge
of rejuvenating the fast deteriorating and exhausting water
resources due to the rapid urbanization. In the present study we
try to identify physically meaningful major spatial and temporal
patterns or signals of changes in TWS for India. TWS data set
over India for a period of 90 months, from June 2003 to December
2010 is use to isolate spatial and temporal signals using
Principal Component Analysis (PCA), an extensively used method
in meteorological studies. To achieve better disintegration of
the data into more physically meaningful components we use a
blind signal separation technique, Independent Component
Analysis (ICA).}",
doi = {10.5194/isprsarchives-XL-8-333-2014},
adsurl = {https://ui.adsabs.harvard.edu/abs/2014ISPAr.XL8..333B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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