The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 333–338, 2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 333–338, 2014

  28 Nov 2014

28 Nov 2014

Identification of prominent spatio-temporal signals in GRACE derived terrestrial water storage for India

C. Banerjee1 and D. Nagesh Kumar1,2 C. Banerjee and D. Nagesh Kumar
  • 1Department of Civil Engineering, Indian Institute of Science, Bangalore, India
  • 2Centre for Earth Sciences, Indian Institute of Science, Bangalore, India

Keywords: GRACE, Terrestrial Water Storage, PCA, ICA, Seasonality, Trend

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).