TY - GEN
T1 - Interpolation of geophysical data using spatio-temporal (3d) block singular value decomposition
AU - Turlapaty, Anish C.
AU - Younan, Nicolas H.
AU - Anantharaj, Valentine
PY - 2010
Y1 - 2010
N2 - Soil moisture data available from the Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard the National Aeronautic and Space Administration's (NASA) AQUA satellite has many inherent gaps. For a region in the Southeast United States, data is collected for years 2005 and 2006. This dataset has nearly 30% missing data due to radio interference, instrument errors, just to mention a few. To address this issue, an improved singular spectral analysis (SSA) -based interpolation scheme is presented. Our approach improves the existing method by utilizing the local variations in the observations for approximation of missing values and thus significantly improving the computational efficiency of the algorithm. For the validation of our interpolation scheme, a subset of SST from GODAE's high resolution sea surface temperature pilot project (GHRSST-PP) is considered. Finally, the presented scheme is also validated and tested on satellite based soil moisture retrievals from AMSR-E. Optimization of the method is based on minimizing the mean square error (MSE) and it is found to be dependent on the nature of the data. The top thirteen dominant SSA modes are usually sufficient for interpolation of missing values.
AB - Soil moisture data available from the Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard the National Aeronautic and Space Administration's (NASA) AQUA satellite has many inherent gaps. For a region in the Southeast United States, data is collected for years 2005 and 2006. This dataset has nearly 30% missing data due to radio interference, instrument errors, just to mention a few. To address this issue, an improved singular spectral analysis (SSA) -based interpolation scheme is presented. Our approach improves the existing method by utilizing the local variations in the observations for approximation of missing values and thus significantly improving the computational efficiency of the algorithm. For the validation of our interpolation scheme, a subset of SST from GODAE's high resolution sea surface temperature pilot project (GHRSST-PP) is considered. Finally, the presented scheme is also validated and tested on satellite based soil moisture retrievals from AMSR-E. Optimization of the method is based on minimizing the mean square error (MSE) and it is found to be dependent on the nature of the data. The top thirteen dominant SSA modes are usually sufficient for interpolation of missing values.
UR - http://www.scopus.com/inward/record.url?scp=78650869141&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2010.5652343
DO - 10.1109/IGARSS.2010.5652343
M3 - Conference contribution
AN - SCOPUS:78650869141
SN - 9781424495658
SN - 9781424495665
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4450
EP - 4453
BT - 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Y2 - 25 July 2010 through 30 July 2010
ER -