A Heuristic Approach to Reduce Atmospheric Effects in InSAR Data for the Derivation of Digital Terrain Models or for the Characterization of Forest Vertical Structure
作者:Ni, WJ (Ni, Wenjian)[ 1 ] ; Sun, GQ (Sun, Guoqing)[ 2 ] ; Zhang, ZY (Zhang, Zhiyu)[ 1 ] ; He, YT (He, Yating)[ 3 ] ; Guo, ZF (Guo, Zhifeng)[ 1 ]
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷: 11  期: 1  页: 268-272
DOI: 10.1109/LGRS.2013.2255579
出版年: JAN 2014

摘要
The differences of two digital terrain models (DTMs) derived from airborne interferometric synthetic aperture radar (InSAR) data of short and long wavelengths are utilized for the estimation of vertical forest structures. However, when the spaceborne repeat-pass InSAR data are used, atmospheric effects must be considered. A simple method for the reduction of atmospheric effects in spaceborne repeat-pass interferometry is proposed in this letter. By subtracting a simulated interferogram using the Shuttle Radar Topography Mission (SRTM) DTM from the interferogram of a pair of Phased Array Type L-Band Synthetic Aperture Radar (PALSAR) InSAR data, the remaining phase includes the phase caused by the height differences of scattering phase centers (SPC) at C- and L-bands and the phases caused by atmospheric effects and other changes during the PALSAR repeat-pass period. A low-pass spatial filtering can reveal the atmospheric effect in the phase image because of the low spatial frequency of the atmospheric effects. The proper size of the filtering window can be determined by the changes of standard deviation of filtered phase images as the window size increases. The changes of the standard deviations of the filtered phase images should be almost constant when only the atmospheric effect remains. After reducing the atmospheric effects, the difference between the SRTM-DTM and the PALSAR-DTM was reduced from 60.17 m +/- 16.2 m to near 0 m (0.52 m +/- 4.3 m) at bare surfaces, and the correlation (R-2) between the mean forest height and the difference between the SRTM-DTM and the PALSAR-DTM was significantly increased from 0.021 to 0.608.

通讯作者地址: Ni, WJ (通讯作者)
Jointly Sponsored Inst Remote Sensing Applicat Ch, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Jointly Sponsored Inst Remote Sensing Applicat Ch, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Univ Maryland, College Pk, MD 20742 USA
[ 3 ] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China