National Aeronautics and Space Administration MERIS and OSCAR: Online Services for Correcting Atmosphere in Radar Eric Fielding and Evan Fishbein Jet Propulsion Laboratory, California Inst. of Tech. Zhenhong Li Dept. of Geography & Geology, Univ. of Glasgow, UK ESA Fringe 2009 InSAR Workshop Frascati, Italy 30 November 2009 2009. All rights reserved. C1-5564,2525
Tropospheric water vapor water vapor delay of radar propagation nondispersive, affects all wavelengths equally large variation in time (random and seasonal) and space (power law) stratified water vapor vertical gradient change and dry atmospheric pressure changes cause delays correlated with topography independent and InSAR-derived estimates of delays possible for correcting ground deformation measurements
InSAR tropospheric water vapor corrections time series filtering } InSAR correlation of phase with topography derived CGPS zenith wet delay interpolated spatially (and temporally) water vapor measurements from absorption of reflected near IR MODIS and MERIS water vapor measurements from thermal IR and microwave radiometers AIRS, MODIS (IR) water vapor estimates from numerical weather models could even assimilate InSAR back into weather
InSAR time series Water vapor largest source of error Dec. 2003 Bam, Iran EQ postseismic 30 dates: Jan. 2004 June 2007 descending track, 10 AM local time plane fit removed from each epoch referenced to epoch 1 (Jan. 2004 not shown) with substantial atmospheric effects Fielding, E.J., Lundgren, P.R., Bürgmann, R., and Funning, G.J., 2009, Shallow fault-zone dilatancy recovery after the 2003 Bam earthquake in Iran: Nature, v. 458, p. 64-68.
OSCAR: Online Services for Correcting Atmosphere in Radar NASA Advanced Information Systems Technology project started May 2009 Paul von Allmen, PI (JPL) Eric Fielding, Evan Fishbein, Zhangfan Xing, Lei Pan (JPL), Zhenhong Li (U. Glasgow), Co-Is Online server to collect and process atmospheric data (tropospheric water vapor) to make corrections for InSAR
OSCAR: Online Services for Correcting Atmosphere in Radar Sources in first stage: MODIS, MERIS, AIRS, NCEP or ECMWF forecast models May add GPS or other GNSS data and mesoscale models Highest resolution data used when available Global weather forecasts provide background coverage every place and time Optimal combination of sources Single-date or interferogram two-date corrections
Water vapor mapping MERIS on Envisat so simultaneous with ASAR high-resolution column WV, 1.2 or 0.3 km day only, affected by clouds AIRS works everywhere, lower resolution (45 km) but has profile need good interpolation
InSAR water vapor correction models GPS Topography-dependent Turbulence Model (GTTM) MODIS water vapour correction model MERIS water vapour correction model MERIS/MODIS combination correction model (MMCC) MERIS/MODIS stacked correction model (MMSC)
MODIS/MERIS Channel Positions Related to PWV PWV retrievals rely on channel ratio techniques MODIS: 2 2 non-absorbing 3 3 absorbing MERIS: 1 1 non-absorbing 1 1 absorbing (Figure adapted from Gao and Kaufman [1998])
Comparisons of GPS, MODIS and MERIS PWV GPS, MODIS and MERIS PWV products are complementary!
MERIS water vapour correction model MERIS vs MODIS MERIS data can be acquired at the same time as ASAR data because both on Envisat (time differences between MODIS and SAR data: ~1 hour) MERIS has better spatial resolution, up to 300 m against 1km for MODIS MERIS near IR water vapour product agrees more closely with GPS than MODIS References: Li, Z., E.J. Fielding, P. Cross, and J.-P. Muller, Interferometric synthetic aperture radar atmospheric correction: MEdium Resolution Imaging Spectrometer and Advanced Synthetic Aperture Radar integration, Geophysical Research Letters, 33,, L06816, 2006. Li, Z., J.-P. Muller, P. Cross, P. Albert, J. Fischer, and R. Bennartz, Assessment of the potential of MERIS near- infrared water vapour products to correct ASAR interferometric measurements, m International Journal of Remote Sensing, 27 (1-2), 349-365, 365, 2006.
InSAR Time Series with water vapor correction & Postseismic motion after the 2003 Bam (Iran) earthquake References: Li, Z., Fielding, E. J., and Cross, P.: Integration of InSAR time e series analysis and water vapour correction for mapping postseismic deformation after the 2003 Bam, Iran Earthquake, ake, IEEE Transactions on Geoscience and Remote Sensing, 2009.
Bam postseismic time series InSAR Time Series (TS) Results Without PWV correction With PWV correction
Interpolating Atmospheric Data at High Spatial Resolution for InSAR How to resolve mismatch between atmospheric data sets having 8-100 km spatial resolution, and InSAR data having 10 100 m resolution? Proposed solutions: High-resolution atmospheric data assimilation models Interpolated gridded atmospheric data High-resolution atmospheric water vapor fields from satellite-based imagers and sounders
Local Atmospheric Correction Algorithms On scales at which approximation is applied: No local sources of water vapor No adiabatic heating or cooling Surface water vapor mixing ratio and temperature along surface is conserved Flow is along slope, not around obstacle Free troposphere structure unperturbed by topography Stretch water vapor and temperature profiles across the boundary layer, holding values at top and bottom fixed Recalculate total precipitable water vapor, surface pressure and wet and dry delays 1 Green the original profile from the forecast 2 Magenta the extrapolated curve Black the stretched boundary layer curve.
Comparison of Techniques Envisat ASAR interferogram 2006/06/03 2006/09/16 MODIS NIR destriped MODIS corrected interf. ECMWF stretched boundary layer model AIRS simple interp. GPS Topo-dependent turbulence model interp.