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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #316416
Title: Feasibility of inter-comparing airborne and spaceborne observations of radar backscattering coefficients

Author
item KIM, S. - Jet Propulsion Laboratory
item Jackson, Thomas
item YUEH, S. - Jet Propulsion Laboratory
item XU, X. - Jet Propulsion Laboratory
item HENSLEY, S. - Jet Propulsion Laboratory

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2015
Publication Date: 12/1/2015
Publication URL: http://handle.nal.usda.gov/10113/61530
Citation: Kim, S., Jackson, T.J., Yueh, S., Xu, X., Hensley, S. 2015. Feasibility of inter-comparing airborne and spaceborne observations of radar backscattering coefficients. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8:3507-3519.

Interpretive Summary: An improved approach to validating the calibration of spaceborne radars based upon aircraft observations was developed. The feasibility of using an airborne synthetic aperture radar (SAR) to validate spaceborne SAR data was evaluated and found to be a suitable approach. This effort is directed at soil moisture sensing and the recently launched Soil Moisture Active Passive (SMAP) satellite. The value of this approach is related to the fact that typically used calibration targets are vicarious in nature, i.e. rain forests and oceans, and only address the extrema of backscattering coefficients and that the relationship between soil moisture and 0 is nonlinear. It is expected that the simulation and data analysis results from this study will be Applied to help design post-launch validation and improve the utility of the soil moisture products for agricultural hydrology.

Technical Abstract: This paper investigates the feasibility of using an airborne synthetic aperture radar (SAR) to validate spaceborne SAR data. This is directed at soil moisture sensing and the recently launched Soil Moisture Active Passive (SMAP) satellite. The value of this approach is related to the fact that vicarious targets such as rain forests and oceans calibrate only the extrema of backscattering coefficients (0) and that the relationship between soil moisture and 0 is nonlinear. Furthermore, corner reflectors are difficult to deploy to calibrate medium resolution (1 to 3 km) spaceborne sensors such as the one onboard SMAP. A challenge with the approach is the varying incidence angle ('inc) of the airborne sensor versus the constant value used by SMAP. The impact of this on inter-comparison is analyzed through simulation and aircraft data analysis. In the absence of the SMAP SAR data, the airborne SAR and scatterometer 0 from the recent field campaign provided the imaging geometry similar to the spaceborne case. The effect of 'inc on the inter-comparison using these two airborne data sets was found to be small if the landcover within the footprint is homogeneous and if 0 (natural unit) changes very little or approximately linearly with 'inc. Over heterogeneous pixels consisting of pasture, grass, forest, and growing corn, the simulation shows that the mean and standard deviation of the difference in 0 between the SAR and scatterometer data are smaller than 0.4 dB and 0.3 dB respectively. The test results with the airborne data are generally consistent with the simulation results: the mean and standard deviation of the difference are smaller than 0.9 dB for HH, VV, and HV. These magnitudes are comparable with those of the major sources of the difference: the relative calibration errors of the airborne instruments (< 0.3 dB), speckle noise (~ 0.35 dB), effect of 'inc variation within the footprint (< 0.4 dB), and geolocation uncertainty in the airborne scatterometer data (< 0.5 dB). The findings from this study are expected to apply to the intercomparison of the SMAP and airborne data when considering the details affecting the comparison: imaging geometry, temporal synchronization, spatial collocation, antenna gain, speckle noise, and spatial resolution. When applied, the inter-comparison will provide more confidence in the calibration of SMAP.