Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements....

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 1997-09, Vol.35 (5), p.1254-1266
Main Authors: Jiancheng Shi, Wang, J., Hsu, A.Y., O'Neill, P.E., Engman, E.T.
Format: Article
Language:English
Subjects:
Applied geophysics
Earth Resources And Remote Sensing
Earth sciences
Earth, ocean, space
Exact sciences and technology
Integral equations
Internal geophysics
L-band
Moisture measurement
Rough surfaces
Soil measurements
Soil moisture
Soils
Surface fitting
Surface roughness
Surface soil
Surficial geology
Synthetic aperture radar
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.
ISSN:0196-2892
1558-0644
DOI:10.1109/36.628792