Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2006
Publication Date: 7/1/2006
Citation: Narayan, U., Lakshmi, V., Jackson, T.J. 2006. A simple algorithm for high resolution change detection of soil moisture using L-band radiometer and radar. IEEE Transactions on Geoscience and Remote Sensing. 44:1545-1554. Interpretive Summary: A simple algorithm for the estimation of change in soil moisture at the spatial resolution of satellite radars using low resolution estimates of soil moisture from radiometer and copolarized backscattering coefficients was developed. Application of the algorithm to data obtained in field experiments yielded excellent results with root mean square errors of prediction of 0.03. The originality of the approach presented here lies in using radiometer to estimate soil moisture change at a lower spatial resolution (but with lower ancillary data requirements as compared to radar estimation of soil moisture) and then using change in radar backscatter to estimate the change in soil moisture at higher spatial resolution. The estimated change in soil moisture is a hydrologic variable of significant interest. It will be possible to relate saturated hydraulic conductivity from the radar/radiometer algorithm derived change in soil moisture with the added advantage of higher spatial resolution that will lead to more accurate estimation of water and energy fluxes. The approach presented developed is applicable to data from the Hydros mission over areas of low vegetation water content variability. This is a positive step towards the development of more robust techniques that will be of value agricultural managers and modeling.
Technical Abstract: The soil moisture experiments held during June – July 2002 (SMEX02), at Iowa demonstrated the potential of the L band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving sub pixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L- band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in-situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100 m resolution, PALS radiometer estimates of soil moisture change at a 400 m resolution have been disaggregated to 100 m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using Integral Equation Model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.