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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: A first-order radiative transfer model for microwave radiometry of forest canopies at L-band

Authors
item Kurum, Mehmet -
item Lang, Roger -
item O'Neill, Peggy -
item Joseph, Alicia -
item Jackson, Thomas
item Cosh, Michael

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 10, 2011
Publication Date: September 30, 2011
Citation: Kurum, M., Lang, R.H., O'Neill, P.E., Joseph, A.T., Jackson, T.J., Cosh, M.H. 2011. A first-order radiative transfer model for microwave radiometry of forest canopies at L-band. IEEE Transactions on Geoscience and Remote Sensing. 49:3167-3179.

Interpretive Summary: A new model for routine microwave soil moisture retrieval that correctly accounts for takes canopy scattering and requires few parameters was developed and validated using ground-based microwave instruments. Soil moisture is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere, yet it is difficult to measure globally using traditional in situ techniques. Several planned microwave space missions, most notably ESA's Soil Moisture Ocean Salinity (SMOS) mission and NASA's Soil Moisture Active Passive (SMAP) mission, are focusing on obtaining accurate soil moisture information over as much of the Earth's land surface as possible. However, current baseline retrieval algorithms are based on an easily implemented but theoretically simple zero-order radiative transfer. The model includes components from the soil and vegetation, but vegetation scattering is not represented properly. This approach essentially places a limit on the density of the vegetation through which soil moisture can be accurately retrieved. The improved model developed here will extend the range of vegetation types for which soil moisture retrieval is possible. The results of this study are very important to current and future spaceborne remote sensing. The improved remote sensing capabilities provided by the new algorithm will be of benefit to hydrologic applications in agriculture and climate.

Technical Abstract: In this study, a first-order radiative transfer (RT) model is developed to more accurately account for vegetation canopy scattering by modifying the basic radiative transfer model (the zero-order RT solution). In order to optimally utilize microwave radiometric data in soil moisture (SM) retrievals over vegetated landscapes, a quantitative understanding of the relationship between scattering mechanisms within vegetation canopies and the microwave brightness temperature is desirable. The first-order RT model is used to investigate this relationship and to perform a physical analysis of the scattered and emitted radiation from vegetated terrain. This model is based on an iterative solution (successive orders of scattering) of the RT equations up to the first-order. This formulation adds a new scattering term to the - model. The additional term represents emission by particles (vegetation components) in the vegetation layer and emission by the ground that is scattered once by particles in the layer. The model is tested against 1.4 GHz brightness temperature measurements acquired over deciduous trees by a truck-mounted microwave instrument system called ComRAD in 2007. The model predictions are in good agreement with the data and they give quantitative understanding for the influence of first-order scattering within the canopy on the brightness temperature. The model results show that the scattering term is significant for trees and modifications are necessary to the radiative transfer model when applied to dense vegetation. Numerical simulations also indicate that the scattering term has a negligible dependence on SM and is mainly a function of the incidence angle and polarization of the microwave observation.

Last Modified: 12/18/2014
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