2686 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 7, JULY 2011 Effect of Radiative Transfer Uncertainty on L-Band Radiometric Soil Moisture Retrieval Alexandra G. Konings, Dara Entekhabi, Senior Member, IEEE, Steven K. Chan, Senior Member, IEEE, and Eni G. Njoku, Fellow, IEEE Abstract—Microwave radiometry soil moisture retrieval meth- ods suffer from uncertainties about the representation of sev- eral effects, including dielectric mixing, surface roughness, and vegetation opacity. These uncertainties lead to two major types of error: systematic bias and random errors. The effect of the uncertainties is studied using the Soil Moisture Active Passive Algorithm Testbed, a simulation environment for evaluating error propagation in retrieval algorithms, and two different common retrieval algorithms (single and dual polarizations). The two types of errors are simulated by using different representations for each factor in the forward and retrieval parts. For both algo- rithms, this approach introduces a spatially variable bias, which is particularly large when using a single-polarization retrieval algorithm. This paper illustrates the emergence of both this bias and the random error due to uncertainty in the representation of vegetation and soil texture effects in retrieval algorithms. The dependence of these two types of error on vegetation and soil texture properties is shown through mapping them over the sim- ulation region. The relative contribution of these errors to the total error is strongly dependent on the simulation conditions and is not necessarily indicative of what may be experienced during actual observations. Uncertainty due to roughness representation causes a lower error than uncertainty in vegetation opacity and dielectric mixing parameterizations in the simulated soil mois- ture retrieval. Summation and compensation of multiple errors can cause the estimate error to increase with improved radiative transfer knowledge, even after bias removal. The retrieval of soil moisture from microwave measurements depends on several other parameterizations that are also uncertain. This paper is limited to only three parameterizations that are considered to be among the larger contributors to bias. Index Terms—Microwave radiometry, radiative transfer, soil moisture. I. I NTRODUCTION T HE HIGH spatial and temporal variability of soil mois- ture limits the use of in situ measurements for global mapping. Remote sensing provides a method for retrieving large-scale soil moisture fields. Microwave radiometry has Manuscript received July 9, 2010; revised November 10, 2010; accepted December 15, 2010. Date of publication March 3, 2011; date of current version June 24, 2011. A. G. Konings is with the Nicholas School of the Environment, Duke University, Durham, NC 27705 USA (e-mail: konings@alum.mit.edu). D. Entekhabi is with the Department of Civil and Environmental Engi- neering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA (e-mail: darae@mit.edu). S. K. Chan and E. G. Njoku are with the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA (e-mail: steven.k. chan@jpl.nasa.gov; eni.g.njoku@jpl.nasa.gov). Digital Object Identifier 10.1109/TGRS.2011.2105495 been well studied for satellite soil moisture retrieval [1]–[3]. L-band (0.95–1.45 GHz) is particularly suited to soil moisture retrieval because it has greater vegetation penetration and re- duced atmospheric attenuation compared to higher frequencies [1]. Nevertheless, retrieval capabilities are limited under dense vegetation cover [4]. In addition, limits on antenna technology force radiometer spatial resolution to be generally low (on the order of 40–50 km, depending on frequency and antenna). Radar measurements may be made at higher spatial resolution (on the order of a few kilometers) but suffer from increased sensitivity to the confounding effects of soil roughness and vegetation [5]. The Soil Moisture Active Passive (SMAP) ob- servatory will combine the two technologies to provide global soil moisture measurements. It is expected to launch around 2014. SMAP will provide data products at several resolutions based on measurements from the two individual instruments and on merging their information (e.g., [6]). The focus of this paper is the 40-km radiometer-only product. Most microwave radiometry soil moisture retrieval algo- rithms are based on a zero-order single-scattering characteriza- tion that accounts for variations due to soil type and vegetation. Nevertheless, uncertainty remains about the optimal parametric representation of several terms in this model. Some data to con- strain and improve existing parameterizations may be obtained by field experiments and measurements from dedicated flights (e.g., [7] and [8]), but the expense and resource needs of such campaigns limit their frequency and coverage. Furthermore, they are hampered by the difficulty of designing a network to obtain validation data at satellite-based measurement scales [9]. Alternatively, retrieval algorithms can be partially tested using an observing system simulation environment [10]. In the SMAP Algorithm Testbed, geophysical fields taken to represent a synthetic “truth” are used to simulate emission (the forward component of the testbed). After coupling this emission model to an orbital sampling module, the resulting brightness temper- atures are passed through the proposed retrieval algorithms to study the retrieval error. Although the dependence of these ex- periments on an explicitly modeled forward method eliminates their applicability for constraining the models themselves, they may still be used to characterize the error structures associated with soil moisture retrieval, including the contributions of land surface heterogeneity [11], parameter uncertainty, and measure- ment error. Crow et al. [10] carried out a simulation experiment for The Hydrosphere State Mission (Hydros) (the cancelled pre- decessor to SMAP) radiometer products in preparation for that project. The Hydros simulation experiment covered the 0196-2892/$26.00 © 2011 IEEE