Influence of vegetation on SMOS mission retrievals

Abstract

Using the proposed Soil Moisture and Ocean Salinity (SMOS) mission as a case study, this paper investigates how the presence and nature of vegetation influence the values of geophysical variables retrieved from multi-angle microwave radiometer observations. Synthetic microwave brightness temperatures were generated using a model for the coherent propagation of electromagnetic radiation through a stratified medium applied to account simultaneously for the emission from both the soil and any vegetation canopy present. The synthetic data were calculated at the look-angles proposed for the SMOS mission for three different soil-moisture states (wet, medium wet and dry) and four different vegetation covers (nominally grass, crop, shrub and forest). A retrieval mimicking that proposed for SMOS was then used to retrieve soil moisture, vegetation water content and effective temperature for each set of synthetic observations. For the case of a bare soil with a uniform profile, the simpler Fresnel model proposed for use with SMOS gave identical estimates of brightness temperatures to the coherent model. However, to retrieve accurate geophysical parameters in the presence of vegetation, the opacity coefficient (one of two parameters used to describe the effect of vegetation on emission from the soil surface) used within the SMOS retrieval algorithm needed to be a function of lookangle, soil-moisture status, and vegetation cover. The effect of errors in the initial specification of the vegetation parameters within the coherent model was explored by imposing random errors in the values of these parameters before generating synthetic data and evaluating the errors in the geophysical parameters retrieved. Random errors of 10% result in systematic errors (up to 0.5°K, 3%, and ~0.2 kg m -2 for temperature, soil moisture, and vegetation content, respectively) and random errors (up to ~2°K, ~8%, and ~2 kg m -2 for temperature, soil moisture and vegetation content, respectively) that depend on vegetation cover and soil-moisture status.

Spatial and temporal variability of biophysical variables in southwestern France from airborne L-band radiometry

“…Over land, the main product of SMOS is surface soil moisture (SSM). The multi-angular bipolarized observations of SMOS permit the retrieval of the vegetation optical depth (VOD), in addition to SSM Lee et al, 2002;Pellarin et al, 2003a). The satellite-derived SSM or soil wetness index (SWI) products from passive (e.g.…”

Using Coupled Land Surface and Microwave Emission Models to Address Issues in Satellite‐Based Estimates of Soil Moisture

“…The proposed retrieval algorithm assumes that the opacity coefficient of the vegetation (equation 8) does not depend on either the polarization of the radiation or the look-angle of the sensor. There is, however, some evidence that it depends on both of these radiometer characteristics (van de Griend and Owe, 1996;Lee et al, 2002b). Figure 4.3 shows the look-angle and polarization dependence of the mean opacity coefficient retrieved (using the method described by Lee et al (2002b)) from the time series of brightness temperatures given in Figures 4.1c and d, this time series having been calculated using the arguably more realistic extended Wilheit (1978) found at look-angle 10…”

Overview of Overland Satellite Rainfall Estimation for Hydro-Meteorological Applications

“…Direct observations of near surface ($1-3 cm) soil moisture is possible based on low frequency (6-19 GHz) passive microwave observations from a limited number of Earth-orbiting satellite platforms such as the Special Sensor Microwave/Imager (SSM/I) onboard the Defense Meteorological Satellite Program (DMSP) satellites, the Advanced Microwave Scanning Radiometer (AMSR) onboard EOS-Aqua, and the Microwave Imager (TMI) onboard Tropical Rainfall Measuring Mission (TRMM) satellite. However, those satellite retrievals are coarse in spatial resolution (>0.5 · 0.5 degree 2 ), infrequent in time, and associated with uncertainty, primarily over areas with dense vegetation cover (Jackson and Schmugge, 1991;Chauhan, 1997;Lee et al, 2002;Bindlish et al, 2003;Lee and Anagnostou, 2004). A perceived solution to the soil moisture estimation problem is to use radiation budget and surface precipitation fields retrieved from remotely sensed data to force offline land data assimilation systems (e.g., Koster and Suarez, 1996;Liang et al, 1996;Mitchell et al, 1999Mitchell et al, , 2000.…”