NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the Combined Radar/Radiometer (ComRAD) truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40 degrees over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval

Cosh, M.

Joseph, A.

Lang, R.

O'Neill, P.

Srivastava, P.

NASA Technical Reports Server

 
SEASONAL PARAMETERIZATIONS OF THE TAU-OMEGA MODEL USING THE 
COMRAD GROUND-BASED SMAP SIMULATOR 
 
 
P. O’Neill1, A. Joseph1, P. Srivastava1, M. Cosh2, and R. Lang3 
 
1Hydrological Sciences Laboratory / Code 617  
NASA Goddard Space Flight Center, Greenbelt, MD  20771  USA 
tel:  1-301-614-5773, fax:  1-301-614-5808,  Peggy.E.ONeill@nasa.gov 
 
2Hydrology and Remote Sensing Laboratory, U.S. Dept. of Agriculture/Agricultural Research Service* 
Beltsville, MD  20105  USA 
 
3Dept. of Electrical & Computer Engineering, George Washington University 
Washington, DC  20052  USA 
 
 
 
ABSTRACT 
NASA’s Soil Moisture Active Passive (SMAP) 
mission is scheduled for launch in November 2014.  In 
the prelaunch time frame, the SMAP team has focused 
on improving retrieval algorithms for the various 
SMAP baseline data products.  The SMAP passive-only 
soil moisture product depends on accurate 
parameterization of the tau-omega model to achieve the 
required accuracy in soil moisture retrieval.  During a 
field experiment (APEX12) conducted in the summer of 
2012 under dry conditions in Maryland, the ComRAD 
truck-based SMAP simulator collected active/passive 
microwave time series data at the SMAP incident angle 
of 40° over corn and soybeans throughout the crop 
growth cycle.  A similar experiment was conducted only 
over corn in 2002 under normal moist conditions.  Data 
from these two experiments will be analyzed and 
compared to evaluate how changes in vegetation 
conditions throughout the growing season in both a 
drought and normal year can affect parameterizations 
in the tau-omega model for more accurate soil moisture 
retrieval. 
 
Keywords (Index Terms) -- soil moisture, microwave, 
tau-omega model, ComRAD, SMAP, seasonal 
parameterization. 
 
1.  INTRODUCTION 
Soil moisture is a critical variable in land 
surface hydrology, and large-scale mapping of soil 
_______________________________________ 
*USDA and NASA are equal opportunity employers. 
moisture based on microwave remote sensing will 
enable new science and applications of benefit to 
society.  NASA’s upcoming SMAP (Soil Moisture 
Active Passive) mission, currently scheduled for 
launch in November 2014, will provide global 
mapping of soil moisture and freeze/thaw state 
every 2-3 days using data from an L-band 
radiometer (40 km resolution) and/or an L-band 
radar (3 km resolution) [1].  SMAP will continue 
the legacy of soil moisture measurements 
produced by ESA’s ongoing L-band SMOS (Soil 
Moisture Ocean Salinity) mission.  Although 
differing in the details of their algorithm 
approaches, passive-only soil moisture retrievals 
from both SMOS and SMAP are fundamentally 
based on the same tau-omega model [2,3].  When 
used for soil moisture retrieval, the tau-omega 
model requires parameterization of the effects of 
the overlying vegetation canopy.  These 
parameterizations have traditionally been 
developed for peak biomass conditions at a single 
polarization – the polarization and seasonal 
variations in the vegetation parameterizations are 
often ignored.  Refinement of these 
parameterizations for use in year-round soil 
moisture retrieval from space requires L-band data 
sets in time series over a long enough time period 
to reflect changing surface conditions (vegetation, 
soil moisture, etc.).  As part of SMAP algorithm 
development activities, NASA GSFC’s ComRAD 
2423U.S. Government Work Not Protected by U.S. Copyright IGARSS 2014
https://ntrs.nasa.gov/search.jsp?R=20160001384 2019-08-31T04:28:05+00:00Z
truck-mounted microwave instrument system 
collected L-band active/passive data throughout 
the growing season over corn and soybean crops 
from June 1 through October 24, 2012, along with 
associated ground truth and vegetation 
characterization data.  These data, along with time 
series data from a similar 2002 experiment in the 
same location, will be used to assess the magnitude 
of the possible seasonal and polarization variations 
in the tau-omega vegetation parameterizations, 
especially as compared to a constant 
parameterization for a given vegetation type where 
changes in the vegetation water content alone 
account for any time dependence in the soil 
moisture retrieval algorithms. 
2.  TAU-OMEGA MODEL 
Retrieval of soil moisture from surface 
brightness temperature (TB) observations is based 
on a well-known zero-order approximation to the 
radiative transfer equation, commonly known as 
the tau-omega model.  A layer of vegetation over a 
soil attenuates the emission of the soil and adds to 
the total radiative flux with its own emission.  
Assuming that scattering within the vegetation is 
negligible at L band frequencies, the vegetation 
may be treated mainly as an absorbing layer.  A 
model following this approach to describe the 
brightness temperature of a weakly scattering layer 
above a semi-infinite medium is shown below and 
includes emission components from the soil and 
the overlying vegetation canopy: 
 
𝑇𝐵𝑝 = 𝑇𝑠𝑒𝑝 exp�−𝜏𝑝 sec𝜃� + 𝑇𝑣�1 − 𝜔𝑝��1 −                exp�−𝜏𝑝 sec𝜃���1 + 𝑟𝑝 exp�−𝜏𝑝 sec𝜃�� 
                (1) 
where the subscript p refers to polarization (V or 
H), Ts is the soil effective temperature, Tv is the 
vegetation temperature, τp is the nadir vegetation 
opacity, ωp is the vegetation single scattering 
albedo, and rp is the rough soil reflectivity.  The 
reflectivity is related to the emissivity (ep) by ep = 
(1 – rp), and ωp, rp and ep are values at the SMAP 
look angle of θ = 40°.  The transmissivity γ of the 
overlying canopy layer is γ = exp(-τp sec θ).  
Equation (1) assumes that vegetation multiple 
scattering and reflection at the vegetation-air 
interface are negligible.  Nadir vegetation opacity 
is related to the total columnar vegetation water 
content (VWC, in kg/m2) by τp = bp*VWC with the 
coefficient bp dependent on vegetation type and 
microwave frequency (and probably polarization) 
[4].  For L-band missions, the coefficient bp is 
often taken to be constant for any given vegetation 
type, such that any variations in tau τp throughout 
the year are caused solely by variations in VWC.   
 
3.  2002 AND 2012 FIELD EXPERIMENTS 
Extensive field experiments from crop planting 
through senescence were conducted in both 2002 
and  2012 at the heavily instrumented USDA-ARS 
(U.S. Dept. of Agriculture-Agricultural Research 
Service) OPE3 [5] test site in Beltsville, MD to 
acquire data needed to address active/passive 
microwave algorithm needs for accurate soil 
moisture retrieval [6,7].  Vegetation cover in the 
2002 experiment consisted of a corn crop, while 
both corn and soybeans were measured in the 2012 
experiment.  2002 was a normal year in terms of 
crop growth, while in 2012 the local Maryland test 
site area experienced drought conditions in June 
and July which significantly stunted the growth of 
the corn canopy (Fig. 1a).  In 2012 in situ soil 
moisture, soil temperature, and leaf wetness 
sensors were installed by USDA to provide 
continuous ground truth data.  These data were 
supplemented by additional soil moisture data 
collected manually twice a week by USDA 
personnel, along with weekly plant architectural, 
biomass/water content, and density measurements.  
The OPE3 site also contains a SCAN 
meteorological station and a flux tower which 
record precipitation and other micrometeorological 
data.  ComRAD microwave measurements were 
made at the SMAP incidence angle of 40° in 2012 
(Fig. 1b) and at 35° in 2002. 
3.  ANALYSIS 
Figure 2 shows the measured vegetation water 
content (by plant constituent) for the corn 
canopies in 2012 (Fig. 2a) and 2002 (Fig. 2b).
2424
  
 
Figure 1.  (a)  Corn canopy on August 11, 2012 showing stunted growth due to drought conditions in June and 
July;  (b)  ComRAD truck-mounted instrument system deployed at the USDA OPE3 test site.  
(a) 
August 11, 2012 
(b) 
2425
Note the similarity of the shape of the curves 
despite the difference in biomass between the two 
years.  Using the vegetation biomass, geometry, 
and dielectric data collected during the 
experiments, a discrete vegetation scattering 
model [8] is used to produce estimates of 
microwave attenuation through the corn canopies 
for 2002 and 2012.  These estimates of attenuation 
are then converted to transmissivity for H and V 
polarization through a simple relationship and 
interpolated to produce a transmissivity value for 
each day during the growing season.  The 
transmissivity curves are also converted into tau 
curves for each polarization, and compared to the 
measured VWC curves to determine whether the 
VWC variations alone are sufficient to enable 
accurate soil moisture retrieval throughout the 
growing season, or whether a non-constant and 
polarization-dependent bp parameterization is 
needed for improved soil moisture estimation, and 
how bp differs in drought and normal years. 
 
5. REFERENCES 
[1]  Entekhabi, D., E, Njoku, P. O’Neill, K. Kellogg,  plus 
19 others, “The Soil Moisture Active Passive (SMAP) 
Mission,”  Proceedings of the IEEE, Vol. 98, No. 5, May, 
2010. 
 
[2]  Kerr, Y., P. Waldteufel, P. Richaume, I. Davenport, P. 
Ferrazoli, and J.-P. Wigneron, SMOS Level 2 Processor Soil 
Moisture Algorithm Theoretical Basis Document (ATBD), 
Toulouse, France, CESBIO,  SM-ESL (CBSA), vol. SO-TN-
ESL-SM-GS-0001, v5.a, 2006. 
 
[3]  O’Neill, P., S, Chan, E. Njoku, T. Jackson, and R. 
Bindlish, SMAP Level 2 & 3 Soil Moisture (Passive) 
Algorithm Theoretical Basis Document (ATBD), Initial 
Release v.1, JPL D-66480, Jet Propulsion Laboratory, 
October, 2012. 
 
[4]  Jackson, T. J. and T. J. Schmugge, “Vegetation effects 
on the microwave emission from soils,” Rem. Sens. 
Environ., vol. 36, pp. 203-212, 1991. 
 
[5]   USDA web site at http://www.ars.usda.gov/Research/. 
 
[6]  O’Neill, P., A. Joseph, G. De Lannoy, R. Lang, C. Utku, 
E. Kim, P. Houser, and T. Gish, “Soil Moisture Retrieval 
Through Changing Corn Using Active/Passive Microwave 
Remote Sensing,” Proc. of IGARSS ’03, Toulouse, France, 
July, 2003. 
 
[7]  O’Neill, P., M. Kurum, A. Joseph, J. Fuchs, P. Young, 
M. Cosh, and R. Lang, “L-Band Active / Passive Time 
Series Measurements over a Growing Season using the 
ComRAD Ground-Based SMAP Simulator,” Proc. of  
IGARSS’13, IEEE, Melbourne, Australia, July 22-26, 2013. 
 
[8]  Chauhan, N., D. Le Vine, and R. Lang, “Discrete scatter 
model for microwave radar and radiometer response to corn:  
comparison of theory and data,”  IEEE Trans. Geos. Rem. 
Sens., 32, pp. 416-426, 1994.
 
Figure 2.  (a) VWC curves (by plant constituent) for corn at the OPE3 test site in 2012; note that peak VWC for 
the canopy was <3 kg/m2;  (b) Similar VWC curves for corn at the OPE3 test site in 2002;  note the more normal 
peak VWC for the canopy was >5 kg/m2.  
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Seasonal Parameterizations of the Tau-Omega Model Using the ComRAD Ground-Based SMAP Simulator

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