An Evaluation of Soil Moisture Predictions Derived from AMSR-E
using Ground Based, Airborne and Ancillary Data During SMEX 02.
McCabe, M. F., Gao, H. and Wood, E. F.
Department of Civil and Environmental Engineering, Princeton University,
Princeton, NJ 08544, USA
Corresponding Author:
Dr Matthew McCabe
Department of Civil and Environmental Engineering
Princeton University, Princeton, NJ 08544, USA
+1 609 258 1551 (Phone)
+1 609 258 2799 (Fax)
mmccabe@princeton.edu (Email)
April 30, 2004
Page
1
Abstract
A land surface microwave emission model (LSMEM) is used to derive soil moisture
estimates over Iowa during the SMEX 02 field campaign using brightness temperature
data from the AMSR-E satellite. Spatial distributions of the near surface soil moisture are
produced using the LSMEM and data from the Land Data Assimilation System (LDAS),
standard soil datasets and vegetation and land surface parameters estimated through
recent MODIS land surface products. In order to assess the value of soil moisture
estimates from the X-band sensor on the AMSR platform, retrievals are evaluated against
ground based sampling data and soil moisture predictions from an airborne polarimetric
scanning radiometer (PSR) operating in the C-band. The PSR offers high resolution detail
of the soil moisture distribution against which an analysis of heterogeneity at the AMSR
pixel scale can be undertaken. Preliminary analysis indicates that predictions from the
AMSR instrument using LSMEM are surprisingly robust, with accuracies of less than 3%
vol./vol. when compared with in-situ samples. Results from the AMSR comparisons
indicate that there is much potential in determining soil moisture patterns over regional
scales and larger, even where vegetation may prove to be a issue. Assessments of soil
moisture determined through local scale sampling with the larger scale AMSR retrievals
reveals a consistent level of agreement over a wide range of hydro-meteorological
conditions, offering much promise for improved land surface hydro-meteorological
characterisation.
Page
2
1. Introduction
Soil moisture plays a critical role in agricultural, hydrological and meteorological
applications and its spatial distribution exhibits a strong correlation with a number of
hydro-meteorological systems. The soil moisture content assumes significant control on
hydrological responses across many spatial and temporal scales, influencing runoff
generation through antecedent conditions, modulating interactions between the land
surface and the atmosphere and comprising a component of the many feedback systems
present in the land-atmosphere interface. The distribution of soil moisture patterns
throughout a catchment plays a critical role in a variety of hydrological processes.
Knowledge of this state variable offers valuable insights into percolation, infiltration and
runoff mechanisms and is a controlling factor in the evaporative process, reflecting the
prevailing water and energy balance conditions at any particular time by influencing the
relative partitioning between latent and sensible heat fluxes. Identifying the spatial
distribution and temporal evolution of the soil moisture would provide greater insight into
larger scale processes, and would undoubtedly see a corresponding development in the
performance of modelling attempts to describe these processes.
Understanding the spatial variation of soil moisture is a perplexing problem and
much research has been directed towards this task (Entekhabi and Rodriguez-Iturbe,
1994; Famiglietti and Wood, 1995; Grayson and Blöschl, 2000; Western et al., 2001;
Wilson et al., 2004). Accurate representation of soil moisture at the catchment scale is
difficult and intensive field instrumentation is required if spatial patterns are desired
(e.g. Western et al., 1999). Remote sensing offers some advantages over instrumented
networks, but also suffers from issues associated with the depth of retrieval, generally
claimed to be less than 5 cm of soil depth (Jackson et al., 1995), the coarse scale of
Page
3
operational measurements (>25km) and in the development of robust retrieval
algorithms. The issue of radio frequency interference (RFI) (Li et al., 2004) in C-band
measurements and the atmospheric and vegetation influences at higher frequencies,
further complicate the accurate retrieval over large areas.
A number of recent studies have compared higher resolution soil moisture retrievals
from airborne microwave radiometers such as the electronically scanned thinned array
radiometer (ESTAR) (Jackson et al., 1995; Le Vine et al., 2001; Gao et al., 2004) and the
polarimetric scanning radiometer (PSR) (Jackson et al., 2002). These sensors offer
excellent detail of the surface dynamics at sub-kilometre resolutions, and offer an
opportunity to examine the scaling characteristics of soil moisture (Kim and Barros,
2002). While the heterogeneous nature of soil moisture is well recognised in a theoretical
sense (Entekhabi and Rodriguez-Iturbe, 1994; Grayson and Blöschl, 2000), few practical
techniques exist to adequately or efficiently characterise this property at large scales. The
insight that is accessible through remotes sensors should facilitate a greater understanding
of the broader scale patterns available from current platforms such as AMSR (Njoku et
al., 2003) and future satellite missions such as SMOS (Kerr et al., 2001) and HYDROS,
but this task has been frustrated by the difficulty in deriving, and then evaluating, robust
interpretive models.
The launch of the Advanced Microwave Scanning Radiometer (AMSR) sensor
offers an opportunity to determine global soil moisture patterns at scales suitable for
inclusion in land surface and general circulation models. While there are numerous
assimilation studies attending to this task (Lakshmi and Susskind, 2001; Reichle et al.,
2001; Crosson et al., 2002; Walker et al., 2002; Francois et al., 2003), there is perhaps a
Page
4
more pressing need for increased evaluation of the derived products to assess the worth of
soil moisture information derived from this sensor. Algorithm assessment and
intercomparison are required before confidence in the global products planned for
development can be ascertained. A number of field experiments undertaken over the last
few years (see http://hydrolab.arsusda.gov/) provide an excellent source of information
with detail sufficient for product evaluation. Such multi-faceted hydrological experiments
offer a level of assessment not normally available for remote sensing studies and facilitate
the critical link between algorithm assessment and product development.
In this paper, an evaluation of soil moisture predictions using a microwave
emission model against field data collected during the SMEX 02 campaign is presented.
Using information from the Land Data Assimilation System (LDAS) and ancillary data,
brightness temperatures from AMSR are incorporated into an emission model (LSMEM)
(Gao et al., 2004) to produce a soil moisture product at the resolution of the LDAS. A
comparison with the dense network of ground based measurements and airborne
information that was collected during this period is undertaken and an assessment of the
derived soil moisture retrieval offered.
2. Land Surface Microwave Emission Model
In the determination of soil moisture from retrieved AMSR brightness temperatures
the Land Surface Microwave Emission Model (LSMEM) (Drusch et al., 2004; Gao et al.,
2004) was utilised. LSMEM makes a number of important assumptions in identifying the
soil moisture which have been shown to hold true over sparse vegetation (Jackson et al.,
1995; Jackson et al., 1999), but which have not been rigorously tested over denser
Page
5
vegetation types, characteristic of the Walnut Creek catchment in Iowa. It is generally
accepted that determining the soil moisture over dense vegetation is problematic
(Ferrazzoli et al., 2002; Schmugge et al., 2002) and the work presented herein represents
a first attempt at retrieving soil moisture values from AMSR over this particular land
surface coverage.
LSMEM is based on a solution of the radiative transfer equation as derived in Kerr
and Njoku (1990), describing the brightness temperature of soil covered by a layer of
vegetation ( Tbv , p ) as :


Tbv, p  Tau    at Tad  Tsky  at 1   p  2 

*
   pTs    TV (1   * )(1    )(1  (1   p )  )
at
*
*
*

(1)
where Tau and Tad are the upward and downward atmospheric contributions from
the atmosphere, Ts is the effective soil temperature, TV the vegetation temperature, Tsky the
cosmic radiation, at the optical depth of the atmosphere and p the rough soil emissivity.
For vegetation having cylindrical structure, * is the single scattering albedo and * is the
optical depth of the vegetation (Chang et al., 1980). In the literature, vegetation single
scattering albedo varies from 0.04 to 1.0 (Pampaloni and Paloscia, 1986; Ulaby et al.,
1996). Since there is no robust database for this value over large area, an average of 0.07
is used in this analysis. Following the approach of Gao et al (2004), Equation 1 can be
simplified to the form introduced by Jackson et al (1982):
*
Tb
 1  (  1)e 2
Ts
(2)
Page
6
Amongst the model inputs, some parameters are assigned constants values such as
the sensor information (10GHz), atmospheric contribution (determined from a radiative
transfer model), and the vegetation structure parameter (Jackson and Schmugge, 1991).
Other parameters maintain temporal stability but have spatial variability, such as the soil
texture (STATSGO), bulk density (LDAS) and the water fractional coverage (LDAS).
Parameters which vary both spatially and temporally include the vegetation fractional
coverage and vegetation water content, which are both monthly averages (see below),
while the soil temperature and the brightness temperature are determined coincident with
the overpass time. The reader is referred to Gao et al. (2004) for a more detailed
description of the LSMEM model and parameter values than is offered here.
One of the key differences to previous applications of the LSMEM is the
accounting of the vegetation cover using a semi-empirical formulation of the Normalized
Differential Vegetation Index (NDVI) (see Baret et al., 1995). Data from the MODIS
NDVI vegetation product (Huete et al., 1994) was reprocessed to provide coverage at
0.125 degrees, consistent with data from the LDAS database, offering an improved
assessment of vegetation cover using this approach. Given the strong influence of
vegetation on the land surface dynamics in the SMEX domain, characterising the
vegetation water content is a critical consideration in achieving accurate representation of
the soil moisture distribution. Vegetation water content was derived from MODIS land
cover classification and LAI data (Myneni et al., 2002) using general relationships
between LAI, foliar and stem biomass, and relative water content estimates for foliar and
stem biomass (pers. comm. Dr. J. S. Kimball, 2004). It should be noted that any seasonal
variability in the vegetation water content is a product of variations in the LAI only.
Page
7
Given the short time period over which the analysis was undertaken, this is not a
pertinent issue to the retrievals undertaken here.
LSMEM is used in an inverted numerical framework to solve for the soil moisture
given knowledge of the brightness temperature. An iterative technique is employed to
identify the soil moisture, starting from an initial estimate of the antecedent moisture
condition – available as output from the LDAS scheme or through a priori knowledge. A
brightness temperature corresponding to the given moisture and emissivity conditions can
be calculated and compared to observations. Successive iterations are performed on the
soil moisture until convergence with the observed horizontal brightness temperature is
reached.
3. Methodology and Data Description
AMSR 10GHz (X band) horizontally polarised brightness temperature records were
processed from June 19 through to the end of July, encompassing the SMEX observation
programme. Analysis of the available data focuses primarily on the Walnut Creek
watershed due to the density of available measurements and the existence of a Soil
Climate Analysis Network (SCAN) installation nearby at Ames which provides a longer
term measurement of profile soil moisture over a variety of depths. The proceeding
sections present an overview of the procedures employed in this analysis, and also a
description of the data sources utilised to determine the near surface soil moisture
predictions.
a. Data Sources
Page
8
The Land Data Assimilation System (LDAS) (Cosgrove et al., 2003) offers a
variety of forcing data for use in land surface and other model simulations. This data
system offers an excellent opportunity to explore regional scale processes, particularly
where extensive ground based records do not exist. Land surface temperatures, used in
Equation 2, were derived from the VIC land surface scheme (Liang et al., 1994; Liang et
al., 1999) nested within LDAS, to facilitate the estimation of soil moisture. Surface
temperature measurement is an integral step in predicting soil moisture value and while
efforts to utilise coincident microwave based temperature measurements show promise
(e.g. Owe et al., 2001), remotely sensed infrared techniques provide a more accurate
source of available data at a variety of resolutions (e.g. Wan et al., 2002). The LDAS
temperatures have recently been evaluated against geostationary satellite data and in-situ
measurements over the ARM-CART region for a select period, with accuracies in the
order of 3-4K (Mitchell et al., 2004). While this level of retrieval accuracy is not ideal for
land surface flux retrieval, estimation of soil moisture is less sensitive to uncertainties in
the surface temperature.
In order that microwave brightness temperatures could be integrated into the
existing LDAS and LSMEM framework, AMSR data were re-grided onto the regularised
LDAS lattice (see Figure 1). Transferring the native 25km resolution to 0.125 degree
inevitably requires some form of data interpolation. In order to retain the information
content of the original data, the re-griding was undertaken in such a way as to minimise
smoothing of the data. Where LDAS grid points coincide with AMSR grid centres, or
within a user-defined search distance, the re-grided brightness temperature is assigned the
original value. Otherwise, an average of the two nearest AMSR brightness temperatures
is determined, weighting each value by the inverse of the distance between the AMSR
Page
9
and LDAS grid centres (i.e. the AMSR value closest to the LDAS grid centre will have
most weight). A simple nearest neighbour allocation could have been assigned, but it was
thought that the scheme proposed above provides a more realistic representation while
retaining the structure of the original data. Alternatively, data from the LDAS could have
been scaled to the AMSR resolution. In doing this however, the information content of
the high resolution vegetation data would have been degraded, as would the surface
temperature information and STATSGO soil property data. The chosen techniques
represent a reasonable compromise given the variety of data resolutions used in this
analysis.
4. Results
A number of assessments of the AMSR-LSMEM soil moisture product were
undertaken against field and aerial measurements during the SMEX observation period.
The following section presents the analysis to examine the retrieval accuracy and
capability of AMSR to capture the local scale dynamics present in the evaluation data.
a. AMSR Comparisons with Ground Based Networks
1) SOIL CLIMATE ANALYSIS NETWORK SITE
The Soil Climate Analysis Network (SCAN) installation offers a continuous and
consistent complimentary data set to the theta probes used during the SMEX campaign.
The Ames SCAN site has been in operation since September 2001 and provides
continuous hourly data measured at a number of depths by a Stevens Vitel Hydra Probe.
Data from the SCAN site were extracted and compared with a collocated AMSR pixel
(the same pixel used in the proceeding watershed analysis). Although clearly representing
Page 10
a scale mismatch, the temporal dynamics of the in-situ measurements are expected to
offer some insight into the ability of AMSR to reproduce local scale observations. Figure
2 illustrates the resulting SCAN response at 2 inches (~50mm) and the measured
precipitation at the site, along with the retrieved AMSR soil moisture. As can be seen,
there is excellent agreement between the data for the period June 20-July 4, with the data
reflecting the drying down after the rain events earlier in the month. There is a fairly
constant offset during this period of approximately 10% vol./vol., likely a result of the
relative depths of measurement (AMSR provides a near-surface soil measure). The onset
of the rain events on the 4, 6 and 10 July incite a marked spike in both responses,
gradually drying down again towards the end of the month and resuming a positive bias.
There are interesting diurnal effects evident in the AMSR response, with PM (2pm)
values generally exceeding the AM (2am) estimates during the same diurnal cycle. The
afternoon overpasses also exhibit a greater degree of variation, perhaps in response to
increased uncertainties in the land surface temperature during the day time. Overall, the
AMSR retrievals, although obviously influenced by pixel-to-point scale and measurement
disparities, reflect well the trends observed in the SCAN response.
2) POINT SCALE MEASUREMENTS IN WALNUT CREEK
During the SMEX watershed sampling, over 4,500 unique theta probe samples were
collected, allowing a detailed accounting of the soil moisture variability within the study
catchment. Of these, 19 (from 33 sites) were within the resampled AMSR footprint,
allowing a truly spatially representative in-situ soil moisture average to be compared with
the model retrieved value. The distribution of sites across the catchment was intended to
effectively capture the level of spatial heterogeneity of the point scale soil moisture.
AMSR morning and afternoon retrievals were averaged and compared with the areal
Page 11
mean of the average soil moisture recorded from each watershed sampling location
within Walnut Creek. Table 1 details the statistical properties of the in-situ distribution
and the coincident AMSR pixel, and Figure 3 compares the collocated retrievals from the
both the PSR and AMSR.
As can be seen, there is a gradual increase in the catchment average soil moisture as
the field campaign progresses, consistent with the precipitation records. Interestingly, the
standard deviation between the sites is not greater than 3%, indicating a stability of the
moisture range across the watershed, although this could be an artefact of averaging the
supplied means rather than the unique point measurements. There is a strong equivalence
with the AMSR pixel, especially considering the scale disparity between the two
approaches and also the different sampling depths of the techniques (60 mm for theta
probe). Although only eight sample days were available for comparison, consistent
agreement between the two measurements is evident. The mean absolute error between
the samples is 2.64% vol./vol. with a correlation coefficient of 0.87. A root mean square
(RMS) error of 4.1% vol./vol. belies the goodness of fit, since half of this error is
attributed to the single offset value evident in Figure 3, which upon removal reduces the
retrieval RMS to 2.17% vol./vol.
3) REGIONAL SAMPLING OVER THE SMEX DOMAIN
A concurrent regional scale sampling strategy conducted during the campaign was
designed to capture the broader scale soil moisture patterns at the satellite footprint scale
and incorporated 46 unique sites distributed across the SMEX domain. At the original
satellite resolution of 25km, it was anticipated that approximately four sites would fall
within the AMSR footprint. The grid of individual sample sites covers a domain of
approximately 50 km by 100 km (2 by 4 AMSR pixels) and measurements were collected
Page 12
during the period 1200 – 1500 local time, to coincide with the afternoon AMSR and PSR
overpasses. Of the sample locations, Site 8 and Site 9 correspond to positions within the
area of the watershed sampling at Walnut Creek and within the resampled AMSR pixel
analysed above (see Figure 1 for location of catchment and grid structure over the
region).
Results of the regional analysis are shown in Figure 4, which illustrates the average
volumetric soil moisture content at Site 8 and Site 9 along with the regional mean of all
sites as measured using hand-held theta probes calibrated to independently evaluated
gravimetric moisture contents. The bars at each of the sample days, identifying the total
standard deviation at the site(s), are comparable to the values obtained in the watershed
analysis, with values between 2.0-3.0% vol./vol. Figure 4-c and Figure 4-d detail the
AMSR distribution for the area encompassing Site 8 and Site 9 and the corresponding
regional value. The general patterns, if not the actual values, are well represented across
the regional and site averages. AMSR predicts a more rapid dry-down phase than the
corresponding in-situ responses, likely a result of the different sampling depths measured.
The stability during the dryer periods preceding July 4 is clearly observed in the AMSR
responses, and is also evident in the in-situ SCAN responses discussed above. A
significant amount of noise however is observed in the regional AMSR response during
the period July 7-14, corresponding to the wet periods of the field campaign.
Interestingly, this seems to be in contradiction to the theta probe samples which show
minimal daily variations in the standard deviations. Given the spatial distributions of soil
moisture evident in the PSR imagery during this period, it would be expected that more
variation should be present than is observed in the in-situ measurements, although again
this is potentially a sampling depth issue.
Page 13
b. AMSR Comparison with the Polarimetric Scanning Radiometer (PSR)
The PSR is an airborne microwave radiometer operated by the NOAA
Environmental Technology Laboratory (Piepmeier and Gasiewski, 2001), which was
flown aboard the NASA P-3 aircraft for the purpose of obtaining polarimetric microwave
emission during SMEX. It has been successfully used in a number of major field
experiments including SGP99 (Jackson et al., 2002). The PSR data provides an excellent
intermediary source of validation information between the scales of the AMSR pixel and
ground based measurements and offers the only feasible means of comparing predictions
with a reasonable spatial equivalence and measurement characteristics to AMSR. Data
from the PSR was supplied in an irregularly spaced grid at a nominal resolution of 800m,
with soil moisture predictions calculated independently by the USDA (pers. comm. Dr R.
Bindlish, 2004). The PSR measurements supplied ten complete moisture maps of the
region, encompassing an area of approximately 0.7 x 1.0. Given the scale difference
between the AMSR and PSR measurements, it is anticipated that a number of underlying
surface physical and hydrological influences evident at higher resolutions will contribute
to soil moisture differences between the sensors.
The PSR retrievals (Bindlish et al., 2004) derive soil moisture values centred
around 7GHz (C band). Comparisons with the AMSR 10GHz are not expected to exhibit
significant divergence, although clearly there will be some effects from the different
dielectric properties of water at the two frequencies as well as surface vegetation and
roughness influences (Jackson et al., 2002). Apart from the defined scale differences,
Jackson et al (2002) indicate that for low soil moistures (high TB) both sensors should
exhibit similar results. The level of agreement might be expected to reduce as the soil
moisture increases, particularly given the level of vegetation characteristic of the study
Page 14
area. To examine scale effects and to allow a more equivalent comparison with the
AMSR footprint, the PSR moisture measurements were resampled to a variety of
resolutions between 1km to 25km in order to assess the consistency of sub-pixel
statistical variation. In this re-analysis, a bilinear interpolation scheme was employed to
make use of the high resolution data – as opposed to the downscaling of the AMSR
information where ‘artificial’ trends were not desired. As anticipated, results indicated a
preservation of the statistical features across scales, retaining many of the visual
characteristics evident in the highest resolution imagery, even at larger scales (Figure 1).
PSR data resampled to 0.125 degree were compared to the single AMSR retrieved
values analysed above for all available PSR overpasses (see Figure 5). There appears to
be a consistent bias between the PSR and AMSR imagery over the Walnut Creek pixel,
with PSR values generally higher than corresponding AMSR predictions. The general
trend however is well reflected, although AMSR values respond more sharply to
precipitation events occurring on the 4, 6 and 10 July respectively. These rainfall induced
spikes are absent from the PSR measurements, likely a result of missed PSR overpasses
around these times. Even with the limited samples, the consistent bias is reflected in the
statistics, with a correlation coefficient of 0.72, a mean absolute error of 6.85% vol./vol
and an RMS error of 7.47% vol./vol.
The relatively poor statistical comparison misrepresents the level of spatial
coherence manifest in the areal imagery. Areal responses derived from both the PSR and
AMSR soil moisture retrievals are shown in Figure 6, illustrating the high level of visual
agreement between the two sensors and indicating that some confidence can be placed in
the remotely sensed retrievals, even at these coarse resolutions. The period preceding the
rainfall event of July 4 (23mm at the SCAN site), characterises a relatively homogeneous
Page 15
regional response reproduced across all scales of the PSR and in the 0.125 degree AMSR
retrievals. Although a significant amount of precipitation occurred on July 4, the majority
fell after the PSR and AMSR (2 p.m.) overpasses, so its influence is not well represented.
Where rain events are significant across the region, as presented in the LDAS derived
daily precipitation totals of Figure 7 (see also Figure 2), the identification of rain affected
areas are captured and reflect a pleasing consistency between the PSR and AMSR
instruments (see July 10-12 in Figure 6).
For the limited imagery that is available for intercomparison, it would seem that
LSMEM retrievals are more sensitive to moisture than the corresponding PSR soil
moisture estimates, with higher volumetric soil moisture prediction generally resulting.
These responses are most likely attributable to scale effects in the AMSR measurements,
given that the coarse PSR imagery is originally determined from a much more spatially
dense data set, which captures more accurately the spatial heterogeneity. The coincidence
of a number of smaller precipitation events with the time of the AMSR overpass would
also tend to exaggerate the retrieval values, although these are not shown here. Overall,
the AMSR retrievals illustrate a considerable level of agreement with the dynamic trends
and statistical structure evident in the PSR imagery, correctly identify the transition from
dry to wet states and the subsequent dry down and wetting up that occurs throughout the
region.
6. Summary and Conclusions
Considerable agreement was observed between watershed samples of the
volumetric soil moisture and AMSR retrievals over a variety of surface and atmospheric
conditions. The level of accord was reduced somewhat upon comparison with regional
Page 16
values, with AMSR values exhibiting significantly more spatial variability across the
region in terms of the standard deviation, than the corresponding ground based samples.
It is surprising that more variability was not evident in the in-situ measurements,
particularly given the variability of the hydrometeorology over the study period. AMSR
performs in line with expectations over these varying conditions, reflecting reduced
regional ranges during dry periods and increased regional ranges during wetter periods.
Such trends are not as evident in the in-situ regional measurements, which illustrate a
relatively constant deviation throughout.
These results raise some issues with regards to evaluating remotely sensed
predictions. Apart from the fact that the AMSR values are sensing a near surface soil
moisture response and evaluation data are invariably representative of the top 60 mm at
best, few data sets exist which adequately capture the statistical variability over areas
large enough to encompass the AMSR footprint. The PSR and similar instruments
represent an excellent compromise, but are limited in their broader and regular
application. Although similar spatial patterns were reflected between the PSR and AMSR
imagery, the soil moisture values from PSR were derived using a different interpretive
model to the emission model used here. It is not known what contribution this makes to
the observed differences, with a consistent bias being observed between AMSR
comparisons and also in the average watershed measurements.
While it is recognised that soil moisture exhibits levels of variability dependant on
the scale at which it is observed, the relative importance of different controls on soil
moisture in space and time is poorly understood (Wilson et al., 2004). Small scale
influences on the soil moisture such as soil properties and vegetation are difficult to
distinguish from larger scale controls such as topography and atmospheric forcing (see
Page 17
Vinnikov et al., 1996; and Entin et al., 2000). Given these factors and the difficulty that
exists in assessing remotely sensed products at large scales and over various domains
using traditional techniques, some effort should be directed towards developing
techniques that would offer a commensurate level of information against which
comparisons could be made. For instance, Jackson et al. (1993) illustrated the relationship
between microwave brightness temperatures and precipitation patterns over the Walnut
Gulch catchment in southern Arizona. Similar pattern based approaches offer an intuitive
technique with which to assess soil moisture distributions, albeit lacking in quantitative
rigour. Alternatively, the use of land surface model output may offer another pathway to
prediction assessment.
The LDAS determines soil moisture within a data assimilation framework, using
observed and modelled atmospheric information to drive a number of land surface
schemes, which in turn provide predictions of a variety of hydrological functions such as
surface fluxes, surface temperature and soil moisture. Soil moisture determination from
these systems has been shown to be variable in both inter-comparisons with the
individual land surface models which comprise the LDAS and against in-situ records (see
Robock et al., 2003; Mitchell et al., 2004; Schaake et al., 2004). While there is potential
for assessing the LDAS predictions against remotely sensed retrievals, there is also an
opportunity for assimilating these values back into the system to improve the spatial
representation and predictive performance.
Issues associated with radio frequency interference (RFI) (Li et al., 2004) have
diminished the utility of the C-band in determining the soil moisture states over large
areas of the globe, particularly across the continental United States. As such, renewed
Page 18
focus on X-band measurements is required to further assess its suitability for soil
moisture insight. Until the launch of L-band missions (SMOS/HYDROS), information
from the 10GHz sensors on board AMSR and TRMM offer the best alternative for
moisture retrieval.
One of the key issues concerning the AMSR programme is whether soil moisture
retrievals can be reliably determined in the presence of agricultural biomass. The work
presented here offers some progress towards this task, addressing the issue of soil
moisture retrieval over landscapes where vegetation cover has a significant seasonal
influence. Results from the AMSR analysis indicate that there is potential in determining
soil moisture patterns over regional scales and larger, even where vegetation may prove
to be an issue. Comparison of soil moisture determined through local scale sampling with
larger scale AMSR retrievals reveals a consistent level of agreement over a wide range of
hydro-meteorological conditions. Further examination of the scale influences on remotely
sensed retrievals and the interactions between precipitation, varying vegetation dynamics
and also the distribution of surface fluxes are the focus of current work and will further
assist in improving our understanding of these inter-related processes.
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable contributions
in improving the manuscript. Particular thanks to Dr Rajat Bindlish who performed the
analysis of the PSR data and also to Dr Tom Jackson for his efforts in organising the
SMEX 02 field experiment. This work was supported by funding from the NASA XXXXXX XXX.
Page 19
References
Baret, F. J., Clevers, G. P. W. and Steven, M. D. (1995). "The robustness of canopy gap
fraction estimates from red and near-infrared reflectances: A comparison of
approaches." Remote Sens. Environ. 54: 141-151.
Bindlish, R., Jackson, T., J. and Cosh, M. H. (2004). "XXX XXX XXX." Remote Sens.
Environ. XXXX(XXX): XX.
Chang, S. L., Kong, J. A. and Tsang, L. (1980). "Radiative transfer theory for passive
microwave remote sensing of a two-layer random medium with cylindrical
structures." J. Appl. Phys. 51: 5588-5593.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J.
C., Robock, A., Marshall, C., Sheffield, J., Duan, Q. Y., Luo, L. F., Higgins, R.
W., Pinker, R. T., Tarpley, J. D. and Meng, J. (2003). "Real-time and
retrospective forcing in the North American Land Data Assimilation System
(NLDAS) project." Journal of Geophysical Research-Atmospheres 108(D22).
Crosson, W. L., Laymon, C. A., Inguva, R. and Schamschula, M. P. (2002).
"Assimilating remote sensing data in a surface flux-soil moisture model."
Hydrological Processes 16(8): 1645-1662.
Drusch, M., Wood, E. F., Gao, H. and Thiele, A. (2004). "Soil moisture retrieval during
the Southern Great Plains Hydrology Experiment 1999: A comparison between
experimental remote sensing data and operational products." Water Resources
Research 40(2).
Entekhabi, D. and Rodriguez-Iturbe, I. (1994). "Analytical Framework for the
Characterization of the Space-Time Variability of Soil-Moisture." Advances in
Water Resources 17(1-2): 35-45.
Page 20
Entin, J. K., Robock, A., Vinnikov, K. Y., Hollinger, S. E., Liu, S. X. and Namkhai, A.
(2000). "Temporal and spatial scales of observed soil moisture variations in the
extratropics." Journal of Geophysical Research-Atmospheres 105(D9): 1186511877.
Famiglietti, J. S. and Wood, E. F. (1995). "Effects of spatial variability and scale on
areally averaged evapotranspiration." Water Resour. Res. 31(3): 699-712.
Ferrazzoli, P., Guerriero, L. and Wigneron, J. P. (2002). "Simulating L-band emission of
forests in view of future satellite applications." IEEE Trans. Geosci. Remote Sens.
40(2700-2708).
Francois, C., Quesney, A. and Ottle, C. (2003). "Sequential assimilation of ERS-1 data
into a coupled land surface-hydrological model using an extended Kalman filter."
J. Hydrometerology 4: 473-487.
Gao, H., Wood, E. F., Drusch, M., Crow, W. and Jackson, T. J. (2004). "Using a
microwave emission model to estimate soil moisture from ESTAR observations
during SGP99." J. Hydrometerology 5(1): 49–63.
Grayson, R. B. and Blöschl, G. (2000). Spatial Patterns in Catchment Hydrology.
Cambridge, Cambridge University Press.
Huete, A., Justice, C. and Liu, H. (1994). "Development of vegetation and soil indices for
MODIS-EOS." Remote Sens. Environ. 49: 224-234.
Jackson, T. J., Gasiewski, A. J., Oldak, A., Klein, M., Njoku, E. G., Yevgrafov, A.,
Christiani, S. and Bindlish, R. (2002). "Soil moisture retrieval using the C-band
polarimetric scanning radiometer during the Southern Great Plains 1999
Experiment." Ieee Transactions on Geoscience and Remote Sensing 40(10): 21512161.
Page 21
Jackson, T. J., Gasiewski, A. J., Oldak, A., Klein, M., Njoku, E. G., Yevgrafov, A.,
Christiani, S. and Bindlish, R. (2002). "Soil Moisture Retrieval Using the C-Band
Polarimetric Scanning Radiometer During the Southern Great Plains 1999
Experiment, submitted to:." IEEE Trans. Geosci. Remote Sens.
Jackson, T. J., Le Vine, D. M., Hsu, A. Y., Oldak, A., Starks, P. J., Swift, C. T., Isham, J.
D. and Haken, M. (1999). "Soil moisture mapping at regional scales using
microwave radiometry: The Southern Great Plains Hydrology Experiment." IEEE
Trans. Geosci. Remote Sens. 37(5 Part 1): 2136-2151.
Jackson, T. J., Le Vine, D. M., Swift, C. T., Schmugge, T. J. and Shchiebe, F. R. (1995).
"Large area mapping of soil moisture using the ESTAR passive microwave
radiometer." Remote Sens. Environ. 54: 27-37.
Jackson, T. J., Levine, D. M., Griffis, A. J., Goodrich, D. C., Schmugge, T. J., Swift, C.
T. and Oneill, P. E. (1993). "Soil moisture and rainfall estimation over a semi-arid
environment with the ESTAR microwave radiometer." IEEE Trans. Geosci.
Remote Sens. 31(4): 836-841.
Jackson, T. J., Levine, D. M., Swift, C. T., Schmugge, T. J. and Schiebe, F. R. (1995).
"Large-Area Mapping of Soil-Moisture Using the Estar Passive Microwave
Radiometer in Washita92." Remote Sensing of Environment 54(1): 27-37.
Jackson, T. J., Schmugge, J. and Wang, J. (1982). "Passive microwave remote sensing of
soil moisture under vegetation canopies." Water Resour. Res. 18: 1137-1142.
Jackson, T. J. and Schmugge, T. J. (1991). "Vegetation Effects on the Microwave
Emission of Soils." Remote Sensing of Environment 36(3): 203-212.
Page 22
Kerr, Y. and Njoku, E. G. (1990). "A semi-empirical model for interpreting microwave
emiision from semi-arid land surfaces as seen from space." IEEE Trans. Geosci.
Remote Sens. 28: 384-393.
Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Martinuzzi, J. M., Font, J. and Berger, M.
(2001). "Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity
(SMOS) mission." Ieee Transactions on Geoscience and Remote Sensing 39(8):
1729-1735.
Kim, G. and Barros, A. P. (2002). "Space-time characterization of soil moisture from
passive microwave remotely sensed imagery and ancillary data." Remote Sensing
of Environment 81(2-3): 393-403.
Lakshmi, V. and Susskind, J. (2001). "Utilization of satellite data in land surface
hydrology: sensitivity and assimilation." Hydrol. Processes 15: 877-892.
Le Vine, D. M., Jackson, T. J., Swift, C. T., Haken, M. and Bidwell, S. W. (2001).
"ESTAR measurements during the Southern Great Plains experiment (SGP99)."
Ieee Transactions on Geoscience and Remote Sensing 39(8): 1680-1685.
Li, L., Njoku, E. G., Im, E., Chang, P. S. and Germain, K. S. (2004). "A preliminary
survey of radio-frequency interference over the US in Aqua AMSR-E data." IEEE
Trans. Geosci. Remote Sens. 42(2): 380-390.
Li, L., Njoku, E. G., Im, E., Chang, P. S. and Germain, K. S. (2004). "A preliminary
survey of radio-frequency interference over the US in Aqua AMSR-E data." Ieee
Transactions on Geoscience and Remote Sensing 42(2): 380-390.
Liang, X., Lettenmaier, D. P., Wood, E. F. and Burges, S. J. (1994). "A Simple
Hydrologically Based Model of Land-Surface Water and Energy Fluxes for
Page 23
General-Circulation Models." Journal of Geophysical Research-Atmospheres
99(D7): 14415-14428.
Liang, X., Wood, E. F. and Lettenmaier, D. P. (1999). "Modeling ground heat flux in
land surface parameterization schemes." Journal of Geophysical ResearchAtmospheres 104(D8): 9581-9600.
Mitchell, K. E., Lohmann, D., Houser, P., R., Wood, E. F., Schaake, J. C., Robock, A.,
Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T.,
Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H., Entin, J. K., Pan, M., Shi, W.,
Koren, V., Meng, J., Ramsay, B. H. and Bailey, A. A. (2004). "The multiinstitution North American Land Data Assimilation System (NLDAS): Utilizing
multiple GCIP products and partners in a continental distributed hydrological
modeling system." J. Geophs. Res. 109(D7): XXX-XXX.
Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., Wang,
Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T.,
Votava, P., Nemani, R. R. and Running, S. W. (2002). "Global products of
vegetation leaf area and fraction absorbed PAR from year one of MODIS data."
Remote Sensing of Environment 83(1-2): 214-231.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K. and Nghiem, S. V. (2003). "Soil
moisture retrieval from AMSR-E." Ieee Transactions on Geoscience and Remote
Sensing 41(2): 215-229.
Owe, M., de Jeu, R. and Walker, J. (2001). "A methodology for surface soil moisture and
vegetation optical depth retrieval using the microwave polarization index." IEEE
Trans. Geosci. Remote Sens. 39(8): 1643-1654.
Page 24
Pampaloni, P. and Paloscia, S. (1986). "Microwave emission and plant water content:A
comparison between field measurements and theory." IEEE Trans. Geosci.
Remote Sens. GE-24: 900-905.
Piepmeier, J. R. and Gasiewski, A. J. (2001). "High-resolution passive microwave
polarimetric mapping of ocean surface wind vector fields." IEEE Trans. Geosci.
Remote Sens. 39: 606-622.
Reichle, R. H., McLaughlin, D. B. and Entekhabi, D. (2001). "Variational data
assimilation of microwave radiobrightness observations for land surface
hydrology applications." Ieee Transactions on Geoscience and Remote Sensing
39(8): 1708-1718.
Robock, A., Luo, L. F., Wood, E. F., Wen, F. H., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Lohmann, D., Cosgrove, B., Sheffield, J., Duan, Q. Y., Higgins, R. W.,
Pinker, R. T., Tarpley, J. D., Basara, J. B. and Crawford, K. C. (2003).
"Evaluation of the North American Land Data Assimilation System over the
southern Great Plains during the warm season." Journal of Geophysical ResearchAtmospheres 108(D22).
Schaake, J. C., Duan, Q. Y., Koren, V., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Robock, A., Lettenmaier, D. P., Lohmann, D., Cosgrove, B., Sheffield, J., Luo, L.
F., Higgins, R. W., Pinker, R. T. and Tarpley, J. D. (2004). "An intercomparison
of soil moisture fields in the North American land data assimilation system
(NLDAS)." Journal of Geophysical Research-Atmospheres 109(D1).
Schmugge, T. J., Kustas, W. P., Ritchie, J. C., Jackson, T. J. and Rango, A. (2002).
"Remote sensing in hydrology." Advances in Water Resources 25(8-12): 13671385.
Page 25
Ulaby, F. T., Dubois, P. C. and van Zyl, J. (1996). "Radar mapping of surface soil
moisture." J. Hydrol. 184: 57-84.
Vinnikov, K. Y., Robock, A., Speranskaya, N. A. and Schlosser, A. (1996). "Scales of
temporal and spatial variability of midlatitude soil moisture." Journal of
Geophysical Research-Atmospheres 101(D3): 7163-7174.
Walker, J. P., Willgoose, G. R. and Kalma, J. D. (2002). "Three-dimensional soil
moisture profile retrieval by assimilation of near-surface measurements:
Simplified Kalman filter covariance forecasting and field application." Water
Resources Research 38(12).
Wan, Z., Zhang, Y., Zhang, Q. and Li, Z.-L. (2002). "Validation of the land-surface
temperature products retrieved from Terra Moderate Resolution Imaging
Spectroradiometer data." Remote Sens. Environ. 83(163-180).
Western, A. W., Bloschl, G. and Grayson, R. B. (2001). "Toward capturing
hydrologically significant connectivity in spatial patterns." Water Resources
Research 37(1): 83-97.
Western, A. W., Grayson, R. B. and Green, T. R. (1999). "The Tarrawarra project: high
resolution spatial measurement, modelling and analysis of soil moisture and
hydrological response." Hydrological Processes 13(5): 633-652.
Wilson, D. J., Western, A. W. and Grayson, R. B. (2004). "Identifying and quantifying
sources of variability in temporal and spatial soil moisture observations." Water
Resour. Res. 40(W02507): doi:10.1029/2003WR002306.
Page 26
Table 1. Statistics for the watershed sampling of Walnut Creek. The average soil
moisture and standard deviations are determined from the provided means at each
site.
Date
Samples Average SM Avg SDev.
AMSR*
6/25/2002
272
12.784
2.626
9.5
6/26/2002
273
12.079
2.805
-
6/27/2002
273
11.253
2.331
7.0
7/1/2002
103
9.511
1.626
8.5
7/5/2002
271
14.837
2.458
-
7/6/2002
273
14.379
2.163
14.0
7/7/2002
273
18.447
2.684
28.5
7/8/2002
273
16.637
2.332
17.0
7/9/2002
273
15.326
2.426
17.0
7/11/2002
260
26.379
1.805
26.5
7/12/2002
273
25.211
2.079
-
* AMSR values indicate the resampled pixel that encompasses the Walnut Creek catchment ~ 65% of the pixel
Page 27
Figure 1. Soil moisture estimates retrieved for July 9 from (a) the original PSR resolution;
(b) the 0.125 degree product; and (c) the coincident AMSR retrieval. The dotted grid
represent the LDAS 0.125 lattice and the dashed line the PSR area overlain on the AMSR
grid.
Figure 2. Profile soil moisture (2”) as measured at the SCAN site during the SMEX 02
study period. AMSR retrievals corresponding to the SCAN site are separated into AM
(red) and PM (yellow) overpasses. Precipitation at the SCAN site is also plotted.
Figure 3. Comparison of the in-situ theta-probe measurements with the 0.125 degree PSR
measurements, and a scatter plot of the retrieved AMSR predictions and the catchment
average theta-probe soil moisture.
Figure
4.
Regional
theta
probe
sampling
results
for
(a)
the
average
of
Site 8 and Site 9 and (b) the entire region, with bars showing the standard deviation of
measurements for each day. The corresponding single pixel AMSR retrievals are shown
in (c), along with the regional average of the AMSR pixels (d) and their daily regional
standard deviation. The solid lines repeat the theta probe samples from (a) and (b).
Figure 5. PSR and AMSR responses during the SMEX 02 campaign (left) and coincident
PSR and AMSR estimates. Dashed lines indicate the precipitation events - more clearly
represented in Figure 3 below.
Figure 6. PSR (left) and AMSR (right) soil moisture retrievals at 0.125 degrees
throughout the SMEX 02 campaign. While images are not strictly collocated due to
geometric differences, for the purposes of visual comparison the spatial and temporal
agreement is sufficient.
Figure 7. LDAS predicted daily precipitation totals for rain events occurring within the
SMEX period.
Page 28
(a)
(b)
(c)
Figure 8. Soil moisture estimates retrieved for July 9 from (a) the original
PSR resolution; (b) the 0.125 degree product; and (c) the coincident
AMSR retrieval. The dotted grid represent the LDAS 0.125 lattice and
the dashed line the PSR area overlain on the AMSR grid.
Page 29
Figure 9. Profile soil moisture (2”) as measured at the SCAN site during
the SMEX 02 study period. AMSR retrievals corresponding to the SCAN
site are separated into AM (red) and PM (yellow) overpasses.
Precipitation at the SCAN site is also plotted.
Page 30
Figure 10. Comparison of the in-situ theta-probe measurements with the
0.125 degree PSR measurements, and a scatter plot of the retrieved
AMSR predictions and the catchment average theta-probe soil moisture.
Page 31
(a)
(c)
(b)
(d)
Figure 11. Regional theta probe sampling results for (a) the average of
Site 8 and Site 9 and (b) the entire region, with bars showing the standard
deviation of measurements for each day. The corresponding single pixel
AMSR retrievals are shown in (c), along with the regional average of the
AMSR pixels (d) and their daily regional standard deviation. The solid
lines repeat the theta probe samples from (a) and (b).
Page 32
Figure 12. PSR and AMSR responses during the SMEX 02 campaign
(left) and coincident PSR and AMSR estimates. Dashed lines indicate the
precipitation events - more clearly represented in Figure 3 below.
Page 33
PSR
AMSR
PSR
AMSR
June 27
Vol./Vol.
July 9
July 1
Vol./Vol.
July 10
July 4
Vol./Vol.
July 11
July 8
Vol./Vol.
July 12
Figure 13. PSR (left) and AMSR (right) soil moisture retrievals at 0.125
degrees throughout the SMEX 02 campaign. While images are not strictly
collocated due to geometric differences, for the purposes of visual
comparison
the
spatial
and
temporal
agreement
is
sufficient.
Page 34
July 4
July 5
July 6
July 7
July 10
July 11
Figure 14. LDAS predicted daily precipitation totals for rain events
occurring within the SMEX period.
Page 35