Year 3 Progress Report and Budget Augmentation
Submitted to
National Aeronautics and Space Administration
Validation Studies for Data Products from the Earth Observing System AQUA (PM)
Platform and EOS-related Spectroscopic Studies
NASA/GSFC
(David Star, EOS Validation Scientist)
Land Surface Modeling Studies in Support of
AQUA AMSR-E Validation
Renewal Date: August 31, 2004
Principal Investigator:
Eric F. Wood
Department of Civil Engineering and Operations Research
Princeton University
Princeton, NJ
08540
E-mail: efwood@princeton.edu
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
Land Surface Modelling Studies in Support of AQUA AMSR-E Validation
1.
INTRODUCTION
The AMSR-E validation plan includes an evaluation of the derived level-2 and level-3
soil moisture product to assure that AMSR-E provides products that are both accurate and
appropriate for hydrological modeling. A combination of process based hydrological modeling,
airborne remote sensing, and the simulation of the AMSR-E measurements, including the
AMSR-E antenna pattern, orbital characteristics, and the gridded products is being carried out to
provide support to the AMSR-E validation. The investigation focuses on understanding the
heterogeneity at various spatial scales for validation and the validation of AMSR-E soil moisture
products with data at different depths from in-situ measurements and hydrological modeling.
The project is structured around four tasks, as follows:
Task 1: Test and Extend the Land Surface Microwave Emission Model (LSMEM)
The LSMEM simulates the microwave emission from the land surface, and includes the
effect of soil moisture, vegetation and temperature on the surface emission, which is measured at
the TOA by AMSR. The LSMEM will be further tested using data from recent field experiments
in the Southern Great Plains. These field data will help us to compare model predictions with
airborne measurements of microwave brightness over a range of frequencies.
Task 2: Produce Real-time AMSR-E Simulated Science Data Products.
Through running LSMEM with the surface conditions estimated from our North
American Land Surface Data Assimilation System (NLDAS) project at 1/8th degree (~10 km),
which produces the land surface hydrology over the contiguous U.S., hourly AMSR-E brightness
temperatures at real time are being simulated. Also, a retrieval of AMSR-E 25 km soil moisture
will be estimated with LSMEM and supplementary data sets from NLDAS. Using LDAS
validation point soil moisture data, we will evaluate the quality of the NLDAS (VIC-3L) surface
hydrology simulations.
Task 3: Produce Retrospective SMMR Simulated Microwave Brightness
Temperatures
In a retrospective mode, we plan to utilize the simulations from NLDAS with our
LSMEM to simulate the SMMR 6.9 GHz brightness temperatures. These simulations will help
the AMSR-E science team better understand the spatial and temporal variability of these
brightness temperatures, and therefore help to better interpret the AMSR-E data. This becomes
more significant due to the high RFI at AMSR-E C-band observations.
Task 4: Evaluate AMSR-E Validation Plans
Through comparing soil moisture data from in-situ measurements, air borne remote
sensing, and hydrological modeling, this task will provide critical insight into the validation
sampling plans. This will involve understanding the sub-grid heterogeneity and how to use
hydrological modeling to upscale the in-situ point data to a product comparable with AMSR-E
25km soil moisture product, as well as the possible problem due to the different depths
represented by different data sources.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
2.
ACTIVITIES DURING THE LAST YEAR
During this year, research at Princeton focused in four areas: (i) completing the task using
LSMEM to estimate soil moisture from the airborne ESTAR observations during SGP99
(Task 1); (ii) retrieving AMSR-E soil moisture from the Soil Moisture Experiment (SMEX02)
AMSR-E field validation campaign in Iowa (Tasks 1 and 4); (iii) comparing AMSR-E and TMI
soil moisture retrievals over the Southern Great Plains using LSMEM (Task 2); and (vi) initiate
joint validation activities in Australia.
2.1 Using LSMEM to Estimate Soil Moisture from ESTAR Observations During SGP99
The SGP99 data provided a comprehensive data set for evaluating microwave remote
sensing of soil moisture algorithms that involve complex physical properties of soils and
vegetation. LSMEM is used to retrieve soil moisture from brightness temperatures collected by
the airborne ESTAR L-band radiometer. This work was published in Gao et al. (2004).
Figure 1 shows the validation of the averaged soil moisture for the sites in each region.
The root mean square errors are: CF=2.8%, ER=2.3%, LW=1.8%, and 2.1% for all sites.
Compared to other microwave soil moisture retrieval algorithms, the LSMEM performs very
well (Jackson et al., 1999). This encourages further application of this physical model in
retrieving soil moisture from AMSR-E.
Figure 1. Validation of the averaged soil moisture for the CF, ER, and LW.
2.2 Soil Moisture Experiment, Iowa, USA 2002
AMSR 25km brightness temperatures were resample to a 1/8th degree grid using a simple
nearest neighbour interpolation scheme. The re-sampling of the data was necessary in order to
integrate the brightness temperature measurements into our existing analysis framework. In
future, soil moisture retrievals will be conducted at the original satellite pixel scale, however, it is
not anticipated that results would be significantly different due to the minimum of data
interpolation inherent in the nearest neighbour technique. Data were analysed to coincide with
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
the SMEX campaign, which extended from June 25 – July 12. AMSR retrievals start from June
19, the earliest data available from the Marshall Space Flight Centre archive
(ariel.msfc.nasa.gov), and continue until the end of July.
A detailed Geographical Information System (GIS) was developed for Iowa, focusing on
the study region, including such data as a digital elevation model of the state, stream network and
hydrology within the region, remotely sensed leaf area index and NDVI information as well as
numerous other ancillary data sets. The details of this analysis are not explored or discussed
herein, but are focus of current and continuing research. Table 1 details the contents of this
developing data inventory.
Table 1. Inventory of GIS compiled for study of AMSR validation (IA).
Name
Description
Source
DEM
90m digital elevation model of Iowa
1:100,000 hydrography network provides a network of
rivers and streams, including intermittent streams,
ditches, and canals.
Site information, spatial extents, sampling locations,
instrument locations etc…
USGS 90m
USDA/NRCS - National
Cartography & Geospatial
Center
SMEX-02 data distribution
at NSIDC
Derived from MODIS Land
Surface Products
Stream Network
Ancillary Data
Vegetation
250m NDVI and 1km LAI data
HYDRO1K
Compound Topographic Index, Slope, Aspect,
Drainage Basins etc…
Land Process DAAC
Analysis of the available data focuses on the Walnut Creek sampling location due to the
density of available measurements (see Figure 2), and the installation of a SCAN site (Soil
Climate Analysis Network) near Ames, which provides a longer term measurement of profile
soil moisture at a variety of depths.
A. AMSR Soil Moisture Comparison with the Polarimetric Scanning Radiometer (PSR)
The PSR, an airborne microwave imaging radiometer operated by the NOAA environmental
Technology Laboratory (Piepmeier and Gasiewski, 2001), was flown aboard the NASA P-3
aircraft for the purpose of obtaining polarimetric microwave emission. It has been successfully
used in several major experiments including SGP99 (Jackson et al., 2002). The PSR data
provides an excellent intermediary source of validation information between the AMSR pixel
and the ground based measurement. The PSR provides the only feasible option to comparing
predictions with a reasonable spatial equivalence to the AMSR footprint and measurement
characteristics. 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.
com. Dr Rajat Bindlish). The PSR measurements supplied ten complete moisture maps of the
region, encompassing an area of approximately 0.7x1 (see Figure 2). Given the scale
difference between the AMSR and PSR measurements, it is expected that a number of
underlying surface physical and hydrological influences would contribute to soil moisture
differences.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
Figure 2. DEM of Iowa with the regional sampling locations across the SMEX
domain. Inset is a sample image of the PSR soil moisture measurement at
supplied resolution (~800m).
To examine these scale effects and to allow a more equivalent comparison with the AMSR
footprint, the PSR moisture measurements were resampled at a variety of resolutions from 1km
to 25km in order to assess the consistency of sub-pixel statistical variation. The analysis was
divided broadly into dry and wet periods, corresponding to the marked meteorological periods
during SMEX 02. As expected, the results indicate a preservation of the statistical features across
scales, retaining many of the visual features evident in the highest resolution imagery even at
larger scales. PSR data resample to 1/8th degree was compared to the corresponding AMSR
retrieved values for all available data, with results presented in Figure 3. There appears to be a
consistent bias between the PSR and AMSR imagery, with PSR values generally higher than
corresponding AMSR predictions. The general trend however is well reflected, although AMSR
responds more sharply to incident precipitation which occurred on the 4, 6 and 10 July. It should
be noted that the PSR soil moisture retrievals are determined from C-Band measurements
(6.9GHz) whereas the AMSR retrievals are based on X-Band measurements (10GHZ). It is not
believed that this should significantly influence the soil moisture distributions seen here,
although there is likely a different sampling depth being sensed.
Figure 3. 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.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
Figure 6 and Figure 7 present the areal imagery derived from the PSR and AMSR soil
moisture retrievals. The level of agreement between the PSR and AMSR visual comparisons is
particularly pleasing, and indicates that some confidence can be placed in the remotely sensed
retrievals, even at apparently coarse resolutions. The results of the AMSR analysis indicates that
there is some utility in assimilating these predictions into the LDAS framework to improve the
representation of the soil moisture dynamics.
B. AMSR Comparisons with Ground Based in-situ SCAN Network
i. Ames SCAN site (4201’, 9344’) http://www.wcc.nrcs.usda.gov/scan/
The Soil Climate Analysis Network (SCAN) site offers a continuous and consistent data set
to the theta probes used during the SMEX campaign. The AMES SCAN site has been in
operation since 9/23/2001 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). Figure 4
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, 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.
Overall, the AMSR retrievals, although obviously influenced by pixel-to-point scale and
measurement disparities, reflects well the trends observed in the SCAN response.
Figure 4. 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 and PM overpasses. Precipitation at the SCAN site is also
plotted. Curves are for visual reference only.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
ii. Regional Sampling over the SMEX Domain
The regional soil moisture sampling during SMEX was designed to capture the broader scale
soil moisture pattern at the satellite footprint scale, and incorporated 46 unique sites distributed
over the SMEX domain. Of these, Site 8 and Site 9 corresponded to positions within the area of
the watershed sampling at Walnut Creek and within one of the resampled AMSR pixels. The grid
of individual sample sites covers a domain of approximately 50 km by 100 km (2 by 4 AMSR
pixels) and measurement sampling was timed to coincide with the afternoon AMSR overpass.
Results for the regional analysis are shown in Figure 5, showing the average volumetric
moisture contents at Site 8 and Site 9 along with the regional mean, as measured using a theta
probe. The bars at each of the sample days indicates the total standard deviation at the site(s).
Figure 5(c,d) also detail the AMSR distribution for the equivalent area encompassing Site 8 and
Site 9 and the entire region. The general patterns are well represented across the regional and site
averages. The stability during the dryer periods preceding July 4 is clearly shown in the AMSR
responses. A significant amount of noise is evident in the AMSR regional 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.
(a)
(c)
(b)
(d)
Figure 5. Regional theta probe sampling results for the (a) average of
Site 8 and 9 and (b) entire region. Bars depict 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).
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
Figure 6. PSR soil moisture measurements for SMEX at 1/8th degree. Values are
not completely equivalent to the AMSR and LDAS measurements in proceeding
figures due to geometric and interpolation restrictions. The date of the overpass
can be seen in the title of each image.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
Figure 7. LSMEM derived AMSR soil moisture measurements at 1/8 degree.
Time stamps represent AM (08) and PM overpasses (20). Where PSR coincident
overpasses were not available, the next nearest AMSR overpass has been used.
To visually compare with the PSR, ignoring one pixel from each of the outside
edges will approximate an equivalent area. The date of the overpass can be seen
in the title of each image, along with the average soil moisture.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
iii. 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 this 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 (Figure 8) was intended to
effectively capture the level of spatial heterogeneity of the point scale soil moisture.
SCAN site
Walnut
Creek
Figure 8. Sampling strategy for the point scale watershed measurements taken
over the Walnut Creek site. Underlying image is a DEM which indicates a 60m
relief from catchment boundary to outlet. The sampling regime composes
approximately 65% of a resampled (1/8) AMSR pixel (dashed line).
AMSR retrievals were compared with the areal mean of the average soil moisture recorded
for each site. Table 2 details the statistical properties of the in-situ distribution and the coincident
AMSR pixel, also shown in Figure 9.
Table 2. Statistics for the watershed sampling of Walnut Creek
Date
6/25/2002
6/26/2002
6/27/2002
7/1/2002
7/5/2002
7/6/2002
7/7/2002
7/8/2002
7/9/2002
7/11/2002
7/12/2002
Samples
272
273
273
103
271
273
273
273
273
260
273
Average SM
12.78421
12.07895
11.25263
9.510526
14.83684
14.37895
18.44737
16.63684
15.32632
26.37895
25.21053
St. Dev. SM
2.626316
2.805263
2.331579
1.626316
2.457895
2.163158
2.684211
2.331579
2.426316
1.805263
2.078947
AMSR*
9.5
7.0
8.5
14.0
28.5
17.0
17.0
26.5
-
* AMSR values indicate the resampled pixel that encompasses the Walnut Creek catchment ~ 65% of the pixel
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 for the region and reflected
in the imagery derived from the PSR measurements (see Figure 6). The equivalence with the
AMSR pixel is excellent, particularly given the scale disparity between the two approaches and
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
also the different sampling depths of the techniques (6cm for theta probe). Although only eight
samples were available to be compared, 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. The
Figure 9. Comparison of the in-situ theta-probe measurements with the PSR
derived measurements, and a scatter plot of the retrieved AMSR predictions and
the catchment average theta-probe soil moisture.
Figure 10 below illustrates all of the available data during the SMEX campaign, allowing
intercomparison of the various data and their agreement with the AMSR predictions.
Figure 10. Available validation data, satellite and airborne retrievals for the
duration of the SMEX field experiment.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
2.3 Oklahoma Mesonet Data and TRMM-TMI Comparisons
A. AMSR-E and TRMM-TMI Soil Moisture Retrieval
Soil moisture values were retrieved from AMSR-E 10.7GHz brightness temperatures in
Oklahoma from Jun. 2002 to Dec. 2003. These products were compared with those from TRMM
Microwave Imager (TMI), the Oklahoma mesonet, and the LDAS-VIC model. The usefulness of
AMSR-E 6.7GHz (C band) is questionable because of RFI. Therefore the 10.7 GHz (X band)
may be the only frequency available for soil moisture estimation. It is therefore useful to intercompare soil moisture estimates from the two instruments, especially since we have a good
understanding the accuracy of the TMI values. The AMSR-E soil moisture was thus evaluated
through comparison with remote sensing products, long-term field observation, as well as
hydrological modeling. A five-year (1998-2002) TRMM/TMI soil moisture product over
southern US has been developed and validated by Princeton University (Gao et al., 2003).
Figure 11 illustrates a sample of the soil moisture patterns retrieved from AMSR-E and TMI,
demonstrating significant similarity in which wet areas and their drying are consistent between
the two sensors. The slight value differences are likely due to different overpass time.
AMSR-E retrieved soil moisture July 23, 2002
TMI retrieved soil moisture July 23, 2002
AMSR-E retrieved soil moisture July 25, 2002
TMI retrieved soil moisture July 25, 2002
Figure 11. Retrieved soil moisture on July 23 and July 25, 2002 from AMSR-E
(left) and TMI (right).
To further examine how the two datasets behave over longer time period and larger scales,
the pixel values over the OK mesonet sites were averaged and plotted in Figure 12, resulting in a
RMSE of approximately 3%. In Figure 13, the pixel/point values over the mesonet sites are
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
averaged for AMSR-E, TMI, mesonet, and the LDAS-VIC model. Considering the physical
properties of the data sources, these curves exhibit a reasonable consistency, indicating that:
a) All responses share similar trends throughout the observation period
b) AMSR-E and TMI are closely related
c) Mesonet data tends to be larger than the remote sensing results as the measurements are
for 5cm sampling depths
d) LDAS-VIC soil moistures, measuring a 10cm average moisture content, are the highest
overall.
Figure 12. Scatter plot of averaged AMSR-E and TMI soil moisture over
Mesonet sites from Jul. 2002 to Nov. 2002.
Figure 13. Soil moisture time series from AMSR-E, TRMM-TMI, mesonet and
LDAS-VIC.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
B. Spatial Heterogeneity Analysis for Oklahoma
Studies of sub-grid heterogeneity are aimed towards improving the AMSR-E validations. At
X-band frequency, vegetation distribution is one of the major sources for brightness temperature
heterogeneity. As an initial investigation, we calculated the variance of vegetation water content
within 1/8th degree boxes using 1/100th degree high-resolution data (Figure 14). In this relatively
simple comparison, the increased heterogeneity evident in the growing season compared to the
winter period is clearly demonstrated. Areas adjacent to water bodies or containing rivers/lakes
within their footprints will require careful discrimination to remove artefacts of pixel
contamination. The removal of such features will be aided through the development of the GIS
(discussed above) and integration of remote sensing information into that database.
Figure 14. Variance of vegetation water content on 1/8th degree resolution in
Iowa for Jan. 2002 (left) and Jul. 2002 (right).
2.4 Using NLDAS real time data as LSMEM inputs to simulate AMSR-E brightness
temperature at C-band and X-band
Using NLDAS real time data (soil moisture and soil temperatures) as LSMEM inputs, we
are starting to simulate the AMSR-E brightness temperatures at C-band and X-band during
SMEX02. We expect to use high resolution MODIS data to improve the needed vegetation data.
In addition, we will do these simulations at MODIS resolutions (1/100th degree spatially). These
simulations will be compared to the PRS airborne brightness temperatures at their full resolution
(see Figure 6). With these high resolution simulations, we will carry out systematic scaling
studies to understand the effects of surface and vegetation heterogeneity on the AMSR-E scale
brightness temperatures and to evaluate the accuracy of AMSR-E retrieved soil moisture from
limited point measurements (like the SCAN sites).
2.5 Participation of SMEX03 in the Southern Great Plains and SMEX04 in
Arizona/Mexico
Two people from my group took part in the SMEX03 experiment that collected data for
AMSR-E validation. In addition, we expect to participate in the SMEX04 experiment in the
NAME region (Arizona and northern Mexico. Our contribution involves watershed sampling,
regional sampling, vegetation sampling, soil property sampling, etc.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
Summary
Based on the research and analysis from SMEX02 and Oklahoma, we can conclude that:

Compared to other microwave soil moisture retrieval algorithms, the LSMEM performs very
well, which encourages further application of this physical model in retrieving soil moisture
from space-borne platforms such as AMSR-E.

Using intensive measurements from Oklahoma Mesonet, validation results during SGP99
and SMEX02 experiment, the retrieved soil moisture using the LSMEM over AMSR-E
footprint scales (watersheds and study areas) is less than 3.5% volumetric soil moisture. This
is within the stated accuracy of the AMSR-E plan.
Spatial heterogeneity is the main problem we need to solve for AMSR-E validation, as can be
seen from the poorer comparisons between AMSR-E and the single SCAN soil moisture station.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
3.
YEAR THREE PLANNED ACTIVITIES
The following activities will be the focus of the next year:
1. Participation in the SMEX04 validation fieldwork, being planned for July in the NAME
region (Arizona and northern Mexico (see http://hydrolab.arsusda.gov/smex04/ for more
details)
2. Produce real-time AMSR-E simulated brightness temperatures with parameters collected
from SMEX02 and SMEX03. This task will carry out the simulations at high spatial
resolution and scale to the AMSR-E resolution to understand the scaling between the
field-scale validation data and the AMSR-E resolution.
3. Initiate AMSR-E validation activities in Australia.
We have a unique opportunity to expand the AMSR-E validation by partnering with colleagues
in Australia, who are carrying out field work that is useful to our validation activities.
3.1
AMSR Validation Studies in the Australian Arid and Semi-Arid Zones
Recent efforts to extend the estimation of soil moisture products to a truly global scale
have focussed on the collection of sampling data in the semi-arid/arid environments of central
Australia. In collaboration with investigators at the University of Melbourne (Dr. Jeff Walker),
inter-platform and inter-algorithm comparisons of soil moisture content with in-situ data are
currently being performed. The results from this analysis are expected to strengthen the
robustness of existing calibrations, and offer insight into the dynamics of surface moisture
responses in this important and globally representative eco-system.
Figure 15. Sites within continental Australia offering facility for soil moisture
evaluations over a number of different surface conditions.
Two three week long validation trips to the Australian arid zone, a region that comprises
more than 70% of the Australian land mass, were undertaken during 2003. Ground measured
near-surface soil moisture content is planned to be compared with both the beta version of the
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
AMSR-E soil moisture product and LSMEM derived soil moisture. The in-situ moisture data
collected at four sites (Figure 15) in the arid zone at two times of the year will compared against
satellite derived estimates. Due to fortuitous timing of meteorological events, duplicate sampling
at several sites under both dry and somewhat wetter conditions was undertaken (Figure 16). A
time series comparison of the two soil moisture retrievals against rainfall records has shown
some encouraging results, with both retrievals responding correctly to rainfall events. The
relative homogeneity of the region makes it an ideal area in which to conduct a remote sensing
validation program.
Figure 16. Sampling regime undertaken in South Australia during the 2003 field
campaign. Dots represent sample locations, with colours depicting the relative
soil moisture content.
An additional study site located on the east coast of Australia offers a further opportunity to
explore the behaviour of soil moisture over varying climatic and hydrological conditions. The
study site for this project is the semi-arid Goulburn River catchment situated approximately
350km north-west of Sydney, on the east coast of Australia. The northern half of this catchment
has predominantly low to moderate vegetation cover and is used for cropping and grazing, while
the southern half of the catchment is more heavily vegetated, and includes a National Park. Soils
in the northern section are predominantly basalt derivates while those in the south are sandstone
derivatives. There are a total of 26 soil moisture monitoring sites, 8 stream gauging stations and
5 climate stations providing data for this study. Further details of this data can be found at
http://www.civag.unimelb.edu.au/~jwalker/data/sasmas/.
AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
YEAR 3 BUDGET
Project: Land Surface Modeling Studies in Support of AQUA AMSR-E Validation
Investigator: Eric F. Wood
Year 3
From:
Sept. 1, 2003 to Aug. 31, 2004
COSTS
A
NASA USE ONLY
B
C
1. Direct Labor
1.1 PI (summer)
13,945
1.2 Research Associates
17,258
1.3 Graduate Student (academic year)
8,450
1.4 Graduate Student (summer)
3,250
1.5 Indirect benefits on 1.1 & 1.2
10,578
Subtotal (salaries, wages and benefits)
53,480
2. Other Direct Costs:
2.1 Graduate Student Tuition
6,950
2.2 Travel
2,500
2.3 Computer Equipment
2.4 Computer Services
2.5 Supplies
2.6 Publication
0
1,400
200
1,000
2.7 Sub-contracts
2.8 Consultants
3. Indirect Costs
33,977
4. Other Applicable Costs
5. SUBTOTAL -- Estimated Costs
99,507
6. Less Proposed Cost Sharing (if any)
7. Carryover Funds (if any)
8. Total Estimated Costs
99,507
9. APPROVED BUDGET
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xxxxxxxxxxx
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AMSR-E progress report NAG5-1111 “Land Surface Modelling Studies in Support of AQUA AMSR-E Validation (EF Wood)
References
Gao, H., Wood, E. F., Drusch, M., McCabe, M. F., Jackson, T. J. and Bindlish, R. (2003). "Using
TRMM/TMI to retrieve soil moisture over Southern United States from 1998 to 2002."
EOS Trans. AGU Suppl 84(46).
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.
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