An evaluation of soil moisture predictions derived from AMSR...

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)

[email protected] (Email)

April 30, 2004

mailto:[email protected]

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.

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

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

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

http://hydrolab.arsusda.gov/

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 ( ) as :

(1)

where and 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):

(2)

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.

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

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

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

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

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

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.

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

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

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

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

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

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 XXX-

XXX XXX.

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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.

* AMSR values indicate the resampled pixel that encompasses the Walnut Creek catchment ~ 65% of the pixel

Page

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 -

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.

(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.

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.

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.

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).

(a) (b)

(c) (d)

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.

PSR AMSR PSR AMSR

June 27 Vol./Vol. July 9

July 1 Vol./Vol. July 10



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.

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.

An evaluation of soil moisture predictions derived from AMSR...

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