1. Introduction
Increasingly, methods of data assimilation are being applied to both hydrological and hydrometeorological problems driven by prospects of better characterization of initial conditions and improved forecasting skill (Mecikalski et al. 1999; Reichle et al. 2001; Crosson et al. 2002; Reichle et al. 2002; Heathman et al. 2003; Merlin et al. 2006; Pan et al. 2008; Wang and Cai 2008; Barrett et al. 2008). The benefits afforded by the application of data assimilation approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models (Jones et al. 2004; Huang et al. 2008), improved energy partitioning between latent and sensible heat fluxes (Pipunic et al. 2008), and a concomitant higher skill in quantitative precipitation forecasts (Koster et al. 2000). For example, it has been shown that updating soil moisture in a numerical weather model using passive microwave observations at daily intervals leads to an increase in precipitation forecast skill (Boussetta et al. 2008). In hydrologic applications, these methods have led to better estimation of antecedent soil moisture (Walker et al. 2001; Reichle et al. 2004; Reichle and Koster 2005; de Lannoy et al. 2007) with the potential for improved prediction of water availability, runoff, streamflow and flood discharge (Bach and Mauser 2003; Oudin et al. 2003; Scipal et al. 2005; Weerts and Serafy 2006), particularly in situations where stream hydrographs are sparse or nonexistent (Barrett et al. 2008). Studies have demonstrated that combining observations of soil moisture with water budget models using data assimilation techniques on time scales of 1–3 days improves the prediction of modeled flows (Aubert et al. 2003; Pan et al. 2008). To capitalize on these benefits, it is necessary to develop and tailor methods for assimilating multiple types of observations into hydrological and hydrometeorological models.
Satellite observations in optical, thermal, and microwave wavelengths can provide indirect information on hydrologic “target” variables, such as profile soil moisture content and evapotranspiration. It is through “observation operators” that model target variables are transformed into the radiometric quantities observed by satellite sensors. The efficacy with which observations constrain model states depends on both the sensitivity of the observation to perturbation in the state and the magnitude of observation error. A yet untested assertion is that through the improved conditioning of model states by satellite observations, the evolution of a hydrologic model in time will more faithfully represent true system dynamics. A limited analysis of observation operators for satellite data and their sensitivity to the perturbation of model states has been completed (e.g., Jones et al. 2004).
Thermal observations from satellites provide information on the earth’s surface radiative properties via derived land surface temperature (LST). For over two decades, models of varying sophistication have been used to relate top-of-canopy LST to evaporative fluxes (Mecikalski et al. 1999; Boni et al. 2001; Caparrini et al. 2004; Sobrino et al. 2007). More challenging, however, is the diagnosis of profile soil moisture from LST using data assimilation methods (Crow et al. 2008), which requires explicit knowledge of the relationships between soil moisture, canopy resistance, and the partitioning of energy between the soil surface, vegetation canopy, and the atmosphere. In principle, these relationships can be exploited in data assimilation to provide information on profile soil moisture content (Entekhabi et al. 1994; Huang et al. 2008), but it remains uncertain as to the range of conditions under which these relationships hold.
Passive microwave observations are used to infer surface soil moisture content from space by exploiting the relationship between brightness temperature and water content via the dielectric properties of the soil mixture (Entekhabi et al. 1994; Njoku et al. 2003; Gao et al. 2006). Experiments have shown (Crow et al. 2001; de Jeu and Owe 2003; Moran et al. 2004; McCabe et al. 2005; Prigent et al. 2005) that inversion schemes based on relatively simple microwave radiative transfer (MRT) theory can yield reliable estimates of volumetric soil moisture content for soil layers 1–5-cm depth, depending on whether C-, X-, or L-band radiation is used. Accurate soil moisture retrieval requires information on surface emissivity, canopy optical thickness, vegetation and soil temperatures, and proportions of soil clay and sand contents. However, the quality of the retrieval may be compromised by radio frequency interference (RFI), standing water, scattering by dense woody vegetation, and liquid water droplets in overlying clouds (Njoku et al. 2003; de Jeu and Owe 2003; Wagner et al. 2007).
In data assimilation, it is of interest to know whether observations, such as thermal and microwave satellite data, are effective “constraints” on the model states and, conversely, under which conditions these observations contribute little to the analysis. We define “observational constraint” as the degree to which an additional observation of specified standard deviation reduces error in the initial model estimate of state variables (i.e., “background” state).
An important component of an assimilation scheme is the observation operator H, which comprises model code that defines the relationship between an observation and the relevant model state. Where H is continuous and differentiable, it is possible to generate the first derivative of the operator known as the Jacobian. The Jacobian is required in variational data assimilation applications to efficiently compute optimal values of state variables and their errors. The most efficient way to calculate the Jacobian is to first determine the tangent linear model (TLM) of the observation operator. The TLM is model code that computes the action of the Jacobian on the state vector (Giering 2000) and can also be used to efficiently analyze the effect of perturbations in state variables on model output at any point on the model trajectory (Giering and Kaminski 1998). Thus, the TLM has intrinsic value as an efficient tool for examining model sensitivity (Errico 1997).
In this paper, we use TLMs to assess the sensitivity of satellite thermal and microwave observations to perturbation in soil moisture. From this analysis, the efficacy of combining these satellite observations in data assimilation as constraints on profile average soil moisture content
2. Observation operators and tangent linear models
a. Land surface temperature observation operator
The starting point for both observation operators is the two-layer surface energy balance (SEB) model of Shuttleworth and Wallace (1985), Friedl (1995), Norman et al. (2003), Anderson et al. (1997), and Friedl (2002). In a two-layer SEB, the soil and vegetation layers are sources of latent and sensible heat fluxes mediated by aerodynamic, canopy, boundary layer, and soil surface resistances. The fluxes are driven by gradients in temperature and water vapor between the sources, air in the canopy volume, and the overlying turbulent atmosphere. The canopy conductance function relates the transfer of water vapor across the resistance network to soil moisture, atmospheric vapor pressure deficit, and light availability by means of scaling functions (e.g., Cox et al. 1998). Six nonlinear equations describing the energy partitioning between vegetation canopy, soil surface, and the overlying atmosphere are solved numerically to yield vegetation, soil and aerodynamic temperatures, and water vapor pressures (Friedl 1995, 2002). The top-of-canopy land surface temperature is determined by the canopy and soil temperatures derived from the SEB model and also from the sensor view zenith angle, which determines the proportion of soil visible to an orbiting satellite sensor.
b. Microwave brightness temperature observation operator
A coupled SEB–MRT model was used to generate the microwave observation operator. This coupled model used all of the equations from the previous subsection to provide soil and vegetation temperatures (Tss and Tυ) to the MRT model of Mo et al. (1982), which itself has widespread use for retrieving land surface layer soil moisture content (Njoku et al. 2003; Owe et al. 2001; de Jeu and Owe 2003). Microwave brightness temperature Tb at a given frequency is related to canopy and soil temperatures, soil emissivity, vegetation optical thickness, and soil dielectric properties. The soil dielectric constant is strongly sensitive to the variation in soil moisture, and this provides the basis for the estimation of surface moisture content from passive microwave emissions.
c. The tangent linear models and model sensitivity
To generate the tangent linear models, each equation in each of the observation operators was differentiated with respect to specified “active” variables (Giering and Kaminski 1998). The active variables comprised all the variables in each operator that were directly a function of the target variables,
The TLMs were used to calculate the variations δTs and δTb based on the perturbations δ
d. Efficacy of satellite observations as constraints
3. Study area and datasets
a. Murrumbidgee River catchment
Investigation focused on a 200 000 km2 region of interest (ROI) located in southeast Australia. The study area surrounds the Murrumbidgee River catchment (∼87 000 km2) and was specifically chosen to illustrate the ranges in Ts and Tb across a diverse set of land uses, terrain, and climates in Australia. Across this ROI topography varies from the alpine areas of the Kosciusko National Park in the southeast to the low-lying plains of the Riverina in the west. Mean annual rainfall varies from nearly 1000 mm in the east to less than 200 mm in the west. Land use in the Murrumbidgee River catchment includes forestry, national park, dryland, irrigated cropping, and grazing. Data used in the investigation are described below and include the static datasets that define model inputs and the time-varying satellite observations and climate forcing data. A subset of these data is shown in Fig. 1.
b. Static input datasets
Both SEB and SEB–MRT models require the input of a number of spatially varying and, for the purposes of this study, temporally static parameters and variables. Inputs range from canopy micrometeorological parameters to soil physical and chemical properties and are available either directly as digital satellite images or as spatially distributed datasets from a variety of sources.
1) Land cover classification
We used the 1-km2 land cover classification of the Australian Government Bureau of Rural Sciences that was prepared for the National Land and Water Resources audit (available online at http://adl.brs.gov.au/anrdl/php/). Land cover classes were grouped into four generalized categories: tall forests, shrublands, grasslands/crops, and water. Through the middle and northwest of the ROI is a mix of irrigated and rainfed cropping, horticulture, and grazing land uses, whereas in the mountain areas to the southeast, plantation forestry, conservation reserves, and grazing predominate.
2) Digital elevation model (DEM)
Topographic information was obtained from the 9-s (∼250-m resolution) DEM of the Australian Government Geoscience Australia (available online at http://www.ga.gov.au/nmd/products/digidat/dem_9s.htm). The elevation data for the ROI is displayed in Fig. 1a. The terrain ranges from the flat, semiarid shrub and grasslands in the northwest to the mountainous alpine terrain of the southeast.
3) Vegetation properties
The digital atlas of Australian vegetation cover of the Australian Survey and Land Information Group (1990) was used to provide vegetation cover classes consisting of growth form, canopy height (up to 30-m height), and species composition of the tallest vegetation stratum. These classes were converted to vegetation heights, zero plane displacement, roughness length, and average leaf width parameters based on generalized values for each vegetation cover class.
4) Soil properties
The Digital Atlas of Australian Soils soil texture classes were converted to porosity, clay and sand content, and field capacity using the interpretations of McKenzie and Hook (1992) and Rawls et al. (1993).
5) Leaf area index (LAI)
Estimates of LAI for the study region were obtained using the numerical inversion of the broadband canopy radiative transfer model of Sellers (1985) and satellite-derived albedo images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The visible (0.3–0.7 μm) and near-infrared (0.7–3.0 μm) albedos were obtained from the version 4 of the MODIS 16-day 0.05° global albedo product (MOD43C1; available online at http://www-modis.bu.edu/brdf/userguide/cmgalbedo.html), which are 16-day composites, normalized to local noon. The estimated LAI appears to be more realistic than that of the MODIS LAI product (MOD15A2; https://lpdaac.usgs.gov/lpdaac/products), which was shown to overestimate LAI in southeastern Australia (Hill et al. 2006). LAI varies from 0 to 1 in the northwest through to 6 in the mountain forests (Fig. 1b).
c. Satellite observations and climate driver data
1) Climate data
Climate data used in the SEB, SEB–MRT, and water budget models were obtained from the archive of interpolated surfaces of Australian meteorological station observations (Jeffrey et al. 2001). These data are gridded for the whole continent at approximately 5-km resolution and date back to 1890. Specific data used by these models were the minimum and maximum daily temperatures, daily total shortwave radiation, 0900 [local time (LT)] vapor pressure, and daily total rainfall. The Murrumbidgee ROI has received well below average rainfalls for the years 2001–08, resulting in a dry soil profile and the lowest inflows by streams into the catchment in recorded history (Cai and Cowan 2008). The period 1 September–1 November 2005 was chosen for this study because it contained a series of rainfall events with which to test the observation operators through wetting and drying cycles.
2) Land surface temperature
The most common way of estimating land surface temperature from satellite brightness temperature observations is via a split-window algorithm (SWA; Yu et al. 2008). LST retrievals via SWAs (e.g., Becker and Li 1990; Sobrino et al. 1994; Wan and Dozier 1996) exploit the differential absorption of radiation by atmospheric water vapor in spectral bands centered approximately on 11 and 12 μm, respectively. They are simple in form (representing linearizations of the thermal infrared radiative transfer equation) and are thus easy to implement operationally for large-scale estimation of LST.
SWA performance is essentially governed by the way in which the algorithm handles atmospheric water concentration, satellite view angle, and surface emissivity effects. Atmospheric water vapor may be estimated from brightness temperature data themselves (Sobrino et al. 1994), or it may be based on climatologies derived from other satellites or surface observations (Pinheiro et al. 2007). Adjustment for the increase in pathlength with off-nadir observations may be achieved explicitly by varying algorithm coefficients as a function of view angle (Wan and Dozier 1996) or by adding path correction terms to the SWA (Sun and Pinker 2005). The independent estimation of land surface emissivity can be achieved by relatively simple normalized difference vegetation index–based approaches (Sobrino and Raissouni 2000; Momemi and Saradjian 2007) or by iterative, computational intensive approaches (Gillespie et al. 1998). The resultant accuracy of LST from these methods ranges from 0.4 to 1.75 K (Sobrino et al. 1994; Wan and Dozier 1996; Qin et al. 2001; Sòria and Sobrino 2007; Wan 2008).
In this work, we have used a vegetation classification–based approach to estimate surface emissivity and applied the SWA of Key et al. (1997) to the 11- and 12-μm channel brightness temperature data from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration-18 (NOAA-18) polar orbiting satellite. These data have an approximate overpass time for the study area of 1400 LT. All NOAA-18 AVHRR imagery for the period September–November 2005 were obtained from Commonwealth Scientific and Industrial Research Organisation (CSIRO) archives (King 2003). The SWA coefficients were derived for a range of atmospheric water content and view angle scenarios with the reported accuracy compared with in situ measured LST of <2 K.
Estimates of LST at 1-km resolution across the study area for the relatively cloud-free image on 2 October 2005 are presented in Fig. 1c. Evident in these data is the northwest–southeast gradient in Ts, with higher values associated with lower LAI in the northwest, whereas cooler Ts occur at higher elevations in mountains and forests. The fine texture variation in Ts shows some coincidence with LAI, but it is mediated by soil water availability via the effect of latent and sensible heat loss on canopy temperatures. Water bodies, snow, and small amounts of residual cloud are visible as a speckling of cooler Ts across the region.
3) Microwave brightness temperatures
The earth’s atmosphere is essentially transparent to microwave radiation in the frequency range 1–15 GHz. Therefore, unlike thermal brightness temperature observations, satellite-based microwave Tb measurements may be considered direct observations of emissions from the land surface. The accuracy of microwave brightness temperature measurements is therefore a function of antenna characteristics and signal deconvolution strategies (Ashcroft and Wentz 2000). Reported instrument errors for Tb from the Scanning Multichannel Microwave Radiometer (SMMR) onboard Nimbus are between 0.3 and 0.7 K (Njoku and Li 1999). Brown et al. (2008) claim that antenna error contributes 0.9 K to the overall radiometric accuracy of 1.68 K for the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the planned Soil Moisture and Ocean Salinity (SMOS) satellite (Barré et al. 2008). Similarly, the planned Soil Moisture Active Passive (SMAP) instrument has specification for a brightness temperature relative accuracy of 1.5 K (available online at http://smap.jpl.nasa.gov). Another factor affecting the microwave brightness temperature accuracy in unguarded frequency bands (e.g., those centered on 6.9 and 10 GHz) is radio frequency interference (RFI). It has been shown that RFI is likely to contaminate microwave signals in the more populous region of the globe (Njoku et al. 2005). In the Murrumbidgee catchment study area, the error introduced by RFI is likely to be negligible based on the global distribution determined by Njoku et al. (2005), therefore this error is ignored here.
In this work, we have used the microwave brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) aboard the Aqua satellite (Njoku et al. 2003) with an approximate overpass time for the ROI of 1330 h (local time). Specifically, we have used the gridded level-3 land surface product resampled to a global 25-km grid [AMSR-E Aqua daily L3 surface soil moisture, interpretive parameters, and quality control Equal-Area Scalable Earth Grids (EASE-Grids; AE_Land3), available online at http://nsidc.org/] for the period September–November 2005.
The microwave brightness temperature observation (Tb) from AMSR-E for October 2005 (Fig. 1d) shows a different spatial pattern to Ts across the study area. The two regions where Tb are highest (∼280 K) occur in the north and southwest as a result of a dry soil surface layer. The spatial distribution of rainfall that occurred three and six days prior to the displayed image has moistened the soil immediately below the surface, leading to the visible band of cooler Tb between 270 and 275 K running northwest–southeast across the ROI. Three areas of coolest Tb are marked A1–A3 in Fig. 1d and are discussed further in section 4.
4. Results
a. Modeled Ts and Tb
Observation operators H t and Hm were used to compute modeled Ts and Tb, respectively, across the study area for daily modeled soil moisture between 1 September 2005–1 November 2005 (see later discussion of temporal variation in proportional constraint) and for two extremes of soil moisture content (i.e., wilting point and field capacity) on 2 October 2005. The single model output provides a spatial estimate of the variability in upper and lower bounds of Ts and Tb across the ROI (Fig. 2) corresponding to wilting point and field capacity modeled temperatures, respectively. Comparison of Fig. 1c with Figs. 2a and 2d show that observed Ts from the AVHRR on this day are bounded by the upper and lower modeled values of Ts, except for pixels contaminated by water, snow, or cloud. Thus, soil moistures for each pixel in Fig. 1c reside within the range between wilting point and field capacity for this region. It can also be seen that the range in modeled Ts (difference between top and bottom panels) is greatest in the lowest relief and lowest LAI parts of the landscape, whereas the least range in Ts occurred in the mountain forests of the southeast.
Modeled Tb (Figs. 2b and 2e) shows a similar southwest–northeast banding as Ts across the ROI. Aggregation of the 1-km grid cells to 25 km (Figs. 2c and 2f) allowed for direct comparison with observed Tb (Fig. 1d). Once again, the observed Tb is bounded by the upper and lower modeled Tb (Figs. 2c and 2f), except for areas marked A1–A3 in Fig. 1d. These areas have lower Tb than estimated by the model even at field capacity, suggesting that either standing water is present on the soil surface at the time of image acquisition or that open water in dams or lakes is “contaminating” observed Tb. Although major dams and lakes are associated with these areas (A1 is centered on Hume Dam, A2 is on the hydroelectric water supply dams of Lakes Eucumbene and Jindabyne, and A3 is on the natural intermittently filled Lake George), there is also temporal variation in Tb for these areas between September and November 2005 (data not presented). This indicates that intermittent standing water is a key factor that is likely to influence microwave emissions from these areas.
The range in Tb is greatest in the northwest where a large fraction of bare soil is visible to the sensor. The range in Tb is least for the high relief, tall forests (marked A2 in Fig. 1d) as a result of the presence of the high biomass, strong relief, and high vegetation moisture contents, which act to scatter passive microwave emissions from the soil. In addition, as explained earlier, the differences in the pixel pattern of modeled and observed Tb in area A2 is due to dams of the Snowy Mountains Hydroelectric Scheme’s remaining snowpack at the highest elevations and surface water, which interferes with the soil passive microwave signal.
The range of modeled Ts in Figs. 2a and 2d is plotted as a function of LAI for the three vegetation types in Figs. 3a and 3c. The highest surface temperatures for soils at wilting point (Fig. 3a) are associated with an increasing fraction of bare soil (LAI < 2). At field capacity (Fig. 3c), higher rates of evapotranspiration from soil and vegetation suppressed the range in modeled Ts across all LAI, with the most pronounced effects when LAI was less than 2.
Similar analyses were conducted on modeled microwave brightness temperature (Fig. 4). The range of modeled Tb in Figs. 2b and 2c is plotted as a function of LAI in Figs. 4a and 4c. These data show an increase in Tb with decreasing LAI in dry soils. This increase in Tb (Fig. 4a) is due to both a decrease in the surface dielectric constant under dry conditions at lower biomass densities and a higher soil source temperature with increased radiation incident on the soil surface. For soils at field capacity (Fig. 4c), modeled Tb was up to 5 K lower than at wilting point for pixels where LAI was <2. At these LAI, the microwave emissions from the soil surface dominated the Tb signal over vegetation moisture. At higher LAI, the soil signal was progressively attenuated by biomass microwave emissions, and the difference between Tb at wilting point and field capacity approached zero.
b. Model sensitivity
Sensitivity of modeled Ts to perturbations in the two extremes of soil moisture status (δTs/δ
Modeled brightness temperature sensitivity (δTb/δθs1) under dry soils is highest (most negative) for LAI < 2.5 (Fig. 4b), but it rapidly diminishes to zero at LAI > 3 as a result of vegetation microwave emissions obscuring the soil signal. For soils at field capacity, the sensitivity remains strong even at relatively high LAI (i.e., between −5 and −10 up to an LAI ≈ 4) with differences in sensitivity evident among vegetation classes (e.g., shrublands are more sensitive than grassland/crops).
c. Analysis error
The relationship between model sensitivity to perturbation in soil moisture (δT/δθ) and analysis, observation, and background variances (σa, σo, and σb) are illustrated for the scalar case [Eq. (3)] in Fig. 5 for “accurate” and “uncertain” modeled soil moistures. Note that this analysis assumes that the model bias in soil moisture (propagated through the observation operators to bias in modeled Ts or Tb) and observation bias has been already eliminated. In each case, the rate at which the analysis error (σa) approached the background error (σb) varied depending on the sensitivity term δT/δθ: the lower the sensitivity (less negative), the more rapidly σa approached σb and the poorer the constraint provided by an observation. The rate at which these curves approached the limit σb also depended on σb itself. In Table 1, we considered the proportional constraint [Eq. (4)] as a measure of efficacy of the observations to constrain modeled soil moisture in the assimilation for three cases of observation error, two background errors (σb = 0.025 m3 m−3 and 0.05 m3 m−3), and three sensitivities (δT/δθ = −2.5, −10, and −25). The three cases of observation error (σo = 1.25, 0.5, and 0.25 K) were chosen based on published errors (from section 3c).
At low sensitivities (δT/δθ = −2.5), an observation of error = 1.25 K provided virtually no constraint on the model. In this case, the proportional constraint [Eq. (4)] was <1%. However, when sensitivities were greater (δT/δθ = −10), a reduction in error of up to 7% was observed with an observation error of 1.25 K. With an increase in the observation accuracy to 0.5 K, the relative constraint increased to >10% for the accurate case but up to ∼30% for the uncertain case. At higher sensitivities, such as those observed for microwave Tb (Figs. 4b and 4d), the proportional constraint provided by σo = 1.25 K is substantial (29%) when the background soil moisture was “uncertain” and increased considerably to ∼63% for observation errors of 0.5 K. Further improvements in the observation error to within the noise of the instrument (σo = 0.25 K) yielded significant improvements in the reduction of analysis error (Table 1) but observations of this accuracy from satellite are unlikely in the foreseeable future.
Temporal variation in the proportional constraint [Eq. (4)] provided by Ts and Tb observations is shown in Figs. 6a, 6b, 7a, and 7b for two contrasting vegetation types: semiarid woodlands and tall forests. In semiarid open woodlands and under moist conditions following rainfall, the reduction in background error in θs1 afforded by Tb observations can be up to 50% or 30% for uncertain and accurate cases, respectively (Fig. 6a). However, this constraint reduces rapidly to zero as soils dry. In contrast, Ts observations provide virtually no reduction in background error in
The bottom panels of Figs. 6c and 7c provide preliminary information on model-observation bias. In the semiarid woodland, modeled Ts consistently underestimated observations by an average of 4.0 K and modeled Tb consistently overestimated observations, particularly when rainfall was frequent and/or heavy, by an average of 9.6 K (Fig. 6c). For tall forests (Fig. 7c), these model-observation biases Ts and Tb were reduced to an average of 1.6 and 1.8 K, respectively, but with the same sign. This result indicates a better performance by the water balance model in tall forests rather than in semiarid woodlands but with potentially similar sources of bias.
5. Discussion
The efficacy of an observation type as a constraint on hydrological and hydrometeorological models is a function of the sensitivity of modeled observation to perturbation in the model state, the observation error, and the background error, and is expressed as the difference (relative or absolute) between the analysis and background errors. This study has shown that both thermal and microwave satellite observations can provide effective observational constraint on the modeled profile and surface soil moisture contents but only under certain conditions. Furthermore, the characteristics at which the observational constraint is maximal for Ts and Tb observations are complimentary—a fact that can be exploited to improve the estimation of soil moisture by satellite observations. Under a weak constraint, the water balance model will operate through time without any feedback from observations. However, under conditions where thermal and microwave observations together or separately provide a strong constraint, the model is benefited by a sequential adjustment that takes into consideration the orthogonal information contained in each observation type.
The fact that the observed Tb and Ts (Figs. 1c and 1d) fall within the endpoints of modeled Tb and Ts (Fig. 2) confirms that the observation operators H t and Hm are functioning realistically in this study, given available climate, soils, and vegetation data. The exception is where errors were introduced by standing water, snow, and cloud, as these conditions are not represented by the observation operators (these would be masked out in an operational assimilation scheme). The analysis of model sensitivities to perturbation in states showed that the sensitivity of Ts to profile moisture content was minimal in circumstances where low maximal stomatal conductance is an inherent plant trait (e.g., in shrublands), large fractions of soil surface were visible to the sensor (i.e., low LAI), soil water holding capacity was large (i.e., large θmax − θmin), and/or profile soil moisture content was near saturation (Fig. 3). Conversely, the sensitivity of Tb to surface layer soil moisture was minimal under conditions of high LAI, dense woody biomass, and low soil moisture content (Fig. 4). Maximal sensitivity for Ts occurred under drying soil —where vegetation stomata were strongly responsive to available water, such as in sands and sandy-loam soils, which have a smaller water holding capacity than clay soils—and at high LAI (Fig. 3). These results highlight the importance of accurate information on soil and vegetation physical properties because δTs/δ
The conditions under which Ts and Tb provide strong constraints on
The magnitude of observation errors is critical in determining the efficacy of satellite data as constraints on models. Increasing measurement error, bias, and infrequent data reduce the effectiveness of observations as constraints (Figs. 5 –7; Table 1). Currently, the range in accuracy for observed Ts from SWAs is 0.4–1.75 K (see section 2c), and the present study has shown that for σo > 1.5 K, these errors are too large for satellite thermal observations to be an effective constraint on modeled soil moisture content, especially when the background error is small (e.g., σb = 0.025 m3 m−3 in Fig. 5). For an uncertain background (0.05 m3 m−3), these observations provide a marginal improvement in the analysis errors but only under conditions when the sensitivity (δTs/δ
In the case of microwave Tb, the requirement for close tolerances on observation errors is to some degree obviated by the strong sensitivity (Fig. 4), and any improvement in the accuracy of these observations would be expected to further increase the strong constraint on modeled θs1 (Fig. 5). These results concur with estimated error reduction using ensemble Kalman filter methods by Reichle et al. (2002). They found a reduction in root-mean-square error for surface soil moisture of up to ∼80% using passive microwave when compared against values obtained without assimilation. However, these improvements can only be achieved where the potential bias from an incorrect specification of canopy optical depth, soil temperature, surface roughness, and surface emissivity is removed—and these biases can be substantial (Draper et al. 2009; Wagner et al. 2007). Assuming that these biases can be eliminated through the accurate and independent estimation of LAI and optical depth, the use of a coupled SEB–MRT model to provide soil and vegetation temperatures, and through accurate information on soil physical properties, the prospects for improved prediction of surface soil moisture from data assimilation constrained by passive microwave observations is good. The exceptions to this are the conditions under which the sensitivity of Tb is low or where the observation operator performs badly, such as in mountainous terrain, high biomass forests, or where standing water or snow is present.
Comparison of modeled observations with satellite data (Figs. 6c and 7c) showed the consistent underestimation of Ts and overestimation of Tb by the water budget but with less bias evident for the tall forest site (Fig. 7c) than for open woodland (Fig. 6c). The identification of biases through the application of multiple constraints data assimilation is one of the major benefits of these methods. The sharp reduction in Tb following rainfall relative to the model (Fig. 6c) is strongly suggestive of surface saturation and possibly pooling of water in run-on parts of the landscape. Consistent underestimation of Ts is indicative of a modeled profile that is too wet relative to observations, which may indicate the possible underestimation of drainage or evapotranspiration terms of the model. Further work is required to elucidate the nature of these biases, to determine whether they arise from observations or from the model, to determine their spatial and temporal variations, and to develop correction functions for use in an operational data assimilation scheme.
6. Conclusions
The tangent linear model (TLM) for two observation operators were used to examine the sensitivities of thermal and microwave satellite observations to variations in profile and surface layer soil moisture. The TLM for Ts showed the strongest sensitivity when soils were relatively dry, in grassland/crop and forest vegetation classes, at low soil water holding capacity, and when canopy leaf area index (LAI) exceeded 2.5. Low sensitivity of Ts was observed where soils were at field capacity (across all vegetation types), in shrublands (as a result of low maximum stomatal conductance), and in sparse canopies where bare soils prevailed. The TLM for Tb showed sensitivities 1–10 times greater than for Ts. Maximal sensitivities of Tb to surface soil moisture content occurred when LAI was <1.5 in dry soils but up to an LAI of ∼4 when soils were at field capacity.
Satellite observations of thermal and microwave emissions of the land surface can provide strong constraints on root-zone soil moisture under specific conditions. Where potential sources of bias are eliminated and the observation errors reduced to ∼1 K, these observations offer good prospects for improved profile soil moisture prediction—particularly when combined in a “multiple constraints” data assimilation context. The multiple constraints methods have additional benefits of using orthogonal information in observations to increase the observational constraint on model dynamics and in identifying the presence of bias in the model and/or observations.
Acknowledgments
The authors acknowledge support and funding for this work from the CSIRO Land and Water “Innovation Bank” and from the CSIRO Water for a Healthy Country Flagship Program. Constructive and helpful suggestions were also provided by Dr. Ray Leuning of CSIRO Marine and Atmospheric Research and three anonymous referees.
REFERENCES
Anderson, M. C., Norman J. M. , Diak G. R. , Kustas W. P. , and Mecikalski J. R. , 1997: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ., 60 , 195–216.
Anderson, M. C., Norman J. M. , Mecikalski J. R. , Otkin J. A. , and Kustas W. P. , 2007: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophys. Res., 112 , D10117. doi:10.1029/2006JD007506.
Ashcroft, P., and Wentz F. J. , 2000: AMSR level 2A algorithm. Algorithm theoretical basis document, Remote Sensing Systems Tech. Rep. 121599B-1, 29 pp.
Aubert, D., Loumange C. , Oudin L. , and Le Hegarat-Mascale S. , 2003: Assimilation of soil moisture into hydrological models: The sequential method. Can. J. Remote Sens., 29 , 711–717.
Australian Survey and Land Information Group, 1990: Vegetation. Vol. 6, Atlas of Australian Resources, Third Series, Department of Administrative Services, Commonwealth of Australia, 64 pp. [Available online at http://www.ga.gov.au/image_cache/GA10358.pdf].
Bach, H., and Mauser W. , 2003: Methods and examples for remote sensing data assimilation in land surface process modelling. IEEE Trans. Geosci. Remote Sens., 41 , 1629–1637.
Barré, H. M. J. P., Duesmann B. , and Kerr Y. , 2008: SMOS: The mission and the system. IEEE Trans. Geosci. Remote Sens., 46 , 587–593.
Barrett, D. J., 2002: Steady state turnover time of carbon in the Australian terrestrial biosphere. Global Biogeochem. Cycles, 16 , 1108. doi:10.1029/2002GB001860.
Barrett, D. J., Hill M. J. , Hutley L. B. , Beringer J. , Xu J. H. , Cook G. D. , Carter J. O. , and Williams R. J. , 2005: Prospects for improved savanna biophysical models by using multiple-constraints model-data assimilation methods. Aust. J. Bot., 53 , 689–714.
Barrett, D. J., Kuzmin V. A. , Walker J. P. , McVicar T. R. , and Draper C. , 2008: Improving stream flow forecasting by integrating satellite observations, in situ data and catchment models using model-data assimilation methods. eWater Cooperative Research Centre Tech. Rep., 54 pp. [Available online at http://ewatercrc.com.au/reports/Barrett_et_al-2008-Flow_Forecasting.pdf].
Becker, F., and Li Z-L. , 1990: Towards a local split window method over land surfaces. Int. J. Remote Sens., 11 , 369–393.
Bell, D. T., and Williams J. E. , 1997: Eucalypt physiology. Eucalypt Ecology: Individuals to Ecosystems, J. E. Williams and J. C. Z. Woinarski, Eds., Cambridge University Press, 168–196.
Boni, G., Entekhabi D. , and Castelli F. , 2001: Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resour. Res., 37 , 1713–1722.
Boussetta, S., Koike T. , Yang K. , Graf T. , and Pathmathevan M. , 2008: Development of a coupled land–atmosphere satellite data assimilation system for improved local atmospheric simulations. Remote Sens. Environ., 112 , 720–734.
Bouttier, F., and Courtier P. , 1999: Data assimilation concepts and methods. Meteorological Training Course Lecture Notes, ECMWF, 58 pp.
Brown, M. A., Torres F. , Corbella I. , and Colliander A. , 2008: SMOS Calibration. IEEE Trans. Geosci. Remote Sens., 46 , 646–658.
Cai, W. J., and Cowan T. , 2008: Evidence of impacts from rising temperature on inflows to the Murray-Darling Basin. Geophys. Res. Lett., 35 , L07701. doi:10.1029/2008GL033390.
Caparrini, F., Castelli F. , and Entekhabi D. , 2004: Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences. J. Hydrometeor., 5 , 145–159.
Carlson, T., 2007: An overview of the “Triangle Method” for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors, 7 , 1612–1629.
Cox, P. M., Huntingford C. , and Harding R. J. , 1998: A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol., 212-213 , 79–94.
Crosson, W. L., Laymon C. A. , Inguva R. , and Schamschula M. P. , 2002: Assimilating remote sensing data in a surface flux-soil moisture model. Hydrol. Processes, 16 , 1645–1662.
Crow, W. T., Drusch M. , and Wood E. F. , 2001: An observation system simulation experiment for the impact of land surface heterogeneity on AMSR-E soil moisture retrievals. IEEE Trans. Geosci. Remote Sens., 39 , 1622–1631.
Crow, W. T., Kustas W. P. , and Prueger J. H. , 2008: Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model. Remote Sens. Environ., 112 , 1268–1281.
de Jeu, R. A. M., and Owe M. , 2003: Further validation of a new methodology for surface moisture and vegetation optical depth retrieval. Int. J. Remote Sens., 22 , 4559–4578.
de Lannoy, G. J. M., Reichle R. H. , Houser P. R. , Pauwels V. R. N. , and Verhoest N. E. C. , 2007: Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour. Res., 43 , W09410. doi:10.1029/2006WR005449.
Dobson, M. C., Ulaby F. T. , Hallikainen M. T. , and El-Rayes M. A. , 1985: Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models. IEEE Trans. Geosci. Remote Sens., GE-23 , 35–46.
Draper, C. S., Walker J. P. , Steinle P. J. , de Jeu R. A. M. , and Holmes T. R. H. , 2009: An evaluation of AMSR-E derived soil moisture over Australia. Remote Sens. Environ., 113 , 703–710.
Entekhabi, D., Nakamura H. , and Njoku E. G. , 1994: Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE Trans. Geosci. Remote Sens., 32 , 438–448.
Errico, R. M., 1997: What is an adjoint model? Bull. Amer. Meteor. Soc., 78 , 2577–2591.
Friedl, M. A., 1995: Modeling land surface fluxes using sparse canopy model and radiometric surface temperature measurements. J. Geophys. Res., 100 , (D12). 25435–25466.
Friedl, M. A., 2002: Forward and inverse modeling of land surface energy balance using surface temperature measurements. Remote Sens. Environ., 79 , 344–354.
Gao, H., Wood E. F. , Jackson T. J. , Drusch M. , and Bindlish R. , 2006: Using TRMM/TMI to retrieve surface soil moisture over the southern United States from 1998–2002. J. Hydrometeor., 7 , 23–38.
Giering, R., 2000: Tangent linear and adjoint biogeochemical models. Inverse Methods in Global Biogeochemical Cycles, Geophys. Monogr., Vol. 114, Amer. Geophys. Union, 33–48.
Giering, R., and Kaminski T. , 1998: Recipes for adjoint code construction. ACM Trans. Math. Software, 24 , 437–474.
Gillespie, A., Rokugawa S. , Matsuna T. , Cothern J. S. , Hook S. , and Kahle A. B. , 1998: A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sens., 36 , 1113–1126.
Gillies, R. R., and Carlson T. N. , 1995: Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. J. Appl. Meteor., 34 , 745–756.
Heathman, G. C., Starks P. J. , Ahuja L. R. , and Jackson T. J. , 2003: Assimilation of surface soil moisture to estimate profile soil water content. J. Hydrol., 279 , 1–17.
Hill, M. J., Senarath U. , Lee A. , Zeppel M. , Nightingale J. M. , Williams R. J. , and McVicar T. R. , 2006: Assessment of the MODIS LAI product for Australian ecosystems. Remote Sens. Environ., 101 , 495–518.
Huang, C., Li X. , and Lu L. , 2008: Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter. Remote Sens. Environ., 112 , 1320–1336.
Jackson, R. B., Canadell J. , Ehleringer J. R. , Mooney H. A. , Sala O. E. , and Schulze E. D. , 1996: A global analysis of root distributions for terrestrial biomes. Oecologia, 108 , 389–411.
Jarvis, P. G., 1976: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. Roy. Soc. London, B273 , 593–610.
Jeffrey, S. J., Carter J. O. , Moodie K. B. , and Beswick A. R. , 2001: Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Modell. Software, 16 , 309–330.
Jones, A. S., Vukicevic T. , and Vonder Haar T. H. , 2004: A microwave satellite observational operator for variational data assimilation of soil moisture. J. Hydrometeor., 5 , 213–229.
Jones, H. G., 1992: Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology. Cambridge University Press, 428 pp.
Key, J. R., Collins J. B. , Fowler C. , and Stone R. S. , 1997: High-latitude surface temperature estimates from thermal satellite data. Remote Sens. Environ., 61 , 302–309.
King, E. A., 2003: The Australian AVHRR data set at CSIRO/EOC: Origins, processes, holdings and prospects. CSIRO Earth Observation Centre Rep. 2003/04, 51 pp. [Available online at http://www.eoc.csiro.au/tech_reps/2003/tr2003_04.pdf].
Koster, R. D., Suarez M. J. , and Heiser M. , 2000: Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeor., 1 , 26–46.
Kustas, W. P., and Norman J. M. , 1999: Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric. For. Meteor., 94 , 13–25.
McCabe, M. F., Gao H. , and Wood E. F. , 2005: Evaluation of AMSR-E-derived soil moisture retrievals using ground-based and PSR airborne data during SMEX02. J. Hydrometeor., 6 , 864–876.
McCabe, M. F., Wood E. F. , Wojcik R. , Pan M. , Sheffield J. , Gao H. , and Su H. , 2008: Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies. Remote Sens. Environ., 112 , 430–444.
McKenzie, N., and Hook J. , 1992: Interpretation of the Atlas of Australian Soils. Consulting report to the Environmental Resources Information Network (ERIN), CSIRO Division of Soils Tech. Rep. 94/1992, 7 pp.
Mecikalski, J. R., Diak G. R. , Anderson M. C. , and Norman J. M. , 1999: Estimating fluxes on continental scales using remotely sensed data in an atmosphere–land exchange model. J. Appl. Meteor., 38 , 1352–1369.
Mein, R. G., and Larson C. L. , 1973: Modeling infiltration during steady rain. Water Resour. Res., 9 , 384–394.
Merlin, O., Chehbouni A. , Boulet G. , and Kerr Y. , 2006: Assimilation of disaggregated microwave soil moisture into hydrologic model using coarse-scale meteorological data. J. Hydrometeor., 7 , 1308–1322.
Mo, T., Choudhury B. J. , Schmugge T. J. , Wang J. R. , and Jackson T. J. , 1982: A model for the microwave emission from vegetation-covered fields. J. Geophys. Res., 87 , (C18). 11229–11238.
Momemi, M., and Saradjian M. R. , 2007: Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by day/night LST algorithm. Remote Sens. Environ., 106 , 190–198.
Monteith, J. L., 1965: Evaporation and environment. The State and Movement of Water in Living Organisms, G. E. Fogg, Ed., Proceedings of the Symposium of the Society of Experimental Biology, Vol. 19, Academic Press, 205–234.
Moran, M. S., Peters-Lidard C. D. , Watts J. M. , and McElroy S. , 2004: Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Can. J. Remote Sens., 30 , 805–826.
Njoku, E. G., and Li L. , 1999: Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Trans. Geosci. Remote Sens., 37 , 79–93.
Njoku, E. G., Jackson T. J. , Lakshmi V. , Chan T. K. , and Nghiem S. V. , 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41 , 215–229.
Njoku, E. G., Ashcroft P. , Chan T. K. , and Li L. , 2005: Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans. Geosci. Remote Sens., 43 , 938–947.
Norman, J. M., and Coauthors, 2003: Remote sensing of surface energy fluxes at 101-m pixel resolutions. Water Resour. Res., 39 , 1221. doi:10.1029/2002WR001775.
Oudin, L., Weisse A. , Loumange C. , and Le Hegarat-Mascle S. , 2003: Assimilation of soil moisture into hydrological models for flood forecasting: A variational approach. Can. J. Remote Sens., 29 , 679–686.
Owe, M., de Jeu R. , and Walker J. , 2001: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans. Geosci. Remote Sens., 39 , 1643–1654.
Pan, M., Wood E. F. , Wojcik R. , and McCabe M. F. , 2008: Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation. Remote Sens. Environ., 112 , 1282–1294.
Pinheiro, A. C. T., Descloitres J. , Privette J. L. , Susskind J. , Iredell L. , and Schmaltz J. , 2007: Near-real time retrievals of land surface temperature within the MODIS rapid response system. Remote Sens. Environ., 106 , 326–336.
Pipunic, R. C., Walker J. P. , and Western A. , 2008: Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study. Remote Sens. Environ., 112 , 1295–1305.
Prigent, C., Aires F. , Rossow W. B. , and Robock A. , 2005: Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements. J. Geophys. Res., 110 , D07110. doi:10.1029/2004JD005087.
Qin, Z., Dall’Olmo G. , Karnieli A. , and Berliner P. , 2001: Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature. J. Geophys. Res., 106 , (D19). 22655–22670.
Raupach, M. R., Rayner P. J. , Barrett D. J. , DeFries R. , Heimann M. , Ojima D. S. , Quegan S. , and Schmullius C. , 2005: Model-data synthesis in terrestrial carbon observation: Methods, data requirements and data uncertainty specifications. Global Change Biol., 11 , 278–397.
Rawls, W. J., Ahuja L. R. , Brakensiek D. L. , and Shirmohammadi A. , 1993: Soil water movement and infiltration. Handbook of Hydrology, D. R. Maidment, Ed., McGraw Hill, 5.1–5.51.
Reichle, D. B., Koster R. , Dong J. , and Berg A. , 2004: Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation. J. Hydrometeor., 5 , 430–442.
Reichle, R. H., and Koster R. D. , 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys. Res. Lett., 32 , L02404. doi:10.1029/2004GL021700.
Reichle, R. H., McLaughlin D. B. , and Entekhabi D. , 2001: Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications. IEEE Trans. Geosci. Remote Sens., 39 , 1708–1718.
Reichle, R. H., McLaughlin D. B. , and Entekhabi D. , 2002: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130 , 103–114.
Renzullo, L. J., Barrett D. J. , Marks A. S. , Hill M. J. , Guerschman J. P. , Mu Q. , and Running S. W. , 2008: Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters. Remote Sens. Environ., 112 , 1306–1319.
Scipal, K., Scheffler C. , and Wagner W. , 2005: Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing. Hydrol. Earth Syst. Sci., 9 , 173–183.
Sellers, P. J., 1985: Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens., 6 , 1335–1372.
Shuttleworth, W. J., and Wallace J. S. , 1985: Evaporation from sparse crops—An energy combination theory. Quart. J. Roy. Meteor. Soc., 111 , 839–855.
Sobrino, J. A., and Raissouni N. , 2000: Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. Int. J. Remote Sens., 21 , 353–366.
Sobrino, J. A., Gomez M. , Jimenez-Munoz J. C. , and Olioso A. , 2007: Application of a simple algorithm to estimate daily evapotranspiration from NOAA-AVHRR images for the Iberian Peninsula. Remote Sens. Environ., 110 , 139–148.
Sòria, G., and Sobrino J. A. , 2007: ENVISAT/AATSR derived land surface temperature over a heterogeneous region. Remote Sens. Environ., 111 , 409–422.
Sun, D., and Pinker R. T. , 2005: Implementation of GOES-based land surface temperature diurnal cycle to AVHRR. Int. J. Remote Sens., 26 , 3975–3984.
Wagner, W., Naeimi V. , Scipal K. , de Jeu R. , and Martinez-Fernandez J. , 2007: Soil moisture from operational meteorological satellites. Hydrogeol. J., 15 , 121–131.
Walker, J. P., Willgoose G. R. , and Kalma J. D. , 2001: One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: A simplified soil moisture model and field application. J. Hydrometeor., 2 , 356–373.
Wan, Z., 2008: New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sens. Environ., 112 , 59–74. doi:10.1016/j.rse.2006.06.026.
Wan, Z., and Dozier J. , 1996: A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens., 34 , 892–905.
Wang, D., and Cai X. , 2008: Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filter. Adv. Water Resour., 31 , 455–472.
Wang, J. R., and Schmugge T. J. , 1980: An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Trans. Geosci. Remote Sens., 18 , 288–295.
Weerts, A. H., and El Serafy G. Y. H. , 2006: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resour. Res., 42 , W09403. doi:10.1029/2005WR004093.
Yu, Y., Privette J. L. , and Pinheiro A. C. , 2008: Evaluation of split-window land surface temperature algorithms for generating climate data records. IEEE Trans. Geosci. Remote Sens., 46 , 179–192.
APPENDIX
Observation Model Operators and TLM Approximations
Observation model operators and their respective TLM representation are given here. The observation operator for land surface temperature H t was developed from the equations of Friedl (1995, 2002) using the Jarvis (1976) formulation for canopy resistance to water vapor and the stomatal resistance scaling functions of Cox et al. (1998). The observation operator for microwave brightness temperature Hm was based on the microwave radiative transfer theory presented in Wang and Schmugge (1980), Mo et al. (1982), Dobson et al. (1985), Njoku et al. (2003), and Owe et al. (2001). (For more details, including model assumptions and limitations, refer to the references above.) Here only the pertinent components of each model for calculating temperature (Ts or Tb) and the TLM are presented. Model variables and parameters are defined in Table A1.
Land surface temperature
The SEB model used in this work comprised six nonlinear equations that describe the portioning of moisture and heat fluxes between the vegetation and soil surface layers and the overlying atmosphere (Friedl 2002). Key TLM equations are denoted by a δ symbol preceding each variable. Active variable in case of LST is
Microwave brightness temperature
The coupled SEB–MRT model used in this work uses the soil and vegetation temperatures computed in the SEB model earlier. Key TLM equations are denoted by a δ symbol preceding each variable. Active variable in case of microwave brightness temperature is θs1. Again, a simple N-loop construct was considered the most efficient method of numerically solving the equations for the temperatures and water vapor pressure values and computing the TLM.
Spatial fields of (a) elevation (m) based on the Australian National University DEM (ANUDEM), and (b) derived 250-m canopy LAI (m2 m−2). (c) Observed 1-km land surface temperature (K) from NOAA-18 AVHRR sensor thermal bands. (d) Observed 25-km 6.9-GHz brightness temperature (K) from Aqua AMSR-E sensor. Satellite imagery acquired for 2 Oct 2005. Polygon boundary shows the Murrumbidgee River catchment of southeast Australia.
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Spatial fields for the Murrumbidgee River catchment region of southeastern Australia of (a),(d) modeled 1-km thermal land surface temperature (K) derived from the observation operator H t. (b),(e) Modeled 1-km microwave land surface brightness temperature (K) derived from the observation operator Hm. (c),(f) Microwave brightness temperature (K) from (b) and (e) aggregated to 25 km. Shown is the output for (top) minimum (wilting point) and (bottom) maximum (field capacity) soil moisture contents.
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Scatterplots of (a),(c) modeled land surface temperature (K) derived from the observation operator H t and (b),(d) sensitivity of land surface temperature to profile average soil moisture content (δTs/δ
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Scatterplots of (a),(c) modeled microwave brightness temperature (K) derived from the observation operator Hm and (b),(d) sensitivity of brightness temperature to surface layer soil moisture content (2.5-cm depth; δTb/δθs1) from the tangent linear model of Hm as a function of LAI. Plots show model output for (a),(b) wilting point and (c),(d) field capacity soil moisture contents. Points derived as in Fig. 3. Black, gray, and white symbols refer to forest, shrub, and grassland/crop vegetation classes, respectively.
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Effect of observation error (σo) on error in analysis state variables (σa). The various curves correspond to different sensitivities of temperature to soil moisture contents (δT/δθ) ranging from −1 to −25 K m3 m−3. Two sets of curves are shown: black curves correspond to a background standard deviation (σb) of 0.025 m3 m−3 soil moisture content and gray curves correspond to σb = 0.05 m3 m−3.
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Temporal sequence of (a) the proportional constraint [Eq. (4); left axis] provided by microwave Tb (σo = 0.5 K; dashed line, σb = 0.025 m3 m−3; and dotted line, σb = 0.05 m3 m−3) and surface layer volumetric moisture content (solid line; right axis). (b) Proportional constraint (left axis) provided by Ts (σo = 0.75 K; dashed line, σb = 0.025 m3 m−3; dotted line, σb = 0.05 m3 m−3) and profile soil moisture content (solid line; right axis). (c) Modeled and observed Ts (dashed line and closed symbols; left axis), Tb (solid line and open symbols; left axis) and rainfall (bars; right axis) for a grid cell/pixel in the western Murrumbidgee catchment. Location was a semiarid open woodland/shrubland (LAI = 1.3 and gmax = 5 mm s−1).
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Same as Fig. 6 but for a grid cell/pixel in the eastern Murrumbidgee catchment located in dense tall forest (LAI = 4.8 and gmax = 10 mm s−1).
Citation: Journal of Hydrometeorology 10, 5; 10.1175/2009JHM1043.1
Proportional constraint (σb − σa/σb) for observations of given error (σo) as quantified by Eq. (4), given three sensitivities of observed temperature against perturbation in soil moisture content [δT/δθ; K (m3 m−3)−1] and two background errors [σb = 0.025 m3 m−3 (accurate case) and 0.05 m3 m−3 (uncertain case)].
Table A1. Symbols used in tangent linear model equations of the appendix.
