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Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3 were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies. [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze–thaw state every 2–3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP. [ABSTRACT FROM PUBLISHER]

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3 cm−3 (one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active–Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (∼36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (∼3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active–passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (∼0.7 dB for co-pol backscatter and ∼1.0 dB for cross-pol backscatter, and ∼1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ∼0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation. [ABSTRACT FROM PUBLISHER]

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m3· m-3, respectively, which generally meet their mission requirement of 0.04 m3· m-3. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m3· m-3. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m3 · m-3 though it underestimates in situ measurements with a bias of 0.059 m3 · m-3, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles. [ABSTRACT FROM AUTHOR]

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements. [ABSTRACT FROM AUTHOR]

Passive microwave remote sensing has experienced significant success for soil moisture (SM) inversion. However, quantifying the uncertainties caused by soil parameter sensitivities has not attracted sufficient attention. Although local sensitivity analysis (SA) has been used to describe parameter sensitivity in the past, it fails to quantify parameter sensitivities, especially interactions, for nonlinear microwave emission models. This paper presents a comprehensive evaluation that combines physically based emission models and various global SA algorithms to evaluate parameter sensitivity. All the algorithms exhibit highly consistent sensitivity measures, which means a reliable SA result is obtained. The results indicate that the sums of the main sensitivity indices of SM and surface roughness parameters—root-mean-square height (RMSH) and correlation length—are greater than 0.92 and 0.95 for emissivity and brightness temperature (TB), respectively. Furthermore, we find that: 1) the parameter probability distributions have little effect on the sensitivity measures; 2) the SM sensitivity decreases and the RMSH sensitivity increases as the frequency increases and the incidence angle decreases; and 3) the SM is more sensitive on V-polarized than on H-polarized emissivity and TB, while the RMSH is much more sensitive on the polarization index. The presented global SA quantitatively explains the optimal frequency, incidence angle, and polarization for SM inversion and extends the parameter SA for microwave emission models to a more general framework, as well as provides an implication for bare soil emission modeling and SM inversion. [ABSTRACT FROM PUBLISHER]

In this paper, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRMs) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at a fine scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM was disaggregated from 10 to 1 km using land cover (LC), precipitation, land surface temperature, leaf area index, and in situ observations of SM. This algorithm was evaluated using multiscale synthetic observations in NC Florida for heterogeneous agricultural LCs. It was found that the rmse for 96% of the pixels was less than 0.02 m 3/m3. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback–Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy. [ABSTRACT FROM PUBLISHER]

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter. [ABSTRACT FROM AUTHOR]

In this paper, the L-band Microwave Emission of the Biosphere (L-MEB) model used in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm is calibrated using L-band (1.4 GHz) microwave measurements over a coniferous (Pine) and a deciduous (mixed/Beech) forest. This resulted in working values of the main canopy parameters optical depth (τ), single scattering albedo (ω), and structural parameters tt(H) and tt(V), besides the soil roughness parameters HR and NR. Using these calibrated values in the forward model resulted in a root mean-square error in brightness temperatures from 2.8 to 3.8 K, depending on data set and polarization. Furthermore, the relationship between canopy optical depth and leaf area index is investigated for the deciduous site. Finally, a sensitivity study is conducted for the focus parameters, temperature, soil moisture, and precipitation. The results found in this paper will be integrated in the operational SMOS Level 2 Soil Moisture algorithm and used in future inversions of the L-MEB model, for soil moisture retrievals over heterogeneous, partly forested areas. [ABSTRACT FROM AUTHOR]

In microwave remote sensing of the Earth's surface, the satellite signal holds information on both soil moisture and vegetation. This necessitates a correction for vegetation effects when retrieving soil moisture. This paper assesses the strengths and weaknesses of the existing vegetation correction as part of the Vienna University of Technology (TU-Wien) method for soil moisture retrieval from coarse-scale active microwave observations. In this method, vegetation is based on a multiyear climatology of backscatter variations related to phenology. To assess the plausibility of the correction method, we first convert the correction terms for retrievals from the Advanced Scatterometer (ASCAT) into estimates of vegetation optical depth \taua using a water-cloud model. The spatial and temporal behaviors of the newly developed \taua are compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model \taup. Spatial patterns correspond well, although low values for \taua are found over boreal forests. Temporal correlation between the two products is high $(R = 0.5)$, although negative correlations are observed in drylands. This comparison shows that \taua and thus the vegetation correction method are sensitive to vegetation dynamics. Effects of the vegetation correction on soil moisture retrievals are investigated by comparing retrieved soil moisture before and after applying the correction term to modeled soil moisture. The vegetation correction increases the quality of the soil moisture product. In areas of high interannual variability in vegetation dynamics, we observed a negative impact of the vegetation correction on the soil moisture, with a decrease in correlation up to 0.4. It emphasizes the need for a dynamic vegetation correction in areas with high interannual variability. [ABSTRACT FROM AUTHOR]

Information on vegetation water content (VWC) is important in retrieving soil moisture using microwave remote sensing. It can be also used for other applications, including drought detection, bushfire prediction, and agricultural productivity assessment. Through the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration, radar data may potentially provide the VWC information needed for soil moisture retrieval from the radiometer data acquired by the same satellite. In this paper, VWC estimation is tested using radar vegetation index (RVI) data from the third SMAP airborne Experiment. Comparing with coincident ground measurements, prediction equations for wheat and pasture were developed. While a good relationship was found for wheat, with r=0.49, 0.62,\ \mboxand\ 0.65 and root-mean-square error (\mboxRMSE)=0.42, 0.37,\ \mboxand\ 0.36 kg/m2, the relationship for pasture was poor, with r=-0.06,-0.14,\ \mboxand\ -0.002 and \mboxRMSE=0.15,0.15,\ \mboxand\ 0.15, kg/m2, for 10-, 30-, and 90-m resolutions, respectively. These results suggested that RVI is better correlated with VWC for vegetation types having a greater dynamic range. However, the results were not as good as those from a previous tower-based study ( $r=0.98$ and \mbox{RMSE}=0.12\ \mbox{kg/m}^2) over wheat. This is possibly due to spatial variation in vegetation structure and surface roughness not present in tower studies. Consequently, results from this study are expected to more closely represent those from satellite observations such as SMAP, where large variation in vegetation and environment conditions will be experienced. [ABSTRACT FROM PUBLISHER]

To retrieve the soil salinity by microwave remote sensing, we must clarify the relation of dielectric properties of saline soils with soil salinity. The objective of this paper was to determine how dielectric properties are affected by soil salinity in the remote sensing range and develop an improved model applying for the saline soil. Laboratory measurements of dielectric constant are made with a microwave vector network analyzer using soil mixture samples of various soil moistures and salinities prepared artificially from natural soils. The results confirmed that the real part is strongly affected by soil moisture, whereas the imaginary part depends on both the soil moisture and salinity, particularly at lower frequencies (1-6 GHz). Thus, as a key factor, soil salinity is introduced into the expression of the imaginary part of dielectric constant in the Dobson semiempirical dielectric mixing model, combining the dielectric model for saline water and the impact of electrical conductivity of soil solution. The improved model yields results, which are in good agreement with the laboratory measurements, the slopes of fitting curves between measurements and simulation nearly being equal to 1, and coefficient R2 are higher than 0.89. In addition, the improve model is independent of soil-texture parameters. However, it should be noted that the improved model can be applied only to the C-band remote sensing. [ABSTRACT FROM PUBLISHER]

A numerical simulator has been developed to study remote sensing from space in the spectral window at 1.413 GHz (L-band), and it has been used to optimize the cold-sky calibration (CSC) for the Aquarius radiometers. The celestial sky is a common cold reference in microwave radiometry. It is currently being used by the Soil Moisture and Ocean Salinity satellite, and it is planned that, after launch, the Aquarius/SAC-D observatory will periodically rotate to view “cold sky” as part of the calibration plan. Although radiation from the celestial sky is stable and relatively well known, it varies with location. In addition, radiation from the Earth below contributes to the measured signal through the antenna back lobes and also varies along the orbit. Both effects must be taken into account for a careful calibration. The numerical simulator has been used with the Aquarius configuration (antennas and orbit) to investigate these issues and determine optimum conditions for performing a CSC. This paper provides an overview of the simulator and the analysis leading to the selection of the optimum locations for a CSC. [ABSTRACT FROM AUTHOR]

Soil moisture is the central focus of land surface and atmospheric modeling because it controls surface water and energy fluxes and consequently affects land–atmosphere interactions. Although global or regional satellite-derived surface soil moisture data sets are readily available, knowledge about assimilating them into numerical weather prediction (NWP) models is limited. The methods of assimilating soil moisture products in NWP models have several limitations, and they cannot be applied in near-real-time applications. As a result, this paper focuses on the development of a system [a Land Data Assimilation System coupled with a mesoscale Atmospheric model (LDAS-A)] that couples satellite land data assimilation with a mesoscale model to physically introduce land surface heterogeneities into the mesoscale model. The LDAS-A consists of a sequential LDAS that directly assimilates the lower frequency passive microwave brightness temperatures, and therefore, its use is feasible for near-real-time NWP applications. The LDAS-A was validated for the Tibetan Plateau using surface, radiosonde, and satellite observations. The simulation results show that the LDAS-A effectively improved the land surface variables (i.e., surface soil moisture and skin temperature) compared with the no-assimilation case and that it has the potential to correct uncertainties resulting from model initialization, model-specific parameters, and model forcing on a wider scale. The improved land surface conditions in the LDAS-A improve the land–atmosphere feedback mechanism, and the assimilated results provide better prediction of atmospheric profiles (i.e., potential temperature and specific humidity) than the no-assimilation case when compared with radiosonde soundings. Improvements in solar radiation, in addition to soil moisture, are necessary to introduce realistic land–atmosphere interactions into a mesoscale model. [ABSTRACT FROM AUTHOR]

In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main “roughness” model parameters: HR , QR, and NR. In many studies, the two parameters QR and NR are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that QR could be set to zero. Second, a refinement of the L-MEB soil model, considering values of NR for both polarizations (namely, NRV and NRH), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters HR and (NRH - NRV) to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions. [ABSTRACT FROM AUTHOR]

The third Stokes parameter will be observed from space for the first time at L-band by the Soil Moisture and Ocean Salinity and Aquarius/SAC-D satellites. The correlation between polarizations, which is the source of the third Stokes parameter, is of interest at L-band to measure Faraday rotation and also to indicate novel features of the surface. However, spurious signals (false indication of correlation) can occur in the third Stokes parameter. For example, this happens when the radiometer crosses boundaries associated with a large change in brightness temperature, such as land–water boundaries. In this paper, calculations with the Aquarius radiometer antennas will be used to show that these spurious signals are due to the cross-polarization coupling and large beamwidth associated with realistic L-band antennas in space. [ABSTRACT FROM AUTHOR]

Water and energy fluxes at the interface between the land surface and atmosphere are affected by the surface water content of the soil, which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite mission concepts as, for example, the Soil Moisture and Ocean Salinity (SMOS) mission, are dedicated to provide global soil moisture information with a temporal frequency of a few days to capture the high temporal dynamics of surface soil moisture. SMOS soil moisture products are expected to have geometric resolutions on the order of 40 km. Mesoscale flood forecasting or water balance models typically operate at much higher spatial resolutions on the order of 1 km. It seems therefore essential to develop appropriate disaggregation schemes to benefit from the high temporal frequency of the SMOS data for hydrological applications as well as, for example, local numerical weather prediction models that are operated at a resolution of a few kilometers. This paper investigates the potential of using prior information on spatially persistent soil moisture fields to disaggregate SMOS scale soil moisture products. The approach is based on a ten-year soil moisture climatology for a mesoscale hydrological catchment, situated in southern Germany, which was generated using a state-of-the-art land-surface process model. The performance of the disaggregation algorithm is verified by comparison of disaggregated soil moisture fields with another ten-year period. To investigate the potential of the suggested disaggregation method for SMOS soil moisture products, a ten-year synthetic brightness temperature data set is generated at the 1-km scale. soil moisture is then retrieved from the aggregated brightness temperature data at the SMOS type scale of 40 km and then disaggregated using the suggested approach. The results are compared against reference soil moisture at the 1-km scale. Uncertainties in the retrieval of the SMOS soil moisture products are explicitly considered, and the uncertainties of the disaggregated fields are quantified. The developed method shows a generally good performance for large parts of the test site, where soil moisture can be disaggregated with an accuracy that is better than the 4 vol.% benchmark of the SMOS mission. As the suggested method shows high sensitivity to biased soil moisture retrievals, uncertainties of the SMOS soil moisture products will directly reflect on the absolute accuracy of the disaggregated soil moisture fields, resulting in a much worse performance under noisy conditions. Nevertheless, the resulting soil moisture distributions show that it is feasible to derive relative soil moisture distributions in these cases. [ABSTRACT FROM AUTHOR]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-rn and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb. edu.au. [ABSTRACT FROM AUTHOR]

Soil Moisture and Ocean Salinity (SMOS) is an Earth observation mission developed by the European Space Agency in cooperation with the Centre National d'Etudes Spatiales, France and the Centre for the Development of Industrial Technology, Spain, whose main objective is to provide global maps of soil moisture over land and sea surface salinity over oceans. This paper describes the SMOS mission in terms of the mission objectives and associated key system requirements, the conceptual implementation of the mission and corresponding system architecture, major building blocks and associated functions, the SMOS selected polar orbit and characteristics, and SMOS satellite attitude modes for the different phases of the mission and for the calibration of the Microwave Imaging Radiometer with Aperture Synthesis instrument. [ABSTRACT FROM AUTHOR]

In this paper, microwave remote sensing, together with in situ moisture probes, is used to investigate temperature effects on the soil dielectric constant. Field and specific laboratory measurements were performed for different soil water content over a wide range of temperatures. The experimental results lead to the following evidences: 1) temperature effect is different for bound and free waters in soil; 2) bound-water relaxation frequency falls within the range of frequencies that are used by impedance soil moisture probes for field measurements; and 3) the increase of bound-water relaxation frequency with soil temperature interferes in a significant way with moisture measurements when bound-water fraction is important. These results have implications in field experimentation since most moisture sensors operate under 500 MHz and are affected by this phenomena of relaxation. [ABSTRACT FROM AUTHOR]

An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated. [ABSTRACT FROM AUTHOR]

Water and energy fluxes at the interface between the land surface and atmosphere are strongly dependent on surface soil moisture content, which is highly variable in space and time. It has been shown in numerous studies that microwave remote sensing can provide spatially distributed patterns of surface soil moisture. In order to use remote-sensing-derived soil moisture information for practical applications as, for example, flood forecasting and water balance modeling in mesoscale areas, frequent large-area coverage is a prerequisite. New sensor generations such as ENVISAT Advanced Synthetic Aperture Radar (ASAR) or RADARSAT allow for image acquisitions in different imaging modes and geometries. Imaging modes with the capability of large-area coverage, such as the Wide Swath Mode of ENVISAT ASAR, are of special interest for practical applications in this context. This paper presents a semiempirical soil moisture inversion scheme for ENVISAT ASAR data. Different land cover types as well as mixed-image pixels are taken into account in the soil moisture retrieval process. The inversion results are validated against in situ measurements, and a sensitivity analysis of the model is conducted. [ABSTRACT FROM AUTHOR]

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Ad- ministration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with twos to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture..The mission data will enable advances in weather and climate prediction and in map- ping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39° with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented. [ABSTRACT FROM AUTHOR]