The capability of high-resolution L-band Synthetic Aperture Radar (SAR) to retrieve field-scale$(< 30\mathrm{m})$soil moisture has been investigated over corn and soybean crop fields. The time-series retrieval algorithm inverts the look-up-table (LUT) representation of physics-based forward scattering model under the assumption of temporally invariant roughness condition. In order to improve the retrieval accuracy of both surface roughness and soil moisture, an enhancement in sensitivity of forward scattering model has been implemented through a linear scaling of LUT. The retrieval algorithm has been applied to the time-series UAVSAR data acquired during AMPM campaign over northern Arkansas in USA. The unbiased RMSE between the forward modeled and observed backscattering coefficients$(\sigma^{0})$are 1.34 dB (HH), 1.83 dB (VV) for corn and 3.77 dB (HH), 3.53 dB (VV) for soybean respectively. The validation of retrieved soil moisture using multi-pol (HH & VV) inputs with in situ measurements shows an unbiased RMSE (correlation) of 0.061$\mathrm{m}^{3}/\mathrm{m}^{3}\ (0.71)$and 0.081$\mathrm{m}^{3}/\mathrm{m}^{3}\ (0.47)$for corn and soybean crop fields respectively.

A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher than CYGNSS Science Data Record Version 3.1 by an average of 5 dB and also smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

A comparison of three different electromagnetic scattering models for delay-Doppler maps (DDMs) of global navigation satellite system reflectometry (GNSS-R) from land is performed along a Cyclone Global Navigation Satellite System (CYGNSS) track over a validation site in the San Luis Valley, Colorado, USA. The peak reflectivity profiles of all three models and of the corresponding CYGNSS data are found to be in general agreement and are strongly influenced by topography. An intercomparison of DDM structure for one acquisition is also included. Efforts to refine the model results using a high resolution lidar survey are ongoing.

Slow-moving landslides are destabilized by precipitation. The continuous soil moisture monitoring aid the understanding of landslide processes. Here we study time-series soil moisture using NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) at 6-m resolution for a seasonally active grassland landslide in the northern California Coast Ranges, USA. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture. The forward model is developed for polarization HH and VV. The soil moisture retrieval using HH&VV shows unbiased RMSE (ubRMSE) of 0.058 m3/m3. From the Freeman-Durden decomposition for UAVSAR's time-series data, the surface scattering and double bounce dominate the landslide area which suggests the strong correlation with soil moisture for the data. The physical-model based algorithm can be applied to other grassland covered landslides in California to retrieve soil moisture.

In this paper, we study the scattering of land surfaces for the Global Navigation Satellite System Reflectometry (GNSS-R) land applications. Topography slopes are introduced to improve the physical patch model. The entire area within the footprint is divided into patches. Each patch is on a slope with elevation. Simulation results have shown that for small rms height, the received power to transmitted power ratio, Pr/Pt is smaller than the coherent model and the patch model with only elevation effects but greater than the incoherent model. The delay-doppler maps are also simulated based on the patch model.

SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density . The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.

Combined active-passive remote sensing has the potential for capturing the relative advantage of each sensing approach in geophysical retrievals. One cornerstone of combined active-passive microwave sensing is the modeling of the covariation of active and passive signals, which arise from equivalent sensitivities of both sensor types to changes in geophysical properties. In this research contribution, we propose a physics-based active-passive combination of active and passive microwave observations based on Kirchhoff's law of energy conservation. This allows establishing a physics-based forward model as well as a fully data-driven, single-pass retrieval methodology for active-passive microwave covariation. The forward model and the retrieval approach are adaptable to different sensor characteristics (incidence angle, frequency & polarization). The theoretical (forward model) as well as applied (retrieval method) physics-based covariation framework is tested with SMAP (LL) and SMAP/Sentinel-1 (LC) data to reveal potentials and constraints for active-passive microwave sensing. As a result of the conducted study, a linear functional relationship between active and passive microwave observations (e.g. assumed for the SMAP mission) is confirmed, if higher-order scattering can be omitted.

L-band airborne synthetic aperture radar observations were made over California shrublands to better understand the effects of soil and vegetation parameters on backscattering coefficient (σ0) for the period of 2011 to 2015. HH was always greater than VV, suggesting the importance of double-bounce scattering by the woody parts. However, the geometric and dielectric properties of the woody parts did not vary significantly over time. Instead the changes in vegetation water content (VWC) were observed to occur primarily in thin leaves that may not meaningfully influence absorption and scattering. Accordingly, unlike in the past literature, the VWC input of the plant to the model was formulated as a function of plant's dielectric property (water fraction) while the plant geometry remains static in time. A physically-based model for single scattering by discrete elements of plants successfully simulated the magnitude of the temporal variations in HH, VV, and HH/VV with a difference of less than 0.9 dB. The modeling results offer an explanation of why soil moisture correlated highly with σ0, which is that the dominant mechanisms for HH and VV are double-bounce scattering by trunk, and soil surface scattering, respectively.

L-band capability to monitor soil moisture at field scale (~100m) is examined using ground and airborne radar data. We report the recent progress in inverting physically-based forward models for radar scattering through time-series retrieval algorithm to systematically correct for the effect of the roughness and vegetation. The retrievals are performed for various vegetation types: pasture, corn, soybean, canola, and shrub over the entire vegetation growth cycles. Various changes to the forward and retrieval algorithm per each species are summarized. Assessed over all available fields of corn, bean, pasture, and wheat, the data-cube time-series inversion has the retrieval rmse of 0.050 to 0.075 m3/m3, and the correlation of 0.5 to 0.9.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

After failure of the SMAP L-band radar, its substitution by the Sentinel-1A/B C-band instruments for combined active-passive retrieval of soil moisture demands an algorithm update for this dual-frequency (L/C) case. In order to account for the different frequencies and acquisition geometries of the two sensor types, the microwave covariation, being the fundamental building block of the moisture retrieval algorithm, is modeled using a fully physics-based approach. Moreover, a data-driven, single-pass retrieval methodology for dual-frequency microwave covariation is proposed and tested on SMAP and Sentinel-1 data. The retrieval is also physics-based, incidence angle independent and therefore globally applicable without any empirical or statistical calibration.

The inundation extent is derived using brightness temperature data acquired by the L-band Soil Moisture Active Passive (SMAP) satellite, to support boreal carbon studies. Exploiting the L-band capabilities to penetrate clouds and vegetation and SMAP's 3-day revisit, the product may complement high-spatial resolution optical products in the high latitudes. The quality of the inundation extent is assessed by comparing with the following data sets: 3-m resolution maps derived using Radarsat synthetic aperture radar (SAR) data in northern Canada and multi-sensor climatology over Siberia. Initial results show encouraging comparisons. SMAP describes the seasonality of inundation more realistically compared with the climatology.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3ubRMSE, −0.015 m3/m3bias, and a correlation of 0.50, compared toin situmeasurements, thus meeting the accuracy target of 0.06 m3/m3ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection ofin situnetworks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3over a variety land cover types.

With emphasis on physics-based techniques, a multi-objective optimization approach to combined radar-radiometer soil moisture estimation is presented in this work. Soil moisture estimation is demonstrated via application of this method to SMAP high resolution radar and coarse resolution radiometer data. Comparisons are then made with the SMAP baseline active-passive soil moisture output data product. A strong agreement between the two techniques, especially in capturing spatial distributions of soil moisture is observed.

This presentation illustrates and discusses the possibility of SMAP-Sentinel combined product for the recovery phase of the SMAP mission post radar failure. Initial assessment and results are preliminary and show great promise.

Surface soil moisture was retrieved globally by systematically correcting for the effects of vegetation and soil surface roughness. The retrieval is enabled by employing physical-models of radar forward scattering for individual vegetation types to account for vegetation scattering and absorption, and by constraining the surface roughness effect using time-series observations. The L-band SMAP multi-polarized (HH/VV/HV) σ° data acquired globally every three days were used from mid-April to early July, 2015. Assessment was conducted over 13 rigorously-chosen core validation sites covering a wide range of biomass types, biomass amount, and soil conditions. The soil moisture retrieval reached an accuracy of 0.06 m3/m3RMSE, a bias of 0.003 m3/m3, and a correlation of 0.56. The successful retrieval demonstrates that the physically-based retrieval method is capable of characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation on a global scale.

A dual-copolarization algorithm to classify inland open water bodies free of flooded vegetation using an L-band radar is presented and evaluated, with a view to applying the method to the Soil Moisture Active Passive (SMAP) mission for hydrological science and soil moisture retrieval applications. Past radar-based water body detection algorithms have applied a threshold to a single-polarization measurement, with water body detection declared if the observed cross section is less than the specified threshold. However, such methods are subject to ambiguities associated with scene variability and terrain slopes, making a universal threshold value difficult to derive and complicating the global application of such methods. Because SMAP will provide measurements in both HH and VV polarizations, the copolarization ratio is also available for water body detection. A threshold of -3 dB applied to the HH/VV polarization ratio is found effective in detecting water bodies at 40° incidence angle based on analysis of theoretical model predictions and measurements from airborne synthetic aperture radar and the spaceborne Aquarius scatterometer. When the water surface is calm and its radar response is very small (i.e., at the radar thermal noise level), the HH/VV ratio method fails. However, a combination of an HH/VV threshold (at -3 dB) and an HH threshold (at -25 dB) is shown to allow water body classification even in this situation. This proposed “combined” algorithm is assessed in four different geophysical scenarios. The resulting water body detection error is shown to be less than 10% for these cases, which satisfies SMAP requirements to allow accurate soil moisture retrieval, and the corresponding false alarm rate is smaller than 2%. The robustness of the proposed approach to subpixel heterogeneity has been also investigated. The performance of the algorithm remains sensitive to the noise level of the radar observations: for SMAP, a radar noise-equivalent sigma 0 of -28.5 dB or less is required in order to facilitate acceptable performance.

We extend the 3-D numerical method of Maxwell's equation (NMM3D) for rough soil surface scattering from L-band to C-, X-, and Ku-bands. We illustrate the results for copolarization, cross-polarization, and polarization ratio (HH/VV). Copolarized and cross-polarized backscattering coefficients from NMM3D are analyzed for frequency dependence, incident angle dependence, and soil moisture dependence. We also cross compare results from analytical and empirical models. The 16 × 16 squared wavelength (λ2) of rough surface is applied for NMM3D using 256 processors on NSF Extreme Science and Engineering Discovery Environment clusters. Polarization ratio, HH/VV, is studied to address the feature of dependence on frequency for same fields (same physical parameters for the model). HH/VV is shown useful to provide additional information to study land surface. Results from NMM3D are also validated with POLARSCAT measurement data-1. NMM3D shows good agreement with data and better performance while considering copolarization, cross-polarization, and polarization ratio (HH/VV) together. The key advancement in computation efficiency in this paper is the implementation of a physically based near-field precondition algorithm in NMM3D to accelerate parallel computation. With precondition, the computation time is faster by ten times for larger root-mean-square height.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

An algorithm for surface soil moisture estimation using L-band radar observations is introduced. The formulation envelops a wide range of land surface conditions based on three limiting cases defined in terms of end-members: smooth bare soil, rough bare soil, and a maximum vegetation covered soil. Parameterizations for these end-members are obtained using forward electromagnetic scattering models. Modulation due to soil surface roughness and overlying vegetation scattering effects between end-members are accounted using the radar vegetation index and the newly introduced radar roughness index. Hence, the retrieval algorithm developed here does not depend on ancillary vegetation or roughness information. The algorithm is tested with ground-based truck-mounted bare soil observations and observations from several airborne field campaigns that represent a wide range of surface conditions.

Physical models for radar backscattering coefficients are developed for the global land surface at L-band (1.26 GHz) and 40°incidence angle to apply to the soil moisture retrieval from the upcoming soil moisture active passive mission data. The simulation of land surface classes includes 12 vegetation types defined by the International Geosphere-Biosphere Programme scheme, and four major crops (wheat, corn, rice, and soybean). Backscattering coefficients for four polarizations (HH/VV/HV/350611873VH) are produced. In the physical models, three terms are considered within the framework of distorted Born approximation: surface scattering, double-bounce volume-surface interaction, and volume scattering. Numerical solutions of Maxwell equations as well as theoretical models are used for surface scattering, double-bounce reflectivity, and volume scattering of a single scatterer. To facilitate fast, real-time, and accurate inversion of soil moisture, the outputs of physical model are provided as lookup tables (with three axes; therefore called datacube). The three axes are the real part of the dielectric constant of soil, soil surface root mean square (RMS) height, and vegetation water content (VWC), each of, which covers the wide range of natural conditions. Datacubes for most of the classes are simulated using input parameters from in situ and airborne observations. This simulation results are found accurate to the co-pol RMS errors of to 3.4 dB (six woody vegetation types), 1.8 dB (grass), and 2.9 dB (corn) when compared with airborne data. Validated with independent spaceborne phased array type L-band synthetic aperture radars and field-based radar data, the datacube errors for the co-pols are within 3.4 dB (woody savanna and shrub) and 1.5 dB (bare surface). Assessed with spaceborne Aquarius scatterometer data, the mean differences range from ~ 1.5 to 2 dB. The datacubes allow direct inversion of sophisticated forward models without empirical parameters or formulae. This capability is evaluated using the time-series inversion algorithm over grass fields.

A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.

The Soil Moisture Active Passive (SMAP) mission will combine spaceborne L-band radar and radiometer observations to provide improved estimates of Earth's surface geophysical parameters. In this paper we present a new robust radar-only snapshot approach (independent of ancillary data on roughness and vegetation) for mapping near real-time soil moisture at high spatial resolution. Simple formulations are developed based on traditional theories and parameterizations are obtained using electromagnetic scattering basis available in the form of “data cubes”. This new algorithm is tested using Passive and Active L- and S-band (PALS) airborne data and in situ soil moisture observations acquired during different field campaigns, i.e., SGP99, SMEX02, CLASIC07 and SMAPVEX08. The soil moisture retrieval root mean square error observed is in the range of the quality target (0.06 cm3/cm3) set for the SMAP mission.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

A time-series algorithm is proposed to retrieve bare surface soil moisture and rms height using two copolarized (HH and VV) L-band backscattering coefficients (σ0). The retrieval approach inverts a forward model for radar scattering from an isotropic bare surface. Because real-time inversion of a complex forward model is often computationally impractical, the inversion is implemented using a precomputed lookup table representation of σ0obtained from numerical Maxwell model in 3-D simulations. The retrieval process assumes that surface roughness properties are constant during the time-series interval, so that only a single rms height estimate is produced for the entire time series. The use of this rms height estimate as a constraint simplifies the associated soil moisture retrievals at each time step. A Monte-Carlo simulation of this algorithm with 0.7 dB radar measurement error (1-sigma) shows that retrievals using six time steps outperform a “snapshot” method (which retrieves rms height and soil moisture at each time step) by a factor of about two in rms soil moisture error. A second study using measured data having 6 to 11 time steps shows an rms error of 0.044 cm3/cm3for soil moisture with a correlation coefficient of 0.89 between retrieved and in situ data. Surface rms height estimates are also found accurate to 10 to 30% of in situ measurements. It is also shown that retrieval performance is not sensitive to errors in knowledge of the surface roughness correlation length for most of the bare surface conditions examined.

A radar-based time-series algorithm is evaluated for retrieving soil moisture (from the surface down to 5 cm depth) and roughness using two co-polarized (HH and VV) backscatter cross-section measurements (σ0). The retrieval approach inverts a forward model for radar scattering from a bare surface using a pre-computed lookup-table representation of σ0obtained from Numerical Maxwell Model in 3D simulations. The retrieval process assumes that surface roughness properties are constant during the time series interval, so that only a single rms height estimate is produced for the entire time series. A study using measured data having 6 to 11 time-steps shows an rms error of 0.044 cm3/cm3for soil moisture with a correlation coefficient of 0.89 between retrieved and in-situ data. Surface rms height estimates are also found accurate to 10 to 30% of in-situ measurements. It is also shown that retrieval performance is not sensitive to errors in knowledge of the surface roughness correlation length for most of the bare surface conditions examined.

This paper studies the identification of inland transient water bodies using an L-band radar, especially the dependence of the identification accuracy on the radar measurement noise (noise-equivalent radar backscatter, σ νε). The different levels for the radar measurement noise are simulated using the images taken by the Uninhabited Aerial Vehicle Syn thetic Aperture Radar (UAVSAR). With the original UAVSAR σ0NEof -45 dB the detection error is 2%. When σ0NEis raised to -28.5 dB and -25.5 dB, the error increases to 7% and 15% respectively. The analyses of the UAVSAR images over two different locations report the consistent results. 15% error in the water detection error translates into approximately 2 kelvin error in brightness temperature, when 10% of a 36-km radiometer pixel of the Soil Moisture Active Passive (SMAP)1 mission is covered by water surfac es. Based on these considerations, it is recommended that σ0NEfor the SMAP mission should be kept as low as the spacecraft and the instrument may support.

Retrieval of the brightness temperature (TB) at the L-band is studied in the context of the remote sensing of ocean surface salinity. The measurement of antenna temperature and the retrieval of TBare simulated with a radiative transfer model and an observing system model of an orbiting spacecraft. Two sets of antenna gain patterns are used: 1) theoretical analysis and 2) measurements of a 1/10th-size scale model. The latter set notably shows the large cross-polarization coupling from the first Stokes transmit into the third Stokes receive. The large cross-polarization coupling causes an error of up to 4° in the estimate of the Faraday rotation angle (the size of the angle itself is mostly less than 15° in the severe ionospheric condition). By this amount of the error, an additional rotation is introduced to the retrieval of the second and third Stokes TBs in front of the feed horn before the Faraday rotation correction. The additional rotation also degrades the performance of the antenna pattern correction (APC). However, when the Faraday rotation correction is performed by the square sum of the two retrieved Stokes, the retrieved TBbelow the ionosphere and at the top of the atmosphere after the Faraday correction becomes insensitive to the additional rotation (i.e., being insensitive to the error in the Faraday angle estimate and the rotational error in the APC). The formal proof of the insensitivity is presented. The first and second Stokes TBs at the top of the atmosphere observed at 5.6-s intervals from space may be retrieved with an error smaller than 0.1-K rms without the accurate ancillary information of the gain pattern and the Faraday rotation angle, assuming correct calibration, 0.08 K NEΔT for the radiometer noise, and accurate correction or flagging of the solar and galaxy radiation.

The Aquarius is a L-band passive/active instrument onboard a spacecraft due for launch in 2010, targeting sea surface salinity retrieval with an accuracy better than 0.2 psu. Measurement and retrieval of salinity by the Aquarius radiometer are simulated using a radiative transfer model. The cross-polarization coupling in the antenna pattern complicates the correction of the Faraday rotation effect in the ionosphere, causing errors in brightness temperature up to several Kelvin. The second-order correction for the Faraday effect is developed and reduces the retrieval error better than the required accuracy (~less 0.1 K).

Time-mean and absolute geostrophic velocities of the Kuroshio current south of Japan are derived from TOPEX/Poseidon altimeter data using a Gaussian jet model. When compared with simultaneous measurements from a shipboard acoustic Doppler current profiler (ADCP) at two intersection points between the altimeter and the ADCP tracks, the time-mean velocity is accurate to 1 cm s -1 to 5 cm s -1 . The errors in the absolute and the mean velocities are similar to those reported previously for other currents. The comparable performance suggests the Gaussian jet model is a promising methodology for determining absolute geostrophic velocities, noting that in this region the Kuroshio does not meander sufficiently, which provides unfavorable environment for the performance of the Gaussian jet model.