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P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

The basic observables of an imaging interferometer by aperture synthesis are the complex visibilities. Under some conditions, they can be simulated with the aid of the van Cittert–Zernike theorem. However, owing to underlying assumptions, some important effects that may alter them cannot be taken into account. This paper is devoted to the numerical simulation of complex visibilities without any reference to the van Cittert–Zernike theorem, in such a way that these effects can be taken into account. The emission from an extended source is modeled using a linear superposition of random waves emitted by a collection of point sources, which are all assumed to behave like black bodies at thermal equilibrium. These random waves are numerically generated with the aid of white noises filtered in such a way that their power spectral densities follow the shape of Planck distributions at the temperature of the point sources over a wide range of frequencies. The radio signal is then transported to the antennas, where the voltage patterns are taken into account as well as the filters response of the bandpass receivers. It is, therefore, sent to the correlator unit for being cross-correlated. From emission to correlation, perturbing effects can be introduced at any time. To illustrate this modeling method, numerical simulations are carried out in the L-band around 1413.5 MHz in reference to the SMOS-next project led by the French Space Agency. The results are discussed and compared with the estimates provided by the van Cittert–Zernike theorem. Owing to the amount of calculations to be performed, massive parallel architectures like that found in GPU have been required. [ABSTRACT FROM PUBLISHER]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is devoted to the monitoring of soil moisture and ocean salinity at a global scale from L-band space-borne radiometric observations obtained with a two-dimensional interferometer. SMOS was launched in November 2, 2009, and is still collecting the interferometric data used to retrieve the brightness temperature of the Earth surface. Several external sources of brightness within the field of view of SMOS contaminate these measurements and their contributions should be removed prior to image reconstruction. One of these sources is the direct solar radiations when the sun is either below the antenna plane array (i.e., in front of the satellite) and seen by the front-lobes antenna patterns or above it (i.e., in the back of the satellite) and seen by the back-lobes antenna patterns. In the case of SMOS, the direct solar radiations are accounted for only when the sun is in front of the satellite. In the second case, their impact is considered negligible. In this paper, we will show evidence of non-negligible direct solar radiations in the retrieved temperatures when the sun is in the back of SMOS. We will also show that the sun correction algorithm implemented in the SMOS level 1 operational processor can be extended efficiently to remove such radiations. [ABSTRACT FROM PUBLISHER]

The quality of science data provided by ESA soil moisture and ocean salinity (SMOS) satellite is degraded by the presence of artificial sources emitting in the protected part of the L-band, which is preserved for passive measurements by ITU regulation. These sources appear as high temperature points in SMOS brightness temperature products (e.g., Level 1C products), and may affect the retrievals of both SMOS (e.g., Level 2 products). In this contribution, a method is presented to quantify the impact of radio-frequency interference (RFI) on each SMOS snapshots, through the definition of an “RFI index” based on the number, position, and intensity of the RFI sources present in the snapshot. The main purpose of RFI indices is to provide the user of SMOS scientific data with information to ease the RFI filtering, thus achieving more accurate results. The comparison of RFI indices with the outputs of two different methods providing similar snapshot-wise information about RFI shows that the use of RFI indices reduces the probability of missed RFI detections, without increasing the risk for false alarms. [ABSTRACT FROM PUBLISHER]

The landscape freeze/thaw (F/T) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and soil moisture and ocean salinity (SMOS) L-band passive microwave measurements using a polarization ratio (PR)-based algorithm for landscape F/T monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: 1) tundra; 2) boreal forest; and 3) prairies. The reference data include in situ measurements of soil temperature (\mathbf T\mathbf{soil}) and air temperature (\mathbf T\mathbf{air}), and moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between L-band F/T detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using \mathbfT\mathbf{air} as the reference. Aquarius radiometer 2 (incidence angle of 39.6°) data give the best accuracies (90.8%), while for SMOS, using the Aquarius temporal characteristics, the best results (87.8% of accuracy) are obtained at higher incidence angles (\mathbf55^\circ\!\!-\!\!\mathbf60^\circ ). The F/T algorithm identifies both freeze onset and end with a delay of about 1 week in tundra and 2 weeks in forest and prairies, when compared to \mathbfT\mathbf{air}. The analysis shows a stronger F/T signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the PR) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar F/T patterns over Canada. This study shows the potential of using L-band spaceborne observations for F/T monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth. [ABSTRACT FROM PUBLISHER]

A single-frequency dielectric model for thawed and frozen Arctic organic-rich (80%–90% organic matter) soil was developed. The model is based on soil dielectric data that were measured over the ranges of volumetric moisture from 0.007 to 0.573\;\textcm^3/\textcm^3, dry soil density from 0.564 to 0.666\;\textg/cm^3, and temperature from 25 °C to -\mathbf30^\circ\mathbfC (cooling run), at the frequency of 1.4 GHz. The refractive mixing model was applied to fit the measurements of the soil’s complex refractive index (CRI) as a function of soil moisture, with the values of temperature being fixed. Using the results of this fitting, the parameters of the refractive mixing model were derived as a function of temperature. These parameters involve the CRIs of soil solids as well as bound, transient, and free soil water components. The error of the dielectric model was evaluated by correlating the predicted complex relative permittivity (CRP) values of the soil samples with the measured ones. The coefficient of determination (\mathbfR^\mathbf2) and the root-mean-square error (RMSE) were estimated to be \mathbfR^\mathbf2= \mathbf0.999, \mathbfRMSE = \mathbf0.27 and \mathbfR^\mathbf2= \mathbf0.993, \mathbfRMSE = \mathbf0.18 for the real and imaginary parts of the CRP, respectively. These values are in the order of the dielectric measurement error itself. The proposed dielectric model can be applied in active and passive remote-sensing techniques used in the areas with organic-rich soil covers, mainly for the SMOS, SMAP, and Aquarius missions. [ABSTRACT FROM PUBLISHER]

In land surface or numerical weather prediction (NWP) models, a soil moisture initialization scheme is important not to drift the prognostic variables to errors. We propose a novel approach for a stationary data assimilation scheme of ensemble optimal interpolation (EnOI) effective for soil moisture and ocean salinity (SMOS) soil moisture initialization. For the optimization of EnOI, the satellite retrieval error specification was conducted rather than ensemble evolution. As combining two ensembles generated from a satellite retrieval and a land surface model, this approach is termed as “two-step EnOI” in this study: (first step) the SMOS soil moisture retrieval ensembles (i.e., errors in brightness temperature, landscape, and geophysical parameters) were merged with SMOS L3 data; (second step) the data assimilation result from the first step was further used for the observations of the EnOI. This two-step EnOI was compared with a sequential ensemble Kalman filter (EnKF) evolving model state ensembles over time but assuming global constant a priori random errors for the SMOS observations. The point-scale comparison results showed that two-step EnOI was better matched with the field measurements than the SMOS L3 data and a sequential ensemble KF scheme. On meso-scale, a spatial average of two-step EnOI reached that of a sequential ensemble KF with the significantly reduced ensemble size. These results suggest that the performance of two-step EnOI is comparable to a sequential ensemble KF but computationally more effective. From this, it is illustrated that appropriate error specification of satellite retrieval is more important than a sequential evolution of model state ensembles, and brightness temperature ensemble mean can reduce the SMOS retrieval biases without sequential evolution. [ABSTRACT FROM PUBLISHER]

Artificial sources emitting in the protected part of the L-band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. Detecting and pinpointing such sources is crucial for the improvement of SMOS science products as well as for the identification of the emitters. In this contribution, we present a method to obtain snapshot-wise information about sources of radio-frequency interference (RFI). The localization accuracy of this method is also assessed for observed RFI sources. We also show that RFI localizations constitute a useful data set for assessing the pointing performance of the satellite, and present how it is possible, using the results of this method, to identify and estimate two systematic errors in the geo-location of the satellite field of view. The potential causes and the approaches to mitigate both these errors are discussed. [ABSTRACT FROM PUBLISHER]

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission. [ABSTRACT FROM PUBLISHER]

In this paper, we derive an expression of the brightness temperatures of a vegetated surface based on the tau-omega model with the rough surface boundary condition that replaces the conventional exp(-h) model by NMM3D (Numerical Simulations of 3D Maxwell equations). A purpose of the paper is that the same physical rough surface scattering model based on NMM3D and the same physical paramters of rms heights and correlation lengths can be used for both passive and active remote sensing of the same scene of vegetated surfaces. The bistatic scattering of rough surfaces are decomposed into the coherent wave and the co-polarization and the cross-polarization of the incoherent waves to quantify the contribution of each of these components. Numerical results are illustrated for a variety of roughness conditions. Comparisons are made with the \exp(-h) model. Results are compared with the experimental passive measurements of PORTOS 1993 for bare soil cases. Data cubes for grassland are calculated for both active and passive signatures at L band. Comparisons are then made with the L band PALS data for the grassland of SGP99 using these data cubes. [ABSTRACT FROM PUBLISHER]

This paper shows global maps of optical depth and soil moisture over land, obtained using the last prototype of SMOS Level 2 retrieval algorithm, which will be implemented in V600 version of Level 2 product made available by European Space Agency (ESA). The focus is on forested areas, where the approach adopted to develop the algorithm can be subdivided into different steps. First a theoretical model, which was previously developed and tested using ground based and airborne measurements, generated parametric outputs. By fitting this output data set, the albedo and the optical depth of a simple first order radiative transfer model were estimated. Then, this simplified forest model was included in the general ESA Level 2 retrieval algorithm over land, described in the Algorithm Theroretical Baseline Document (ATBD). The paper describes the details of this procedure and shows some retrieval results. First, the prototype algorithm was run with three free parameters: Soil moisture, optical depth, and albedo. The retrieved albedo resulted to be close to the initial estimate (0.08) for Boreal forests, while it was lower for Tropical forests. Running again the algorithm with the albedo fixed, a global map of optical depth was generated. The spatial features of the map follow the global information about forest biomass and forest height available in the literature. Finally it was found that, on average, the influence of seasonal effects on optical depth is moderate. [ABSTRACT FROM PUBLISHER]

The recently available Soil Moisture and Ocean Salinity (SMOS) 1.4 GHz based soil moisture retrievals for the year of 2010 and the first nine months of 2011 are assessed over the continental United States (CONUS) region, along with soil moisture retrievals produced at Princeton University based on the Advanced Microwave Scanning Radiometer (AMSR-E) 10.7 GHz channel using the Land Surface Microwave Emission Model (LSMEM) and in-situ measurements from the Natural Resource Conservation Service's (NRCS) Soil Climate Analysis Network (SCAN). The assessment is carried out using a performance metric developed by Crow (J. Hydromet., 2007), which calculates the ability of soil moisture estimates to correct errors in surface moisture predictions through a linear Kalman filter. Within the Crow framework, SMOS retrievals show the same level of skill as AMSR-E/LSMEM or SCAN when evaluated on the days where both are available. But the SMOS product is significantly less available than AMSR-E/LSMEM or SCAN, especially on rainy days, therefore it is less able to reproduce the rainfall-moisture dynamics and consequently achieves a lower performance metric if all available data are used from all products. Detailed analysis shows that, with uncertainties, the performance of both SMOS and AMSR-E/LSMEM generally decays with thicker vegetation and wetter climate but is not significantly influenced by topography. We expect SMOS to further improve its accuracy through validation studies and its availability under rainy conditions as well. [ABSTRACT FROM PUBLISHER]