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A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer--Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures (Tb) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E Tb's to soil temperature (Tsoil ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E Tb's to retrieve Tsoil is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and Tsoil information was chosen to obtain SM in the 2003-2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe's surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003-2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region. [ABSTRACT FROM AUTHOR]

As part of the Soil Moisture and Ocean Salinity (SMOS) validation process, a comparison of the skills of three satellites [SMOS, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) or Advanced Microwave Scanning Radiometer, and Advanced Scatterometer (ASCAT)], and one-model European Centre for Medium Range Weather Forecasting (ECMWF) soil moisture products is conducted over four watersheds located in the U.S. The four products compared in for 2010 over four soil moisture networks were used for the calibration of AMSR-E. The results indicate that SMOS retrievals are closest to the ground measurements with a low average root mean square error of 0.061 m3·m-3 for the morning overpass and 0.067 m3·m-3 for the afternoon overpass, which represents an improvement by a factor of 2-3 compared with the other products. The ECMWF product has good correlation coefficients (around 0.78) but has a constant bias of 0.1-0.2 m3·m-3 over the four networks. The land parameter retrieval model AMSR-E product gives reasonable results in terms of correlation (around 0.73) but has a variable seasonal bias over the year. The ASCAT soil moisture index is found to be very noisy and unstable. [ABSTRACT FROM PUBLISHER]

Overlapping soil moisture time series derived from two satellite microwave radiometers (the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer-Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U.S. with different climatologies. The two methods, cumulative density function (CDF) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. Both methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However, CDF matching improves the correlation, whereas copulas improve the root-mean-square error. [ABSTRACT FROM AUTHOR]

Abstract: SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the triple collocation method. This statistical method is based on the comparison of three datasets and produces global error maps by statistically inter-comparing their variations. Only the variable part of the errors are considered here, the bias errors are not treated by triple collocation. This method was applied to the following datasets: SMOS Level 2 product, two soil moisture products derived from AMSR-E (Advanced Microwave Scanning Radiometer)–LPRM (Land Parameter Retrieval Model) and NSIDC (National Snow and Ice Data Center), ASCAT (Advanced Scatterometer) and ECMWF (European Center for Medium range Weather Forecasting). The resulting errors are not absolute since they depend on the choice of the datasets. However this study showed that the spatial structure of the SMOS was independent of the combination and pointed out the same areas where SMOS performed well and where it did not. This global SMOS error map was then linked to other global parameters such as soil texture, RFI (Radio Frequency Interference) occurrence probabilities and land cover in order to identify their influences in the SMOS error. Globally the presence of forest in the field of view of the radiometer seemed to have the greatest influence on SMOS error (56.8%) whereas RFI represented 1.7% according to the analysis of variance from a multiple linear regression model. These percentages were not identical for all the continents and some discrepancies in the proportion of the influence were highlighted: soil texture was the main influence over Europe whereas RFI had the largest influence over Asia. [Copyright &y& Elsevier]

The SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched in November 2009. This ESA led mission for Earth Observation is dedicated to providing soil moisture over continental surface (with an accuracy goal of 0.04 m³/m³) and ocean salinity (with a goal of 0.1psu). These two geophysical features are important as they control the energy balance between the surface and the atmosphere. Their knowledge is of interest at global scales for climatic and weather research in particular for improving model forecasts. But it also has impact on various domains, ranging from hurricane monitoring to water resource management. The first six months after the launch, the so called commissioning phase, was dedicated to testing the functionalities of the spacecraft, the instrument and the ground segment including data processing. This phase was successfully completed in May 2010, and SMOS has since been in the routine operation phase, providing data products for the scientific community for over two years. The instrument performance and data quality fit the specifications. However, radio frequency interferences have been detected over large parts of Europe, China, Southern Asia, and the Middle East. The generation of Level 2 soil moisture and ocean salinity data is an on-going activity with continuously improved processings. [ABSTRACT FROM AUTHOR]

A simple reflectivity model of a bare soil at L-band is developed to account for the effects of soil roughness at different angles and polarizations. This model was developed using a long-term dataset acquired over the bare soil in the framework of the Surface Monitoring Of the Soil Reservoir EXperiment (SMOSREX). It is shown that the roughness effects are different depending on the measurement configuration, in terms of incidence angle and polarization. However, in this paper, a simple parameterization that is based on a single roughness parameter was calibrated in order to account for this angular and polarization dependencies. This parameter was found to be dependent on soil moisture: drier conditions were associated to higher "roughness" conditions. The root-mean-square error between the measured and modeled reflectivities on days when no precipitation events were detected at vertical polarization (V-pol) is 0.0275, and at horizontal polarization (H-pol), the rmse is 0.0237; all incidence angles were considered. When all data are considered, the rmsd for V-pol is 0.0350, and for H-pol, the rmse is 0.0373. This new simple model is suitable for soil moisture retrieval from Soil Moisture and Ocean Salinity data. By means of this simple parameterization, almost two years of soil moisture data were retrieved with a good accuracy. The SMOSREX dataset allowed to ensure a long-term suitability of the proposed parameterization. [ABSTRACT FROM AUTHOR]

In the framework of the Soil Moisture and Ocean Salinity mission, a two-year (1987–1988) global simulation of brightness temperatures (TB) at L-band was performed using a simple model [L-band microwave emission of the biosphere, (L-MEB)] based on radiative transfer equations. However, the lack of alternative L-band spaceborne measurements corresponding to real-world data prevented from assessing the realism of the simulated global-scale TB fields. In this study, using a similar modeling approach, TB simulations were performed at C-band and X-band. These simulations required the development of C-MEB and X-MEB models, corresponding to the equivalent of L-MEB at C-band and X-band, respectively. These simulations were compared with Scanning Multichannel Microwave Radiometer (SMMR) measurements during the period January to August 1987 (corresponding to the end of life of the SMMR mission). A sensitivity study was also carried out to assess, at a global scale, the relative contributions of the main MEB parameters (particularly the roughness and vegetation model parameters). Regional differences between simulated and measured TBs were analyzed, discriminating possible issues either linked to the radiative transfer model (C-MEB and X-MEB) or due to land surface simulations. A global agreement between observations and simulations was discussed and allowed to evaluate regions where soil moisture retrievals would give best results. This comparison step made at C-band and X-band allowed to better assess how realistic and/or accurate the L-band simulations could be. [ABSTRACT FROM AUTHOR]

Presents information on a study which described the antenna synthesis methods for Soil Moisture and Ocean Salinity mission. Mission description; Data acquisition and processing; Calibration and validation; Conclusion.

The Above Ground Biomass (AGB) is an essential component of the Earth carbon cycle. Yet, large uncertainties remain on its spatial distribution and temporal evolution. Improving the accuracy of the AGB estimates requires precise and regular monitoring. Satellite remote sensing offers such capabilities. In particular, the L-Band (1.41 GHz) Vegetation Optical Depth (VOD) derived from the SMOS (Soil Moisture and Ocean Salinity mission) multi-angle brightness temperatures is a good AGB proxy. Averaging the SMOS L-VOD over a year and linking it to a pre-existing AGB map is a well-established method to derive a spatial relationship between both quantities. After temporal extrapolation of this relation, global AGB time series are derived from the L-VOD, allowing to retrieve vegetation biomass values up to 300 Mg ha-1 from 2011 onwards. This study focuses on this protocol to produce a harmonized AGB dataset from the L-VOD and analyses the impact of three factors on the AGB/VOD calibration. First, the influence of the orbit type (ascending or descending) on the estimation is quantified. Second, the relevance of using a single global spatial calibration or several regional ones is thoroughly discussed for the first time. Third, the AGB time series from this new dataset are compared against other published AGB time series to assess the validity of extrapolating a spatial relationship over time. These comparisons highlight that the produced dataset has more inter-annual variability than the other available time series and presents globally lower AGB estimates, particularly over the equatorial part of Africa. These two limitations are inherent to the input data and method used. Overall, the resulting AGB is coherent with the AGB map from the CCI Biomass version 4 and can be used in AGB studies. The freely accessible AGB dataset has been produced from the level 2 SMOS products, mixing ascending and descending orbits altogether and using a single global relationship between the AGB and the VOD. The spatial bias associated with the AGB estimates is also provided in the files. The AGB dataset is open access and the NetCDF files are available at: https://doi.org/10.12770/95f76ff0-5d89-430d-80db-95fbdd77f543 (Boitard et al., 2024). [ABSTRACT FROM AUTHOR]

The introductory overview and 27 papers in this special issue present information on instrument performance and first results of the SMOS mission. [ABSTRACT FROM AUTHOR]

This article presents an introduction to this special issue devoted to the electronic sensing of soil moisture and ocean salinity of the Earth from a spacecraft to be launched to a position above the planet.

Soil Moisture and Ocean Salinity (SMOS) mission has successfully contributed to global soil moisture products since 2009. Validation and calibration activities were conducted worldwide, yet some of the validation results do not fulfill the targeted accuracy of ±0.04 $\text{m}^{3}\text{m}^{-3}$. This paper presented the site-specific calibration of the V620 retrieval algorithm with in situ data collected at selected agricultural sites in the humid tropical regions, Malaysia. This set of data has been validated where low accuracy of SMOS soil moisture products was found. To improve the SMOS soil moisture retrieval, calibration of SMOS soil moisture retrieval algorithm based on the L-band Microwave Emission and Biosphere model and SMOS Level 1C $\text{T}_{\mathrm {B}}$ products, considering the local parameters was conducted. The calibration proves that these site-specific parameters improve the product’s accuracy. Validation of SMOS Level 2 product with in situ data showed bias, root-mean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 $\text{m}^{3}\text{m}^{-3}$ , 0.068 to 0.142 $\text{m}^{3}\text{m}^{-3}$ , and 0.069 to 0.103 $\text{m}^{3}\text{m}^{-3}$ , respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020–0.056 $\text{m}^{3}\text {m}^{-3}$ and RMSE of 0.026–0.065 $\text{m}^{3}\text{m}^{-3}$. The ubRMSE ranges from 0.017 to 0.034 $\text{m}^{3}\text{m}^{-3}$. Recently released SMOS-IC V105 product was also validated, where small improvements were noticed when compared to the accuracy of SMOS Level 2. This paper shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global generalized parameters that are used in the original SMOS soil moisture retrieval algorithm. [ABSTRACT FROM AUTHOR]

In a recent paper, Youths et al. propose an apparently interesting methodology for the computation of the interference received by a spaceborne passive sensor from terrestrial interferences. However, the paper seems to require some clarifications, as some of the conclusions are questionable. This comments paper alms at identifying the most outstanding issues of the publication. [ABSTRACT FROM AUTHOR]

Presents the foreword to the October 2004 issue of the journal "IEEE Transactions on Geoscience and Remote Sensing."

Calibration and validation activities on Soil Moisture and Ocean Salinity (SMOS)-derived soil moisture products have been conducted worldwide since the data became available, but this has not been the case over tropical regions. This study focuses on the setting up of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia and on the validation of SMOS soil moisture products. Thein-situdata over a one-and-a-half-year period was analysed and the validation of the SMOS soil moisture products with thisin-situdata was conducted. Bias and root mean square error (RMSE) were computed between the SMOS soil moisture products and thein-situsurface soil moisture collected at the satellite passing times (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with a vegetation water content higher than 5 kg m−2, an overestimation of SMOS soil moisture products toin-situdata was noticed in this study. The bias ranged from 0.064 to 0.119 m3m−3and the RMSE was from 0.090 to 0.158 m3m−3, when both ascending and descending mode data were measured. This RMSE was found to be similar to those of a number of studies conducted previously at different regions. However, a wet bias was found during the validation, while previous validation activities at other locations showed dry biases. The result of this study is useful to support the continuous development and improvement of the SMOS soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially in tropical regions. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact. [ABSTRACT FROM PUBLISHER]

The Soil Moisture and Ocean Salinity (SMOS) radiometer operates within the Earth Exploration Satellite Service passive band at 1400–1427 MHz. Since its launch in November 2009, SMOS images are strongly impacted by radio frequency interference (RFI). So far >500 RFI sources distributed worldwide have been detected. Up to 42% of these RFIs could be suppressed thanks to the co-operation of the National Spectrum Management Authorities. Some of the strongest RFI sources might mask other weaker sources underneath, hence it is expected the total number of RFI detected may increase as strong ones are progressively identified and switched off. Most RFIs are located in Asia and Europe, which together hold \sim73\% of the active sources and >90\% of the strongest interference. The areas affected by RFI may experience either an underestimation in the retrieved values of soil moisture and ocean salinity or data loss, with the associated detrimental impact on the scientific return. ESA and the teams participating in SMOS mission have put in place different strategies to alleviate this RFI situation. [ABSTRACT FROM AUTHOR]

Effective water management in agriculture requires a comprehensive understanding of the distribution of water content throughout the soil profile to the root zone. This knowledge empowers farmers and water managers to make informed decisions regarding irrigation timing and quantity for optimizing crop growth. To estimate the soil moisture profile, this study utilized combined L- and P-band radiometry with four incoherent radiative transfer models, including three multi-layer models based on a zero-order (IZ), first order (IF) and incoherent solution (IS) approximation, and a uniform model (UM) model, as well as the stratified coherent Njoku model (NM). The impact of vegetation was considered through the conventional tau-omega model. Linear (Li) and second-order polynomial (Pn2) functions were used to represent the shape of the soil moisture profile. Observations from a tower-based experiment under various land cover conditions, including bare, bare-weed, grass, wheat and corn, were used. The root mean square error (RMSE) was calculated between the observed and estimated soil moisture profiles. The results revealed comparable RMSE values for all five radiative transfer models, with the Pn2 function outperforming the Li function in estimating the soil moisture of deeper layers. Regardless of the employed radiative transfer model, utilizing combined L- and P-band radiometry and employing the Pn2 function yielded RMSEs of 0.03 m3/m3, 0.08 m3/m3, and 0.1 m3/m3 over depths of 0–5 cm, 0–30 cm, and 0–60 cm, respectively. A comparison between the incoherent and stratified coherent Njoku radiative transfer models indicated that the latter slightly outperformed the former under the dry bare soil conditions, exhibiting a 0.003 m3/m3 lower RMSE at the surface while nearly equal performance at the bottom of the profile. Furthermore, the multi-layer incoherent radiative transfer models provided only a slightly better estimate than the UM model, especially for shallow layers, with the average RMSE over the entire profile being 0.002 m3/m3 lower. Consequently, the complexity of the multi-layer incoherent and coherent radiative transfer models is not justified for this small gain in performance. The depth for which the UM model provided a reasonable soil moisture estimate ranged from 1 cm (under wet corn) to 39 cm (under dry bare), and depended on the soil moisture profile gradient and soil moisture content values in the shallow layers. These important findings pave the way for estimating soil moisture profile on a global scale using combined L- and P-band radiometry from future satellite missions. • Combined L-band and P-band radiometry was used to estimate soil moisture profiles. • Second-order polynomial surpassed linear function in estimating deep soil moisture. • Multilayer coherent models slightly outperformed uniform model for shallow layers. • Differences between models were minor, particularly for second-order polynomial. • The uniform model estimates: 1 cm (wet corn) to 39 cm (dry bare). [ABSTRACT FROM AUTHOR]

Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Jülich: 10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) satellite with a passive L-band radiometer monitors surface soil moisture. In addition to soil moisture, vegetation optical thickness \tauNAD is retrieved (L2 product) from brightness temperatures (TB, L1C product) using an algorithm based on the L-band Microwave Emission of the Biosphere (L-MEB) model with initial guesses on the two parameters (derived from ECMWF products and ECOCLIMAP Leaf Area Index, respectively) and other auxiliary input. This paper presents the validation work carried out in the Skjern River Catchment, Denmark. L1C/L2 data and the most sensitive algorithm parameters were analyzed by network and airborne campaign data collected within one SMOS pixel (44 km diameter). The SMOS retrieval is based on the prevailing low vegetation class. For the L1C comparison, TB's were calculated from in situ soil moisture using L-MEB. Consistent with worldwide findings, the initial/retrieved SMOS soil moisture captures the in situ dynamics well but with significant wet/dry biases and too large amplitudes in case of the latter. While the initial \tauNAD is in range with an in situ estimate for low agricultural vegetation, the retrieved \tauNAD is too high with too pronounced temporal variability. A filter based on L2 criteria removed radio frequency interference (RFI) and improved the R^2 between retrieved and network soil moisture from 0.49 to 0.61, while the bias remained (-0.092/-\!0.087\ \m^3/\m^3). Likely error sources include the following: 1) still present RFI; 2) potential link between high retrieved \tauNAD and other L-MEB parameters, e.g., low roughness parameter (HR); 3) \sim18% lower sand and \sim8% higher clay fractions while \sim\!\!0.35\ \g/cm^3 lower bulk density in SMOS algorithm than in situ; and 4) caveats in the Dobson dielectric mixing model implemented in the L-MEB model. A previous study at the Danish validation site had revealed superior performance of the Mironov dielectric mixing model at the 2 \times 2 km scale. Studies are ongoing to address the aforementioned issues, and the role of organic surface layers will be investigated. [ABSTRACT FROM PUBLISHER]

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]

Soil moisture retrieval models from the Soil Moisture and Ocean Salinity (SMOS) mission, which is an L-band microwave interferometer, are based on multiangular measurements and make use of the emissivity angular signature. Mountainous areas modify local incidence angles, implying significant impacts on brightness temperatures and, consequently, on soil moisture retrievals. The purpose of this paper is to establish a criterion in quantifying the relevance of topographic impacts at the SMOS scale (∼40 km). The goal is thus to define a method of flagging the pixels according to the relative impact of topography on the brightness temperature. The proposed method uses the variogram of digital elevation model images. As a result, a map of the pixels to be flagged is produced to ensure that no soil moisture retrievals are carried out on pixels that are affected by strong topographic effects. As validation, a model was also used to simulate differences between brightness temperature variations between mountainous areas and flat surfaces. [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]

Abstract: The primary goal of the SMOS mission is to deliver global fields of sea surface salinity and surface soil moisture using L-band (1.4 GHz) radiometry. Within the context of the preparation of SMOS activities over land, a field campaign, SMOSREX (Surface Monitoring Of the Soil Reservoir EXperiment), has been in operation since January 2001 in Mauzac, near Toulouse in France. Continuous ground measurements of meteorological variables, soil moisture and temperature profiles have been taken over bare soil and a grass plot left fallow. Since January 2003, SMOSREX has been providing accurate field measurements of dual polarized L-band brightness temperature up-welling from both bare soil and fallow plots, together with multi-spectral (from visible to infrared frequencies) remote sensing surface data. The scientific objectives are presented in this paper and the corresponding experimental design is described. The experimental concept is totally new since (i) SMOSREX combines land–surface–atmosphere observations, passive microwave measurements and VIS to NIR remote sensing, (ii) SMOSREX is based on highly accurate L-band measurements carried out by a radiometer specifically designed for the experiment, and (iii) SMOSREX provides a unique continuous data set of L-band measurements over several years. The characteristics of the L-band emission are presented at diurnal, seasonal and annual temporal scales, and the emissions are compared over bare soil and natural grass. The surface emissions over bare soil and fallow area are shown to be counter-phased at the diurnal scale due to small variations in vegetation water content and bare soil surface moisture. Innovative long term results using L-band measurements for both bare soil and natural grass are presented in this paper, and the relationship between the surface emission at L-band and surface bare soil moisture is shown to be suitable for a long term period (19 months). Soil freezing is shown to be drastically different for bare soil and vegetation covered plots, with a large threshold effect on microwave surface emission. [Copyright &y& Elsevier]

The spectral window at L-band (1.413 GHz) is important for passive remote sensing of surface parameters such as soil moisture and sea surface salinity that are needed to understand the hydrological cycle and ocean circulation. Radiation from celestial sources (mostly galactic) is strong in this window, and an accurate accounting of this background radiation is often needed for calibration. This paper presents a comparison of the background radiation predicted by a model developed from modern radio astronomy measurements with measurements made with several modern L-band remote sensing radiometers. The comparison validates the model and illustrates the magnitude of the correction necessary in remote sensing applications. [ABSTRACT FROM AUTHOR]

A new physically based disaggregation method is developed to improve the spatial resolution of the surface soil moisture extracted from the Soil Moisture and Ocean Salinity (SMOS) data. The approach combines the 40-km resolution SMOS multiangular brightness temperatures and 1-km resolution auxiliary data composed of visible, near-infrared, and thermal infrared remote sensing data and all the surface variables involved in the modeling of land surface-atmosphere interaction available at this scale (soil texture, atmospheric forcing, etc.). The method successively estimates a relative spatial distribution of soil moisture with fine-scale auxiliary data, and normalizes this distribution at SMOS resolution with SMOS data. The main assumption relies on the relations ship between the radiometric soil temperature inverted from the thermal infrared and the microwave soil moisture. Based on synthetic data generated with a land surface model, it is shown that the radiometric soil temperature can be used as a tracer of the spatial variability of the 0-5 cm soil moisture. A sensitivity analysis shows that the algorithm remains stable for big uncertainties in auxiliary data and that the uncertainty in SMOS observation seems to be the limiting factor. Finally, a simple application to the SGP97/AVHRR data illustrates the usefulness of the approach. [ABSTRACT FROM AUTHOR]

In the framework of the preparation of the Soil Moisture and Ocean Salinity (SMOS) mission, several field experiments are required so as to address specific modeling issues. The goal is to improve current models and to test retrieval algorithms. However, adequate ground instrumentation is scarce and not readily available "off the shelf." In this context, a high-accuracy L-band radiometer was required for a specific long-term campaign for the preparation of the SMOS mission. For this purpose, a dual-polarized radiometer was designed and built to check algorithms for surface soil moisture retrieval from multiangular dual-polarized brightness temperatures. This radiometer has been tested in the field for 20 months and is operational since end of January 2003. The aim of this paper is to give details of the system architecture, calibration procedures, together with the performances obtained and some preliminary results. [ABSTRACT FROM AUTHOR]

The issue of deriving cross-scale aggregation rules has been extensively investigated over the last two decades. A widely used approach consists of formulating grid-scale surface radiances using the same equations that govern the patch-scale behavior but whose arguments are the aggregate expressions of those at the patch-scale. This approach derives the area-averaged or effective radiative surface temperature as might be observed using low spatial resolution satellite data. The problem however is that such satellite data exhibit large directional effects and no study has addressed this issue. The present work tackles this problem in the thermal infrared domain. The directional effects are studied by modeling. Thus, an infrared sensor observing a two-dimensional (2-D) heterogeneous plane surface is modeled. The 2-D heterogeneous plane surface is simulated by a grid with two homogeneous elements (vegetation-bare soil). The angular properties of the local surfaces, assumed homogeneous, are calculated by a multiple scattering model. The equivalent angular radiance of the complete heterogeneous scene is then determined by applying the aggregation method. This radiance is very sensitive to the surface heterogeneity, especially when the spatial variation of the surface temperature is significant and when the directional behavior of the surface is non-Lambertian. As a result, an angular variation of 6% on radiance was obtained on a heterogenous surface between a zenith angle of 70° and on-nadir measurements. [ABSTRACT FROM AUTHOR]

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

Presents information on the first images obtained by a demonstrator of the Soil Moisture and Ocean Salinity (SMOS) over land surfaces at the Avignon test site in 1999. Details of the SMOS mission; Function of L-band microwave radiometry; Positions of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) during the SMOS experiment; Information on the MIRAS demonstrator; Description of the images obtained over land surfaces.

Presents information on a study which described a model for simulating the L-band microwave emission from bare soils. Experimental details; Derivation of surface emissivity from the measurements; Discussion and conclusions.

Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy of accumulated rainfall estimates over land. Such satellite enhanced precipitation products, available with a short latency (< 1 day), represent an important and new source of information for river flow prediction and water resources management, particularly in developing countries in which ground observations are scarcely available and the access to such data is not always ensured. In this study, three recently developed rainfall products obtained from the integration of GPM rainfall and satellite soil moisture products have been used; namely GPM+SM2RAIN, PRISM-SMOS, and PRISM-SMAP. The prediction of observed daily river discharge at 10 basins located in Europe (4), West Africa (3) and South Africa (3) is carried out. For comparison, we have also considered three rainfall products based on: (1) GPM only, i.e., the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (GPM-ER), (2) rain gauges, i.e., the Global Precipitation Climatology Centre, and (3) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. Three different conceptual and lumped rainfall-runoff models are employed to obtain robust and reliable results over the 3-year data period 2015–2017. Results indicate that, particularly over scarcely gauged areas (West Africa), the integrated products outperform both ground- and reanalysis-based rainfall estimates. For all basins, the GPM+SM2RAIN product is performing the best among the short latency products with mean Kling–Gupta Efficiency (KGE) equal to 0.87, and significantly better than GPM-ER (mean KGE = 0.77). The integrated products are found to reproduce particularly well the high flows. These results highlight the strong need to disseminate such integrated satellite rainfall products for hydrological (and agricultural) applications in poorly gauged areas such as Africa and South America. [ABSTRACT FROM AUTHOR]

Melt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010–2011 and 2017–2018. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from the special sensor microwave imager (SSM/I) and special sensor microwave imager sounder (SSMIS). The comparison of daily melt occurrence retrieved from 1.4 and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, a theoretical analysis is performed using a microwave emission radiative transfer model. It shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimetres of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present in depths up to hundreds of metres. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher-frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack. [ABSTRACT FROM AUTHOR]

• SWAF-HR data is new water surface extent product at 1 km over the Amazon basin from 2010 to 2016 every 3 days. • SWAF-HR provided from the fusion of three datasets: water surface fraction at coarse spatial resolution from an L-band passive microwave sensor (Soil Moisture and Ocean Salinity – SMOS), Global Surface Water Occurrence from Landsat (GSWO) and the Digital Elevation Model (DEM) Multi-Error-Removed-Terrain (MERIT) based on the Shuttle Radar Topography Mission. • The flooded area of the Branco floodplain (Roraima) varies from 0.2 times 104 to 2.7 × 104 km2 whereas the extent of the Bolivian floodplain (Llanos de Moxos) inundation ranges between 0.8 × 104 and 8.1 × 104 km2 during 2010–2016. • The choice of the DEM in the downscaling process introduces 5% of error in the instantaneous water surface extent estimate but can reach up to 10% in the flood probability estimations over seven years. • (MERIT) based on the Shuttle Radar Topography Mission (SRTM). In this paper, we present a methodology to map inland water in tropical areas under dense vegetation at high spatial and temporal resolution using multi-source remote sensing data. A new inundation product (SWAF-HR) is presented. It is characterized by a high spatial resolution (30′, 1 km) and high temporal resolution (3 days). The SWAF-HR product is estimated over the Amazon basin for the 2010–2016 period. It is based on a downscaling procedure and the synergistic use of: (1) water surface fraction at coarse spatial resolution from an L-band passive microwave sensor (Soil Moisture and Ocean Salinity – SMOS), (2) Global Surface Water Occurrence from Landsat (GSWO) and (3) the Digital Elevation Model (DEM) Multi-Error-Removed-Improved-Terrain (MERIT) based on the Shuttle Radar Topography Mission (SRTM). Thanks to the high capability of L-band microwave emission to reveal surface water under all-weather conditions and beneath the vegetation, the inundated area extent estimated by the SWAF-HR product is always larger than GSWO estimates obtained by the optical sensor (Landsat). SWAF-HR data is compared to ESA CCI and IGBP land covers, two SAR images and flooded areas over the Purus basin computed by the MGB-IPH model simulation. The results show the coherence of spatial and temporal dynamics of the SWAF-HR data. We show that the flooded area of the Branco River floodplain in Roraima (Brazil) varies from 0.2 × 104 to 2.7 × 104 km2 whereas the extent of the Bolivian floodplain (Llanos de Moxos) inundation ranges between 0.8 × 104 and 8.1 × 104 km2 during 2010–2016. The flooded area in the Branco floodplain gradually decreased from 2010 to 2015 but in 2016, the flooded area has increased during the rainy season. During 2010–2016, the minimum of the inundated surface extent was reached during 2015–2016 reflecting to a drought event related to ENSO. The most important uncertainties of the DEM are located over tropical areas but this information is essential in the downscaling procedure. Therefore, we investigate the impact of the choice of the DEM for the downscaling procedure. It is found that the choice of the DEM introduces 5% of error in the instantaneous water surface extent estimate but can reach up to 10% in the flood probability estimations over seven years. This new SWAF-HR product will be helpful for the understanding of the water, carbon and biogeochemical cycles of the Amazon. [ABSTRACT FROM AUTHOR]

In the Sahel, crop growth and yield are strongly linked to climate fluctuations. The low and erratic rainfall the Sahel region has experienced for several years led to poor harvests, associated with dramatic food crises and famines. Consequently, numerous studies were conducted to develop innovative techniques to estimate crop yield based on satellite measurements. Unlike most approaches which use rainfall, temperature or vegetation indices to derive crop yield estimates, the present study investigates the potential of satellite-derived soil moisture products. This study focuses on millet, a major food crop in Africa. A first step was devoted to analyzing the relation between soil moisture and millet yield at the local scale using ground-based soil moisture and millet yield measurements obtained at ten site locations in Niger. Then, the statistical relationship obtained at the local scale was assessed at the regional scale (Niger, Mali, Senegal and Burkina Faso) using satellite-based soil moisture mapping (based on a simple land-surface model and a satellite precipitation product) and compared to millet yield estimates from the Food and Agriculture Organization (FAO) database. It was shown that millet yield variations are closely linked to soil moisture variations during two key periods of the plant growth: the “grain filling” and the “reproductive” periods. Soil moisture variations during these two periods led to explain 81% (R² = 0.81) of the FAO millet yield variations from 1998 to 2014 in the Sahel. [ABSTRACT FROM AUTHOR]

This paper investigated the sensitivity of passive microwave L-band soil moisture (SM) retrieval from multiangle airborne brightness temperature data obtained under morning and afternoon conditions from the National Airborne Field Experiment conducted in southeast Australia in 2006. Ground measurements at a dryland focus farm including soil texture, soil temperature, and vegetation water content were used as ancillary data to drive the retrieval model. The derived SM was then in turn evaluated with the ground-measured near-surface SM patterns. The results of this paper show that the Soil Moisture and Ocean Salinity target accuracy of 0.04 m3·m-3 for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions≥0.15 m3·m-3. Additional tests on the use of the air temperature as proxy for the vegetation temperature also showed no preference for the acquisition time. The performance of multiparameter retrievals of SM and an additional parameter proved to be satisfactory for SM modeling--independent of the acquisition time--with root-mean-square errors less than 0.06 m3·m-3 for the focus farm. [ABSTRACT FROM AUTHOR]

Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003-2013) from two different satellite sensors, i.e., SMOS (2010-2013) and AMSR-E (2003-2011), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the Rvalue), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land-atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The Rvalue and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil moisture datasets based on a combination of these sources are a valuable source of information for climate research. [ABSTRACT FROM AUTHOR]

Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km²). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10-20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr-1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources. [ABSTRACT FROM AUTHOR]

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX ( Surface Externalisée )). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale. [ABSTRACT FROM AUTHOR]

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 1 cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “ Q / H ” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between Δ N and root-mean-square height ( σ ) or σ 2 / L C can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters ( Q , N p and h ) generally increase when soil get rougher. The calibrated Q , N h and N v show exponential dependence on the effective parameter h . The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero- Q fitting models have comparable performance in Tb modeling with zero- Q models but may be more physically realistic. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of different soil moisture products from AMSR2 and SMOS against the most representative stations within the Yanco study area in the Murrumbidgee catchment, in southeast Australia. AMSR2 Level 3 (L3) soil moisture products retrieved from two versions of brightness temperatures using the Japanese Aerospace eXploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithm were included. For the LPRM algorithm, two different parameterization methods were applied. Furthermore, two versions of SMOS L3 soil moisture product were assessed. The results are contrasted against the use of “random” stations. Accounting for all versions, frequencies and overpasses, the latest versions of the JAXA (JX2) and LPRM (LP3) products were found to surpass the earlier versions (JX1, LP1 and LP2). Soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used but not X-band. However, X-band retrievals (r: 0.71, MAE: 0.07, RMSD: 0.08 m 3 /m 3 ) were found to perform better than C-band (r: 0.68–0.70, MAE: 0.07–0.09 m 3 /m 3 , RMSD: 0.09–0.10 m 3 /m 3 ). Moreover, an intercomparison between different acquisition times (morning and evening) of AMSR2 X-band products found a better performance from evening overpasses (1:30 pm; r: 0.69–0.77) as opposed to morning overpasses (1:30 am; r: 0.47–0.66). In the case of SMOS, morning (6:00 am; r: 0.77) retrievals were found to be superior over evening (6:00 pm; r: 0.69) retrievals. Overall, both versions of JAXA products, the second and third versions of LPRM X-band products, and two versions of SMOS products were found to meet the mean average error (MAE) goal accuracy of the AMSR2 mission (MAE < 0.08 m 3 /m 3 ) but none of the products achieved the SMOS goal of RMSD < 0.04 m 3 /m 3 . Furthermore, performance of the products differed depending on the statistic used to evaluate them. Consequently, considering the results in this study, JX2 products are recommended if both absolute and temporal accuracy of the soil moisture product is of importance, whereas LP3 X products from evening observations and SMOS version 3.00 (SMOS2) products from morning overpasses are recommended if temporal accuracy is of greater importance. [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]

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes. [ABSTRACT FROM AUTHOR]

For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD) dataset by utilizing Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Land Data Assimilation System (GLDAS) soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS-) based AOD products were used as reference datasets to validate the modeled AOD (MA). The SMOS-based MA (SMOS-MA) dataset showed good correspondence with observed AOD (R-value: 0.56) compared to AMSR2- and GLDAS-based MA datasets, and it overestimated AOD compared to observed AOD. The AMSR2-based MA dataset was found to underestimate AOD, and it showed a relatively low R-value (0.35) with respect to observed AOD. Furthermore, SMOS-MA products were able to simulate the short-term AOD trends, having a high R-value (0.65). The results of this study may allow us to acknowledge the utilization of microwave-based soil moisture datasets for investigation of near-real time dust outbreak predictions and short-term dust outbreak trend analysis. [ABSTRACT FROM AUTHOR]

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir ( τ nad ) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB), soil roughness is modelled with a semi-empirical equation using four main parameters ( Q r , H r , N rp , with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N rp and H r on the SM and τ nad retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011–2015). In this study, Q r was set equal to zero and we assumed that N rH = N rV . The retrievals were performed by varying N rp from −1 to 2 by steps of 1 and H r from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions. [ABSTRACT FROM AUTHOR]

The remote signatures measured at microwave frequency above land surfaces are strongly dependent on the permittivity of the soil, which is linked to its moisture content. Thus, soil permittivity is a key parameter when algorithms are developed for the retrieval of hydrologic parameters from remote sensing data. Soil permittivity measurements are generally carried out in the laboratory because in-situ measurements are more difficult to obtain. The study presents the development of two probes (N and SMA probes) for in situ soil permittivity measurements ( i.e . measurements of dielectric properties). They are based on the end effect phenomenon of a coaxial waveguide and so are called end effect probes in this paper. Results obtained on well-known materials (water and polytetrafluoroethene) are compared with corresponding data obtained by laboratory approaches (Von Hippel’s method and resonant cavity) and show good agreement from 0.5 GHz up to ∼3.5 GHz and 6 GHz for N and SMA probes respectively. Then measurements made on concrete and mineral soil are reported to underline the efficiency of end effect probes for in-situ dielectric measurements. Finally, through work undertaken in the framework of the European Space Agency’s SMOSHiLat project, we demonstrate the applicability of the two probes for measurements performed within these frequency ranges in complex material such as organic soil horizons. [ABSTRACT FROM AUTHOR]

Global surface soil moisture products are derived from passive L-band microwave satellite observations. The applied retrieval algorithms include dielectric models (relating soil water content to relative permittivity) developed for mineral soils. First efforts to generate equivalent models for areas where organic surface layers are present such as in the high-latitude regions have recently been undertaken. The objective of this study was to improve our still insufficient understanding of L-band emission of organic substrates in prospect of enhancing soil moisture estimations in the high latitudes undergoing most rapid climatic changes. To this end, L-band relative permittivity measurements using a resonant cavity were carried out on a wide range of organic surface layer types collected at different sites. This dataset was used to evaluate two already existing models for organic substrates. Some samples from underlying mineral layers were considered for comparison. In agreement with theory the bulk relative permittivity measured in organic substrate was decreased due to an increased bound water fraction (where water molecules are rotationally hindered) compared to the measured mineral material and corresponding output of the dielectric model for mineral soils used in satellite algorithms. No distinct differences in dielectric response were detected in the measurements from various organic layer types, suggesting a generally uniform L-band emission behavior. This made it possible to fit a simple empirical model to the data obtained from all collected organic samples. Outputs of the two existing models both based on only one organic surface layer type were found to lie within the spread of our measured data, and in close proximity to the derived simple model. This general consensus strengthened confidence in the validity of all these models. The simple model should be suitable for satellite soil moisture retrieval applications as it is calibrated on a wide range of organic substrate types and the entire wetness range, and does not require any auxiliary input that may be difficult to obtain globally. This renders it generically applicable wherever organic surface layers are present. [ABSTRACT FROM AUTHOR]

Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm-Merge Active and Passive microwave Soil Moisture-uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m3/m3 and 0.069 m3/m3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data. [ABSTRACT FROM AUTHOR]