Understanding the vertical distribution of soil moisture is crucial for making informed decisions in various applications, ranging from precision agriculture to hydrological modeling. Four machine learning algorithms, including random forest, extreme gradient boosting, deep learning, and support vector regression were employed to estimate the soil moisture profile from collected tower-based L-band and P-band brightness temperature observations in Victoria, Australia. The results showed that random forest outperformed the other algorithms, with root mean square error (RMSE) values of 0.03, 0.04, and 0.06 m3/m3for depths of 0-5 cm, 0-30 cm, and 0-60 cm, respectively

The Fine Resolution Explorer for Salinity, Carbon and Hydrology (FRESCH) is presented. The science case and the mission objectives are discussed before presenting the mission concept. FRESCH is an L-band antenna array operated in beamforming mode providing data at a spatial resolution of 10-15 km to study the biogeochemical and physical phenomena taking place at the interfaces of ocean, land and ice. FRESCH has been submitted to the European Space Agency Earth Explorer 12 program.

The impact of the initial spatial resolution of soil moisture maps on the quality of downscaled maps by merging with a higher resolution dataset was addressed. Soil moisture maps acquired with airborne sensors in four different campaigns in different climate regions with resolutions of 500 m to 1 km were aggregated to 4-5 km, 8-10 km, 18-20 km and 36-40 km before applying a downscaling algorithm to compute 1 km maps. These maps were compared to the original maps at 1 km resolution. Using different quality metrics, it is shown that the downscaled maps are 30%-75% more accurate when the initial resolution is in the range of 5-10 km with respect to initial resolutions of 36-40 km.

This paper reports on the main results of SMOS-HR instrument technical study, a new interferometric radiometer dedicated to Soil Moisture and Ocean Salinity measurement from space. The instrument aims at providing enhanced spatial resolution with respect to SMOS and integrating robust RFI (RF Interference) mitigation technique. The instrument key performance requirements and the architecture features and trade-offs are exposed in this paper.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on SMOS and SMAP surface soil moisture (SSM) data. This paper demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated area at the seasonal scale and at a 25 km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5-10%). More importantly, the amplitude of the second SSM peak is significantly correlated to the rice cover fraction within 25×25 km2pixels (R=0.81 for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information.

As an emerging technique, P-band (0.3-1 GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in Victoria, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content reached ~20 kg/m2, and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the SCA (Single Channel Algorithm) and DCA (Dual Channel Algorithm) approaches presented a clear reduction in vegetation impact at P-band compared to L-band. While the root-mean-square error (RMSE) for P-band did not achieve the 0.04-m3/m3target accuracy of SMOS and SMAP, i.e., 0.054 m3/m3for the SCA and 0.074 m3/m3for the DCA, this performance can be regarded as acceptable considering the extremely high vegetation water content. In comparison, the RMSEs at L-band were larger than 0.1 m3/m3for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at P-band were found to apply to broader conditions than those at L-band, likely due to the reduced vegetation impact.

Digital Beam Forming (DBF) and Synthetic Aperture Inter-ferometry (SAI) are signal processing techniques that mixes the signals collected by an antenna array to produce high resolution images. This study aims at comparing these two approaches with the aid of simulations conducted at microwaves frequencies within the frame of the Soil Moisture and Ocean Salinity (SMOS) mission which is providing for more than a decade systematic passive L-band measurements from space. Although the two techniques are using the same signals and sharing the same goal, there are few differences that deserve attention. This is the case of the reconstruction floor error whose level and angular signature are significantly lower with the DBF paradigm than with the SAI one.

Remote sensing observations of variables related to vegetation at microwave and optical/infrared wavelengths are presented over three regions in Europe in the Iberian peninsula, northern Finland and central Europe. They include the instrumented sites of Las Majadas, Sodankyla and Reusel. The final goal is to better constrain land carbon cycle models using the complementarities of vegetation optical depth derived at different frequencies from active and passive instruments (related to vegetation water content and biomass) as well as optical data of the fraction of absorbed photosynthetically active radiation or solar induced fluorescence, closely linked to photosynthesis. The first results confirm this complementarity. For instance, time series of different variables exhibit positive correlations in some areas and negative correlations in other areas.

International audience

Above ground biomass (AGB) maps were estimated directly from microwave brightness temperatures (TB) using a machine learning approach. The accuracy of AGB retrievals from Artificial Neural Networks (ANN) is explored using both a multi-angular (using TBs products from the SMOS mission) and multi-frequency approach (using multi frequency measurements from the AMSR-E mission), an additional ANN inversion including optical indexes (MODIS-NDVI) is also discussed. Higher incidence angles have been shown to provide more information during the inversion process for AGB estimates than lower angles. Retrievals from the multi-angular lower-frequency inversion (SMOS - 1.4GHz) performed better than any individual higher-frequency retrievals. The addition of multi-frequency TBs (AMSR-E) to lower-frequency multi-angular TBs improves the performance of ANN models (from$\mathrm{R}^{2}\approx 0.94$to 0.96). Adding MODIS-NDVI to the inversion process improves the performance in an additional ~0.05%.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420 - 1427 MHz) measurements from space with a spatial resolution of ~v40 Km. Within the frame of the studies conducted by CESBIO and CNES for the next generations of high-resolution L-band imaging radiometers based on aperture synthesis, the concept of unconnected interferometry plays a key role in the roadmap because it is a solution for improving the spatial resolution. Before deploying such a mission, many issues have to be solved. This is why CNES has initiated the Unconnected L-band Interferometer Demonstrator (ULID) which aims at demonstrating the capability to operate unconnected interferometry in orbit. ULID system relies on a small constellation of three identical nano-satellites flying in close range formation and transmitting to ground the signals acquired by similar detectors onboard all satellites to allow synchronization and correlation computation. This contribution is concerned by the demonstration of our ability to cope with synchronization problems between the detectors.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420 - 1427 MHz) measurements from space with a spatial resolution of ~40 Km. Preliminary results of studies conducted within the frame of a High Resolution (HR) follow-on mission are presented. The SMOS-HR project has undergone a Phase 0 study by the French space agency. The aim of this contribution is to improve the spatial resolution capabilities of SMOS-HR from ~10 Km to ~4 Km with the aid of a swarm of nano-satellites orbiting close to the connected array selected for SMOS-HR. After SMOS and SMOS-HR, this third generation named SMOS-NEXT will be the very first one to perform aperture synthesis from space with both connected and unconnected interferometric measurements.

The last decade has witnessed a series of extreme droughts across the globe. The impacts of these droughts have been devastating for the ecosystem and human activities. In this paper we present the assessment of the drought events in the last decade from the remote sensing-based root zone soil moisture anomalies. The root zone soil moisture is obtained from the SMOS surface soil moisture. And the drought index is defined as the monthly anomaly of the root zone soil moisture. Our results show the distribution of droughts over the last decade in various regions across the globe.

Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the opacity of the vegetation layer. This study focuses on the behaviour of L-band VOD (L-VOD) retrieval algorithm over seasonally inundated areas, as previous observations have shown an unexpected decline in VOD during floods. The signal emitted by a mixed scene composed of soil and standing water was simulated, leading to an overestimation of the retrieved soil moisture (SM) and an underestimation of the retrieved L- VOD, typically by ~ 1 0% over flooded forests and up to 100% over flooded grasslands. We evaluated the induced underestimation of aboveground biomass (AGB) by 15/20 Mg ha-1 in the largest seasonal wetlands, which can represent more than 50% of the actual AGB of the savanna wetland, and up to higher values during exceptional years. Surface water seasonality needs to be taken into account in passive microwave retrieval algorithms to better estimate the global biomass.

The SMOS mission, launched in 2009, has been followed by Aquarius and SMAP, but follow-on missions are still in the preliminary phases. One of the main expected improvements is on the spatial resolution, for which a 10-fold increase is needed. Regardless of the choice on acquisition principle (real aperture or interferometry) such a massive improvement cannot be addressed with current technology. But interferometry has an advantage here in the sense that it can be distributed over multiple satellites. The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements. Such a massive improvement cannot be addressed with current technology and requires a major revisit of the acquisition of interferometric measurements. Of course, technological advances targeted by this mission concept is of far wider interest than L-band interferometry. The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements.

The effects of dry and fresh matter on microwave remote sensing data represent a great challenge, for which vegetation permittivity models are essential. To develop this model, in situ permittivity sensors are needed. There are few commercial equipment dedicated to these in situ measurements and none of them are developed in order to leave the instrument on site for automatic measurements with specific constraints such as communication, wide frequency band, non-invasive measurements and low price. We developed an instrument addressing these challenges. In this article, we present the first collected measurements and the computed permittivity data.

Monitoring properties of soil from microwave remote sensing is a very effective tool. In this context, the knowledge of soil permittivity values is important in order to guarantee the accuracy of the monitoring. Previous studies have showed the impact of temperature and moisture gradients in the top soil layer permittivity profile on remote sensing monitoring of soil. To increase our knowledge of these phenomena, we developed a new low cost equipment for continuous in-situ measurements of microwave [100 MHz-3GHz] permittivity soil profiles.

After almost 12 years in operation (SMOS- Aquarius - SMAP) the very high potential of L band radiometry is clearly demonstrated. Several applications are already operational (assimilation at ECMWF, food security, hurricanes, and natural risks, for sea ice etc.) so it is crucial to maintain such measurements. To do so while satisfying the current missions specifications is also of prime importance. Degrading spatial resolution is thus a significant step back which will impact science and applications. These missions are now getting older and the goal now is to ascertain the achievements done during the last 10 years or so, and to prepare the next generation so as to ensure data continuity for these unique measurements.

Constructing long time records of soil moisture (SM) requires the merging of data derived from different instruments while insuring the removing of the bias from different sensors time series. For instance, the ESA Climate Change Initiative (CCI) for SM currently uses the GLDAS v2.1 model as the reference to re-scale active and passive microwave time series. This paper discusses the possibility to use data from an L-band sensor as the reference in order to remove model dependency. AMSR-2 SM time series were re-scaled using different SMAP and SMOS datasets and evaluated against in-situ measurements. The results show that L-band data can be used to re-scale other sensor data with good performances. In addition, using the 11-years SMOS SM times series, the optimal length of the reference time series was studied.

A method to retrieve surface soil moisture (SM), at global scale, from L-Band telemetry of SMOS satellite using artificial Neural Networks (NNs) is presented. The NNs are trained using in-situ SM measurements as reference data, and SMOS Level-3 Temperature Brightness (TB) values and other auxiliary information, like MODIS NDVI, soil texture, and Skin Temperature from ECMWF as input. The retrieval is done in three steps. First multiple NN s, one per available in-situ site, are trained. Then a “representative” reference SM dataset is defined by examining the statistical relationships which link measurements from individual insitu SM sites and the input data. Finally, this representative reference SM data set is used to train an artificial NN using SMOS TBs and other inputs over the period 2011–2014. The resulting NN is in turn applied to 2017 SMOS TBs and others input data to retrieve SM at a global scale. The NN predicted SM is compared against SMOS Level 2 SM products as well as ECMWF forecast and is found to well capture the temporal and spatial variability of SM.

The Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) and European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength ) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 m3/m3target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band ( wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top ~10-cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

Launched in 2009, the SMOS satellite provides measurement of Soil Moisture (SM) and Vegetation Optical Depth (VOD) at L-band over land at high temporal resolution. L-VOD is known for being a good proxy for biomass, and 10 years of measurements are now available to analyze the behaviour of biomass over the whole world. Here, we investigate the links between L- VOD and SM seasonality, together with other vegetation and climatic variables (LAI, precipitation, temperature, and insolation). We also present time series of climatic variables for specific areas, showing remarkable trends of the L- VOD over the 10 year period (2010-2019), linked to climate change.

After almost 10 years in operation (SMOS- Aquarius - SMAP) the very high potential of L band radiometry is clearly demonstrated. Several applications are already operational (assimilation at ECMWF, for hurricanes, for sea ice etc.) so it is crucial to maintain such measurements. To do so while satisfying the current missions specifications is also of prime importance. Degrading spatial resolution is thus a significant step back which will impact science and applications). These missions are now getting older and the goal of the study presented in this paper is to assess which planned mission could fulfill the requirements to ensure data continuity. For this purpose, an extensive users' requirements study was performed in 2018-2019 assessing what would be required in the near future as well as when L band radiometry was absolutely necessary to satisfy the requirements. From the gathered results a cluster analysis was performed and the only.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (~ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ~ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are close to each other but SMAP observations show a warmer TB bias (about 0.64 K: V pol and 1.14 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures (SMOS-SMAP) were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. The SMOS-SMAP soil moisture retrievals compared with in situ observations show a retrieval accuracy of less than 0.04 m3/m3. Results from the development and validation of the integrated soil moisture product will be presented.

The vegetation optical depth measured at L-Band (LVOD) by the SMOS satellite provides a high temporal resolution information of the vegetation water content that can be linked to the total above ground biomass (AGB). Nevertheless, its coarse spatial resolution (~40 km) can be limiting for a number of applications. This study is devoted to the downscaling of the SMOS LVOD using high spatial resolution L-Band backscatter data from ALOS1 synthetic aperture radar. The goal is to improve the spatial resolution of the LVOD to estimate AGB at 1 km.

Continuation of brightness temperature measurements at L- band is a major asset for soil moisture and ocean salinity studies in the long term. The SMOS mission, launched in 2009, has been followed by Aquarius and SMAP, but follow-on missions are still in the preliminary phases. One of the main expected improvements is on the spatial resolution, for which a 10-fold increase is needed.Such a massive improvement cannot be addressed with current technology and requires a major revisit of the acquisition of interferometric measurements.The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements.

Since 2009, the SMOS mission has been acquiring brightness temperature measurements to derive soil moisture and ocean salinity. With a revisit time of three days everywhere on the globe, it has become one of the first mission to provide global assessment of these two parameters in near real time. The only instrument carried by SMOS satellite is a two dimensional radiometric interferometer, operating between 1400 and 1420 MHz. Despite this being a protected band, it has been obvious since day one that man-made emissions, either close and powerful or directly in-band, were contaminating the measurements. This was foreseen to some extent and some filtering algorithms had been designed prior to the launch. Although quite efficient, the very high diversity of RFI source characteristics made it very difficult to identify reliably all contaminations. Thus, since then, it has been a constant effort to try to identify better all these sources and assess their impact on SMOS measurements.

The ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit over nine years, with its L-Band Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. Between October 2011 and March 2012, a sudden increase in the distribution of RF interference (RFI) was observed over most urban areas in Japan, and this RFI scenario has remained unchanged during the last years. The interference has also polluted the observations of the SMAP (Soil Moisture Active/Passive) and Aquarius missions operated by NASA. This case of harmful interference was reported by ESA to the Japanese Regulatory Authorities in accordance to the procedure contained in the ITU Radio-Regulations. Investigations have shown that the main RFI source is originated by the aggregate radiations from the intermediate frequency (IF) circuits of broadcasting-satellite receiving installations. The main RFI causes appear to be due to poor quality cable, poor connections and/or issues with installation practices resulting in insufficient RF screening. This paper presents the SMOS experience in addressing this unique RFI problem encountered, provides an overview of the investigations conducted and the on-going actions led by the Japanese authorities in order to mitigate the impact of the interference.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 9 years. The whole data currently being reprocessed (Version 721 for levels 1 and 2 and version 4 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 9 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

Neural networks were used to estimate the ability of different sets of predictors to capture the variability of above ground biomass (AGB). SMOS brightness temperatures (TBs) for only two incidence angles capture 85% of the AGB variance. Adding soil moisture or L-band vegetation optical depth (L-VOD) increase the ability to capture the AGB variance to 90 % and 92 %, respectively. With respect to using only TBs, L-VOD improves the AGB estimation in regions of low vegetation.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, ...), ocean (salinity, meso-scale phenomena, river plumes, high winds, ...) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1-10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d'Etudes Spatiales).

An Emission Models (EM) adequate for a retrieval algorithm requires being simple while still capturing the responses of brightness temperatures TBp,θto the retrieval parameters. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the "Tau-Omega" (TO) EM to retrieve soil Water Content WC and vegetation optical depth τ from L-band TBp,θ. For sparse and low-scattering vegetation TB,EMp,θsimulated with EM = TO and EM = 2S converge, which is not the case for dense and strongly scattering vegetation. WCRCand τRCare retrieved with Retrieval Configurations RC = {TO, 2S} from TBp,θ: i) from a tower within a deciduous forest, and ii) by the "Soil Moisture and Ocean Salinity" (SMOS) mission. Using 2S EM instead of TO EM resulted in marginally lower WCRCretrievals while τRCretrievals are reduced more considerably. With respect to in-situ WCin-situ, retrievals WC2Sderived from tower-based TBp,θperformed better than forest soil water-content WCTOretrieved via the inversion of the "reference" TO EM. Likewise, SMOS based WC2Sretrievals revealed better agreement with ECMWF WC simulations than WCTOachieved with the "reference" RC = TO. In short, our study provides clear evidence that it is meaningful to replace TO EM used for current SMOS and SMAP land retrieval with 2S EM.Further advantages of the 2S EM over the TO EM are outlined in this study.

The SMOS (Soil Moisture and Ocean Salinity) satellite was launched on 2 November 2009, and it is the ESA second Earth Explorer Opportunity mission. After almost 10 years of successful Operations, the status of the SMOS mission is excellent. However, SMOS observations are significantly affected by RF interference (RFI) in several world areas.Since the first SMOS mission observations, its radiometer in the passive band 1400-1427 MHz detected RFI sources. Any emission in this band is prohibited by ITU Radio-Regulations (RR No.5340) [1]. To successfully accomplish the decrease in the global number of interfering sources, two parties are required: SMOS RFI team - to detect, monitor and report the cases of harmful interference - and national regulatory authorities - to investigate and take remedial actions to solve the RFI case (i.e. remove unauthorized devices, fix malfunctioning equipment or optimize operational settings). Some examples of devices causing interference are presented in this paper, showing the impact on the SMOS science data according to the topology of the interfering device.

In this letter, the results obtained with the last version (level 2, version 650) of SMOS retrieval algorithm are compared against independent measurements, over tropical forests. In particular, the climate research unit meteorological variables and data bases of forest height and forest biomass are considered. Comparisons with results obtained by AMSR-2 under similar conditions are also illustrated. Vegetation optical depth shows a generally good correlation with forest height and forest biomass, particularly in Africa and South America. Spatial and temporal trends of retrieved soil moisture follow trends of rainfall, particularly in regions of dry winter.

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 m3m-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 TB products, considering the local parameters was conducted. The calibration proves that these sitespecific parameters improve the product's accuracy. Validation of SMOS Level 2 product with in situ data showed bias, rootmean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 m3m-3, 0.068 to 0.142 m3m-3, and 0.069 to 0.103 m3m-3, respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020-0.056 m3m-3and RMSE of 0.026-0.065 m3m-3. The ubRMSE ranges from 0.017 to 0.034 m3m-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.

SMOS is a space mission led by the European Space Agency and designed to provide global maps of Soil Moisture and Ocean salinity, two important geophysical parameters for understanding the water cycle variations and climate change. The SMOS payload is a 2-D interferometer operating at L-band that consists of 69 elementary antennas located along a Y-shaped structure. Important spatial biases persist in the retrieved brightness temperature (BT) images mainly due to the phenomenon of aliasing inside the field of view of SMOS but also due to the Gibbs oscillations near land/ocean transitions. To minimize these biases, a differential image reconstruction algorithm is used in the operational processor that reduces the contrast of the image to be retrieved. To do that, the contribution of a constant artificial temperature map is removed from the measurements prior to reconstruction and then added back after the reconstruction. In this paper, we show that strong residual biases are still present in the retrieved images. To reduce them, we propose to improve the bias correction algorithm by using a more realistic artificial temperature scene based on separating the land and ocean regions and assigning a constant temperature over land and a Fresnel BT model over the ocean. The artificial scene is also improved by means of representing each pixel by its water fraction percentage to smooth the land/ocean transitions. The improved algorithm is validated over the ocean by comparing the retrieved temperatures to a forward geophysical model but also over land by comparing the retrieved soil moisture toin situmeasurements.

Soil moisture retrieval from microwave remote sensing brightness temperatures is in continuous development. In this study, the most recent and latest microwave remote sensing soil moisture products were evaluated against ground-based measurements. We compared, for the first time, the latest versions of SMOS (L2V650 and SMOS-IC V105), SMAP (L3V4), and CCI (V03.2) soil moisture products with respect to ground-based measurements obtained from ISMN (International Soil Moisture Network). Time series were plotted over some sites and it was found that all these products capture well the temporal dynamics over all the sites used in this study. However, CCI was wetter than the in situ measurements over Niger and both SMOS products (IC and L2) and SMAP were drier than the in situ observations over Biebrza site in Poland.

In this study, we compared coupled and uncoupled land surface model simulations of surface soil moisture (SM) with passive microwave remote sensing observations of SM over the contiguous US (CONUS). For this purpose, we used the following: (i) the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved L3 surface SM; (ii) the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) SM derived using LPRM; and (iii) the coupled and uncoupled ORCHIDEE land surface model. The comparison was achieved by computing the temporal mean difference between the normalized remotely-sensed and the model-SM datasets. It was found that both coupled and uncoupled models are drier than the remotely sensed data (particularly the SMOS data) over the principal aquifers of the CONUS. These aquifers are known as one of the places where irrigation is intensive. Therefore, the reason behind this model' dryness could be the fact that the models do not take into account the irrigation activities, which can be monitored by the remotely-sensed observations. Time series of SM over irrigated pixels also showed that SMOS was wetter than the models mainly during the irrigation period.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent. SMAP observations show a warmer TB bias (about 1.27 K: V pol and 0.62 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. Results from the development and validation of the integrated soil moisture product will be presented.

Soil moisture measurement using L-band radiometry is now widely accepted as the state-of-art remote sensing approach, and has been adopted by both the SMOS and SMAP soil moisture dedicated satellite missions. However, it suffers from the shallow depth of its soil moisture measurement, and the confounding effects of vegetation and soil roughness on soil moisture retrieval. P-band, which is a longer wavelength measurement, provides the potential to retrieve deeper soil moisture information, and to do so more accurately due to reduced soil roughness and vegetation effects. This paper presents some pioneering work on the use of P-band for soil moisture retrieval. The Polarimetric P-band Multibeam Radiometer (PPMR) used in this research operates at 740 MHz / wavelength of 40 cm. It is used together with the Polarimetric L-band Multibeam Radiometer (PLMR) which operates at 1.4 GHz / wavelength of 21 cm. The PPMR and PLMR are mounted onto a 10m high tower in an agricultural farm located at Cora Lynn, Victoria. This paper outlines the initial set up for the study and the experimental plan for understanding PPMR's performance, along with some initial data.

Monitoring live fuel moisture content (LFMC) in Mediterranean area is of great importance for fire risk assessment. LFMC has extensively been estimated based on optical remote sensing data. But the latter can be affected by atmospheric effects. As a complementary data source, microwave data can be used as they are relatively insensitive to atmospheric effects. Yet further evaluations are needed to investigate the potential of microwave observations to monitor LFMC. In this study, we assess the capability of long-term microwave vegetation optical depth (VOD) to capture the temporal variability of in situ measured LFMC in 14 Mediterranean shrub species in southern France during 1996-2014. Microwave-derived VOD at X band (VODX-15) displayed a high sensitivity to LFMC with correlation coefficients of 0.56. Similar evaluations were made using four optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI). The comparisons showed that VARI performs better than VODX-15 and other optical indices with highest median of correlation coefficients of 0.65. Overall, this study shows that passive microwave-derived VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.

Tropical aboveground carbon changes during 2010–2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010–2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010–2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.

The ongoing ESA funded `SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of dedicated observation operators that link the state of the terrestrial biosphere model to simulated VOD and surface layer soil moisture. We present our carbon assimilation system around a terrestrial biosphere model and demonstrate its operation through simultaneous assimilation of the SMOS VOD product over seven sites covering a range of plant functional types.

After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

After almost 9 years in orbit L-band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. However, so as to cover specific applications use of other sensors can prove very valuable. In particular use of altimetry, optical and thermal infrared measurements can offer new avenues. Many of them were tested with existing satellites at the time of SMOS launch, but with the Copernicus' Sentinel-3 mission's data now available, new applications and operational products can be envisioned.

In Antarctica, the coastal and ice-shelves areas are affected by snowmelt during the austral summer. The length and extend of this melting period are key parameters in the study of climate and its interannual variations in these regions. Melting events have a significant impact on the microwave emissivity of the surface. Thus, satellite microwave observations can be used in order to provide useful information over the whole Antarctic coast and ice-shelves. Several studies exploited the 19 and 37 GHz long time series to retrieve snowmelt events. In this study, these algorithms previously developed have been used to detect melt from the Soil Moisture and Ocean Salinity (SMOS) satellite observations at 1.4 GHz. Snowmelt dataset was obtained from April 2010 and March 2017 with SMOS observations. Finally, the potential of combined low and high frequencies to provide a synergetic description of surface melting events in Antarctica have been highlighted.

The European Space Agency's (ESA) SMOS mission, in orbit since more than 8 years, carries a passive microwave interferometric radiometer measuring in L-Band and provides accurate global observations of emitted radiation originating from the Earth's surfaces since the atmosphere is almost transparent in this spectral range. In addition, over land the effect of vegetation on the measurements is smaller than for shorter wavelengths. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system.

The SMOS (Soil Moisture and Ocean Salinity) mission, together with other passive microwave based missions (AMSR, SMAP), provides soil moisture estimates at resolutions ranging from 30 to 55 km. These estimates are validated by direct comparison to in situ measurements that typically measure over an area of a few centimeters. There exist a spatial scale mismatch between the satellite (large support) and the in situ measurements (point support), which contributes to the differences observed. Their magnitude depends on the spatial representativeness of the in situ measurements, which varies in time and with the selected location. This communication will show how the spatial scale mismatch evolves through timescales. It is characterized by using modeled, in situ and satellite soil moisture time series. Timescales, from 0.5 to 128 days, are obtained using wavelet transforms and the spatial representativeness is assessed with a new approach that uses wavelet-based correlations (WCor).

Wetlands and open waters are key components of the hydrological and carbon cycles but their spatio-temporal dynamics are still not well known, mostly over tropical areas. In this paper, a new water surface product at high spatial resolution (30 arcsec) and high temporal resolution (3 days) over the Amazon basin for recent years (2010-2016) is presented. This product comes from the synergy between recent products: (1) water surface fraction at coarse spatial resolution from L-band microwave sensor (Soil Moisture and Ocean Salinity - SMOS), (2) Global Surface Water Occurrence (GSWO) from Landsat sensor and (3) the new Digital Elevation Model (DEM) Multi-Error-Removed-Improved-Terrain (MERIT) based on the Shuttle Radar Topography Mission (SRTM) observations.

A set of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) data assimilation (DA) experiments are presented. The SMOS soil moisture dataset used in this study was produced training a neural network (NN) using SMOS brightness temperatures as input and ECMWF H-TESSEL SM fields as reference for the training. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. SMOS NN SM DA experiments were compared to Advanced Scat-terometer (ASCAT) SM DA. In both cases, experiments with and without 2 metre air temperature and relative humidity DA are discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives similar results to the model open loop with no assimilation. The effect of the soil moisture analysis on the Numerical Weather Prediction (NWP) was evaluated using the analysed surface fields to perform atmospheric forecast experiments. In the Northern Hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in Northern Asia for up to 72 hours.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new L-Band (1.4 GHz) VOD dataset (SMOS-IC L-VOD) obtained from the SMOS (Soil Moisture and Ocean Salinity) satellite. SMOS IC L-VOD is evaluated through a comparison with several vegetation-related quantities such as tree height and above ground biomass (AGB) for different land cover types. SMOS L-VOD shows monotonic relationships with respect to the variables extracted from these different datasets without signs of saturation at high values. The relationships between L-VOD and AGB were also compared to those obtained using the Normalized Difference Vegetation Index (NDVI) and K/X/C-VOD (VOD measured at 19, 10.7, and 6.9 GHz). In contrast to NDVI and K/X/C-VOD, L-VOD shows a relationship to AGB that is closer to a linear one without significant signs of saturation. SMOS L-VOD is a very promising dataset for large scale monitoring of biomass, at coarse scale spatial resolution (~ 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

This paper presents the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyse soil moisture for Numerical Weather Prediction (NWP) applications. Two different approaches are presented based on SMOS brightness temperature and SMOS neural network soil moisture data assimilation, respectively. For the first approach, SMOS brightness temperature data assimilation relies on forward modelling. Long term results, spanning the SMOS period, of SMOS forward modelling, monitoring and data assimilation are presented. They emphasize the relevance of SMOS data for monitoring and to support NWP model developments. For the second approach, a SMOS soil moisture product has been produced based on a Neural Network (NN) trained on ECMWF soil moisture. So, the SMOS-ECMWF NN soil moisture product captures the SMOS signal variability in time and space, while by design its climatology is consistent with that of the ECMWF soil moisture, which makes it suitable for data assimilation purpose. This approach, initially tested for 2012 in a global scale stand alone approach, shows that SMOS NN data assimilation slightly improves the two-metre air temperature forecast in the short range at regional scale. For NWP applications this approach has been further developed with a near real time production of the SMOS-ECMWF NN soil moisture product, with the implementation of the SMOS NN data assimilation in the ECMWF Integrated Forecasting System (IFS), and with high resolution (9km) global scale testing compatible with the current ECMWF NWP system.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission operates in the 1400-1427MHz frequency band, which is allocated to the EESS(passive) service in the ITU Radio-Regulations. The measurements of SMOS radiometer are perturbed by radio frequency interferences (RFIs) that jeopardize part of its scientific retrieval in certain areas of the World. This paper summarizes the strategies initiated by the European Space Agency to mitigate the impact of RFIs, and in particular the collaboration established with national telecommunication Authorities for the investigation of interference cases and switching-off the unauthorised RFI sources. The RFI detection, monitoring and reporting process is described in more detail, focused on the tasks carried out by the SMOS Operations team at ESAC, the ESA centre located near Madrid. The RFI tools developed and the planned evolution are also presented.

In this paper, retrieval results obtained using the last version (V650) of SMOS level 2 algorithms are tested considering pixels of Africa and South America. Yearly average values of vegetation optical depth are compared against forest height estimates at continental scale. For selected areas of African woody savannah, multitemporal trends of SM and VOD are compared against environmental variables available from Climatic Research Unit data base.

In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center.

A fundamental limitation of current soil moisture remote sensing technology is that can only provide moisture information on the top 5 cm layer of soil at most, being one-tenth to one-quarter of the wavelength (21 cm at L-band; 1.4 GHz) using the current SMAP and SMOS soil moisture dedicated missions of NASA and ESA. Consequently, we have developed an airborne passive microwave sensing capability at P-band to develop a new state-of-the-art satellite concept that will provide soil moisture data for the top 10 cm layer of soil using radiometer observations at P-band (40 cm; 750 MHz). Not only would P-band provide soil moisture information on a soil layer thickness that more closely relates to that affecting crop and pasture growth, but it is expected to produce greater spatial coverage with improved accuracy to that from L-band. This is because P-band should be less affected by surface roughness conditions and have a reduced attenuation by the overlaying vegetation. This paper describes a series of small airborne field experiments at P-band, and presents some early results of P-band passive microwave observations in comparison with L-band and K-band passive microwave from initial trial flights.

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-3for 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-3for the focus farm.

In this study, we evaluated a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product, developed by the collaboration of INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphère), against the operational SMOS level 3 SM product (SMOSL3). This new product (hereinafter referred to as SMOS-INRA-CESBIO, i.e. SMOSIC in short) differs from SMOSL3 three ways: (i) the SMOSIC algorithm considers the pixel as homogeneous and does not take into account the heterogeneity of the pixel; (ii) uses a new calibration of the effective scattering albedo and soil roughness parameters; (iii) no time correlation is applied on the optical depth. The evaluation was done over North America using the (European Center for Medium range Weather Forecasting) ECMWF SM simulation as a reference, using data for 2011. A better performance of the SMOSIC SM product with respect to ECMWF was found: (i) SMOSIC had higher correlation coefficients (temporal dynamics) and lower unbiased RMSD (absolute values) values with ECMWF over most of the study area and (ii) the spatial patterns of the SMOSIC temporal mean SM maps were in a better agreement with ECMWF.

Soil Moisture Active Passive (SMAP) mission and the ESA Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m3/m3at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m3/m3. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

This study presents a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product based on a different scattering albedo and soil roughness parameterization: the SMOS-IC (SMOS INRA-CESBIO) data set. In this study, several parameterizations of the vegetation and soil roughness parameters (ω, HRand NRP, P = H, V) were tested and the retrieved SM was compared against in situ observations obtained from the International Soil Moisture Network (ISMN). Firstly, values of ω = 0.10, HR= 0.4 and NRP= −1 (P = H, V) were found globally. Secondly, a calibration of these parameters was obtained for the different land cover categories of the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HRvaried, respectively, in the ranges 0.08–0.12 and 0.1–0.5. The IGBP-based calibration is currently used in the SMOS-IC product algorithm. Using as reference the ISMN sites, a better performance of the SMOS-IC product over the operational SMOSL3 (SMOS level 3) SM product was found: R = 0.62, bias = −0.019 m3/m3, ubRMSE = 0.061 m3/m3 for SMOS-IC; against R = 0.54, bias = −0.037 m3/m3 and ubRMSE = 0.069 m3/m3 for SMOSL3.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 7 years. The whole data set has been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 7 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that need improvement, and how the instrument could be enhanced if we were to build it again. This paper presents the lessons learnt from SMOS after 7 years in orbit.

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0-5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Données SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0-5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NNinSitu) is evaluated against in situ data not used during the training phase and against maps of the SMOS level 3 SM product and ECMWF SM models. NNinSituSM values are closer to ECMWF values for wet areas. A method to use NNs as a tool to classify in situ sites representative of the remote sensing observations scale is briefly discussed.

This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First, to compare and provide feedback to the operational algorithm. Second, to evaluate this approach in the context of pre-launch algorithm development. The performance of the L-MEB NN dataset was evaluated by comparing with time series of in situ measurements in North America. The correlation, standard deviation and bias of NN SM and in situ SM are similar to those obtained with the SMOS L3 SM product and with ECMWF models. The L-MEB NN dataset was also compared globally to the SMOS Level 3 SM and SM from ECMWF models. The L-MEB NN dataset is in general wetter than SMOS L3 SM, closer to ECMWF models. Some possible reasons are briefly discussed.

This letter aims to estimate the effective scattering albedo ($\omega _{p}$) over the tropical forests using L-band (1.4 GHz) microwave remote sensing. It is carried out using Soil Moisture and Ocean Salinity (SMOS) mission data over five years (2011–2015). We find similar values of$\omega _{p}$computed over the Congo and Amazon forests. The$\omega _{p }$values depend slightly on the polarization. The values of$\omega _{p }$at H-polarization and at 52° ± 5° (40° ± 5°) of incidence angle are within the range 0.064 – 0.069 ± 0.01 (0.061 – 0.067 ± 0.012). At V-polarization, the values of$\omega _{p }$are slightly lower (0.060 – 0.061 ± 0.013 at 52° ± 5° of incidence angle and 0.052 – 0.055 ± 0.013 at 40° ± 5° of incidence angle). These findings should contribute to a better calibration of the value of$\omega _{p }$over the tropical forests in both the SMOS and SM active and passive retrieval algorithms, leading to increase the SM retrieval accuracy over heterogeneous pixels.

This letter illustrates results obtained using brightness temperature data collected by the Soil Moisture and Ocean Salinity radiometer and a three-parameter algorithm, retrieving soil moisture, vegetation optical depth, and the effective albedo of vegetation. Four eight-day time intervals have been considered. Only areas with a forest cover higher than 90% have been selected. For tropical forests, the retrieved albedo is in a range 0.05-0.07 and tends to decrease with an increasing optical depth. For these forests, the results are scarcely influenced by seasonal variations. For boreal forests of North America, the range of retrieved albedo is again 0.05-0.07 in July, but lower values are found in November for needleleaf forests of Canada and Northern U.S. For forests with higher optical depth, retrieved values of effective albedo show a lower spread.

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.

In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

Verifying the calibration of the SMAP radiometer over land observations is an important mission requirement. Inter-comparison of L-band brightness temperature observations from different satellites (SMAP, SMOS and Aquarius) is a useful tool for radiometer calibration. Brightness temperatures observations made at the same frequency, polarization, incidence angle and coincident in time and location should be consistent with each other. SMAP brightness temperature observations were compared with SMOS observations at 40o incidence angle. The observations from the two satellites were found to be consistent with each other over the entire dynamic range (both ocean and land). The RMSD between the two missions was less than 3 K. The two observations exhibit a strong linear relationship and the observed bias was less than 0.5 K for both polarizations. This bias is within the required target accuracy requirement of the SMAP radiometer (requirement of 1.3 K).

This study focuses on the calibration of the effective scattering albedo (ω) of vegetation in the soil moisture (SM) retrieval at L-Band. Currently, in the SMOS Level 2 and 3 algorithms, the value of ω is set to 0 for low vegetation and ∼ 0.06 – 0.08 for forests. Different parameterizations of vegetation (in terms of ω values) were tested in this study. The possibility of combining soil roughness and vegetation contributions as a single parameter (“combined” method) leads to an important simplification in the algorithm and was also evaluated here. Following these assumptions, retrieved values of SMOS SM were compared with SM data measured over many in situ sites worldwide from the International Soil Moisture Network. These validation sites were classified using the International Geosphere-Biosphere Programme (IGBP) classification scheme. In situ SM measurements and SM retrievals were compared, and statistical scores were computed. The optimum albedo configuration was then found for each class of the IGBP landcover classification. Preliminary results yield values of albedo between 0.07 to 0.12 under the assumption of homogeneous pixels.

Field experiments have played a critical role in the development and implementation of satellite soil moisture missions. A review of key experiments is presented that includes tower-, aircraft, and satellite-focused efforts conducted over four decades that have supported two dedicated satellite missions; Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active passive (SMAP).

The Soil Moisture and Ocean Salinity mission has been collecting data for 6 years. The whole data set has just been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS). This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 6 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce 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 SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The main objective of SMOS over land is to retrieve soil moisture. Anyway, for soils covered by vegetation two model parameters, soil moisture and vegetation optical depth, are provided as outputs of the Level 2 algorithm. The precisions in the retrieval of the two parameters are linked, since improvements in soil moisture estimates can only be achieved if vegetation parameters are correctly estimated. The vegetation optical depth retrieval itself is also interesting since contains important information about vegetation, allowing us to open new perspectives for the monitoring of forests at global scale. In this work, vegetation optical depth products for July 2015, obtained over forests by the last 620 version of the SMOS retrieval algorithm, are considered and tested. The results of a comparison with three independent remote sensing datasets, containing forest height, forest biomass and Leaf Area Index (LAI) respectively, are presented. A further test is made considering AMSR2 data in order to evaluate the differences in the retrieval accuracy of the vegetation optical depth due to frequency. The analysis is conducted at global scale, and is focused on the pan-tropical biomes.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Soil Moisture and Ocean Salinity (SMOS) satellite has been providing surface soil moisture (SSM) and ocean salinity (OS) retrievals at L-band for five years (2010-2014). During these five years, the SSM retrieval algorithm i.e. the L-MEB (L-Band Microwave Emission of the Biosphere [1] model has been progressively improved and hence results in different versions of the SMOS SSM products. This study aims at evaluating the last improvement in the SSM products of the most recent SMOS level 3 (SMOSL3) reprocessing (SMOSL3_2.72) vs. an earlier version (SMOSL3_246). Correlation, bias, Root Mean Square Difference (RMSD) and unbiased RMSD (unbRMSD) were used as performance criteria in this study using the ECMWF SM-DAS-2 product as a reference. Results show that the SMOS SSM estimates have been improved: (i) SMOSL3_272 was closer to SM-DAS-2 over most of the globe-with the exception of arid regions-in terms of unbRMSD (ii) SMOSL3_272 was closer to SM-DAS-2 over Spain, Brazil, parts of Sahel, high latitude and equator regions but comparable with SMOSL3_246 over most of the rest of the globe in terms of correlations.

This paper focuses on a new approach to account for soil roughness effects in the retrieval of soil moisture (SM) at L-band in the framework of the SMOS (Soil Moisture and Ocean Salinity) mission: the Simplified Roughness Parameterization (SRP). While the classical retrieval approach considers SM and τNAD(vegetation optical depth) as retrieved parameters, this approach is based on the retrieval of SM and the TR parameter combining τNADand soil roughness (TR = τNAD+ HR/2). Different roughness parameterizations were tested to find the best correlation (R), bias and unbiased RMSE (ubRMSE) when comparing homogeneous retrievals of SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) vineyard field. The highest R (0.68) and lowest ubRMSE (0.056 m3m−3) were found using the SRP method. Using the SMOS observations comparisons against several SM networks were also made: AACES, SCAN, watersheds and SMOSMANIA. SM was retrieved over all these stations. The SRP and another similar approach (SRP2) improved the averaged ubRMSE, while the SRP2 method leaded to higher correlation values (R). A global underestimation of SM was noticed, which may be linked to the differences in the sampling depths of the L-band observations (∼ 0–3cm for both Elbara-II and SMOS) and of the in situ measurements (∼ 0–5 cm).

The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.

Two electromagnetic models were used to simulate snow emission at L-band from in situ measurements of snow properties collected at Dome C in Antarctica. Two different approaches were used: one based on the radiative transfer theory and the other on the wave approach. The soil moisture ocean salinity (SMOS) satellite observations performed at 1.4 GHz (21 cm) were used to check the validity of these models. Model results based on the wave approach were in good agreement with SMOS observations, particularly for incidence angles lower than 55°. Comparisons suggest that the wave approach is more suitable to simulate brightness temperature at L-band than the transfer radiative theory, because interference between the layers of the snowpack is better taken into account. The model based on the wave approach was then used to investigate several L-band characteristics at Dome C. The emission e-folding depth, i.e., 67% of the signal, was estimated at 250 m, and 99% of the signal emanated from the top 900 m. L-band brightness temperature is only slightly affected by seasonal variations in surface temperature, confirming the high temporal stability of snow emission at low frequency. Sensitivity tests showed that good knowledge of density variability in the snowpack is essential for accurate simulations in L-band.

The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The retrieval algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS retrieval algorithm converges to realistic soil moisture retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures,$T_{b} $'s) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using$T_{b} $'s measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated againstin situmeasurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the variable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation applications, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

This study investigated the use of physically based statistical regressions to retrieve a global and long term (e.g. 2003–2014) surface soil moisture (SSM) record based on a combination of passive microwave remote sensing observations from the Advanced Microwave Scanning Radiometer (AMSR-E; 2003-Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS; 2010–2014) sensors. Statistical regression methods based on bi-polarization (horizontal and vertical) brightness temperatures (Tb) observations obtained from AMSR-E. The coefficients of these regression equations were calibrated using SMOS level 3 SSM maps (SMOSL3) as a reference. This calibration process was carried out over the June 2010-Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients global SSM maps could be computed from the AMSR-E Tb observations over the whole 2003–2011 period. In this study, the SSM maps were successfully evaluated against the SMOSL3 SSM products over the period of calibration (Jun. 2010-Sept. 2011). Correlations (R) and Root Mean Square Error (RMSE) were computed between the AMSR-E retrievals and the reference (SMOSL3) SSM products. The R (mostly > 0.75) and RMSE (mostly3/m3) maps showed a good agreement between the retrieved and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In conclusion, the statistical regression method is capable of retrieving a coherent "SMOS-AMSR-E" SSM time series for the period 2003–2014.

Performances of two global satellite-based surface soil moisture (SSM) retrievals with respect to model-based SSM derived from the MERRA (Modern-Era Retrospective analysis for Research and Applications) rea-nalysis were explored in this paper: (i) Soil Moisture and Ocean Salinity (SMOS; passive) Level-3 SSM (SMOSL3) and (ii) the Advanced Scatterometer (ASCAT; active) SSM. Temporal correlation was used to investigate the performance of SMOSL3 and ASCAT SSM products during the period 05/2010–2012 on a global basis. Both SMOSL3 and ASCAT (slightly better) captured well (R>0.70) the long-term variability of the modelled SSM, particularly, over the Indian subcontinent, the Great Plains of North America, and the Sahel. However, ASCAT had negative correlations in arid regions, in particular across the Sahara and the Arabian Peninsula. This may be due to complex scattering mechanisms over very dry surfaces. To explore the land cover dependence of the analyzed statistical indicators, the global correlation results were averaged per biome extracted from a global map of biomes. In general, SMOSL3 and ASCAT performances behaved differently from one biome to another. For SMOSL3, the highest average correlation was observed over “tropical semi-arid” (R = ∼ 0.5) and “temperate semi-arid” biomes, whereas for ASCAT, the highest correlations were observed over “tropical semi-arid” (R = ∼ 0.7) and “tropical humid” biomes. The poorest agreement for both SMOSL3 and ASCAT was generally found over “tundra” and “desert temperate” biomes, particularly for ASCAT. This study showed that the performance of both SMOSL3 and ASCAT is highly dependent on vegetation. We also showed that both of them provide complementary information on SSM, which implies a potential for data fusion which would be pertinent for the ESA climate change initiative (CCI).

The Soil Moisture Ocean Salinity (SMOS) mission was launched in 2009 and provides derived soil moisture globally using a forward modelling approach that incorporates a number of auxiliary data sets. By default, the SMOS mission uses global land cover and soils data sets to run the soil moisture retrieval models. This study examines the use of national data sets from Agriculture and Agri-Food Canada (AAFC) to determine if improvements in land cover and soils accuracies achieved using these national data sets can provide an improvement in SMOS soil moisture retrieval. Results show that changing the land cover produced the greatest differences, with a reduction in the fraction of the land area identified as forest, but also an increase in the number of failed model retrievals. The use of the AAFC soils resulted in a greater fraction of clay in the surface soil layer, but this did not have a large impact on the overall retrieval accuracy at the study sites. This suggests that the default SMOS parameterization can provide adequate estimation of soil moisture over most sites, but areas where forest, wetland or urban land cover may be over or underestimated should be more closely evaluated.

Microwave remote sensing instruments are an adequate way to provide soil moisture information at large scale. Microwave remote sensing data are linked to the electromagnetic properties of soil. In that context, the objective of this study is to develop an integrated approach to estimate effective electromagnetic properties of soils layers at different scale using ground-penetrating radar (GPR), L-band radiometer, dielectric laboratory measurements, modelling approaches and in situ measurements of essential state variables.

A physically based downscaling algorithm, DISPATCH (DISaggregation based on Physical And Theoretical scale CHange), has been used to estimate soil moisture at 1 km scale from coarse resolution SMOS soil moisture estimates over the agricultural site of the CanEx-SM10 campaign. In this paper, we test the applicability of the algorithm and analyze the linearity of the relationship between the soil evaporative efficiency (SEE) and the near-surface soil moisture (SM) for very wet soils. In such conditions, the results show that the linear model is not suitable due to a difficulty in estimating Tsmax (dry edge) within the SMOS pixel. The use of a neighboring area to estimate a reliable value of Tsmax was tested and validated in order to improve the linear model results.

In this paper, roughness parameterizations providing best retrievals of soil moisture (SM) at L-band were evaluated. Different parameterizations were tested to find the best correlation R, bias and ubRMSE when comparing retrieved SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) over a vineyard field. Roughness measurements were always performed after the agricultural practices in the vineyard. These in situ data was used as input of the L-MEB (L-band Microwave Emission of the Biosphere) model, which permits the retrieval of SM and TAU (vegetation optical depth). In addition, a simplified method consisting on the retrieval of a parameter which combines the effects of roughness and TAU was tested. Significantly higher correlation (R=0.86) for SM was found using this method, while the absolute bias (-0.062) and RMSE (0.069) were slightly higher than for other roughness parameterizations.

The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture (SM). SMOS operates at L-band and at this frequency, the signal depends on soil moisture but is also significantly affected by surface soil roughness. Using the Combined soil Roughness & Vegetation Effects (CRVE) method detailed in this paper, the effect of vegetation and soil roughness can be combined using a single parameter, referred to as TR here. SM and TR were retrieved by inverting the SMOS observations using the forward emission model (L-MEB). Assuming a linear relationship between TR and LAI obtained by the MODIS data, an Australian map of soil roughness was computed. This map could lead to improved soil moisture retrievals for present and future microwave remote sensing missions such as SMOS and the Soil Moisture Active Passive (SMAP) scheduled for launch in November 2014.

In this study, vegetation optical depth from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite mission is combined with other existing remote sensing, meteorological and literature data in order to obtain values of gravimetric vegetation water content (Mg). The methodology combines an effective medium model valid at passive microwave frequencies with a vegetation dielectric constant model. The algorithm is calibrated for 11 global vegetation classes. The resulting product consists of temporally dynamic ~25 km global grids of Mg. The first maps clearly show seasonal differences in vegetation water, which vary for the different continental regions due to variations in e.g. latitude, climate and landcover type. This new vegetation water product is unique and offers important complementary information to existing vegetation indices.

In this communication we present an update on the RFI detection used in the SMOS processing chain and some elements on quantified impact of RFIs on level 2 soil moisture products. The level 2 soil moisture algorithms which included since the beginning a screening mechanism to reject contaminated brightness temperatures is now stricter. New approaches at the level 1 processors are also emerging and will be operational at their next release in 2014. Despite these strengthen procedures, RFIs are still impacting strongly SMOS observations and examples of quantified deterioration are given.

A methodology to retrieve soil moisture (SM) from multiinstrument remote sensing data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures), active microwaves (C-band ASCAT backscattering coefficients), and visible and infrared observations by MODIS. The reference SM data used to train the NN are ECMWF model predictions or SMOS L3 SM. After determining the best configuration of input data to retrieve SM using a NN, the NN soil moisture product is evaluated with respect to other global SM products and with respect to in situ measurements. The NN is able to capture the spatial and temporal dynamics of SM, and the SM computed with NNs compares well with the other SM datasets.

The usefulness of microwave remote sensing to retrieve near-surface soil moisture has already been demonstrated in many studies. However, obtaining high quality estimates of soil moisture is influenced by many effects from soil, vegetation and atmosphere; one of the key parameters is surface roughness. This research focusses on a semi-empirical method to evaluate the roughness effects from space borne observations. Global maps of roughness effects are evaluated at C-band from AMSR-E measurements.

Current and future satellite missions with L-band passive microwave radiometers could provide useful information for monitoring the soil moisture and freeze/thaw state at a global scale. The soil surface roughness plays a significant role in microwave emission from land surfaces. In this study, a simple parameterized model from exponentially correlated surface was developed. Results indicated the model can be very useful in understanding the effects of surface roughness on microwave emission.

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-3for the morning overpass and 0.067 m3·m-3for 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-3over 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.

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.

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${<}{4}~{\rm K}$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.

The Soil Moisture and Ocean Salinity (SMOS) satellite was launched by the European Space Agency on November 2, 2009. Its payload, i.e., Microwave Imaging Radiometer with Aperture Synthesis, which is a 2-D L-band interferometric radiometer, measures the brightness temperatures (BTs) in the protected 1400-1427-MHz band. Although this band was preserved for passive measurements, numerous radio frequency interferences (RFIs) are clearly visible in SMOS data. One method to get rid of these interferences is to create a synthetic signal as close as possible to the measured interference and subtract it from the instrument visibilities. In this paper, we describe an approach to create such a signal and on how to use it for geolocalization of the emitters. Then, different methods for assessing the quality of the mitigation are introduced. Due to the complexity of estimating the effects of mitigation globally, it is finally proposed to use mitigation results to create flag maps about the estimated RFI impact, to be associated with BT measurements.

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

This paper shows results obtained by using the SMOS retrieval algorithm over forests at the prototype level. In each SMOS node, the algorithm estimates the soil moisture and the vegetation optical depth. For the optical depth, values retrieved in July 2011 in all forests of the American continent are shown and compared against forest height estimated by GLAS LIDAR of ICESAT satellite. A significant correlation between the two variables is observed. For each forest height estimated by LIDAR, the standard deviation of optical depth is slightly higher than 0.1. For soil moisture, 30 nodes of the SCAN/SNOTEL network have been considered. Over one year of data, retrieved values are compared against ground measurements. Overall, the rms error is of the order of 0.1 m3/m3. In general better results are obtained in the Eastern deciduous forest. The algorithm was run using different versions, corresponding to different initial guesses of soil permittivity and Leaf Area Index, but variations in the retrieved values are moderate.

Artificial sources emitting in the protected part of the L-band are polluting the retrievals of ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. Detection and localization of such sources are of interest for the exploitation of science products as well as for the identification of the emitters. A simple and fast method that provides snapshot-wise information is presented. From a statistical analysis of the results, some systematic errors are reported along with their potential causes and an approach to mitigate them. In the case of sources at high geomagnetic latitudes a seasonal variation of the localization error is also noticed; the origin of such phenomenon is still under investigation.

The Soil Moisture Ocean Salinity (SMOS) mission has been providing L-band multi-angular 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 are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio frequency interference issues and aliasing at lower look angles increases the uncertainty of observations and thereby affects the soil moisture retrieval that utilizes observations at specific angles. In this study, we propose a processing chain that uses a mixed objective function based on SMOS L1c data products to refine the characteristics of multi-angular observations. The approach was validated using simulations from a radiative transfer model and analyzed over three external targets: Amazon rainforest, Sahara desert, and Antarctic ice. These results could provide insights for selecting and utilizing external targets as part of the upcoming Soil Moisture Active Passive (SMAP) mission.

In this letter, a monofrequent dielectric model for moist soils taking into account dependences on the temperature and texture is proposed, in the case of an electromagnetic frequency equal to 1.4 GHz. The proposed model is deduced from a more general model proposed by Mironov and Fomin (2009) that provides estimations of the complex relative permittivity (CRP) of moist soils as a function of frequency, temperature, moisture, and texture of soils. The latter employs the physical laws of Debye and Clausius-Mossotti and the law of ion conductance to calculate the CRP of water solutions in the soil. The parameters of the respective physical laws were determined by using the CRPs of moist soils measured by Curtis (1995) for a wide ensemble of soil textures (clay content from 0% to 76%), moistures (from drying at 105 °C to nearly saturation), temperatures (10 °C -40 °C), and frequencies (0.3-26.5 GHz). This model has standard deviations of calculated CRPs from the measured values equal to 1.9 and 1.3 for the real and imaginary parts of CRP, respectively. In the model proposed in this letter, the respective standard deviations were decreased to the values of 0.87 and 0.26. In addition, the equations to calculate the complex dielectric permittivity as a function of moisture, temperature, and texture were represented in a simple form of the refractive mixing dielectric model, which is commonly used in the algorithms of radiometric and radar remote sensing to retrieve moisture in the soil.

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$\tau_{\rm NAD}$is retrieved (L2 product) from brightness temperatures ($T_{B}$, 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,$T_{B}$'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$\tau_{\rm NAD}$is in range with an in situ estimate for low agricultural vegetation, the retrieved$\tau_{\rm NAD}$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$(-0.092/-\!0.087\ \hbox{m}^{3}/\hbox{m}^{3})$. Likely error sources include the following: 1) still present RFI; 2) potential link between high retrieved$\tau_{\rm NAD}$and other L-MEB parameters, e.g., low roughness parameter$(H_{R})$; 3)$\sim$18% lower sand and$\sim$8% higher clay fractions while$\sim\!\!0.35\ \hbox{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.

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 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 ~73% of the active sources and 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.

In this paper, a numerical modeling approach was used to evaluate the semiempirical H-Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H-Q model uses four parameters, HR, QR, NRH, and NRV, which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights σ and autocorrelation length Lc. First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H -Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H -Q model. It was found that the retrieved HRcould be related directly to Zs = σ2/Lcand that QR, NRV, and NRHwere dependent on HR. HRwas found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H-Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of ~ 0.03 m3/m3, which was within the desired 0.04-m3/m3error margin for the SMOS mission.

In the above paper (ibid., vol. 49, no. 4, pp. 1177-1189, Apr. 2011), there is an error in equation (7). The explanation and corrected equation are presented here.

The SMOS radiometer operates within the Earth Exploration Satellite Service passive band at 1400-1427 MHz. Since its launch in November 2009, SMOS images have been strongly impacted by Radio Frequency Interference (RFI). So far approximately 500 RFI sources distributed worldwide have been detected. Some of the strongest RFI sources might mask other weaker RFI underneath, hence it is expected the total number of RFI detected may increase as strong ones are progressively located and switched off. Most RFIs are located in Asia and Europe, which together hold approximately 80% of the active sources and more than 90% of the strongest interference. The areas affected by RFI may experience either an underestimation in the retrieval values of soil moisture and ocean salinity or data loss, with the associated detrimental impact in the scientific return. ESA and the teams participating in SMOS mission have put in place different strategies to alleviate this RF interference situation.

In the context of the Soil Moisture and Ocean Salinity mission, we present a study of the emission of rough surfaces at 1.4 GHz and the effects of moisture and temperature gradients.

The Salar de Uyuni is a large, flat and radiometrically homogeneous area. Its extent is several tenths the SMOS footprint and its emissivity is high and rather homogeneous except when flooded (about 3 months per year). It is located in a rather un-inhabitated area with no RFI. Therefore it provides a perfect calibration site for SMOS brightness temperature and the dielectric constant product validation. In this paper several calibration/validation issues were assessed: SMOS and AMSR-E brightness temperature cross-validation over the site, the influence of Full/Dual polarisation mode on SMOS angular signature and L2 dielectric constant product validation.

This study investigates the temporal behavior of the gravimetric vegetation water content (Mg) derived from SMOS (L-band) optical depth (τ) values. The analysis is done for the year 2010 over a coniferous forest site in the U.S. Resulting values of Mgare compared to values of leaf water potential obtained with the Soil-Plant-Atmosphere model and in situ data. A significant nonlinear correlation is found between the two (R=0.72, pg, based on plant water status, is different from that of existing optical-based vegetation indices, which are mainly based on chlorophyll/biomass changes. Therefore, τ-derived Mgcould provide complementary information for future global-scale dynamic monitoring of vegetation water status.

In this paper a single-frequent temperature and texture dependent dielectric model for moist soils is proposed. This model is designed for the frequency of 1.4 GHz at which the European sensor SMOS operates to monitor moisture of the Earth surface from space. Earlier, the dielectric model was created, that provides estimations of the complex dielectric constant of moist soils as a function of frequency, temperature, moisture and mineralogy of soils. This model was developed based on extensive measurements of the dielectric constant for a wide ensemble of soils (clay content from 0 to 76%), moistures (from zero to molecular moisture capacity), temperatures (10° C to 40° C), and frequencies (0.3 to 26.5 GHz). This model provides for fairly good accuracy. But, being cumbersome to account for frequency dependencies, the model is not always convenient for practical use at a single frequency. Exclusion of the frequency dependence allowed us to obtain a substantially more simple dielectric model to estimate the complex dielectric constant of soil at a single frequency of 1.4 GHz as a function of moisture, temperature, and clay content.

Estimates of soil moisture and surface salinity are of significant importance to improve meteorological and climate prediction. The SMOS mission monitor these quantities, by measuring the brightness temperature by means of L-band aperture synthesis interferometry. Despite the L-band being reserved for Earth and space exploration, SMOS images reveal large number of strong outliers, produced by illegal antennas emitting in this band. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map. The measurements are modeled as the superposition of three super-resolved components in the spatial domain: the target brightness temperature map u, an image o modeling the outliers, and Gaussian noise n. This decomposition allows to isolate each of its constituent parts, thanks to a sparsity operator that acts on o, and a bounded variation prior on u that extrapolates its spectrum promoting a non-oscillating behavior. The proposed model is interesting in itself, as it is general enough to be applied to other restoration problems. Experiments on real and synthetic data confirm the suitability of the proposed approach.

This paper introduces a revised version of the forward model adopted by the SMOS SM algorithm to represent forest emission. The forward model is tested against multitemporal measurements of brightness temperature obtained over a US forest. World map of retrieved optical depth and soil moisture are also presented.

Vegetation indices are indicators for analyzing the properties of vegetation. The Normalized Difference Vegetation Index (NDVI) from optical remote sensing data is one of the most commonly used vegetation indices, which can exhibit the ecological characteristics of leafy materials, but lacks the ability to directly provide information on the woody materials. In this paper, we developed Microwave Vegetation Indices (MVIs) from the L-band Soil Moisture and Ocean Salinity (SMOS) data, which is an effective means to detect the information of branches and trunks. The theory of MVIs is derived from the tau-omega model. To minimize the influence from the uncertain soil surface radiation, a parameterized database was built by the Advanced Integral Equation Model (AIEM). We selected two incidence angles (40° and 50°) and combined them to compute the MVIs. A simple method to evaluate the L-band optical thickness efficiently based on the MVI b-parameter (MVI-B). The MVI optical thickness(MVI-τ) was compared with the optical thickness retrieved from SMOS(SMOS-τ), and the results showed that the overall pattern was very similar.

The Soil Moisture and Ocean Salinity (SMOS) mission delivers global surface soil moisture fields at high temporal resolution which is of major relevance for water management and climate predictions. Between April 26 and May 9, 2010, an airborne campaign with the L-band radiometer EMIRAD-2 was carried out within one SMOS pixel (44 × 44 km) in the Skjern River Catchment, Denmark. Concurrently, ground sampling was conducted within three 2 × 2 km patches (EMIRAD footprint size) of differing land cover. By means of this data set, the objective of this study is to present the validation of SMOS L1C brightness temperaturesTBof the selected node. Data is stepwise compared from point via EMIRAD to SMOS scale. From ground soil moisture samples,TB's are pointwise estimated through the L-band microwave emission of the biosphere model using land cover specific model settings. TheseTB's are patchwise averaged and compared with EMIRADTB's. A simple uncertainty assessment by means of a set of model runs with the most influencing parameters varied within a most likely interval results in a considerable spread ofTB's (5-20 K). However, for each land cover class, a combination of parameters could be selected to bring modeled and EMIRAD data in good agreement. Thereby, replacing the Dobson dielectric mixing model with the Mironov model decreases the overall RMSE from 11.5 K to 3.8 K. Similarly, EMIRAD data averaged at SMOS scale and corresponding SMOSTB's show good accordance on the single day where comparison is not prevented by strong radio-frequency interference (RFI) (May 2, avg. RMSE = 9.7 K). While the advantages of solid data sets of high spatial coverage and density throughout spatial scales for SMOS validation could be clearly demonstrated, small temporal variability in soil moisture conditions and RFI contamination throughout the campaign limited the extent of the validation work. Further attempts over longer time frames are planned by means of soil moisture network data as well as studies on the impacts of organic layers under natural vegetation and higher open water fractions at surrounding grid nodes.

The Soil Moisture and Ocean Salinity (SMOS) satellite has opened the era of soil moisture products from passive L-band observations. In this paper, validation of SMOS products over continental U.S. is done by using the Soil Climate Analysis Network (SCAN)/SNOwpack TELemetry (SNOTEL) soil moisture monitoring stations. The SMOS operational products and the SMOS reprocessing products are both used and compared over year 2010. First, a direct node-to-site comparison is performed by taking advantage of the oversampling of the SMOS product grid. The comparison is performed over several adjacent nodes to site, and several representative couples of site-node are identified. The impact of forest fraction is shown through the analysis of different cases across the U.S. Also, the impact of water fraction is shown through two examples in Florida and in Utah close to Great Salt Lake. A radiometric aggregation approach based on the antenna footprint and spatial description is used. A global comparison of the SCAN/SNOTEL versus SMOS is made. Statistics show an underestimation of the soil moisture from SMOS compared to the SCAN/SNOTEL local measurements. The results suggest that SMOS meets the mission requirement of 0.04 m3/m3over specific nominal cases, but differences are observed over many sites and need to be addressed.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

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

The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The bestwSestimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission was launched on the 2nd of November 2009. The first six months after launch, the so-called commissioning phase, were dedicated to test the functionalities of the spacecraft, the instrument, and the ground segment including the data processors. This phase was successfully completed in May 2010, and SMOS has since been in the routine operations phase and providing data products to the science community for over a year. The performance of the instrument has been within specifications. A parallel processing chain has been providing brightness temperatures in near-real time to operational centers, e.g., the European Centre for Medium-Range Weather Forecasts. Data quality has been within specifications; however, radio-frequency interference (RFI) has been detected over large parts of Europe, China, Southern Asia, and the Middle East. Detecting and flagging contaminated observations remains a challenge as well as contacting national authorities to localize and eliminate RFI sources emitting in the protected band. The generation of Level 2 soil moisture and ocean salinity data is an ongoing activity with continuously improved processors. This article will summarize the mission status after one year of operations and present selected first results.

Disaggregation based on Physical And Theoretical scale Change (DisPATCh) is an algorithm dedicated to the disaggregation of soil moisture observations using high-resolution soil temperature data. DisPATCh converts soil temperature fields into soil moisture fields given a semi-empirical soil evaporative efficiency model and a first-order Taylor series expansion around the field-mean soil moisture. In this study, the disaggregation approach is applied to Soil Moisture and Ocean Salinity (SMOS) satellite data over the 500 km by 100 km Australian Airborne Calibration/validation Experiments for SMOS (AACES) area. The 40-km resolution SMOS surface soil moisture pixels are disaggregated at 1-km resolution using the soil skin temperature derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and subsequently compared with the AACES intensive ground measurements aggregated at 1-km resolution. The objective is to test DisPATCh under various surface and atmospheric conditions. It is found that the accuracy of disaggregation products varies greatly according to season: while the correlation coefficient between disaggregated and in situ soil moisture is about 0.7 during the summer AACES, it is approximately zero during the winter AACES, consistent with a weaker coupling between evaporation and surface soil moisture in temperate than in semi-arid climate. Moreover, during the summer AACES, the correlation coefficient between disaggregated and in situ soil moisture is increased from 0.70 to 0.85, by separating the 1-km pixels where MODIS temperature is mainly controlled by soil evaporation, from those where MODIS temperature is controlled by both soil evaporation and vegetation transpiration. It is also found that the 5-km resolution atmospheric correction of the official MODIS temperature data has a significant impact on DisPATCh output. An alternative atmospheric correction at 40-km resolution increases the correlation coefficient between disaggregated and in situ soil moisture from 0.72 to 0.82 during the summer AACES. Results indicate that DisPATCh has a strong potential in low-vegetated semi-arid areas where it can be used as a tool to evaluate SMOS data (by reducing the mismatch in spatial extent between SMOS observations and localized in situ measurements), and as a further step, to derive a 1-km resolution soil moisture product adapted for large-scale hydrological studies.

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

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by radio frequency interferences (RFIs) that jeopardize part of its scientific retrieval in certain areas of the world, particularly over continental areas in Europe, Southern Asia, and the Middle East. Areas affected by RFI might experience data loss or underestimation of soil moisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put strategies in place that, one year after launch, have already improved the RFI situation in Europe where half of the sources have been successfully localized and switched off.

The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m3/m3. Further analysis found the following: 1) The roughness valueHRwas too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameterstthandttvrequired optimization, yielding new values oftth= 0.2 andttv= 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.

Radiative properties at 1.4 GHz of vine vegetation are investigated by measuring brightness temperatures with the ETH L-band Radiometer II (ELBARA II) operated on a tower at the Mediterranean Ecosystem L-band Characterisation Experiment III (MELBEX III) field site in Spain. To this aim, experiments with and without a reflecting foil placed under the vines were performed for the vegetation winter and summer states, respectively, to provide prevailingly information on vegetation transmissivities. The resulting parameters, which can be considered as “ground truth” for the MELBEX III vineyard, were retrieved from brightness temperature at horizontal and vertical polarization measured at observation angles between 30° and 60°. These MELBEX III “ground-truth” values are representative for the Mediterranean Soil Moisture and Ocean Salinity (SMOS) Valencia Anchor Station (VAS) and therefore valuable for the corresponding calibration and validation activities over the VAS site. Likewise, quantifying the uncertainties of the measured brightness temperatures was also important, particularly as several equivalent ELBARA II instruments are currently operative in ongoing SMOS-related field campaigns.

On one hand, the SMOS mission is an ESA project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. The single payload of the mission is MIRAS, a Microwave Imaging Radiometer with Aperture Synthesis. It has been successfully lofted into orbit on November 2nd, 2009. On the other hand, AQUARIUS/SAC-D mission is a partnership between NASA and CONAE for monitoring sea surface salinity from space. The observatory includes AQUARIUS, an L-band radiometer/radar combination and the mission is scheduled for launch on June 9th, 2011. This work is concerned with the synergy between both instruments. It is shown how the brightness temperature maps retrieved from MIRAS interferometric measurements can be resampled down to the ground resolution achieved by the three beams of AQUARIUS without introducing any artifact.

SMOS observations provide an opportunity to develop a testbed for the evaluation of different SMAP algorithm options. The use of real-world global observations will help in the development and selection of different land surface parameters and ancillary observations needed for the soil moisture algorithms. In this study, SMOS observations were used with one soil moisture retrieval algorithm and the results were evaluated using in situ soil moisture measurements. The SMOS soil moisture product, which exploits multiple incidence angle observations, compares well with the ground-based observations (RMSE 0.043 m3/m3(ascending) and 0.047 m3/m3(descending)). The alternative SMAP compatible algorithm also performed well (RMSE 0.040 m3/m3(ascending) and 0.043 m3/m3(descending)). Although preliminary, these initial results are encouraging for the potential of SMAP to meet its required soil moisture accuracy.

Soil moisture is one of the most important variables regarding climate evolution. In this work, we use the triple collocation method with SMOS, AMSR-E (LPRM) and ASCAT soil moisture products at a global scale. The goal of this study is to compare SMOS soil moisture product to already existing data sets, to evaluate its accuracy and to identify the kind of areas where SMOS performs better (type of vegetation or soil).

We evaluate a new 3-D numerical modeling approach for calculating the rough-surface scattering and emission of a soil layer. The approach relies on the use of Ansoft's numerical computation software High-Frequency Structure Simulator, which solves Maxwell's equations directly using the finite-element method. The interest of this approach is that it can be easily extended to studies of heterogeneous media. However, before being applied in this way, it must first be validated for the rough-surface case. In this letter, we perform this validation by comparing the results of rough-surface scattering and emission with the results of the method of moments (MoM) for a range of different roughness and permittivity conditions and with both Gaussian and exponential rough-surface autocorrelation functions. For the scattering case, we obtain results that are in agreement with the MoM to within approximately 1-3 dB for angles up to and including 40° and 2-4 dB for angles from 50° to 70°. Agreement for emissivity is to within 3.2 and 3.6 K for low and high roughness conditions, respectively. We then illustrate the application of the new approach by calculating the emission of a two-layer system with rough surfaces, representing the soil-litter system in forests.

With the recent launch of the Soil Moisture and Ocean Salinity (SMOS) mission, the passive microwave remote-sensing community is currently planning and undertaking airborne validation campaigns. Given the financial and logistical constraints on the size of validation area that can be covered by airborne simulators and the experiments underway that cover only a part of a satellite footprint, timely and scientifically sound advice on fractional footprint coverage requirements by campaigns for these low-resolution sensors is of paramount importance. Using high-resolution airborne data from an extensive airborne campaign in Southeast Australia, the fractional coverage requirement for L-band passive microwave satellite missions is assessed using a subsampling technique of flight lines through a passive microwave footprint. It is shown that minimum 50% coverage of the total footprint size will typically be required, given a spatial variability value of 20 K at 1-km resolution, to ensure that the footprint mean is estimated with an expected sampling error of less than 4 K, which is the design sensitivity of SMOS.

A downscaling approach to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture estimates with the use of higher resolution visible/infrared (VIS/IR) satellite data is presented. The algorithm is based on the so-called “universal triangle” concept that relates VIS/IR parameters, such as the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (Ts), to the soil moisture status. It combines the accuracy of SMOS observations with the high spatial resolution of VIS/IR satellite data into accurate soil moisture estimates at high spatial resolution. In preparation for the SMOS launch, the algorithm was tested using observations of the UPC Airborne RadIomEter at L-band (ARIEL) over the Soil Moisture Measurement Network of the University of Salamanca (REMEDHUS) in Zamora (Spain), and LANDSAT imagery. Results showed fairly good agreement with ground-based soil moisture measurements and illustrated the strength of the link between VIS/IR satellite data and soil moisture status. Following the SMOS launch, a downscaling strategy for the estimation of soil moisture at high resolution from SMOS using MODIS VIS/IR data has been developed. The method has been applied to some of the first SMOS images acquired during the commissioning phase and is validated against in situ soil moisture data from the OZnet soil moisture monitoring network, in South-Eastern Australia. Results show that the soil moisture variability is effectively captured at 10 and 1 km spatial scales without a significant degradation of the root mean square error.

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:$H_{R}$,$Q_{R}$, and$N_{R}$. In many studies, the two parameters$Q_{R}$and$N_{R}$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$Q_{R}$could be set to zero. Second, a refinement of the L-MEB soil model, considering values of$N_{R}$for both polarizations (namely,$N_{\rm RV}$and$N_{\rm RH}$), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters$H_{R}$and$(N_{\rm RH} - N_{\rm RV})$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.

The error induced by some soil dielectric models used to retrieve the soil moisture from the SMOS radiobrightnesses is statistically evaluated, based on the measured dielectric data. Three dielectrical models most frequently used in the radio thermal remote sensing algorithms are tested in conjunction with the dielectric data base covering all soil types and moisture variations.

Soil Moisture and Ocean Salinity, European Space Agency, is the first satellite mission addressing the challenge of measuring sea surface salinity from space. It uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) that generates brightness temperature images, from which both geophysical variables are computed. The retrieval of salinity requires very demanding performances of the instrument in terms of calibration and stability. This paper highlights the importance of ocean salinity for the Earth's water cycle and climate; provides a detailed description of the MIRAS instrument, its principles of operation, calibration, and image-reconstruction techniques; and presents the algorithmic approach implemented for the retrieval of salinity from MIRAS observations, as well as the expected accuracy of the obtained results.

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.

The emission of bare soils at microwave L-band (1-2 GHz) frequencies is known to be correlated with surface soil moisture. Roughness plays an important role in determining soil emissivity although it is not clear which roughness length scales are most relevant. Small-scale (i.e., smaller than the resolution limit) inhomogeneities across the soil surface and with soil depth caused by both spatially varying soil properties and topographic features may affect soil emissivity. In this paper, roughness effects were investigated by comparing measured brightness temperatures of well-characterized bare soil surfaces with the results from two reflectivity models. The selected models are the air-to-soil transition model and Shi's parameterization of the integral equation model (IEM). The experimental data taken from the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) consist of surface profiles, soil permittivities and temperatures, and brightness temperatures at 1.4 GHz with horizontal and vertical polarizations. The types of correlation functions of the rough surfaces were investigated as required to evaluate Shi's parameterization of the IEM. The correlation functions were found to be clearly more exponential than Gaussian. Over the experimental period, the diurnal mean root mean square (rms) height decreased, while the correlation length and the type of correlation function did not change. Comparing the reflectivity models with respect to their sensitivities to the surface rms height and correlation length revealed distinct differences. Modeled reflectivities were tested against reflectivities derived from measured brightness, which showed that the two models perform differently depending on the polarization and the observation angle.

The future Soil Moisture and Ocean Salinity satellite time of overpass is 6 A.M. and 6 P.M. at the equator. In many regions, morning dew is expected at the time of the satellite overpass and might play a role in soil moisture retrievals. The aim of this study was to assess the effects of dew on L-band measurements. Radiometric, biomass, and dew measurements were performed over a natural grass field. Our results show that at the diurnal scale, vegetation internal water content changes play a major role in emission. A direct impact of dew on measurements was not identified. However, a brightness temperature increase of 0.5 and 1 K was observed at vertical and horizontal polarizations, respectively. This increase was detected later after dew, suggesting that it was not directly caused by the presence of dew on the grass blades. Our hypothesis is that the observed increase in brightness temperatures is due to the absorption of dew water by the litter layer.

The Soil Moisture and Ocean Salinity (SMOS) mission is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the current lack of global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. The paper will give an overview on the scientific objectives and the (access to the) available data products.

The community microwave emission model (CMEM) is the low frequency forward observation operator developed at ECMWF. It is used in this paper to simulate brightness temperatures at local and regional scales over SMOSREX (France) and AMMA (West Africa), respectively. Background errors in simulated brightness temperatures are quantified at different frequencies and incidence angles for these two sites.

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.

The Soil Moisture and Ocean Salinity (SMOS) mission is a joint ESA-CNES (F)-CDTI (E) mission within the ESA Living Planet Program, and it was the second ESA Earth Explorer Opportunity Mission to be selected. The mission objectives of SMOS are to provide soil moisture and ocean salinity observations for weather forecasting, climate monitoring, and the global freshwater cycle. This paper will describe the scientific campaigns performed to date, as well as the plans for the on-orbit calibration and validation activities.

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

The aim of this special issue to provide as much as possible an overview of the Soil Moisture and Ocean Salinity (SMOS) project now that it is reaching completion.

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-m resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-m ldquocoarse-scalerdquo passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): (1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and (2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave soil moisture.

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.

The Soil Moisture and Ocean Salinity (SMOS) mission aims at observing two variables critical for a large scientific community, from biosphere dynamics to climate monitoring. The mission should also provide information on root zone soil moisture and vegetation and contribute to significant research in the field of the cryosphere. The original design, 2D interferometric radiometer at L-band, and principle of measurement makes SMOS a challenge at various technical levels. Moreover, stringent requirements on the estimated variables make the complete processing of SMOS data even more challenging. One of these requirements is concerned with the ability to accurately localize all the footprints of the instrument on the surface of the earth. Based on simulation and sensitivity studies with respect to the final retrieval of soil moisture, this accuracy requirement has been established so that the localization error on each footprint presents a zero mean and a standard deviation of 400 m. This high accuracy is mainly due to the need for knowledge of open water within a footprint, not to bias soil moisture estimation. This accuracy is highly challenging and unprecedented for sensors of this class and resolution. The on board devices that will help characterize the geolocation of the SMOS products include stellar sensor and gyroscopes, which can achieve an accuracy consistent with the requirements in terms of standard deviation. But the overall localization budget is also contaminated by an important bias, due to the mechanical deployment of the instrument antenna arms after launch, and to the launch shift that impacts all the alignments on the satellite (mechanical shift of stellar sensor due to shocks and vibrations, moisture desorption in mechanical brackets...). The purpose of this study is to characterize these biases, obviously inaccessible to on ground measurement and expected not to evolve once in orbit, so that they can be accounted for in the ground processing prior to initiate the soil moisture retrieval.

The CoSMOS (Campaign for validating the Operation of the Soil Moisture and Ocean Salinity mission) campaign was conducted during November of 2005 in the Goulburn River Catchment, in SE Australia. The main objective of CoSMOS was to obtain a series of L-band measurements from the air in order to validate the L-band emission model that will be used by the SMOS (Soil Moisture and Ocean Salinity) ground segment processor. In addition, the campaign was designed to investigate open questions including the sun-glint effect over land, the application of polarimetric measurements over land, and to clarify the importance of dew and interception for soil moisture retrievals. This paper summarises the campaign activities, and presents progress on the analysis of the CoSMOS data set.

This paper compares L-band measurements from three different experiments in areas covered by grass. The main objective is to assess soil moisture retrievals based on the L-band Microwave Emission of the Biosphere model (L-MEB) used by the Soil Moisture and Ocean Salinity mission (SMOS). Results indicate that over grass the vegetation is isotropic to the microwave propagation at horizontal polarisation, while at vertical polarisation non-zero scattering is observed for all the grass data sets. Surface soil moisture is retrieved with enough accuracy for all data sets as long as the soil roughness and litter emission are calibrated beforehand. The study also highlights the importance of detecting strong attenuation by wet vegetation and litter due to rainfall interception. We show that strong rainfall interception can be flagged using a microwave polarisation index.

This contribution summarizes prominent features of the Level 2 algorithm aimed at processing land surface geophysical quantities from the ESA-led SMOS mission. It emphasizes the soil moisture retrieval and describes the decision tree built in order to select appropriate retrieval configurations. The expected performance is illustrated by preliminary results of the algorithm validation.

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 ldquoroughnessrdquo 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.

In this paper, microwave remote sensing, together within situmoisture 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.

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

In a recent paper, Younis 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 aims at identifying the most outstanding issues of the publication.

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 relationship 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.

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.

This article is concerned with the apodization windows to be applied to brightness temperature maps reconstructed from complex visibilities provided by the MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) instrument on board the SMOS (Soil Moisture and Ocean Salinity space mission) spacecraft in order to achieve a close to uniform pixel at the Earth's surface level

This paper is part of an ongoing study aimed at exploring the potential of biangular measurements at L band to estimate near surface soil moisture. A statistical approach based on a physical radiative transfer model is tested over a natural grassland. Soil moisture shows an encouraging correlation with a combination of brightness temperatures measurements performed at two different angles. Nevertheless, the assumptions involved into the definition of a valid statistical index need to be studied. For that purpose, an insight into the modeling of grassland L band emission is also presented

Radiation from the celestial sky in the spectral window at 1.413 GHz is strong and an accurate accounting of this background radiation is needed for calibration and retrieval algorithms. Modern radio astronomy measurements in this window have been converted into a brightness temperature map of the celestial sky at L-band suitable for such applications. This work presents a comparison of the background predicted by this map with the measurements of several modern L-band remote sensing radiometers.

A simple approach for correcting the effect of vegetation in the estimation of soil moisture (w/sub S/) from L-band passive microwave observations is presented in this study. The approach is based on statistical relationships, calibrated from simulated datasets, which requires only two observations made at distinct incidence angles (/spl theta//sub 1/,/spl theta//sub 2/). A sensitivity study was carried out, and best retrieval remote sensing configurations, in terms of polarization and couple of incidence angles (/spl theta//sub 1/,/spl theta//sub 2/), were investigated. Best estimations of w/sub S/ could be made at H polarization, for /spl theta//sub 1/ varying between 15/spl deg/ and 30/spl deg/, and with a difference (/spl theta//sub 2/-/spl theta//sub 1/) larger than 30/spl deg/. The method was tested against two experimental datasets acquired over crop fields (soybean and wheat). The average accuracy in the soil moisture retrievals during the whole crop cycle was found to be about 0.05 m/sup 3//m/sup 3/ for both crops.

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/spl deg/ and on-nadir measurements.

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

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.

This paper analyzes the signal measured by optical remote sensors when acquiring data over a shaded part of an urban scene. The signal is much lower for this kind of target than for others because there is no direct downward irradiance. Here, a simple urban scene is considered with a shaded area. The signal observed by a high spatial resolution satellite sensor over an ordinary panchromatic band (500-700 nm) is computed thanks to a radiative transfer code [advanced modeling of the atmospheric radiative transfer for inhomogeneous surfaces (Amartis)] capable of dealing with ground topography and heterogeneity. The signal is analyzed, and it appears that environmental effects play a significant role. Moreover, because of the scattering that occurs at shorter wavelengths, it is also shown that a widening of the band to 440 nm sharpens the difference between signals coming from two different ground types (for whose the difference of reflectance is constant and equal to 0.1) by about 10%. This demonstrates that the band widening may be beneficial to observation in shadow, mainly because of scattering effects. A more realistic scene is also considered, in which each part is associated with realistic spectral properties. This simply shows the importance of the thematic in the choice of band, as it determines the effect of the widening.

A number of studies have shown the feasibility of estimating surface soil moisture from L-band passive microwave measurements. Such measurements should be acquired in the near future by the Soil Moisture and Ocean Salinity (SMOS) mission. The SMOS measurements will be done at many incidence angles and two polarizations. This multiconfiguration capability could be very useful in soil moisture retrieval studies for decoupling between the effects of soil moisture and of the various surface parameters that also influence the surface emission (surface temperature, vegetation attenuation, soil roughness, etc.). The possibility to implement N-parameter (N-P) retrieval methods (where N = 2, 3, 4, ..., corresponds to the number of parameters that are retrieved) was investigated in this study based on experimental datasets acquired over a variety of crop fields. A large number of configurations of the N-P retrievals were studied, using several initializations of the model input parameters that were considered to be fixed or free. The best general configuration using no ancillary information (same configuration for all datasets) provided an rms error of about 0.059 m/sup 3//m/sup 3/ in the soil moisture retrievals. If a priori information was available on soil roughness and at least one vegetation model parameter, the rms error decreased to 0.049 m/sup 3//m/sup 3/. Using specific retrieval configurations for each dataset, the rms error was generally lower than 0.04 m/sup 3//m/sup 3/.

To retrieve soil moisture over vegetation-covered areas from microwave radiometry, it is necessary to account for vegetation effects. At L-band, many retrieval approaches are based on a simple model that relies on two vegetation parameters: the optical depth (/spl tau/) and the single-scattering albedo (/spl omega/). When the retrievals are based on multiconfiguration measurements, it is necessary to take into account the dependence of /spl tau/ and /spl omega/ on the system configuration, in terms of incidence angle and polarization. In this paper, this dependence was investigated for several crop types (corn, soybean, wheat, grass, and alfalfa) based on L-band experimental datasets. The results should be useful for developing more accurate forward modeling and retrieval methods over mixed pixels including a variety of vegetation types.

In order to prepare the Soil Moisture and Ocean Salinity (SMOS) mission, we present 1) the sea surface salinity precision that could be achieved with the SMOS radiometer measurements and 2) the time and space scales over which averaged SMOS Tb should remain relatively constant in order to prepare after-launch monitoring of radiometer drifts. Leaving aside errors due to the instrument and the image reconstruction process, the SSS averaged over 200 /spl times/ 200 km/sup 2/ areas and over 10 days retrieved from SMOS measurements should meet the GODAE requirements with a precision better than 0.1 psu in most oceanic regions, assuming random noise on W and SST of 2 m s/sup -1/ and 1 /spl deg/C, respectively. On another hand, this requirement will not be met if no a priori information on the wind speed is available. However, it is likely that SMOS Tb will suffer from temporal drifts and/or from regional biases linked to sun disturbances for instance. In these biases are going to be monitored using Tb averages, it will be necessary to take into account wind speed variability.

Surface soil moisture is a key variable of water and energy exchanges at the land surface/atmosphere interface. But currently there are no means to assess it on a global and timely fashion. The ESA Earth Explorer Opportunity mission Soil Moisture and Ocean Salinity (SMOS) is the first attempt to fill such a gap. SMOS is based upon an L-band 2-D interferometer, an innovative concept of bi-dimensional aperture synthesis method to obtain surface measurement with an appropriate resolution from a tractable (in terms of dimensions) space-borne instrument. Moreover, the sensor has new and very significant capabilities especially in terms of multi-angular view configuration. However as for most of space borne instrument, retrieval of surface parameters/variables will be hampered by several factors. This fact is enhanced for sensors having a coarse spatial resolution. In the specific case of SMOS the ground resolution of 40 km means that the influence of different contributors to the signal has to be accurately assessed and eventually corrected. Finally many pixels will be affected by topography effects. It is thus important to assess exactly which level of topography distorts significantly the brightness temperatures and to which extend so as to be able to either correct soil moisture retrieval for topography effects or flag the data. The goal of this paper is to present the topography effects. The latter has been analyzed in depth by modeling the signal issued from mountainous terrain including moisture and vegetation gradient. The adjacency and shadowing effects were in particular addressed. The second step was to develop a simplified characterization of the topography through a statistical description which is then used to assess exactly the level of topography which has an influence of the signal and from which one has to take it into account in the retrieval process. The potential of SMOS, depending on the view angle configuration and the use of the sole 1.4 GHz is thus investigated over complex targets. These questions are key issues to define the exact range of configurations where SMOS meets the scientific requirements of the mission.

Surface soil moisture is a key variable of water and energy exchanges at the land surface/atmosphere interface. But currently there are no means to assess it on a global and timely fashion. Similarly, our current knowledge of sea surface salinity is very reduced. One way to overcome this issue would be to use an adequate space-borne instrument. The most promising instrument would then be an L-band microwave remote sensing sensors as they are able to provide estimates of surface soil moisture and sea surface salinity, on spatial and temporal scales compatible with applications in the fields of climatology, meteorology and large scale hydrology. The ESA Earth Explorer Opportunity mission SMOS is the first to attempt to fulfill such a gap. SMOS is based upon an L-band 2-D interferometer. It is thus an innovative concept of bi-dimensional aperture synthesis method to obtain surface measurement with an appropriate resolution from a tractable (in terms of dimensions) space-borne instrument. Moreover, the sensor has new and very significant capabilities especially in terms of multi-angular view configuration. This paper will describe the SMOS concept in terms of instrument (characteristics) and will investigate the main aspects of the retrieval capabilities of the 2-D microwave interferometer for monitoring soil moisture, vegetation biomass and sea surface salinity. The analysis is based on model inversion taking into account the instrument characteristics. The standard error of estimate of the surface variables is computed as a function of the sensor configuration system and of the uncertainties associated with the spatial measurements. The inversion process is based on a standard minimisation routine that computes both retrieved variables and standard error associated with the retrievals. Nevertheless, retrieving surface variables from such as instrument is not necessarily straightforward. Over the oceans, a very high sensitivity and accuracy are acquired. Over the land the main issues are linked to mixed pixels and topography. Using other sensors/mission (such as Aquarius over the oceans) and assimilation techniques will be used to address these issues. The potential of SMOS, depending on the view angle configuration and the use of the sole 1.4 GHz is thus investigated. These questions are key issues to define the observation configuration of SMOS that meets the scientific requirements and the technical constraints of the spatial missions.

This paper presents a methodology to interpret and then assimilate the SMOS surface soil moisture data into a modelling framework. This methodology is designed for multiscale applications in hydrology.

The main objective of the SMOS (Soil Moisture and Ocean Salinity) mission over the land surfaces, is to monitor soil moisture (SM) with a frequent (3-day revisit) and global coverage (ground resolution of /spl sim/50 km). In this paper, we present some examples of the current activities aiming at developing and improving SM retrieval methods.

This letter presents a synthetic L-band (1.4 GHz) multiangular brightness temperature dataset over land surfaces that was simulated at a half-degree resolution and at the global scale. The microwave emission of various land-covers (herbaceous and woody vegetation, frozen and unfrozen bare soil, snow, etc.) was computed using a simple model [L-band Microwave Emission of the Biosphere (L-MEB)] based on radiative transfer equations. The soil and vegetation characteristics needed to initialize the L-MEB model were derived from existing land-cover maps. Continuous simulations from a land-surface scheme for 1987 and 1988 provided time series of the main variables driving the L-MEB model: soil temperature at the surface and at depth, surface soil moisture, proportion of frozen surface soil moisture, and snow cover characteristics. The obtained global maps constitute a useful dataset for a first evaluation of the sensitivity of future satellite-based L-band radiometry data to soil moisture.

The US Salinity Temperature and Roughness Remote Scanner (STARRS) was exploited during an European campaign in 2001 (EuroSTARRS) supporting the scientific preparation of the Soil Moisture and Ocean Salinity (SMOS) mission. This paper is intended to introduce the EuroSTARRS experiment set-up and to provide an overview of all activities performed during this campaign.

In preparation of the SMOS mission, synthetic L-band brightness temperatures are produced at the global scale and soil moisture retrieval algorithms are assessed.

The Soil Moisture and Ocean Salinity (SMOS) space mission is currently undergoing phase-B studies at the European Space Agency. The SMOS payload is an L-band interferometric radiometer based on a two-dimensional aperture synthesis concept. This paper presents the first images obtained by a demonstrator of the SMOS instrument over land surfaces at the Avignon test site in 1999.

Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, large antennas (several meters) are required to achieve an adequate spatial resolution at L-band. So as to reduce the problem of putting into orbit a large filled antenna, the possibility of using antenna synthesis methods has been investigated. Such a system, relying on a deployable structure, has now proved to be feasible and has led to the Soil Moisture and Ocean Salinity (SMOS) mission, which is described. The main objective of the SMOS mission is to deliver key variables of the land surfaces (soil moisture fields), and of ocean surfaces (sea surface salinity fields). The SMOS mission is based on a dual polarized L-band radiometer using aperture synthesis (two-dimensional [2D] interferometer) so as to achieve a ground resolution of 50 km at the swath edges coupled with multiangular acquisitions. The radiometer will enable frequent and global coverage of the globe and deliver surface soil moisture fields over land and sea surface salinity over the oceans. The SMOS mission was proposed to the European Space Agency (ESA) in the framework of the Earth Explorer Opportunity Missions. It was selected for a tentative launch in 2005. The goal of this paper is to present the main aspects of the baseline mission and describe how soil moisture will be retrieved from SMOS data.

A simple model for simulating the L-band microwave emission from bare soils is developed. The model is calibrated on a large set of measurements obtained during a three-month period over seven plots covering a wide range of surface roughness (representing the total range which can be expected on agricultural fields), soil moisture, and temperature conditions. The approach is based on the parameterization of an effective roughness parameter as a function of surface characteristics: surface roughness (standard deviation of height and correlation length) and the surface soil moisture. The parameterizations that are developed are independent of incidence angle and polarization and are valid over a large range in surface roughness conditions, representative of most of typical agricultural bare fields, from very smooth (rolled field after sowing) to very rough surfaces (deeply plowed soil). This approach will enable the use of microwave radiometric observations for soil moisture retrieval over agricultural areas.

This study focuses on an area in the Sahelian zone, Niger, Western Africa, where the HAPEX-Sahel experiment took place in 1992. During the hydrologic atmospheric pilot experiment in the Sahel (HAPEX-Sahel), passive microwave data were acquired with airborne radiometer, the multifrequency (5 to 90 GHz) and dual polarization sensor, PORTOS, and the four-beam sensor push broom microwave radiometer (PBMR), operating at 1.4 GHz in H-polarization. The aim of this investigation is to monitor soil moisture and vegetation parameters by combining L-band C-band passive microwave airborne measurements. Through the relationships between soil moisture measurements from the 2 cm and 0.5 cm top layers, soil moisture is estimated for PORTOS data using the estimated soil moisture along the transects covered by the PBMR flights. The simplified radiative transfer model is then used to extract the optical thickness and the single scattering albedo of vegetation at C-band, and to evaluate the vegetation effect on the estimated soil moisture at L-band. An attempt to relate the estimated optical thickness from PORTOS data to the measured vegetation biophysical parameters [water content, biomass, leaf area index (LAI)] is presented.

An attempt is made to derive the evolution of the temperature and the water status of the Amazon forest canopy from satellite microwave radiometry. The Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) temperature-corrected tapes data are analyzed for the 6.6, 10.7, 18, and 37 GHz frequencies, at daytime and nighttime, over a zone near Manaus (3/spl deg/S, 60/spl deg/W), Brazil. Two periods are investigated: the wet (April-May) and dry (July-August) seasons of 1985. After separating forest- from river-contaminated pixels, atmospheric corrections are performed for water vapor, clouds, and rain, using surface and satellite data. Algorithms are developed to model the microwave thermal emission of vegetation following a continuous approach and a discrete approach. A sensitivity study is performed in order to determine which frequencies are relevant to retrieve land surface parameters. The models are then used along with an optimization procedure so as to carry out the inversion of the canopy structure parameters. The vegetation temperature and water content are retrieved through the continuous model.>

The effects of leaf characteristics on the microwave emission of land surfaces are analyzed. In order to simulate these effects, a radiative transfer model is presented. The medium consists of a vegetated layer containing randomly oriented leaves, modeled as elliptic-shaped scatterers, over the ground surface. Radiative transfer equations are solved with a discrete-ordinate-eigenanalysis method. The calculation of the phase matrix of the elliptic scatterers is based on the generalized Rayleigh-Gans approximation, which increases the frequency range of the modeling. The sensitivity of brightness temperature and polarization ratio to leaf characteristics, volume fraction, gravimetric moisture, size, shape, and inclination distribution is investigated at C-, and X-band. The behavior of the simulated emission of a soybean canopy versus frequency and incidence angle is studied for different soil moisture levels. Up to 10 GHz the microwave emission appears to contain significant information on underlying soil moisture.>