Search

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 $\text{m}^{3}/\text{m}^{3}$ target 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 $\sim 10$ -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.

Microwave radiometry has provided valuable spaceborne observations of Earth's geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.

We address the optimization problem of antenna placement on satellite-mounted interferometric synthetic-aperture instruments. In classic designs, the antennas on satellites are aligned regularly on the satellite’s frame. Inspired by methods proposed for the placement of antennas in astronomical interferometers, such as ALMA or SKA, we explore irregular layouts and show that they are a valid alternative in terms of spatial resolution and reconstruction error. We formalize mathematically the optimization problem of irregularly placed antennas and we show that this kind of arrays can still be calibrated with the same methods used for regular arrays. Finally, this strategy is evaluated in the context of soil moisture and ocean salinity (SMOS) follow-up concepts, such as SMOS-HR (high resolution), for which the new optimized irregular configurations are compared to the regular ones.

We report a unique multiyear L-band microwave radiometry dataset collected at the Maqu site on the eastern Tibetan Plateau and demonstrate its utilities in advancing our understandings of microwave observations of land surface processes. The presented dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. Measurement cases in winter, pre-monsoon, monsoon and post-monsoon periods are presented., Measurement(s) microwave radiation • soil moisture • atmospheric wind • Solar Radiation • rain • soil • CO2/H2O flux • thermal radiation • temperature of soil Technology Type(s) Radiometry (Microwave) • Sensor Device • pyranometer • Gauge or Meter Device • particle size analyzer • pyrgeometer Sample Characteristic - Environment land Sample Characteristic - Location Tibetan Plateau Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12818261

International audience; The Soil Moisture and Ocean Salinity (SMOS) satellite has been providing global data for more than 11 years. The algorithm to retrieve surface soil moisture (SM) and vegetation optical depth (VOD) from Brightness Temperatures (TB) has constantly evolved as our understanding of the instrument and of the responses to various ecosystems increased. The evolution of the radiative transfer modelling used in the inversion process is in part based upon ground measurements done on experimental sites. We present the particular case of an L-band radiometer placed on top of a 800- meter-high cliff. This design allows us to study large and various scenes when compared to classical experiments where a radiometer is placed 10–20 m above a surface. With our set up, the footprints cover areas larger than 300 m in diameter, covering thus several land use types and the footprint can be directed towards different dominant land classes (forest, crops, urban etc…). This paper presents the whole set up consisting of in situ measurements (soil moisture and surface temperatures) and L-band radiometry acquired by the LEWIS instrument at high incidence angles. We perform SM/VOD retrievals from brightness temperatures using various configurations which are compared to the in situ observations. We test in particular a 2-VOD retrieval approach that consists in deriving one SM for the entire scene but two VOD, one for agricultural/low vegetation and a VOD for the forest parts, which improves the performance of the SM/VOD retrievals.

International audience

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

Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the attenuation of the Earth's thermal emission at microwave wavelengths by the vegetation layer. At L-band, VOD can be used to estimate and monitor aboveground biomass (AGB), a key component of the Earth's surface and of the carbon cycle. We observed a strong anti-correlation between SMOS (Soil Moisture and Ocean Salinity) L-band VOD (L-VOD) and soil moisture (SM) anomalies over seasonally inundated areas, confirming previous observations of an unexpected decline in K-band VOD during flooding (Jones et al., 2011). These results could be, at least partially, due to artefacts affecting the retrieval and could lead to uncertainties on the derived L-VOD during flooding. To study the behaviour of SMOS satellite L-VOD retrieval algorithm over seasonally inundated areas, the passive microwave L-MEB (L-band Microwave Emission of the Biosphere) model was used to simulate the signal emitted by a mixed scene composed of soil and standing water. The retrieval over this inundated area shows an overestimation of SM and an underestimation of L-VOD. This underestimation increases non-linearly with the surface water fraction. The phenomenon is more pronounced over grasslands than over forests. The retrieved L-VOD is typically underestimated by ~10% over flooded forests and up to 100% over flooded grasslands. This is mainly due to the fact that i) low vegetation is mostly submerged under water and becomes invisible to the sensor; and ii) more standing water is seen by the sensor. Such effects can distort the analysis of aboveground biomass (AGB) and aboveground carbon (AGC) estimates and dynamics based on L-VOD. Using the L-VOD/AGB relationship from Rodriguez-Fernandez et al. (2018), we evaluated that AGB can be underestimated by 15/20Mg ha-1 in the largest wetlands, and up to higher values during exceptional meteorological years. Such values are more significant over herbaceous wetlands, where AGB is ~30 Mg ha-1, than over flooded forests, which have typical AGB values of 150-300 Mg ha-1. Consequently, to better estimate the global biomass, surface water seasonality has to be taken into account in passive microwave retrieval algorithms.

Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as &ldquo, irrigation-driven&rdquo, No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20&ndash, 30% in the irrigation period. Hybrid datasets perform better (+15&ndash, 20%) than single-technology measurements, among which active data provide slightly better results (+5&ndash, 10%) than passive data.

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.

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 $\text{m}^{3}\cdot \text{m}^{-3}$ for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions $\le 0.15~\text{m}^{3}\cdot \text{m}^{-3}$ . 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 $\text{m}^{3}\cdot \text{m}^{-3}$ for the focus farm.

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

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

Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of 3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 1 cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “Q/H” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between ΔN and root-mean-square height (σ) or σ2/LC can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters (Q, Np and h) generally increase when soil get rougher. The calibrated Q, Nh and Nv show exponential dependence on the effective parameter h. The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero-Q fitting models have comparable performance in Tb modeling with zero-Q models but may be more physically realistic.

International audience; L-band radiometer measurements collected over the Dome C area from 2010 to 2015 indicated that the brightness temperature (T B) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014-2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5 o incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects.

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

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

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d’études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 – 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R > 0.80) over the Oznet sites and worst (R = 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R= 0.51 and R= 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

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

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 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).

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.

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.

An Emission Models (EM) adequate for a retrieval algorithm requires being simple while still capturing the responses of brightness temperatures $T_{\text{B}}^{p,\theta }$ to 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 $T_{\text{B}}^{p,\theta }$. For sparse and low-scattering vegetation $T_{{\text{B,}}EM}^{p,\theta }$ simulated with EM = TO and EM = 2S converge, which is not the case for dense and strongly scattering vegetation. WC RC and τ RC are retrieved with Retrieval Configurations RC = {TO, 2S} from $T_{\text{B}}^{p,\theta }$ measured: 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 WC RC retrievals while τ RC retrievals are reduced more considerably. With respect to in-situ WC in-situ , retrievals WC 2S derived from tower-based $T_{\text{B}}^{p,\theta }$ performed better than forest soil water-content WC TO retrieved via the inversion of the "reference" TO EM. Likewise, SMOS based WC 2S retrievals revealed better agreement with ECMWF WC simulations than WC TO achieved 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.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

In this paper, we present a methodology to map inland water in tropical areas under dense vegetation at high spatial and temporal resolution using multi-source remote sensing data. A new inundation product (SWAF-HR) is presented. It is characterized by a high spatial resolution (30', 1 km) and high temporal resolution (3 days). The SWAF-HR product is estimated over the Amazon basin for the 2010-2016 period. It is based on a downscaling procedure and the synergistic use of: (1) water surface fraction at coarse spatial resolution from an L-band passive microwave sensor (Soil Moisture and Ocean Salinity - SMOS), (2) Global Surface Water Occurrence from Landsat (GSWO) and (3) the Digital Elevation Model (DEM) Multi-Error-Removed-Improved-Terrain (MERIT) based on the Shuttle Radar Topography Mission (SRTM). Thanks to the high capability of L-band microwave emission to reveal surface water under all-weather conditions and beneath the vegetation, the inundated area extent estimated by the SWAP-HR product is always larger than GSWO estimates obtained by the optical sensor (Landsat). SWAF-HR data is compared to ESA CCI and IGBP land covers, two SAR images and flooded areas over the Purus basin computed by the MGB-IPH model simulation. The results show the coherence of spatial and temporal dynamics of the SWAF-HR data. We show that the flooded area of the Branco River floodplain in Roraima (Brazil) varies from 0.2 x 10(4) to 2.7 x 10(4) km(2) whereas the extent of the Bolivian floodplain (Llanos de Moxos) inundation ranges between 0.8 x 10(4) and 8.1 x 10(4) km(2) during 2010-2016. The flooded area in the Branco floodplain gradually decreased from 2010 to 2015 but in 2016, the flooded area has increased during the rainy season. During 2010-2016, the minimum of the inundated surface extent was reached during 2015-2016 reflecting to a drought event related to ENSO. The most important uncertainties of the DEM are located over tropical areas but this information is essential in the downscaling procedure. Therefore, we investigate the impact of the choice of the DEM for the downscaling procedure. It is found that the choice of the DEM introduces 5% of error in the instantaneous water surface extent estimate but can reach up to 10% in the flood probability estimations over seven years. This new SWAF-HR product will be helpful for the understanding of the water, carbon and biogeochemical cycles of the Amazon.

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.

International audience; 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 to in situ measurements.

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/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Calibration and validation activities on Soil Moisture and Ocean Salinity SMOS-derived soil moisture products have been conducted worldwide since the data became available, but this has not been the case over tropical regions. This study focuses on the setting up of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia and on the validation of SMOS soil moisture products. The in-situ data over a one-and-a-half-year period was analysed and the validation of the SMOS soil moisture products with this in-situ data was conducted. Bias and root mean square error RMSE were computed between the SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing times 6 am and 6 pm local time. Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with a vegetation water content higher than 5 kg m−2, an overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias ranged from 0.064 to 0.119 m3 m−3 and the RMSE was from 0.090 to 0.158 m3 m−3, when both ascending and descending mode data were measured. This RMSE was found to be similar to those of a number of studies conducted previously at different regions. However, a wet bias was found during the validation, while previous validation activities at other locations showed dry biases. The result of this study is useful to support the continuous development and improvement of the SMOS soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially in tropical regions.

European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by Radio Frequency Interference (RFI) that jeopardizes part of its scientific retrieval in certain areas of the world. 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 several strategies into place that help improve the RFI situation, filter the SMOS data from RFI perturbed measurements and bring awareness to the RFI problem.

International audience; Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). 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, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations.

L-band passive measurements (1.4 GHz) over continental areas are known to be related to surface soil moisture. Two satellite missions were recently launched to measure land surface emissions at this frequency band (SMOS-Soil Moisture and Ocean Salinity in 2009 and SMAP-Soil Moisture Active/Passive in 2015). In order to improve soil moisture retrievals from satellite data, ground-based radiometer systems operating at the same frequency were deployed over specified areas to investigate the L-band emission of various land covers under various climatological conditions. In this study, three years of L-band passive measurements from a radiometer installed on top of a steep mountain in the French Alps were analyzed and compared to L-band passive simulations. The innovative radiometer location led to large footprints due to the distance between the radiometer and the area under study. This experiment also produced microwave measurements affected by various potential difficulties typically encountered in SMOS/SMAP satellite missions: topography, heterogeneous footprints, dry/wet snow events, dew and vegetation litter. Based on in situ and modeling data, this paper investigates the potential of a radiative transfer model (L-band Microwave Emission of the Biosphere, L-MEB) to simulate L-band measurements and analyzes the differences with ELBARA observations. First, it was found that the topography generated a mixing of the horizontal and vertical polarizations. In addition, a large positive bias was found on ELBARA measurements (31 K and 12 K in horizontal and vertical polarizations respectively). Investigations showed that the sky reflection measured by the radiometer was partially substituted by land reflection coming from the surrounding topography. Second, the low-vegetation emission was investigated and highlighted the inability of the MODIS NDVI product to correctly represent the vegetation dynamics. Finally, dry snow conditions were found to have non-negligible impact at L-band and a particular signature was found during snow melting periods, with potential applications at the SMOS/SMAP spatial scales (~ 40 km).

The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. Although the L-band is a protected band, the data collected by SMOS are contaminated by radio frequency interferences (RFI) which degrade the performance of the mission. A precise location of the RFI emitters is required for switching off illegal transmissions or for fixing malfunctioning equipments. This work is concerned with the geolocation of such sources with a sub-kilometric accuracy from SMOS interferometric data themselves. Such a precise location has never been reached by any other published methods using only SMOS measurements.

International audience; The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

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

International audience; Plant water storage is fundamental to the functioning of terrestrial ecosystems by participating in plant metabolism, nutrient and sugar transport, and maintenance of the integrity of the hydraulic system of the plant. However, a global view of the size and dynamics of the water pools stored in plant tissues is still lacking. Here, we report global patterns of seasonal variations in ecosystem-scale plant water storage and their relationship with leaf phenology, based on space-borne measurements of L-band vegetation optical depth. We find that seasonal variations in plant water storage are highly synchronous with leaf phenology for the boreal and temperate forests, but asynchronous for the tropical woodlands, where the seasonal development of plant water storage lags behind leaf area by up to 180 days. Contrasting patterns of the time lag between plant water storage and terrestrial groundwater storage are also evident in these ecosystems. A comparison of the water cycle components in seasonally dry tropical woodlands highlights the buffering effect of plant water storage on the seasonal dynamics of water supply and demand. Our results offer insights into ecosystem-scale plant water relations globally and provide a basis for an improved parameterization of eco-hydrological and Earth system models.

© 2018 IEEE. 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. ispartof: pages:1451-1454 ispartof: IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM vol:2018-July pages:1451-1454 ispartof: 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) location:Valencia Spain date:22 Jul - 27 Jul 2018 status: Published online

International audience; 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 (similar to 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

International audience; 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.

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.

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.

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.

International audience; 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.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission operates in the 1400–1427 MHz 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.

International audience; 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.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse ( ∼ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.

International audience; After the successful acquisition by a coarse L-band radiometer on board Skylab in the early seventies, the potential of L-band radiometry was made clear in spite of a strict limitation linked to minimum antenna dimensions required for appropriate spatial resolution. More than 20 years later new antenna concepts emerged to mitigate this physical constraint. The first to emerge, in 1997, and to become a reality, was the Soil Moisture and Ocean Salinity (SMOS) mission (Kerr, 1997, Kerr, 1998). It is European Space Agency’s (ESA’s) second Earth Explorer Opportunity mission (Kerr et al., 2001), launched in November 2009. It is a joint program between ESA, CNES (Centre National d’Etudes Spatiales), and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D 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 from space. Surface emissivity can 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.Soil moisture retrieval from SMOS observations with a required accuracy of 0.04 m3/m3 is challenging and involves many steps. The retrieval algorithms are developed and implemented in the ground segment, which processes level 1 and level 2 data. Level 1 consists mainly of directional brightness temperatures, while level 2 consists of geophysical products in swath mode, i.e., for successive imaging snapshots acquired by the sensor during a half orbit from pole to pole. Level 3 consists in composites of brightness temperatures, or geophysical products over time and space, i.e., global maps over given temporal periods from 1 day to 1 month. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis Documents (ATBD), used to in operational soil moisture and sea salinity retrieval algorithms (Kerr et al., 2010a).The principle of the level 2 soil moisture 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 brightness temperature (TB) at horizontal and vertical polarizations, for a variety of incidence angles. The algorithm finds the best set of parameters, e.g., soil moisture (SM) and vegetation characteristics, which drive the TB model and minimizes the cost function. From this algorithm, a more sophisticated one was developed to take into account multiorbit retrievals (i.e., level 3). Subsequently, after several years of data acquisition and algorithm improvements, a neural network approach was developed so as to be able to infer soil moisture fields in near-real time. In parallel, several simplified algorithms were tested, the goal being to achieve a seamless transition with other sensors, along with other studies targeted on specific targets such as dense forests, organic rich soils, or frozen and snow-covered grounds. Finally, it may be noted that most of these approaches deliver not only the surface soil moisture but also other quantities of interest such as vegetation optical depth, surface roughness, and surface dielectric constant. The goal of this article is to give an overview of these different approaches and corresponding results and adequate references for those wishing to go further. Sea surface salinity is not covered in this article, while the focus is SMOS data.

We investigate to what extent correlating the Fourier components at slightly shifted frequencies of the fluctuations of the electric field measured with a one-dimensional antenna array on board of a satellite flying over a plane, allows one to measure the two-dimensional brilliance temperature as function of position in the plane. We find that the achievable spatial resolution resulting from just two antennas is of the order of $h\chi$, with $\chi=c/(\Delta r \omega_0)$, both in the direction of flight of the satellite and in the direction perpendicular to it, where $\Delta r$ is the distance between the antennas, $\omega_0$ the central frequency, $h$ the height of the satellite over the plane, and $c$ the speed of light. Two antennas separated by a distance of about 100m on a satellite flying with a speed of a few km/s at a height of the order of 1000km and a central frequency of order GHz allow therefore the imaging of the brilliance temperature on the surface of Earth with a resolution of the order of one km. For a single point source, the relative radiometric resolution is of order $\sqrt{\chi}$, but for a uniform temperature field in a half plane left or right of the satellite track it is only of order $1/\chi^{3/2}$, indicating that two antennas do not suffice for a precise reconstruction of the temperature field. Several ideas are discussed how the radiometric resolution could be enhanced. In particular, having $N$ antennas all separated by at least a distance of the order of the wave-length, allows one to increase the signal-to-noise ratio by a factor of order $N$, but requires to average over $N^2$ temperature profiles obtained from as many pairs of antennas., Comment: 39 pages of latex in preprint format, 2 figures