Determination of agricultural fields carbon budget is needed to devise the best sustainable agriculture strategies, and quantify future subsidies in the agricultural sector. One major sustainable practice is cover crops that are planted in between main crops cycles, enabling storage of carbon and reduction of reflected heat. Assimilation of Earth Observation data is crucial for such exercise due to the sheer heterogeneity and complexity of crop modeling in the real environment. In this paper, we present the impact of cover crops on Net Ecosystem Exchange (NEE) and Biomass over a large area in the south-west of France using the AgriCarbon-EO. This tool is an end-to-end solution that enables a Bayesian assimilation of multi-temporal and entire tiles (100x100 km) Sentinel-2 reflectance data into a radiative model and a crop model. Our results show an increase of NEE of about due to cover crops over maize fields.

International audience

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.

Inland water storage is a key reservoir in the continental water cycle but the scientific knowledge about its spatio-temporal dynamic is still poor, especially over tropical areas. By coupling the Soil and Water Assessment Tool (SWAT) model and the Soil WAter Fraction (SWAF) data to increase the inundation delineation precision, inland water storage in the Madeira Basin located in Southern Amazon Basin was computed from 1998 to 2018 each day. During this period, the maximum of water storage was reached in March every year and varied from$3.01\times 10^{11}\ \mathrm{m}^{3}$to$1.22\times 10^{11}\ \mathrm{m}^{3}$. The Madeira Basin and each floodplain section have a peculiar temporal hydrologic response. The methodology presented in this paper can be extended to the entire Amazon Basin and other large-scale watersheds.

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.

Wetlands and open waters are key components of the hydrological and carbon cycles but their spatio-temporal dynamics are still not well known at global scale. Current paper presents a new methodology to retrieve water fraction at coarse scale and high temporal resolution (one week) using L-Band multi-angular and dual polarisation remote sensing data from SMOS mission. The dataset labeled G-SWAF (or Global-SWAF) is an extention of the SWAF approach which did not consider the separate contributions of the Soil, Vegetation and water fractions. The comparison to existing datasets shows more water fraction detecting in Tropical areas but better consideration of high latitudes is still needed to be included in future studies. The use of such datasets with the future data from the SWOT (NASA/CNES) mission will provide global evaluation of inland open water volumes at 10 days scale.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

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 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).

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture in agricultural tropical areas. Simultaneously to several radar acquisitions made between June and October 2018, using ALOS2-PALSAR sensor over the Berambadi site (south of India), ground measurements of soil roughness, soil water content, LAI were recorded. The sensitivity of the ALOS-2 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study, even for dense crops. The radar signals are simulated using different types of backscattering models (physical and semi-empirical) over bare soil and vegetation cover for different types of crops (tumeric, etc). WCM model parameterized with LAI for vegetation contribution allows a good estimation of soil moisture for tumeric.

Models that simulate the radiative budget (RB) and remote sensing (RS) observation of landscapes with physical approaches and consideration of the three-dimensional (3-D) architecture of Earth surfaces are increasingly needed to better understand the life-essential cycles and processes of our planet and to further develop RS technology. DART (Discrete Anisotropic Radiative Transfer) is one of the most comprehensive physically based 3-D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet to thermal infrared. It simulates the optical 3-D RB and signal of proximal, aerial and satellite imaging spectrometers and laser scanners, for any urban and/ or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. Here, an application is presented after a summary of its theory and recent advances: inversion of Sentinel 2 images for simulating time series of urban radiative budget `Q*sw' maps through the determination of maps of urban surface material. Results are very encouraging: satellite and in-situ Q*sware very close (RMSE ≈ 15W/m2; i.e., 2.7% mean relative difference).

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 (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.

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.

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.

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.

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.

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.

We introduced an approach to simulate and separate atmospheric contribution in ground-based thermal-infrared (TIR) camera measurements. Different from the traditional approach which uses the look-up table built from 1-D radiative transfer model (RTM), this approach directly simulates 3-D ray propagations and interactions in the heterogeneous urban environment by using the Discrete Anisotropic Radiative Transfer (DART) model. The atmospheric turbid cells that occupy every part of the urban scene are created using the vertical constituent distribution and the optical property profiles in the existing databases or from the actual meteorological measurements. The two components of atmospheric effects on the TIR at-sensor radiance are attenuated transmission and path thermal emission. Taking both into account, the at-surface radiance corresponding to the signal emitted only from the urban surface can be derived.

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.

The recently launched H2020 project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of EO to retrieve anthropogenic heat flux, as a key component in the Urban Energy Budget (UEB). URBANFLUXES advances existing Earth Observation (EO) based methods for estimating spatial patterns of turbulent sensible and latent heat fluxes, as well as urban heat storage flux at city scale and local scale. Independent methods and models are engaged to evaluate the derived products and statistical analyses provide uncertainty measures. Optical, thermal and SAR data are exploited to improve the accuracy of the UEB components spatial distribution calculation. Synergistic use of different types and of various resolution EO data allows estimates in local and city scale. Ultimate goal of the URBANFLUXES is to develop a highly automated method for estimating UEB components to use with Copernicus Sentinel data, enabling its integration into applications and operational services.

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.

The need of better accuracy for analyzing remote sensing (RS) data of complex Earth surfaces explains the increasing need of models that simulate RS data with physical approaches. Similarly, the study of Earth surfaces functioning requires physical models that simulate the 3D radiative budget (RB) of these surfaces. DART (Discrete Anisotropic Radiative Transfer is one of the most comprehensive physically based 3D models that model the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It simulates optical signals at the entrance of terrain/airborne/satellite imaging radiometers and laser scanners, as well as the 3D RB, of urban/natural landscapes for any experimental and instrumental configurations. Its licenses are free for research and teaching activities. Here, we present its major recent advances.

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.

In this paper we present a study on the use of remote sensing data combined to the 3D modeling of radiative transfer (RT) and energy balance in urban canopies in the aim to improve our knowledge on anthropogenic heat fluxes in several European cities (London, Basel, Heraklion, and Toulouse). The approach is based on the forcing by the use of LandSAT8 data of a coupled radiative transfer model DART (Direct Anisotropic Radiative Transfer) (www.cesbio.upstlse.fr/dart) with an energy balance module. LandSAT8 visible remote sensing data is used to better parametrize the albedo of the urban canopy and thermal remote sensing data is used to enhance the anthropogenic component in the coupled model. This work is conducted in the frame of the H2020 project URBANFLUXES, which aim is to improve the efficiency of remote-sensing data usage for the determination of the anthropogenic heat fluxes in urban canopies [5].

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.

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.

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).

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.

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.

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.

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.

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.

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.