Soil Moisture Ocean Salinity (SMOS) mission was the first L-band radiometer launched in 2010 and is operational for retrieving global scale soil moisture and Vegetation Optical Depth (VOD). SMOS-INRA-CESBIO (SMOS-IC) version-2 is the latest retrieval algorithm for SMOS radiometers that outperforms existing SMOS retrieval algorithms. Research is underway to enhance the SMOS-IC product by improving surface roughness information that influences soil moisture and VOD retrievals. In the present study, we developed a new global parameterization of soil roughness using SMOS-IC retrievals. For this purpose, we retrieved the soil moisture and surface roughness (through the Hr parameter) values over bare soils using the SMOS-IC algorithm. A Random Forest (RF) model was trained with soil textural and terrain properties as inputs (explanatory variables) to model Hr over bare soils. Later, we extrapolated the Hr values obtained over bare soils to a global scale using the RF model. The newly calibrated Hr values were further used in the SMOS-IC algorithm for soil moisture and VOD retrievals (SMOS-IC v2.1). The SMOS-IC v2.1, the soil moisture product, is wetter than the original SMOS-IC v2 product and has improved performance compared to in situ ISMN soil moisture and modeled ECMWF soil moisture data sets. Regarding VOD, the SMOS-IC v2.1 VOD product showed improved spatial correlation with the reference aboveground biomass product. In addition, SMOS-IC v2.1 VOD product showed improved temporal correlation with MODIS NDVI over low-to-moderate vegetated regions.

The Copernicus Imaging Microwave Radiometer (CIMR) is a Copernicus Expansion Mission with an expected launch in 2028+. The satellite will carry a multi-frequency microwave radiometer operating in the L-, C-, X-, Ku-, and Ka-bands. In addition to providing L-band continuity from other missions (e.g., SMOS, SMAP), the CIMR mission brings new soil moisture remote sensing opportunities due to its multi-frequency, multi-resolution, and high temporal revisit characteristics. This study outlines a preliminary version of a soil moisture retrieval approach designed within CIMR preparatory activities. It aims to provide two soil moisture products: the first is based on the inversion of L-band brightness temperature measurements at their native resolution (<60 km). The second product is based on the inversion of enhanced resolution L-band measurements (<15 km) achieved through sharpening the L-band with higher resolution C/X-band measurements. In both cases, the soil moisture retrieval is based on the inversion of the zeroth-order tau-omega radiative transfer model. We present current efforts of algorithm development and performance evaluation based on simulated CIMR L1B data. The results presented here showcase the potential of CIMR to provide soil moisture estimates not only at hydroclimatological scales (<60 km, from L-band) but also at hydrometeorological scales (~10 to 25 km, from L-band, sharpened with C/X bands), meeting the needs of a wide range of science and applications.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

The future Copernicus Imaging Microwave Radiometer (CIMR) mission is planned to be launched in the 2027+ time frame. At its present phase, the first version of each Algorithm Theoretical Basis Document (ATBD) must be defined. CIMR will provide observations at L (1.4 GHz), C (6.9 GHz), X (10.65 GHz), Ku (18.7 GHz) and Ka (36.5 GHz) microwave frequencies. These observations will be relevant to develop high resolution land surface products. Here we present a preliminary study with the aim of exploring the future capabilities that the synergy of CIMR frequencies can provide. Focused on the 0th-order Tau-Omega (τ-ω) model, we analysed the influence of soil roughness (H) and scattering albedo (ω) to retrieve soil moisture (SM) and vegetation optical depth (VOD) at L-band and how these parameters can be potentially estimated from higher frequency bands. We evaluated our results over CONUS, concluding that the soil roughness (H) parameter is affecting VOD and ω mainly in non-forested areas: in those areas, the increase of H produces a decrease in VOD. Our maps of ω revealed dependence with land cover type: generally, the lowest ω values were found in forested areas. Instead, our H map yielded patterns that could be mostly associated with topographic effects. Furthermore, by utilizing a depolarization index, TBdep, we discovered that its values were constrained to nearly zero (indicating minimal soil impact) in areas with vegetation, whereas in bare soils, topography had a significant influence on TBdep. We hypothesize that the use of this index could help in finding relationships among the multi-frequency information from CIMR, allowing us to understand the degree of sensitivity of each band to vegetation and topography.

Between 2018 and 2022, INRAE Bordeaux (IB) has developed a series of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) retrieval products from SMOS and SMAP, which are currently the only two operational L-band passive microwave satellite missions. These IB products rely on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information. The products are found to be accurate, and very well-suited for application in hydrology, agriculture, climate and vegetation monitoring. In this communication, we present an overview of the development, evaluation and new applications of these IB SM or L-VOD products.

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.

The present study focuses on retrieving soil and canopy temperatures, which are key parameters to estimate soil moisture and vegetation optical depth from multi-frequency microwaves information. Several retrieval algorithms assume that canopy and vegetation temperatures are similar in thermal equilibrium conditions, while others separate their contributions, as SMOS-IC, one of the consolidated retrieval algorithms for the Soil Moisture and Ocean Salinity (SMOS) satellite mission. Soil and canopy temperatures in SMOS-IC are modelled from the ECMWF (European Centre for Medium-Range Weather Forecasts) centre. Both SMOS and the Soil Moisture Active Passive (SMAP) missions are currently the only passive L-band (1.4 GHz) missions in operation, but their lifetime is limited. In this context, the upcoming Copernicus Imaging Microwave Radiometer (CIMR) mission will provide continuity on L-band measurements with complementary information in a range of microwave frequencies, from 1.4 to 36.5 GHz. This study uses in situ soil moisture information from the International Soil Moisture Network (ISMN) as input in the SMOS-IC algorithm to retrieve vegetation optical depth (VOD) and soil/canopy effective temperature (TGC). The retrieved effective temperature is then compared with modelled temperatures from ECMWF and with data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), which acquires the higher frequency bands (C, X, Ka, and Ku) present in the future CIMR mission. Results confirm the potential of all high-frequency bands to estimate TGC, with C and X-bands being the most correlated. This study is a first approach to evaluate how microwave multi-frequency information can help modelling soil and canopy temperatures in the SMOS-IC retrieval algorithm, from which the upcoming CIMR mission may benefit.

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear fusion method. We found that SMOS and SMAP TB are strongly correlated (R > 0.90 over most of the globe) but present a small bias at both the horizontal and vertical polarizations. The preliminary evaluation results show that this bias can be adjusted using a linear fit, but further evaluation procedures are still needed. In the near future, we will develop a long-term time series of SM and L-VOD products based on this merged SMOS-SMAP TB record.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass$(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$, which is better than V16 VOD$(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$and VODCA VOD$(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$. Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.

Vegetation optical depth (VOD) is used to parameterize microwave extinction effects within the vegetation layer. Many studies have showed VOD presents interesting features for applications in ecology, water and carbon cycles, and VOD is only marginally impacted by signal disturbances and artefacts from atmospheric, cloud and sun illumination effects. As soil moisture (and not VOD) has generally been the main factor of interest in retrieval studies from microwave observations, there is room for improvement in the retrieved VOD products. In this context, INRAE Bordeaux recently developed alternate VOD products from the SMOS, AMSR2 and ASCAT sensors, by addressing specifically the ill-posed problem of retrieving both SM and VOD from observations which may be strongly cross-correlated. Promising results were obtained particularly in terms of spatial correlation of these alternate VOD indices with biomass.

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.

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.

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

This 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.

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring by Global Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced.

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

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

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

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

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