In the context of soil monitoring from microwave radiometric observations, the knowledge of the soil permittivity is an important factor in order to guarantee the accuracy of models inversions. Previous studies have showed the impact of temperature and moisture gradients in the top soil layer on remote sensing monitoring of soil. So, we aim here to investigate a way to increase our knowledge of the permittivity profile of the top soil layer, in particular at the P and L band. The solution we selected is based on a new equipment for continuous in-situ measurements of dielectric soil profiles. First measurement results over a sandy soil after rainfall are presented.

Spatially distributed hydrological models are traditionally calibrated and evaluated against few spatially aggregated observations such as river discharge. This model evaluation approach does not enable an assessment of the model predictive capabilities of other hydrological states and fluxes nor does it give any insight into the model ability to mimic the spatial patterns within a catchment. The current study explores a multivariable optimization of a complex coupled surface–subsurface–atmosphere model at the catchment scale in an attempt to move beyond simple run‐off calibration. The model is evaluated against five independent observational data sets of discharge (Q), hydraulic head (h), actual evapotranspiration (ET), soil moisture (SM), and remotely sensed land surface temperature (LST). It is shown that a balanced optimization can be achieved where errors on objective functions for all five observation data sets can be reduced simultaneously. Additionally, the multivariable calibration proved more robust, compared with calibration against Q and h only, during the validation period, even for Q and h. The current parameterization and calibration framework was mainly suitable for reducing model biases and allowed only limited improvements in the spatio‐temporal patterns of the model simulations. This points towards development of better parametrization schemes that will allow simulated spatial patterns to adjust during calibration. Additionally, analysis showed that systematic spatial patterns in the errors of the LST maps could be a very valuable diagnostic tool for assessing deficiencies in the model structure, spatial parameterization, or process description.

Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

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 study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 − 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m3 m−3, ubRMSE = 0.062 m3 m−3 for the IGBP-based calibration; against R = 0.54, bias = −0.034 m3 m−3 and ubRMSE = 0.070 m3 m−3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product.

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, H R and N RP , 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, H R = 0.4 and N RP = −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 H R varied, 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.

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m3m-3, 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m3m-3, 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

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.

Ce travail s’inscrit dans le projet SMOSHiLat de l’ESA (Agence Spatiale Europeenne) visant a ameliorer le suivi a l’echelle planetaire des sols organiques en zones boreales (pergelisol ou permafrost) a l’aide du radiometre de la mission spatiale SMOS (capteur radiometrique en bande L – 1,4 GHz). Ces sols etaient perpetuellement geles mais le rechauffement climatique provoque le degel partiel de ces sols. Outre le suivi de ces phenomenes de gel/degel comme indicateur du rechauffement climatique, ce suivi est aussi necessaire car ces sols renferment des quantites considerables de gaz. Un degel de ces structures pourrait provoquer une liberation massive de gaz (methane...) qui provoquerait un feed-back positif important sur le rechauffement climatique. Ce travail a consiste a concevoir des techniques de metrologie adaptees pour la caracterisation electromagnetique des sols organiques (en fonction de leur nature, de la temperature et de l’humidite) en laboratoire et in-situ. Pour ce dernier point nous avons concu une sonde de mesure dediee a la mesure in-situ des proprietes dielectriques des sols sur une large bande de frequence (100MHz-6GHz).

Two passive microwave missions are currently operating at L-band to monitor surface soil moisture (SM) over continental surfaces. The SMOS sensor, based on an innovative interferometric technology enabling multi-angular signatures of surfaces to be measured, was launched in November 2009. The SMAP sensor, based on a large mesh reflector 6 m in diameter providing a conically scanning antenna beam with a surface incidence angle of 40°, was launched in January of 2015. Over the last decade, an intense scientific activity has focused on the development of the SM retrieval algorithms for the two missions. This activity has relied on many field (mainly tower-based) and airborne experimental campaigns, and since 2010–2011, on the SMOS and Aquarius space-borne L-band observations. It has relied too on the use of numerical, physical and semi-empirical models to simulate the microwave brightness temperature of natural scenes for a variety of scenarios in terms of system configurations (polarization, incidence angle) and soil, vegetation and climate conditions. Key components of the inversion models have been evaluated and new parameterizations of the effects of the surface temperature, soil roughness, soil permittivity, and vegetation extinction and scattering have been developed. Among others, global maps of select radiative transfer parameters have been estimated very recently. Based on this intense activity, improvements of the SMOS and SMAP SM inversion algorithms have been proposed. Some of them have already been implemented, whereas others are currently being investigated. In this paper, we present a review of the significant progress which has been made over the last decade in this field of research with a focus on L-band, and a discussion on possible applications to the SMOS and SMAP soil moisture retrieval approaches.

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels'' at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m(3) m(-3) or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m(3) m(-3) (0.030 m(3) m(-3)) at the 9-km scale and 0.035 m(3) m(-3) (0.026 m(3) m(-3)) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m(3) m(-3) (0.032 m(3) m(-3)). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

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.

A meta‐analysis data‐driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02–0.56, a sand fraction range of 0.05–0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance (rss) formulation based on surface soil moisture (θ) and two resistance parameters rss,ref and θefolding. The data‐driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut‐off soil moisture value θ1/2 at which SEE=0.5, and first derivative of SEE at θ1/2 , named Δθ1/2−1 . An analytical relationship between (rss,ref;θefolding) and (θ1/2;Δθ1/2−1) is first built by running a soil energy balance model for two extreme conditions with rss = 0 and rss∼∞ using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair (θ1/2;Δθ1/2−1) either from the time series of SEE and θ observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear SEE(θ) relationships and potentially large random deviations of observed SEE from the mean observed SEE(θ) . The second method parameterizes θ1/2 as a multi‐linear regression of clay and sand percentages, and sets Δθ1/2−1 to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol‐Biosphère‐Atmosphère), H‐TESSEL (Hydrology‐Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land‐surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.

This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are based on the here-proposed natural log fit. The newly derived ThetaProbe fit functions should be used for hand-held applications only, but prove to be of value for the acquisition of instantaneous large-scale soil moisture estimates.

From the passive L-band microwave radiometer onboard the Soil Moisture and Ocean Salinity (SMOS) space mission global surface soil moisture data is retrieved every 3 days. Thus far, the empirical L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model applied in the SMOS soil moisture retrieval algorithm is exclusively calibrated over test sites in dry and temperate climate zones and the included dielectric mixing model relating soil moisture to permittivity accounts only for mineral soils. However, soil moisture monitoring over the higher northern latitudes is crucial since these regions are especially sensitive to climate change and a considerable feedback is expected due to carbon liberated from thawing ground of these extremely organic soils. Due to differing structural characteristics and thus varying bound water fractions, the permittivity of organic material is lower than the one of most mineral soils at a given water content. This assumption was verified by means of measurements in organic and mineral substrates from various sites in Denmark, Finland, Scotland and Siberia. For this purpose, conventional soil moisture sensors were used as well as weak perturbation and waveguide techniques in order to infer effective soil permittivity at the microwave L-band (1-2 GHz). Based on these data, a generic L-band soil moisture – permittivity relation for organic soils was derived and validated with dielectric mixing model runs as well as literature data. Furthermore, the derived function was tested in the L-MEB model. Results showed that modeled data agreed with measurements from a tower-based passive L-band microwave radiometer observing organic-rich soil over a 2 months period in a highly controlled set-up. The generic «organic» empirical model was then implemented in the SMOS Prototype Algorithm to retrieve soil moisture over a site in Northern Finland. The validation with in situ soil moisture observations calibrated for organic soils showed a distinct improvement in the agreement between the satellite and ground datasets when using the «organic» instead of the operational SMOS processor version. This analysis is to be continued in more detail and the validation effort needs to be expanded over as many regions with abundant soil organic matter content as possible. Appropriate in situ observations are currently available from various sites in Alaska, Canada, and the Netherlands. In this communication, first the derivation of the generic L-band «organic» soil moisture-permittivity model will be presented. Focus will then be on the comparison of «organic» SMOS soil moisture retrievals with corresponding operational SMOS products as well as in situ observations over all available sites.