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This article presents the “screen-level and surface analysis only” (SSA) system at the European Centre for Medium-Range Weather Forecasts (ECMWF). SSA is a simplification of the operational land–atmosphere weakly coupled data assimilation (WCDA). The goal of SSA is to provide 1) efficient research into land surface developments in NWP and 2) land reanalyses with land–atmosphere coupling. SSA maintains a coupled forecast model between assimilation cycles, but the atmospheric analysis is not performed; rather, it is forced from an archived analysis. Hence, SSA is much faster than WCDA, although it lacks feedback between the land and atmospheric analyses. A global sensitivity analysis was performed over one year to compare the WCDA and SSA systems. Prescribed proxy 2-m temperature/humidity screen-level observation errors were approximately doubled in the soil moisture data assimilation, thereby reducing the average size of the root-zone soil moisture analysis increments by about 60%. The systematic impact of these changes on the WCDA surface and near-surface atmospheric dynamics was effectively captured by SSA, although the short-term impact was underestimated. Importantly, the SSA forecast verification scores accurately reflected those of WCDA: atmospheric 1–10-day temperature/humidity forecasts were degraded in the tropics and lower midlatitudes up to about 700 hPa. The soil moisture analysis performance was not significantly impacted. These results endorse SSA as an NWP research tool and confirm the role of assimilating proxy screen-level observations in the soil moisture analysis to improve weather forecasts. Appropriate use and limitations of SSA are considered.

International audience

This contribution presents the main findings of a recently published review on high-resolution satellite soil moisture applications (https://doi.org/10.1016/j.rse.2020.112162). The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This presentation summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. This presentation also discusses the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of soil moisture remote sensing are discussed, providing guidance for the further development of operational soil moisture products and for bridging the gap between the soil moisture user and supplier communities.The published review is:Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M.H., Crow, W.T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M.W.J., de Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y.H., Lovergine, F., Mahecha, M.D., Marzahn, P., Mattia, F., Musial, J.P., Preuschmann, S., Reichle, R.H., Satalino, G., Silgram, M., van Bodegom, P.M., Verhoest, N.E.C., Wagner, W., Walker, J.P., Wegmüller, U., & Loew, A. (2021). A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements. Remote Sensing of Environment, 252, 112162

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities.

International audience; Land surfaces are characterised by strong heterogeneities of soil texture, orography, land cover, soil moisture, snow, and other variables. The complexity of the surface properties is very challenging to represent accurately in radiative transfer models, which have a limited reliability over land, especially for observations such as given. This has resulted in difficulties in assimilating land‐surface related satellite observations in numerical weather prediction models. Simple statistical relationships between satellite observations and surface variables have therefore been considered in the last 20 years. In this study, we propose to compare two such approaches: cumulative distribution function (CDF)‐matching (used as a normalisation and an inversion technique) and neural network (NN) methods. CDF‐matching finds a simple monovariate relationship at the local scale and is very dependent on the land‐surface model (LSM) on which it is calibrated. NNs are global multivariate models able to exploit auxiliary information and the synergy of multiple instruments, but the solution is global and no local characteristics constrain the solution. One of these two methods will be better suited, depending on the application—in particular the simplicity of the satellite/variable relationship. We illustrate these concepts here using Advanced SCATerometer (ASCAT) observations for soil moisture (SM) retrieval in an assimilation context. The two approaches are compared and combined. We also compare the more traditional inversion scheme and forward modelling, which could be attractive for assimilation purposes. We show that, in the context of ASCAT, the inversion approach seems better suited than the forward modelling. We also show that it is possible to combine the global model obtained using the NN and the localised information of the LSM offered by CDF‐matching.

International audience; This paper presents the forward modelling aspects of the SMOS (Soil Moisture and Ocean Salinity) activities at ECMWF (European Centre for Medium-Range Weather Forecasts). Several parameterizations of the Community Microwave Emission Modelling Platform (CMEM) are used to simulate L-band Brightness Temperatures (TBs) and compared to the SMOS TBs for 2010-2011. We show that simulated TBs are primarily sensitive, by order of importance, to the soil roughness model, the vegetation opacity and the soil dielectric model. In particular, best CMEM results are obtained with the simple Wigneron soil roughness model and the Wigneron model for the vegetation opacity. For the soil dielectric model, performances of the Wang and Schmugge and the Mironov models are shown to be similar and better than the Dobson model. The Wang and Schmugge model is then used in the next steps of this paper combined with the Wigneron roughness and vegetation models. The paper describes a multi-angular multi-polarised bias correction method based on a linear rescaling (mean and variance) computed at the monthly scale using SMOS observations and ECMWF-CMEM re-analysed TBs for a four year period (2010-2013). Results show that for 2010-2013 the seasonal multi-angular multi-polarisation bias correction approach reduces global RMSE to 7.91 K, compared to 16.7 K before bias correction, whereas the mean absolute bias is reduced to 1.39 K, compared to 11.04 K before bias correction. The consistency between the seasonality of simulated and the observed TBs is also improved by using a monthly bias correction, leading to correlation values improvement to 0.62 after bias correction compared to 0.56 before. The 2010-2013 bias correction applied to the 2014-2016 period at 40°incidence reduces the global RMSE from 15.56 K to 8.19 K, and the mean absolute bias from 10.16 K to 2.51 K, with no impact on the correlation values that remain at 0.61 in both cases. Long term monitoring of SMOS TB is presented covering a 7-year period (2010-2016) at both polarisations, at 40°incidence angle. Results show that the consistency between SMOS and ECMWF reanalysis-based TBs progressively improved between 2010 and 2016, pointing out improvements of level 1 SMOS TB products quality through the SMOS lifetime.

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).

International audience; Soil moisture is a crucial variable for numerical weather prediction. Accurate, global initialization of soil moisture is obtained through data assimilation systems. However, analyses depend largely on the way observation and background errors are defined. In this study, a wide range of short experiments with contrasted specifications of the observation error and soil moisture background were conducted. As observations, screen-level variables and brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission were used. The region of interest is North America, given the good availability of in situ observations and mixture of different climates, making it a good test for global applications. The impact of these experiments on soil moisture and the atmospheric layer near the surface were evaluated. The results highlighted the importance of assimilating observations sensitive to soil moisture for air temperature and humidity forecasts. The benefits on predicting the soil water content were more noticeable with increasing the SMOS observation error, and with the introduction of soil texture dependency in the soil moisture background error.

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

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April−September, while NNSM alone has a significant positive effect in July−September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is - with the exception of arid environments -comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.

Land surface processes control the water and energy fluxes between the continental surfaces and the atmosphere, as well as the interaction of the water and carbon cycles. They are complex and characterized by strong heterogeneities both spatially (orography, land use, soil types, etc.) and temporally (variability of diurnal and seasonal cycles). Due to the complexity and the large range of spatial and temporal scales involved in these processes, land surface data assimilation has not yet reached levels of maturity, which are found in the areas of atmospheric (notably, in weather forecast) and oceanographic data assimilation. Nevertheless, considerable progress has been made in recent years in the development of the data assimilation on continental surfaces, in conjunction with the expansion of spatial observation of the Earth.

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.

This paper assessed the SMOS soil moisture values from Level 3 (SMOS L3SM) product provided by the French CNES-CATDS. The evaluation was conducted at the local scale through comparison with ground-based soil moisture measurements acquired in Mali, Niger and Benin from 2010 to 2012. The SMOS L3SM product was compared to three other satellite-based soil moisture products. It was found that, in average over the three sites, the SMOS L3SM product provided the best coefficients of correlation and the lowest root mean square errors (RMSE). The second part of the paper is devoted to retrieve soil moisture estimates between successive SMOS soil moisture measurements in order to increase the temporal resolution. The result of the methodology allows obtaining 3-hour soil moisture mapping over West Africa with a coefficient of correlation greater than 0.82, and an RMSE lower than 0.030 m3 m− 3 in Niger and Mali and lower than 0.044 m3 m− 3 in Benin.

International audience; The L-band brightness temperature of natural grass fields is strongly influenced by rainfall interception. In wet conditions, the contribution of the soil, mulch, and vegetation to the overall microwave emission is difficult to decouple, thus rendering the retrieval of surface soil moisture from a direct emission model difficult. This paper investigates the development and assesses the performances of statistical regressions linking passive microwave measurements to surface soil moisture in order to assess the potential of soil moisture retrievals over natural grass. First, statistical regressions were analytically derived from the L-Band Emission of the Biosphere model (L-MEB). Single configuration (1 angle, 1 polarisation), and multi-configuration regressions (2 angles, or 2 polarisations) were developed. Second, the performance of statistical regressions was evaluated under different rainfall interception conditions. For that purpose, a modified polarisation ratio at L-band was used to build three data sets with different interception levels. In the presence of interception, a regression based on one observation angle (50°) and two polarisations was able to reduce the effects of vegetation and soil roughness on the soil moisture retrievals. The methodology presented in this study is also able to provide estimates of the vegetation and soil roughness contribution to the brightness temperature.

This paper explores the effect of rain intercepted by vegetation and mulch on the L-band emission of natural grass. The study is based on radiometric, meteorological, and biophysical measurements obtained during the SMOSREX Experiment (Toulouse, France). Several approaches were followed to evaluate interception effects. Firstly, the analysis of microwave brightness temperature (TB) measurements at L-band indicated that interception increases vegetation emission at both polarisations. Secondly, the use of microwave indices to detect the presence/absence of interception was examined. In particular, a modified polarisation ratio at 50° was found to be well related to the interception status of the standing vegetation. Finally, the vegetation optical depth (?), which parameterises the extinction across the vegetation layer, was retrieved from the TB observations. It was found that ? increases with the increase in the water content stored within the vegetation and mulch after rainfall. The study highlights the strong impact of intercepted water in otherwise weakly attenuating covers such as grasses. Interception might therefore be an issue to consider in order to improve soil moisture retrieval algorithms from L-band observations.

[1] The contrast between the point-scale nature of current ground-based soil moisture instrumentation and the ground resolution (typically >102 km2) of satellites used to retrieve soil moisture poses a significant challenge for the validation of data products from current and upcoming soil moisture satellite missions. Given typical levels of observed spatial variability in soil moisture fields, this mismatch confounds mission validation goals by introducing significant sampling uncertainty in footprint-scale soil moisture estimates obtained from sparse ground-based observations. During validation activities based on comparisons between ground observations and satellite retrievals, this sampling error can be misattributed to retrieval uncertainty and spuriously degrade the perceived accuracy of satellite soil moisture products. This review paper describes the magnitude of the soil moisture upscaling problem and measurement density requirements for ground-based soil moisture networks. Since many large-scale networks do not meet these requirements, it also summarizes a number of existing soil moisture upscaling strategies which may reduce the detrimental impact of spatial sampling errors on the reliability of satellite soil moisture validation using spatially sparse ground-based observations.

Rainfall distribution and the soil moisture regime have been recognized to be the key drivers of the phenological rhythms in Sahelian woody plants, although different climate triggers have been assumed to be involved in determining the date of the onset of the phenophase. However, almost no comparisons have been made of the actual relative predictive power of these environmental factors. The aim of our study was to quantify the ability of several factors to predict phenophase occurrence in the dominant woody populations of northern Mali. Canopy leafing, flowering and fruiting were monitored from May 2005 to July 2007. Multiple logistic regressions were used to test the predictive power of cumulative rainfall, soil moisture, air temperature, air humidity and day length, with time lags of up to 2 months. Artificial variables derived from time lags observed in phenophases were included as predictors to account for possible auto-correlation and cross-correlation among phenophases. Surprisingly, a decrease in temperature associated with different time lags was most often found to be the strongest predictor of both leafing and reproductive phenophases. In Sahelian shrubs, morphological and physiological adaptations strongly contribute to the relative independence of their activity from water availability, leaf phenology being a way to adjust the plant water balance to current water availability and atmospheric water content. This study provides insight towards the development of a mechanistic understanding of phenological control in the Sahel, which is becoming increasingly important in the context of expected climate changes. © 2012 Springer Science+Business Media B.V.

In situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system (Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle and short term variability. Correlations with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.

The study quantifies the relationships at local scale between phenology and determinants of climate and soil water resources at two sites located along the latitudinal gradient of West Africa, one in the central Sahel (Mali), the other in the Sudanian bioclimatic zone (Benin). The aim is to improve our knowledge on possible vegetation response to possible climate change. Within the Sudanian site, average annual rainfall is 1200 mm, extending from April to October, while, in the Sahelian site, it is 370 mm, occurring from June to September. Physical data were collected from the African Monsoon Multidisciplinary Analysis research programme. The phenology of the dominant species was monitored in four types of vegetation cover at the wetter site, and in three types of vegetation cover at the drier site. For each sampled plant, leafing, flowering and fruiting were recorded as binary variables in terms of the presence/absence of phenophases. A small proportion of the variability of each phenophase occurrence is explained by the logit models. However, rainfall rise is significantly linked to leafing probability increase in the Sahelian site but not in the Sudanian site. Day length extension and temperature decrease are significantly correlated with an increase in leafing in the Sudanian site, but not in the Sahelian. On both sites, the increase in cumulative rainfall is not found to be linked to an increased probability of reproductive phenophases (negative or non-significant relationships). Air temperature is positively correlated with flowering rate in the Sudanian site, but, all other factors being constant, no climate factors are found to be highly significant of flowering occurrence in the Sahel. Fruiting probability is positively correlated mainly with temperature within the Sahelian site. Leafing occurrence is positively correlated with soil moisture in the 0-1 m layer for the Sudanian site, but not for the Sahelian site. Significant relationships between fruiting occurrence and soil moisture may reflect a prior selection of plants on fruiting period that maximizes seed dispersion and germination differently at the two sites. While vegetative and reproduction schedules may be determined by specific genetic factors, the physical environment controls the possibility of their expression. Reduction of the rainfall amount and intensity may increase reproduction rates in wet areas. Although this factor should decrease leafing rate, it does not influence reproduction at dry sites, except through the decrease in air humidity. In wetter areas, increasing temperature may reduce leafing, but may increase reproduction rates. Cover reduction may have an impact on local physical factors and, consequently, probably also affects vegetation phenology. © 2009 Elsevier B.V. All rights reserved.

International audience; The primary goal of the SMOS mission is to deliver global fields of sea surface salinity and surface soil moisture using L-band (1.4 GHz) radiometry. Within the context of the preparation of SMOS activities over land, a field campaign, SMOSREX (Surface Monitoring Of the Soil Reservoir EXperiment), has been in operation since January 2001 in Mauzac, near Toulouse in France. Continuous ground measurements of meteorological variables, soil moisture and temperature profiles have been taken over bare soil and a grass plot left fallow. Since January 2003, SMOSREX has been providing accurate field measurements of dual polarized L-band brightness temperature up-welling from both bare soil and fallow plots, together with multi-spectral (from visible to infrared frequencies) remote sensing surface data. The scientific objectives are presented in this paper and the corresponding experimental design is described. The experimental concept is totally new since (i) SMOSREX combines land–surface–atmosphere observations, passive microwave measurements and VIS to NIR remote sensing, (ii) SMOSREX is based on highly accurate L-band measurements carried out by a radiometer specifically designed for the experiment, and (iii) SMOSREX provides a unique continuous data set of L-band measurements over several years. The characteristics of the L-band emission are presented at diurnal, seasonal and annual temporal scales, and the emissions are compared over bare soil and natural grass. The surface emissions over bare soil and fallow area are shown to be counter-phased at the diurnal scale due to small variations in vegetation water content and bare soil surface moisture. Innovative long term results using L-band measurements for both bare soil and natural grass are presented in this paper, and the relationship between the surface emission at L-band and surface bare soil moisture is shown to be suitable for a long term period (19 months). Soil freezing is shown to be drastically different for bare soil and vegetation covered plots, with a large threshold effect on microwave surface emission.

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated. It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snow-cover fraction as a function of snow depth is observed at the catchment but not in any of the models.

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