We propose a methodology combining a change detection approach with a neural network algorithm to monitor soil moisture. The methodology utilizes Sentinel-1 and Sentinel-2 data, incorporating various metrics such as radar signals (VV and VH polarization), surface soil moisture index (I_SSM), radar incidence angle, normalized difference vegetation index (NDVI), and VH/VV ratio. In situ data from the International Soil Moisture Network (ISMN) across diverse climatic contexts are used for testing. The results demonstrate improved soil moisture estimations using the hybrid algorithms.

Despite the current short temporal coverage of high spatial resolution SM maps estimated from Synthetic Aperture Radars such as Sentinel-1(S1), their evaluation is important in the context of the ESA CCI as potential future high resolution (HR) SM long time series, and also as benchmarking references for HR SM data sets that could be obtained by downscaling coarser resolution sensors. In this context, 1 km HR SM maps obtained making a synergistic use of S 1 and Sentinel 2 (or Sentinel 3) using the$S^{2}MP$algorithm were compared to the HR SM data sets from the Copernicus Global Land Service produced from S 1 data over three regions in Europe and one in Tunisia. In addition, the$S^{2}MP$maps were also compared to the SMAP+S 1 downscaled product in those regions and in two additional ones in North America and Australia. The HR SM maps show an overall good agreement for croplands and herbaceous land covers while showing significant differences for other land cover classes. All the 1 km SM maps data sets, in addition to coarse scale SMAP, SMOS and CCI data, were evaluated against in-situ measurements. The results show that the HR products are in good agreement but they show a lower correlation with respect to in-situ data than the coarse resolution products.

A global land data assimilation system (LDAS-Monde) was developed by CNRM. It uses a version of the interactions between soil, biosphere, and atmosphere (ISBA) land surface model able to simulate photosynthesis and plant growth. Vegetation variables such as leaf area index (LAI) and surface soil moisture can be jointly assimilated in the model. Sequential assimilation of LAI is possible thanks to the fully photosynthesis-driven phenology. The simulated LAI is flexible and can be analyzed at a given date. Also, the assimilation of LAI alone can be used to analyze the root-zone soil moisture. The assimilation of level 1 microwave observations is being investigated. Recent results and potential applications of LDAS-Monde are presented.

The European Space Agency (ESA) leads on observing the Earth's changing climate from space. Its flagship programme, the Climate Change Initiative (CCI), draws together over 40 years of data from ESA's own satellite missions and those from other space agencies - from past as well as currently active in -orbit ins trumentation. The CCI science teams focus on R&D activities to generate long -term, global climate data records that describe the evolution of key components of the Earth's climate system, as defined by the Global Climate Observing System (GCOS) (see https://public.wmo.int/en/programmes/global-climate-observing-system) in support of the United Nations Framework Convention on Climate Change (see https://unfccc.int/). Currently more than 20 Essential Climate Variables (ECV) of the 54 GCOS defined ECVs have been addressed by science teams involved in the CCI. All ECV datasets are fully validated and have high levels of traceability and consistency, including quantitative estimates of uncertainty required by both climate science and modelling communities. In its contribution to climate and Earth system science, this programme has published over 700 peer-reviewed articles, and supported the Intergovernmental Panel on Climate Change's (IPCC) headline statements on climate in both its fifth Assessment Report and subsequent reports, such as the ‘Special Report on Oceans and Cryosphere in a Changing Climate’, with ongoing involvement in the IPCC's sixth assessment cycle. Besides providing an overview on CCI, this presentation will make the link between CCI and ESA's Soil Moisture and Ocean Salinity (SMOS) mission, demonstrating the contribution that SMOS data can make in the creation of climate data records (CDR). Several CDRs are already including SMOS data on a regular basis, such as the sea surface salinity and soil moisture long-term data sets. There is also potential for using SMOS data for sea ice, biomass and vegetation climate data records.

A set of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) data assimilation (DA) experiments are presented. The SMOS soil moisture dataset used in this study was produced training a neural network (NN) using SMOS brightness temperatures as input and ECMWF H-TESSEL SM fields as reference for the training. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. SMOS NN SM DA experiments were compared to Advanced Scat-terometer (ASCAT) SM DA. In both cases, experiments with and without 2 metre air temperature and relative humidity DA are discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives similar results to the model open loop with no assimilation. The effect of the soil moisture analysis on the Numerical Weather Prediction (NWP) was evaluated using the analysed surface fields to perform atmospheric forecast experiments. In the Northern Hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in Northern Asia for up to 72 hours.

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.

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.

The 2-D interferometric radiometer on board the Soil Moisture and Ocean Salinity (SMOS) satellite has been providing a continuous data set of brightness temperatures, at different viewing geometries, containing information of the Earth's surface microwave emission. This data set is affected by several sources of noise, which are a combination of the noise associated with the radiometer itself and the different views under which a heterogeneous target, such as continental surfaces, is observed. As a result, the SMOS data set is affected by a significant amount of noise. For many applications, such as soil moisture retrieval, reducing noise from the observations while keeping the signal is necessary, and the accuracy of the retrievals depends on the quality of the observed data set. This paper investigates the averaging of SMOS brightness temperatures in angular bins of different sizes as a simple method to reduce noise. All the observations belonging to a single pixel and satellite overpass were fitted to a polynomial regression model, with the objective of characterizing and evaluating the associated noise. Then, the observations were averaged in angular bins of different sizes, and the potential benefit of this process to reduce noise from the data was quantified. It was found that, if a 2° angular bin is used to average the data, the noise is reduced by up to 3 K. Furthermore, this method complements necessary data thinning approaches when a large volume of data is used in data assimilation systems.

Within the Climate Change Initiative of the European Space Agency a multi-satellite soil moisture product covering the period 1979-2010 was released. In this study we first assess its quality by comparing it with soil moisture from ground-based stations and several land surface model estimates. Secondly, the dynamics in the dataset were assessed using trend analysis and comparisons with ancillary data sets of precipitation and vegetation. Significant changes over time were found that largely correspond to changes in precipitation and vegetation vigorousness. However, the influence of changing observation density and data set quality over time need to be better understood for a more precise interpretation of the observed trends.

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The bestwSestimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions.

Ground-based multifrequency (L-band to W-band, 1.41-90 GHz) and multiangular (20°-50°) bipolarized (V and H) microwave radiometer observations, acquired over a dense wheat field, are analyzed in order to assess the sensitivity of brightness temperatures (Tb) to land surface properties: surface soil moisture (mv) and vegetation water content (VWC). For each frequency, a combination of microwaveTbobserved at either two contrasting incidence angles or two polarizations is used to retrievemvand VWC, through regressed empirical logarithmic equations. The retrieval performance of the regression is used as an indicator of the sensitivity of the microwave signal to eithermvor VWC. In general, L-band measurements are shown to be sensitive to bothmvand VWC, with lowest root mean square errors (0.04 m3·m-3and 0.52 kg ·m-2, respectively) obtained at H polarization, 20° and 50° incidence angles. In spite of the dense vegetation, it is shown thatmvinfluences the microwave observations from L-band to K-band (23.8 GHz). The highest sensitivity to soil moisture is observed at L-band in all configurations, while observations at higher frequencies, from C-band (5.05 GHz) to K-band, are only moderately influenced bymvat low incidence angles (e.g., 20°). These frequencies are also shown to be very sensitive to VWC in all the configurations tested. The highest frequencies (Q- and W-bands) are shown to be moderately sensitive to VWC only. These results are used to analyze the response of W-band emissivities derived from the Advanced Microwave Sounding Unit instruments over northern France.

A long term profile soil moisture data acquisition effort is currently under way at 12 monitoring stations across southwestern France. The spatial distribution and set-up of those sites was specifically designed for validating remote sensing and model soil moisture estimates [1], [2]. Those m-situ measurements are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter using a time constant, T, is used to compute a profile soil water index from the surface observations. Through optimisation, a unique value for T of 6 days is found to result in a satisfactory correlation between observation and estimate for all stations. The surface soil moisture observed is also used to evaluate the normalized surface soil moisture estimates derived from coarse resolution (25 km) active microwave data of the ASCAT C-band. For 11 stations, significant correlation levels are found. The best correlation between a soil water index derived from ASCAT and the in-situ observations is obtained for a T of 14 days.