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The two vegetation transfer parameters of [Formula: see text] (Vegetation Optical Depth,VOD) and [Formula: see text] (Omega) could vary significantly across microwave channels in terms of frequencies, polarizations, and incidence angles, and their channel-dependent characteristics have not yet been fully investigated. In this study, we investigate the channel dependence of vegetation effects on microwave emissions from soils using a higher-order vegetation radiative transfer model of Tor Vergata. Corn was selected as the subject of investigation, and a corn growth model was developed utilizing field data collected from the multifrequency and multi-angular ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River (SMELR). Upon compilation of the simulation dataset of microwave emissions of the corn field, the effective scattering albedo across different channels were calculated using the Tor Vergata model. Results show that vertical polarization of the vegetation optical depth is more affected by incidence angle changes, while horizontal polarization exhibits lower variations in vegetation optical depth due to incidence angle adjustments. The channel dependence of vegetation optical depth can be described as the polarization dependence parameter ([Formula: see text]) and the frequency dependence parameter ([Formula: see text]). These two parameters enable the calculation of vegetation optical depth at any channel under three adjacent frequencies (L-band, C-band and X-band). The effective scattering albedo of vegetation does not vary significantly with vegetation height or angle. It primarily depends on frequency and polarization, showing an overall increasing trend with increasing frequency. The effective scattering albedo with vertical polarization is slightly higher than that with horizontal polarization at higher frequencies, while both are lower in the L-band. This investigation is helpful for understanding the vegetation effects on microwave emissions from soils, ultimately advancing the accuracy of large-scale soil moisture retrieval in vegetated areas.

Abstract A data-driven approach insensitive to the initial conditions was developed to extract governing equations for the concentration of CO2 in the Altamira cave (Spain) and its two main drivers: the outside temperature and the soil moisture. This model was then reformulated in order to use satellite observations and meteorological predictions, as a forcing. The concentration of CO2 inside the cave was then investigated from 1950 to 2100 under various scenarios. It is found that extreme levels of CO2 were reached during the period 1950–1972 due to the massive affluence of visitors. It is demonstrated that it is possible to monitor the CO2 in the cave in real time using satellite information as an external forcing. For the future, it is shown that the maximum values of CO2 will exceed the levels reached during the 1980s and the 1990s when the CO2 introduced by the touristic visits, although intentionally reduced, still enhanced considerably the micro corrosion of walls and pigments.

Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m[Formula: see text] m[Formula: see text]) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the [Formula: see text] model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m[Formula: see text] m[Formula: see text] and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

Precision irrigation management and crop water stress assessment rely on accurate estimation of root zone soil moisture. However, only the top 5 cm soil moisture can be estimated using the two current passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), which operate at L-band (wavelength of ∼21 cm). Since the contributing depth of the soil to brightness temperature increases with observation wavelength, it is expected that a P-band (wavelength of ∼40 cm) radiometer could potentially provide soil moisture information from deeper layers of the soil profile. Moreover, by combining both L- and P- bands, it is hypothesized that the soil moisture profile can be estimated even beyond their individual observation depths. The aim of this study was to demonstrate the potential of combined L-band and P-band radiometer observations to estimate the soil moisture profile under flat bare soil using a stratified coherent forward model. Brightness temperature observations at L-band and P-band from a tower based experimental site across a dry (April 2019) and a wet (March 2020) period, covering different soil moisture profile shapes, were used in this study. Results from an initial synthetic study showed that the performance of a combined L-band and P-band approach was better than the performance of using either band individually, with an average depth over which reliable soil moisture profile information could be estimated (i.e. with a target root mean square error (RMSE) of less than 0.04 m3/m3) being 20 cm for linear and 15 cm for second-order polynomial functions. Other functions were also tested but found to have a poorer performance. Applying the method to the tower-based brightness temperature achieved an average estimation depth of 28 cm (20 cm) and 5 cm (5 cm) during the dry and wet periods respectively when using a second-order polynomial (linear) function. These findings highlight the opportunity of a satellite mission with L-band and P-band observations to accurately estimate the soil moisture profile to as deep as 30 cm globally.

Microwave radiometry has provided valuable spaceborne observations of Earth's geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process.

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m3/m3 for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m3/m3 and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF’s (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval process. In the first step, we retrieved a parameter (referred to as the parameter) accounting for the combined effects of roughness and vegetation. Then, global MODIS NDVI data were used to decouple the effects of vegetation from those of surface roughness. Finally, global maps of the Hr parameters were produced and discussed. Initial results showed that some spatial patterns in the values could be associated with the main vegetation types (higher values of were retrieved generally in forested regions, intermediate values were obtained over crops and grasslands, and lower values were obtained over shrubs and desert) and topography. For instance, over the USA, lower values of were retrieved in relatively flat regions while relatively higher values were retrieved in hilly regions.

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.

The authors wish to make the following corrections to this paper [...]

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

The SMOS satellite mission, launched in 2009, allows global soil moisture estimations to be made using the L-band Microwave Emission of the Biosphere (L-MEB) model, which simulates the L-band microwave emissions produced by the soil–vegetation layer. This model was calibrated using various sources of in situ and airborne data. In the present study, we propose to evaluate the L-MEB model on the basis of a large set of airborne data, recorded by the CAROLS radiometer during the course of 20 flights made over South West France (the SMOSMANIA site), and supported by simultaneous soil moisture measurements, made in 2009 and 2010. In terms of volumetric soil moisture, the retrieval accuracy achieved with the L-MEB model, with two default roughness parameters, ranges between 8% and 13%. Local calibrations of the roughness parameter, using data from the 2009 flights for different areas of the site, allowed an accuracy of approximately 5.3% to be achieved with the 2010 CAROLS data. Simultaneously we estimated the vegetation optical thickness (t) and we showed that, when roughness is locally adjusted, MODIS NDVI values are correlated (R2 = 0.36) to t. Finally, as a consequence of the significant influence of the roughness parameter on the estimated absolute values of soil moisture, we propose to evaluate the relative variability of the soil moisture, using a default soil roughness parameter. The soil moisture variations are estimated with an uncertainty of approximately 6%.

The “Cooperative Airborne Radiometer for Ocean and Land Studies” (CAROLS) L-Band radiometer was designed and built as a copy of the EMIRAD II radiometer constructed by the Technical University of Denmark team. It is a fully polarimetric and direct sampling correlation radiometer. It is installed on board a dedicated French ATR42 research aircraft, in conjunction with other airborne instruments (C-Band scatterometer—STORM, the GOLD-RTR GPS system, the infrared CIMEL radiometer and a visible wavelength camera). Following initial laboratory qualifications, three airborne campaigns involving 21 flights were carried out over South West France, the Valencia site and the Bay of Biscay (Atlantic Ocean) in 2007, 2008 and 2009, in coordination with in situ field campaigns. In order to validate the CAROLS data, various aircraft flight patterns and maneuvers were implemented, including straight horizontal flights, circular flights, wing and nose wags over the ocean. Analysis of the first two campaigns in 2007 and 2008 leads us to improve the CAROLS radiometer regarding isolation between channels and filter bandwidth. After implementation of these improvements, results show that the instrument is conforming to specification and is a useful tool for Soil Moisture and Ocean Salinity (SMOS) satellite validation as well as for specific studies on surface soil moisture or ocean salinity.

In the Sahelian region, the West African Monsoon (WAM) is an important phenomenon for land water storage evolution, as demonstrated by The Gravity Recovery and Climate Experiment (GRACE) estimations. The Monsoon leads to an annual increase of the water mass. However, GRACE data also displays the existence of a semi-annual cycle whose its origin is still uncertain. This cycle is characterized by a gain of water mass at the beginning of the dry season. In this study, 10-days GRACE data are used to understand the characteristics of this semi-annual cycle. Investigations of the rainfall events, rivers discharge peaks, and the Lake Chad water level variations suggest that they are not at the origin of this cycle. However, MODIS evapotranspiration data display an increase each 6 months, during the rainy season, and at the same time as the semi-annual cycle estimated by GRACE. This increase occurs in regions where the amount of clays at the surface exceeds 30%. The link between both signals and the proportion of clays at the surface leads us to the conclusion that the seasonal variation of the vertical permeability of clays controls the amount of water present in the unsaturated zone.

Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003–2013) from two different satellite sensors, i.e., SMOS (2010–2013) and AMSR-E (2003–2011), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the Rvalue), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land–atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The Rvalue and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil moisture datasets based on a combination of these sources are a valuable source of information for climate research.

Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km2). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10–20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr−1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources.

To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with.

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.

La mission SMOS (Soil moisture and Ocean Salinity) a été lancée avec succès le 2 novembre 2009. Cette mission menée par l'ESA (Agence Spatiale Europénne) est dédiée à la mesure de l'humidité superficielle des sols sur les continents (avec une précision recherchée de 0,04m3/m3) et la salinité des océans (objectif 0.1psu). Ces deux quantités géophysiques sont très importantes car elle contrôle le budget énergétique à l'interface sol-atmosphère. Leur connaissance à l'échelle globale est utile pour les recherches sur le climat et la météorologie, en particulier pour les modèles de prévision numérique. Elles ont aussi un très grand potentiel un très grand nombre d'application, comme par exemple pour le suivi des ouragans ou la gestion des ressources en eau. Les six premiers mois ont été dédiés à la recette en vol qui a permis de vérifier le satellite le segment sol et l'étalonnage. Cette phase s'est achevée avec succès en mai 2010 et SMOS fonctionne de façon opérationnelle depuis, fournissant de données à la communauté internationale. Les performances de l'instrument sont globalement conformes aux spécifications. Cependant, les interférences radio sont présentes au-dessus de l'Europe, du Moyen-Orient et de l'Asie. Ces émissions parasites dans la bande protégée perturbent la mesure de façon significative. La génération des produits de niveau 2 et 3 est une activité en cours avec des améliorations régulières de sorties.

Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm—Merge Active and Passive microwave Soil Moisture—uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m3/m3 and 0.069 m3/m3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.