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International audience; This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future.

International audience; Soil moisture (SM) is a fundamental environmental variable for characterizing climate, land surface and atmosphere. In recent years, several SM products have been developed based on remote sensing (RS), land surface model (LSM) or land data assimilation system (LDAS). However, little knowledge is available in understanding spatial patterns of the uncertainty of different SM products and potential regional drivers over the Qinghai-Tibet Plateau (QTP), a complex environment for accurate SM estimation. This paper investigates the sensitivity of the SM uncertainties based on the three-cornered hat (TCH) method and a generalized additive model (GAM) in terms of underlying surface characteristics (sand fraction, soil organic matter, vegetation, land surface temperature, and topography) and near-ground meteorology (precipitation and air temperature) in the third pole environment over the 2015–2018 period. Eleven SM products are involved in this work, including Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity INRA-CESBIO (SMOS-IC), Japan Aerospace Exploration Agency (JAXA), Land Surface Parameter Model (LPRM), Climate Change Initiative - Active/Combined (CCI_A/CCI_C), Global Land Data Assimilation System (GLDAS), European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), Global Land Evaporation Amsterdam Model product a/b (GLEAM_a/GLEAM_b), and Random Forest Soil Moisture (RFSM). Results show that most of the SM products perform well across QTP, while SMOS-IC is strongly affected by radio-frequency interference in this region, JAXA has a relatively higher noise level over QTP, and LPRM has larger relative uncertainties (RUs) in the southeast of QTP. Nonlinear regression analysis demonstrates that variations of RUs in SMOS-IC and JAXA are driven by topography, while LPRM's are mainly controlled by vegetation. In addition, two groups of opposite (positive/negative) effects from topography and vegetation, topography and precipitation, and precipitation and land surface temperature affect the spatial variations of RUs in CCI_A, RFSM, and ERA-Interim, respectively. Meanwhile, more complex relationships are found between multiple surface factors and RUs of different products. In general, the underlying surface factors explain on average 39.41% and 28.34% of the variations in RS and LSM/LDAS SM RUs, respectively. Comparatively, the near-ground meteorology factors have a slightly larger effect on LSM/LDAS products than that on RS products.

International audience; The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as ‘Mattar’ and ‘Saleh’). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. As no in situ SM data was used, the evaluation made here is relative to the use of this specific reference data set. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R2 ≈ 0.90, RMSE varying between 0.035 and 0.056 m3/m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R2 ≈ 0.93, RMSE ≈ 0.035 m3/m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the ‘reference’ SM time series (R2 ≈ 0.75, RMSE ≈ 0.055 m3/m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.

International audience; In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

International audience; This study focused on atmospheric corrections of airborne thermal infrared remote sensing data acquired with a multidirectional and single broadband channel sensor during the ReSeDA experiment. For single channel sensors, atmospheric corrections are generally performed using atmospheric radiative transfer models such as MODTRAN 3.5 along with radiosoundings. A sensitivity study was performed using MODTRAN 3.5 simulations to assess the accuracy of the processing regarding the experimental context. It was shown that the local topography and the atmosphere spatial variability could affect significantly the radiosounding representativeness, whereas the fluctuations of the flight altitude around the nominal value induced non-negligible inaccuracies. Moreover, using a broadband sensor induced non-linear effects that required a second order correction and also the use of look-up tables to reduce computation time. A theoretical error was next proposed to account for both the sensor accuracy and the experimental uncertainties considered when performing the sensitivity study. This theoretical error was about VC, and agreed well with the results obtained when validating against field measurements.

International audience; A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave-Scanning Radiometer-Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Q(p) model, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Q(p) at different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface enrissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronorniques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the available experimental data at the incidence angles of 50 degrees and 60 degrees. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50 degrees and 55 degrees.

International audience; This study compared surface emissivity and radiometric temperature retrievals derived from data collected with the MODerate resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensors, onboard the NASA's Earth Observation System (EOS)-TERRA satellite. Two study sites were selected: a semi-arid area located in northern Chihuahuan desert, USA, and a Savannah landscape located in central Africa. Atmospheric corrections were performed using the MODTRAN 4 atmospheric radiative transfer code along with atmospheric profiles generated by the National Center for Environmental Predictions (NCEP). Atmospheric radiative properties were derived from MODTRAN 4 calculations according to the sensor swaths, which yielded different strategies from one sensor to the other. The MODIS estimates were then computed using a designed Temperature-Independent Spectral Indices of Emissivity (TISIE) method. The ASTER estimates were derived using the Temperature Emissivity Separation (TES) algorithm. The MODIS and ASTER radiometric temperature retrievals were in good agreement when the atmospheric corrections were similar, with differences lower than 0.9 K. The emissivity estimates were compared for MODIS/ASTER matching bands at 8.5 and 11 μm. It was shown that the retrievals agreed well, with RMSD ranging from 0.005 to 0.015, and biases ranging from −0.01 to 0.005. At 8.5 μm, the ranges of emissivities from both sensors were very similar. At 11 μm, however, the ranges of MODIS values were broader than those of the ASTER estimates. The larger MODIS values were ascribed to the gray body problem of the TES algorithm, whereas the lower MODIS values were not consistent with field references. Finally, we assessed the combined effects of spatial variability and sensor resolution. It was shown that for the study areas we considered, these effects were not critical.

International audience; A spatialized surface energy balance model was validated over the database acquired in the framework of the ReSeDA program. The benefit of the SEBAL model we considered was to compute wind speed and air temperature using the information contained in the spatial variability of convective fluxes. The multitemporal database allowed performing a validation over cycles of several crops. Problems induced by mixed pixels were reduced using high spatial resolution remote sensing data. We verified the validity of the model basic assumption, i.e. the simultaneous presence of partial areas with very high and very low evaporation rates, and the resulting relation between surface temperature and albedo. Besides, the model provided estimates of wind speed and air temperature close to the field references. The validation of soil heat flux showed the inadequacy of the empirical relationship used through a significant underestimation of the references. The validation of sensible heat flux provided similar results as compared to previous studies that dealt with model validations over databases including numerous situations.; Nous avons validé un modèle de bilan d'énergie de surface en utilisant la base de données ReSeDA. L'intérêt du modèle SEBAL était d'estimer la vitesse du vent et la température de l'air à partir de l'information contenue dans la variabilité spatiale des flux convectifs. La base de données multitemporelle permettait d'effectuer une validation en considérant les cycles de plusieurs cultures. Les problèmes dûs aux pixels mixtes étaient réduits par l'utilisation de données télédétectées à haute résolution spatiale. Nous avons pu vérifier la validité de l'hypothèse de base du modèle, i.e. la présence simultanée de zones très évaporantes ou peu évaporantes, ainsi que la relation entre albedo et température qui en résulte. De plus, le modèle a fourni des estimations de la vitesse du vent et de la température de l'air proche des mesures de référence. La validation du flux de chaleur dans le sol a montré que la relation empirique générait une sous-estimation importante. La validation du flux de chaleur sensible a fourni des résultats comparables à ceux rencontrés dans la littérature et concernant la validation de modèles sur des bases de données incluant de nombreuses situations.