Search

International audience; The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr$^{−1}$) associated with drying trends, and a net change of −0.02 Pg C yr$^{−1}$ was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS Level 2, Level 3 and SMOS-IC) were compared with data sets on tree height, visible/infra-red indexes (NDVI, EVI), cumulated precipitation, and above ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationship linking L-VOD to tree height (R = 0.87) and Baccini's AGB (R = 0.94) was strong and linear. The relationships between L-VOD and three other AGB data sets were linear per land cover class, but with a changing slope depending on the land cover type. For low vegetation classes, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlation with the amount of the average annual precipitations. SMOS L-VOD showed a higher sensitivity to AGB as compared to NDVI and K/X/C-VOD (VOD measured, respectively, at 19, 10.7, and 6.9 GHz). The results showed that although the spatial resolution of L-VOD is coarse (~ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising index for large scale monitoring of the vegetation status, in particular biomass.

We investigate to what extent correlating the Fourier components at slightly shifted frequencies of the fluctuations of the electric field measured with a one-dimensional antenna array on board of a satellite flying over a plane, allows one to measure the two-dimensional brilliance temperature as function of position in the plane. We find that the achievable spatial resolution resulting from just two antennas is of the order of $h\chi$, with $\chi=c/(\Delta r \omega_0)$, both in the direction of flight of the satellite and in the direction perpendicular to it, where $\Delta r$ is the distance between the antennas, $\omega_0$ the central frequency, $h$ the height of the satellite over the plane, and $c$ the speed of light. Two antennas separated by a distance of about 100m on a satellite flying with a speed of a few km/s at a height of the order of 1000km and a central frequency of order GHz allow therefore the imaging of the brilliance temperature on the surface of Earth with a resolution of the order of one km. For a single point source, the relative radiometric resolution is of order $\sqrt{\chi}$, but for a uniform temperature field in a half plane left or right of the satellite track it is only of order $1/\chi^{3/2}$, indicating that two antennas do not suffice for a precise reconstruction of the temperature field. Several ideas are discussed how the radiometric resolution could be enhanced. In particular, having $N$ antennas all separated by at least a distance of the order of the wave-length, allows one to increase the signal-to-noise ratio by a factor of order $N$, but requires to average over $N^2$ temperature profiles obtained from as many pairs of antennas., Comment: 39 pages of latex in preprint format, 2 figures

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse ( ∼ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.

International audience; Live fuel moisture content (LFMC) is an important factor in fire risk management in the Mediterranean region. Drawing upon a large network of stations (the Réseau Hydrique) measuring LFMC for operational fire danger assessment in the south-eastern region of France, this study assesses the ability of several long-term passive microwave remote sensing indices to capture the LFMC temporal dynamic of various Mediterranean shrub species. Microwave remote sensing has a high potential for monitoring LFMC independently of several constraints (e.g., atmospheric and cloud contamination effects) associated with optical-infrared and thermal remote sensing observations. The following four microwave-derived indices are considered: (1) the Essential Climate Variable near-surface soil moisture (ECV_SM); (2) the root-zone soil moisture (ECV_RZSM) derived from ECV_SM; (3) the microwave polarization difference index (MPDI) computed from five microwave frequencies (C, X, Ku, K and Ka-band corresponding to 6.9, 10.7, 18.7, 23.8 and 36.5 GHz respectively); and (4) the vegetation optical depth (VOD) at C- and X-band (from the Advanced Microwave Scanning Radiometer for the Earth observing system, AMSR-E). Firstly, an evaluation of the root-zone soil moisture ECV_RZSM against a network of soil moisture measurements (SMOSMANIA in southern France) gave satisfactory results. For most of the Réseau Hydrique sites, the present study found good agreement between LFMC and individual microwave indices, including root-zone soil moisture, VOD at X-band, and MPDI at X and Ku-bands, all averaged over the 15 days preceding the in-situ LFMC measurements. VOD at X-band showed the best agreement with the in situ LFMC data (median of correlation coefficients over all in situ sites = 0.43). Further comparisons between LFMC data and several optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI), normalized difference infrared index 6 (NDII6), normalized difference infrared index 7 (NDII7) and global vegetation moisture index (GVMI) were made. The comparisons showed that VARI and SAVI, as optical greenness indices, outperform the microwave indices and other optical indices with median of correlation coefficients of 0.66 and 0.65, respectively. Overall, this study shows that passive microwave indices, particularly VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.

Crop detection from remote sensed images is of major interest for land use and land cover mapping. Classification techniques often require multi-temporal images. However, most of these techniques assume that the cultural cycle occurs at the same dates across plots or for a given crop and do not take into account the sensitivity to initial conditions of the dynamical behaviors. Such hypotheses are not well adapted when a wide diversity of practices is observed for the same crops from one crop field to another, which is often the case in tropical context. To cope with these difficulties, a new classification technique based on the global modeling technique is introduced in this paper. It is first applied to a case study based on chaotic oscillators. It is then tested on crop classification observed from satellite data. The Berambadi watershed (South India) is taken as a case study to test this new classification approach. Crop classification is a difficult problem in Southern India where optical satellite images are scarce during the monsoon season due to cloud cover, and where crop land is divided in parcels (i.e. crop fields) of very small sizes with diversified crops. The Landsat-8 images were used to monitor an ensemble of 104 parcels of ten different crops (irrigated and non-irrigated). Using global modeling, a bank of crop models was first obtained for the ten crops considered in the study. A metric is introduced to compare the observed signal to the obtained crop-models used as reference for each crop dynamic. Based on this metric, the possibility to use global models as references for distinguishing crops is investigated. The results provide a good proof-of-concept and show promising potential for crop classification. (c) 2017 Elsevier Ltd. All rights reserved.

International audience; After the successful acquisition by a coarse L-band radiometer on board Skylab in the early seventies, the potential of L-band radiometry was made clear in spite of a strict limitation linked to minimum antenna dimensions required for appropriate spatial resolution. More than 20 years later new antenna concepts emerged to mitigate this physical constraint. The first to emerge, in 1997, and to become a reality, was the Soil Moisture and Ocean Salinity (SMOS) mission (Kerr, 1997, Kerr, 1998). It is European Space Agency’s (ESA’s) second Earth Explorer Opportunity mission (Kerr et al., 2001), launched in November 2009. It is a joint program between ESA, CNES (Centre National d’Etudes Spatiales), and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D interferometric radiometer in the 1400–1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence, the instrument probes the Earth surface emissivity from space. Surface emissivity can be related to the moisture content in the first few centimeters of soil, and after some surface roughness and temperature corrections, to the sea surface salinity over ocean.Soil moisture retrieval from SMOS observations with a required accuracy of 0.04 m3/m3 is challenging and involves many steps. The retrieval algorithms are developed and implemented in the ground segment, which processes level 1 and level 2 data. Level 1 consists mainly of directional brightness temperatures, while level 2 consists of geophysical products in swath mode, i.e., for successive imaging snapshots acquired by the sensor during a half orbit from pole to pole. Level 3 consists in composites of brightness temperatures, or geophysical products over time and space, i.e., global maps over given temporal periods from 1 day to 1 month. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis Documents (ATBD), used to in operational soil moisture and sea salinity retrieval algorithms (Kerr et al., 2010a).The principle of the level 2 soil moisture retrieval algorithm is based on an iterative approach, which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled brightness temperature (TB) at horizontal and vertical polarizations, for a variety of incidence angles. The algorithm finds the best set of parameters, e.g., soil moisture (SM) and vegetation characteristics, which drive the TB model and minimizes the cost function. From this algorithm, a more sophisticated one was developed to take into account multiorbit retrievals (i.e., level 3). Subsequently, after several years of data acquisition and algorithm improvements, a neural network approach was developed so as to be able to infer soil moisture fields in near-real time. In parallel, several simplified algorithms were tested, the goal being to achieve a seamless transition with other sensors, along with other studies targeted on specific targets such as dense forests, organic rich soils, or frozen and snow-covered grounds. Finally, it may be noted that most of these approaches deliver not only the surface soil moisture but also other quantities of interest such as vegetation optical depth, surface roughness, and surface dielectric constant. The goal of this article is to give an overview of these different approaches and corresponding results and adequate references for those wishing to go further. Sea surface salinity is not covered in this article, while the focus is SMOS data.

To ensure sustainable agriculture production, the availability of water in the right quantity and at the right time is critical, with extremes in availability resulting in severe impacts on the agricultural sector. Delivery of timely and accurate soil moisture data can play a vital role in monitoring the status of available water reserves for this sector. Passive microwave sensors, such as the Soil Moisture and Ocean Salinity (SMOS), are well suited for monitoring vast landscapes given their all-weather capabilities, large spatial footprint, frequent revisit, and the sensitivity of microwave emissions to the soil dielectric. This study examines the impact of exploiting Canadian soil and land cover datasets in the retrieval of soil moisture from SMOS over an agricultural area in the province of Manitoba (Canada). Results demonstrate that global datasets that are integrated within the current SMOS processor perform adequately when field measured soil moisture is compared to estimates of soil moisture by SMOS ( ${{\rm R}^2}$ of 0.70 ( ${\rm p} ) and root-mean-square error (RMSE) of 7.15% with a negative (dry) bias of $-0.05\%$ ). Overall, this study showed that ingesting high-quality national datasets into the SMOS soil moisture retrieval algorithm did not fully resolve the underestimation of soil moisture, suggesting that further investigation is required to understand this bias. Also, several approaches were evaluated to improve statistical field-derived soil moisture representation in the validation of SMOS soil moisture retrieval and it is clear that good representation of soil moisture as a function of soil textures is crucial to accurately validate SMOS soil moisture products.

The landscape freeze/thaw (F/T) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and soil moisture and ocean salinity (SMOS) L-band passive microwave measurements using a polarization ratio (PR)-based algorithm for landscape F/T monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: 1) tundra; 2) boreal forest; and 3) prairies. The reference data include in situ measurements of soil temperature ( ${\mathbf {T}}_{\mathbf{soil}}$ ) and air temperature ( ${\mathbf {T}}_{\mathbf{air}}$ ), and moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between L-band F/T detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using ${\mathbf{T}}_{\mathbf{air}}$ as the reference. Aquarius radiometer 2 (incidence angle of 39.6°) data give the best accuracies (90.8%), while for SMOS, using the Aquarius temporal characteristics, the best results (87.8% of accuracy) are obtained at higher incidence angles ( ${\mathbf{55}}^\circ\!\!-\!\!{\mathbf{60}}^\circ $ ). The F/T algorithm identifies both freeze onset and end with a delay of about 1 week in tundra and 2 weeks in forest and prairies, when compared to ${\mathbf{T}}_{\mathbf{air}}$ . The analysis shows a stronger F/T signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the PR) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar F/T patterns over Canada. This study shows the potential of using L-band spaceborne observations for F/T monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth.

A single-frequency dielectric model for thawed and frozen Arctic organic-rich (80%–90% organic matter) soil was developed. The model is based on soil dielectric data that were measured over the ranges of volumetric moisture from 0.007 to ${0.573}\;\text{cm}^{{3}}/\text{cm}^{{3}}$ , dry soil density from 0.564 to $0.666\;\text{g/cm}^{3}$ , and temperature from 25 °C to $-\mathbf{30}^{\circ}\mathbf{C}$ (cooling run), at the frequency of 1.4 GHz. The refractive mixing model was applied to fit the measurements of the soil’s complex refractive index (CRI) as a function of soil moisture, with the values of temperature being fixed. Using the results of this fitting, the parameters of the refractive mixing model were derived as a function of temperature. These parameters involve the CRIs of soil solids as well as bound, transient, and free soil water components. The error of the dielectric model was evaluated by correlating the predicted complex relative permittivity (CRP) values of the soil samples with the measured ones. The coefficient of determination ( $\mathbf{R}^{\mathbf{2}}$ ) and the root-mean-square error (RMSE) were estimated to be $\mathbf{R}^{\mathbf{2}}= \mathbf{0.999}$ , $\mathbf{RMSE} = \mathbf{0.27}$ and $\mathbf{R}^{\mathbf{2}}= \mathbf{0.993}$ , $\mathbf{RMSE} = \mathbf{0.18}$ for the real and imaginary parts of the CRP, respectively. These values are in the order of the dielectric measurement error itself. The proposed dielectric model can be applied in active and passive remote-sensing techniques used in the areas with organic-rich soil covers, mainly for the SMOS, SMAP, and Aquarius missions.

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 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) 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.

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Donnees SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the va riable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

In land surface or numerical weather prediction (NWP) models, a soil moisture initialization scheme is important not to drift the prognostic variables to errors. We propose a novel approach for a stationary data assimilation scheme of ensemble optimal interpolation (EnOI) effective for soil moisture and ocean salinity (SMOS) soil moisture initialization. For the optimization of EnOI, the satellite retrieval error specification was conducted rather than ensemble evolution. As combining two ensembles generated from a satellite retrieval and a land surface model, this approach is termed as “two-step EnOI” in this study: (first step) the SMOS soil moisture retrieval ensembles (i.e., errors in brightness temperature, landscape, and geophysical parameters) were merged with SMOS L3 data; (second step) the data assimilation result from the first step was further used for the observations of the EnOI. This two-step EnOI was compared with a sequential ensemble Kalman filter (EnKF) evolving model state ensembles over time but assuming global constant a priori random errors for the SMOS observations. The point-scale comparison results showed that two-step EnOI was better matched with the field measurements than the SMOS L3 data and a sequential ensemble KF scheme. On meso-scale, a spatial average of two-step EnOI reached that of a sequential ensemble KF with the significantly reduced ensemble size. These results suggest that the performance of two-step EnOI is comparable to a sequential ensemble KF but computationally more effective. From this, it is illustrated that appropriate error specification of satellite retrieval is more important than a sequential evolution of model state ensembles, and brightness temperature ensemble mean can reduce the SMOS retrieval biases without sequential evolution.

International audience; Artificial sources emitting in the protected part of the L-band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. Detecting and pinpointing such sources is crucial for the improvement of SMOS science products as well as for the identification of the emitters. In this contribution, we present a method to obtain snapshot-wise information about sources of radio-frequency interference (RFI). The localization accuracy of this method is also assessed for observed RFI sources. We also show that RFI localizations constitute a useful data set for assessing the pointing performance of the satellite, and present how it is possible, using the results of this method, to identify and estimate two systematic errors in the geo-location of the satellite field of view. The potential causes and the approaches to mitigate both these errors are discussed.

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.

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.

International audience; The Soil Moisture and Ocean Salinity mission has been collecting data for over 7 years. The whole data set has been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 7 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

International audience; This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First, to compare and provide feedback to the operational algorithm. Second, to evaluate this approach in the context of pre-launch algorithm development. The performance of the L-MEB NN dataset was evaluated by comparing with time series of in situ measurements in North America. The correlation, standard deviation and bias of NN SM and in situ SM are similar to those obtained with the SMOS L3 SM product and with ECMWF models. The L-MEB NN dataset was also compared globally to the SMOS Level 3 SM and SM from ECMWF models. The L-MEB NN dataset is in general wetter than SMOS L3 SM, closer to ECMWF models. Some possible reasons are briefly discussed.

International audience; This study presents a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product based on a different scattering albedo and soil roughness parameterization: the SMOS-IC (SMOS INRA-CESBIO) data set. In this study, several parameterizations of the vegetation and soil roughness parameters (co, H-R and N-RP, P = H, V) were tested and the retrieved SM was compared against in situ observations obtained from the International Soil Moisture Network (ISMN). Firstly, values of omega = 0.10, H-R = 0.4 and N-RP = -1 (P = H, V) were found globally. Secondly, a calibration of these parameters was obtained for the different land cover categories of the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of co and HR varied, respectively, in the ranges 0.08 - 0.12 and 0.1 - 0.5. The IGBP-based calibration is currently used in the SMOS-IC product algorithm. Using as reference the ISMN sites, a better performance of the SMOS-IC product over the operational SMOSL3 (SMOS level 3) SM product was found: R = 0.62, bias = -0.019 m3/m3, ubRMSE = 0.061 m3/m3 for SMOS-IC; against R = 0.54, bias = -0.037 m3/m3 and ubRMSE = 0.069 m3/m3 for SMOSL3.

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0–5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Donnees SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2–3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

International audience; In this study, we evaluated a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product, developed by the collaboration of INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphere), against the operational SMOS level 3 SM product (SMOSL3). This new product (hereinafter referred to as SMOS-INRA-CESBIO, i.e. SMOSIC in short) differs from SMOSL3 three ways: (i) the SMOSIC algorithm considers the pixel as homogeneous and does not take into account the heterogeneity of the pixel; (ii) uses a new calibration of the effective scattering albedo and soil roughness parameters; (iii) no time correlation is applied on the optical depth. The evaluation was done over North America using the (European Center for Medium range Weather Forecasting) ECMWF SM simulation as a reference, using data for 2011. A better performance of the SMOSIC SM product with respect to ECMWF was found: (i) SMOSIC had higher correlation coefficients (temporal dynamics) and lower unbiased RMSD (absolute values) values with ECMWF over most of the study area and (ii) the spatial patterns of the SMOSIC temporal mean SM maps were in a better agreement with ECMWF.

An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0–5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NN inSitu ) is evaluated against in situ data not used during the training phase and against maps of the SMOS level 3 SM product and ECMWF SM models. NN inSitu SM values are closer to ECMWF values for wet areas. A method to use NNs as a tool to classify in situ sites representative of the remote sensing observations scale is briefly discussed.

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 soil moisture information in near-real-time (NRT), typically not later than 3 hours after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure soil moisture 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 30º to 45º. 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 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 shows a standard deviation of the difference with respect to the L2 data of

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.

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze–thaw state every 2–3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

This letter aims to estimate the effective scattering albedo ( $\omega _{p}$ ) over the tropical forests using L-band (1.4 GHz) microwave remote sensing. It is carried out using Soil Moisture and Ocean Salinity (SMOS) mission data over five years (2011–2015). We find similar values of $\omega _{p}$ computed over the Congo and Amazon forests. The $\omega _{p }$ values depend slightly on the polarization. The values of $\omega _{p }$ at H-polarization and at 52° ± 5° (40° ± 5°) of incidence angle are within the range 0.064 – 0.069 ± 0.01 (0.061 – 0.067 ± 0.012). At V-polarization, the values of $\omega _{p }$ are slightly lower (0.060 – 0.061 ± 0.013 at 52° ± 5° of incidence angle and 0.052 – 0.055 ± 0.013 at 40° ± 5° of incidence angle). These findings should contribute to a better calibration of the value of $\omega _{p }$ over the tropical forests in both the SMOS and SM active and passive retrieval algorithms, leading to increase the SM retrieval accuracy over heterogeneous pixels.

International audience; For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (τnad) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB), soil roughness is modelled with a semi-empirical equation using four main parameters (Qr, Hr, Nrp, with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of Nrp and Hr on the SM and τnad retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011–2015). In this study, Qr was set equal to zero and we assumed that NrH = NrV. The retrievals were performed by varying Nrp from −1 to 2 by steps of 1 and Hr from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA, ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that need improvement, and how the instrument could be enhanced if we were to build it again. This paper presents the lessons learnt from SMOS after 7 years in orbit

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

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

The Soil Moisture and Ocean Salinity (SMOS) satellite was launched by the European Space Agency on November 2, 2009. Its payload, i.e., Microwave Imaging Radiometer with Aperture Synthesis, which is a 2-D L-band interferometric radiometer, measures the brightness temperatures (BTs) in the protected 1400–1427-MHz band. Although this band was preserved for passive measurements, numerous radio frequency interferences (RFIs) are clearly visible in SMOS data. One method to get rid of these interferences is to create a synthetic signal as close as possible to the measured interference and subtract it from the instrument visibilities. In this paper, we describe an approach to create such a signal and on how to use it for geolocalization of the emitters. Then, different methods for assessing the quality of the mitigation are introduced. Due to the complexity of estimating the effects of mitigation globally, it is finally proposed to use mitigation results to create flag maps about the estimated RFI impact, to be associated with BT measurements.

Summary Since soil moisture is an important influencing factor of the hydrological cycle, knowledge of its spatio-temporal dynamics is crucial for climate and hydrological modeling. In recent years several soil moisture data products from satellite information have become available with global coverage and sub-monthly resolution. Since the remote sensing of soil moisture is an indirect measurement method and influenced by a large number of factors (e.g. atmospheric correction, vegetation, soil roughness, etc.), a comprehensive validation of the resulting soil moisture products is required. However, the coarse spatial resolution of these products hampers the comparison with point-scale in situ measurements. Therefore, upscaling of in situ to the scale of the satellite data is needed. We present the validation results of the soil moisture products of the years 2010–2012 retrieved from the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Scatterometer (ASCAT) for the Rur and Erft catchments in western Germany. For the upscaling of in situ data obtained from three test sites of the Terrestrial Environmental Observatories (TERENO) initiative we used the hydrological model WaSiM ETH. Correlation of the SMOS product to modeled and upscaled soil moisture resulted in a mean correlation coefficient of 0.28 whereas for ASCAT a correlation coefficient of 0.50 was obtained. However, for specific regions the SMOS product showed similar correlation coefficients as the ASCAT product. While for ASCAT correlation was mainly dependent on topography and vegetation, SMOS was also influenced by radiofrequency interferences in our study area. Both products show dry biases as compared to the soil moisture reference. However, while SMOS showed relatively constant bias values, ASCAT bias is variable throughout the year. As an additional validation method we performed a temporal stability analysis of the retrieved spatio-temporal soil moisture data. Through investigation of mean relative differences of soil moisture for every pixel, their standard deviations and their rankings, we analyzed the temporal persistence of spatial patterns. Our results show high standard deviations for both SMOS and ASCAT soil moisture products as compared to modeled soil moisture, indicating a lower temporal persistence. The consistence of ranks of mean relative differences was low for SMOS and relative ASCAT soil moisture compared to modeled soil moisture, while ASCAT soil moisture, converted to absolute values, showed higher rank consistence.

The launch of the Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009 marked a milestone in remote sensing for it was the first time a radiometer capable of acquiring wide field of view images at every single snapshot, a unique feature of the synthetic aperture technique, made it to space. The technology behind such an achievement was developed, thanks to the effort of a community of researchers and engineers in different groups around the world. It was only because of their joint work that SMOS finally became a reality. The fact that the European Space Agency, together with CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnologico e Industrial), managed to get the project through should be considered a merit and a reward for that entire community. This paper is an invited historical review that, within a very limited number of pages, tries to provide insight into some of the developments which, one way or another, are imprinted in the name of SMOS.

This paper focuses on the sensitivity of L-band multi-parameter retrievals across the range of angular measurements available from the SMOS (Soil Moisture and Ocean Salinity) mission. The SMOS core algorithm was used to evaluate two-parameter retrieval scenarios including soil moisture and one of either i) vegetation water content, ii) surface roughness, iii) vegetation temperature, or iv) surface soil temperature. For all pairs a range of parameter value combinations were compiled to run the model in forward mode. Subsequently, the resulting angular brightness temperature simulations with two unknown parameters were compared against the brightness temperature response derived from reference simulations using data from the National Airborne Field Experiment 2005 (NAFE'05) in Australia. This paper showed that the two-parameter retrieval accuracy of soil moisture is strongly affected by the surface moisture conditions, the polarization of the brightness temperature data, and the choice of the secondary ancillary parameter to be retrieved. The synthetic analysis demonstrated a tendency for better retrievals from dual-polarized data at large incidence angles (40–50°). Validation with airborne brightness temperature observations at L-band did not demonstrate such a strong angular dependency, although it confirmed that the simultaneous retrieval of soil moisture and vegetation properties is not preferable as opposed to i) soil moisture and surface roughness or ii) soil moisture and surface soil temperature, especially under dry moisture conditions.

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact.

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.

The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the growing season and decrease during senescence in these areas, as did MODIS vegetation indices. SMOS' tau values generally peaked later than MODES LAI values, with an estimated time difference of about 19 days. A linear regression between tau and the MODIS products was carried out for each node and values of the determination coefficient, R-2, slope, b' and intercept, b '' were found. The average R-2 value varied from 0.32 to 035 for the different vegetation indices. The linear regression between LAI and tau produced an average slope of b' = 0.06, and an average intercept of b '' = 0.14. The effects of crop fraction and dominant crop type were investigated and crop fraction was found to have a low effect on R-2 values. R-2 values appeared to be lower for wheat and hay and higher for corn. b' and b '' values had higher standard deviations for wheat but were generally close to the mean values for corn, soybean and hay. (C) 2013 Published by Elsevier Inc. (Less)

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.

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.

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon’s rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

In this paper, a numerical modeling approach was used to evaluate the semiempirical H-Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H-Q model uses four parameters, HR, QR, NRH , and NRV, which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights σ and autocorrelation length Lc. First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H -Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H -Q model. It was found that the retrieved HR could be related directly to Zs = σ2/Lc and that QR, NRV, and NRH were dependent on HR. HR was found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H-Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of ~ 0.03 m3/m3, which was within the desired 0.04-m3/m3 error margin for the SMOS mission.

In this paper, we derive an expression of the brightness temperatures of a vegetated surface based on the tau-omega model with the rough surface boundary condition that replaces the conventional exp(-h) model by NMM3D (Numerical Simulations of 3D Maxwell equations). A purpose of the paper is that the same physical rough surface scattering model based on NMM3D and the same physical parameters of rms heights and correlation lengths can be used for both passive and active remote sensing of the same scene of vegetated surfaces. The bistatic scattering of rough surfaces are decomposed into the coherent wave and the co-polarization and the cross-polarization of the incoherent waves to quantify the contribution of each of these components. Numerical results are illustrated for a variety of roughness conditions. Comparisons are made with the exp(-h) model. Results are compared with the experimental passive measurements of PORTOS 1993 for bare soil cases. Data cubes for grassland are calculated for both active and passive signatures at L band. Comparisons are then made with the L band PALS data for the grassland of SGP99 using these data cubes.

The Soil Moisture and Ocean Salinity (SMOS) satellite with a passive L-band radiometer monitors surface soil moisture. In addition to soil moisture, vegetation optical thickness $\tau_{\rm NAD}$ is retrieved (L2 product) from brightness temperatures ( $T_{B}$ , L1C product) using an algorithm based on the L-band Microwave Emission of the Biosphere (L-MEB) model with initial guesses on the two parameters (derived from ECMWF products and ECOCLIMAP Leaf Area Index, respectively) and other auxiliary input. This paper presents the validation work carried out in the Skjern River Catchment, Denmark. L1C/L2 data and the most sensitive algorithm parameters were analyzed by network and airborne campaign data collected within one SMOS pixel (44 km diameter). The SMOS retrieval is based on the prevailing low vegetation class. For the L1C comparison, $T_{B}$ 's were calculated from in situ soil moisture using L-MEB. Consistent with worldwide findings, the initial/retrieved SMOS soil moisture captures the in situ dynamics well but with significant wet/dry biases and too large amplitudes in case of the latter. While the initial $\tau_{\rm NAD}$ is in range with an in situ estimate for low agricultural vegetation, the retrieved $\tau_{\rm NAD}$ is too high with too pronounced temporal variability. A filter based on L2 criteria removed radio frequency interference (RFI) and improved the $R^{2}$ between retrieved and network soil moisture from 0.49 to 0.61, while the bias remained $(-0.092/-\!0.087\ \hbox{m}^{3}/\hbox{m}^{3})$ . Likely error sources include the following: 1) still present RFI; 2) potential link between high retrieved $\tau_{\rm NAD}$ and other L-MEB parameters, e.g., low roughness parameter $(H_{R})$ ; 3) $\sim$ 18% lower sand and $\sim$ 8% higher clay fractions while $\sim\!\!0.35\ \hbox{g/cm}^{3}$ lower bulk density in SMOS algorithm than in situ; and 4) caveats in the Dobson dielectric mixing model implemented in the L-MEB model. A previous study at the Danish validation site had revealed superior performance of the Mironov dielectric mixing model at the 2 $\times$ 2 km scale. Studies are ongoing to address the aforementioned issues, and the role of organic surface layers will be investigated.

International audience; This study focuses on the calibration of the effective scattering albedo (ω) of vegetation in the soil moisture (SM) retrieval at L-Band. Currently, in the SMOS Level 2 and 3 algorithms, the value of ω is set to 0 for low vegetation and ∼ 0.06 – 0.08 for forests. Different parameterizations of vegetation (in terms of ω values) were tested in this study. The possibility of combining soil roughness and vegetation contributions as a single parameter (“combined” method) leads to an important simplification in the algorithm and was also evaluated here. Following these assumptions, retrieved values of SMOS SM were compared with SM data measured over many in situ sites worldwide from the International Soil Moisture Network. These validation sites were classified using the International Geosphere-Biosphere Programme (IGBP) classification scheme. In situ SM measurements and SM retrievals were compared, and statistical scores were computed. The optimum albedo configuration was then found for each class of the IGBP landcover classification. Preliminary results yield values of albedo between 0.07 to 0.12 under the assumption of homogeneous pixels.

International audience; The Soil Moisture and Ocean Salinity mission has been collecting data for 6 years. The whole data set has just been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS). This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 6 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events.

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.

Verifying the calibration of the SMAP radiometer over land observations is an important mission requirement. Inter-comparison of L-band brightness temperature observations from different satellites (SMAP, SMOS and Aquarius) is a useful tool for radiometer calibration. Brightness temperatures observations made at the same frequency, polarization, incidence angle and coincident in time and location should be consistent with each other. SMAP brightness temperature observations were compared with SMOS observations at 40o incidence angle. The observations from the two satellites were found to be consistent with each other over the entire dynamic range (both ocean and land). The RMSD between the two missions was less than 3 K. The two observations exhibit a strong linear relationship and the observed bias was less than 0.5 K for both polarizations. This bias is within the required target accuracy requirement of the SMAP radiometer (requirement of 1.3 K).