Soil Moisture And Ocean Salinity TB Research Articles

Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.

Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

SMOS brightness temperature forward modelling and long term monitoring at ECMWF

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

A comparison of SMAP and SMOS L-band brightness temperature observations over the global landmass

ABSTRACT This study compared the consistency of brightness temperature (Tb) measurements over the global landmass as observed by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) radiometers. We evaluated the consistency of horizontally and vertically polarized (H-Pol and V-Pol) SMAP and SMOS Tb data via the Pearson correlation coefficient (r), the bias (B, SMOS minus SMAP), and the Hovmöller diagram analysis. The r and B results were displayed and analyzed through the global land covers and Koppen climates. The results indicate a strong consistency of SMAP and SMOS Tb data for the r (mostly > 0.8) and B (mostly varying from −5 to 5 K) metrics. For different land covers and climates, relatively low r values (< 0.8) were found for the continental climates, deciduous needleleaf forests, mixed forests, and barren/sparsely vegetated land covers. The SMOS Tb gave warmer values with respect to the SMAP Tb over the global landmass at the dual polarizations. But the B values are larger at the V-Pol (global average is 0.70 K) than at the H-Pol (global average is 0.16 K). Only the croplands yielded significantly positive B values at the H-Pol. The largest discrepancies of SMAP and SMOS Tb data were observed in Eurasia and area of eastern North Africa and we speculate that the RFI (Radio Frequency Interference) contamination from the SMOS is a reason that may explain this phenomenon. We suggest users pay careful attention to the RFI contamination when using SMOS Tb in applications. As revealed by the Hovmöller diagrams, SMAP and SMOS Tb data all resolved the same seasonally dynamic changes for both dual polarizations. This study is helpful to construct a long-term and reliable Tb product by combining SMAP and SMOS Tb data to retrieve soil moisture information.

Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System

The assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB) data in numerical weather prediction systems influences the state of the soil, which in turn affects the exchange of energy and water fluxes between the soil and the near‐surface atmosphere, with potential implications for the prediction of atmospheric variables. In this paper, the impact of assimilating SMOS TB alone or in combination with screen‐level observations and Advanced Scatterometer (ASCAT) soil moisture retrievals is assessed. Independent quality controlled insitu soil moisture observations belonging to several networks included in the International Soil Moisture Network, were used to validate the quality of both the new soil moisture analyses and the skill to predict soil moisture up to 5 days ahead. The impact on atmospheric variables is indirect and was evaluated through computation of the forecast skill at different lead times. The analysis period was selected to be around the boreal summer, a period of the year when evaporatranspiration fluxes are stronger, and when it is therefore expected that the assimilation of remote‐sensing data provides the largest impact on the state of the soil. The results show that the soil moisture state benefits from the direct assimilation of SMOS TB, especially in better representing the temporal variations of soil moisture. The skill of atmospheric variables is mainly driven by the screen‐level observations. Despite the clear benefits to the soil state, remote‐sensing data need to be used with screen‐level variables to add value to the state of the atmosphere, pointing to inconsistencies in the physical coupling between the land and near‐surface components of the ECMWF Earth system.

Land Contamination Analysis of SMOS Brightness Temperature Error Near Coastal Areas

For Soil Moisture And Ocean Salinity (SMOS) data, significant errors exist near coastal areas because of the contamination by the nearby land. For analyzing the origin of the land contamination, in this letter, it is found, from the global map of SMOS data error, that although the SMOS data error due to land contamination near coasts varies in different regions, the characteristic that the error increases significantly in proximity to land is similar in global scope. The detailed analysis of SMOS brightness temperature (TB) error in the selected area reveals a decreasing trend with the increase of the distance to coast (DC). An SMOS TB measurement model according to geophysical parameters of the selected area and SMOS/microwave imaging radiometer aperture synthesis antenna array is established to analyze the origin of the TB error variation due to land contamination. The simulation results, which agree with the SMOS TB data analysis, show that within 40 km of DC, TB error is large and decreases sharply from ~60 to ~4 K with the increase of DC, since the mainlobe of the antenna array is departing from land to ocean; during 40–400 km of DC, TB error decreases smoothly from ~4 to ~0.15 K, since the lower near sidelobes are observing and leaving land; TB error slightly decreases to 0 from 400 to 800 km when the lower far sidelobes leave land. The study of the origin of land contamination on ocean TB will be helpful for improving the observation of salinity near coastal areas.

Considering combined or separated roughness and vegetation effects in soil moisture retrievals

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 (43km 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.

Roughness and foam signature on SMOS-MIRAS brightness temperatures: A semi-theoretical approach

Abstract The influence of wind speed (WS) on sea surface emission at L-band is revisited using the updated version v6.2 of Soil Moisture and Ocean Salinity (SMOS) brightness temperatures (TB) and an incoherent two-layer foam emissivity model. The influence of the roughness effect due to surface waves is consistent with the one found with an older version of SMOS TB. The two-layer incoherent model of foam emissivity accounts for weak volume scatterings and multiple reflections within a medium with an exponential vertical permittivity profile. The foam emissivity simulated using this model at L band varies from 0.35 to close to 1 with thickness varying from 0.01 cm to 2 cm. The wind induced brightness temperature components deduced from the multi-angular SMOS TB is used to optimize the foam void fraction (defined as the fraction of a unit volume of seawater that is occupied by air) at the air–sea interface, an effective thickness of foam layer and the dependency of foam coverage with wind speed. A new set of parameters for the foam emissivity model and the foam coverage model that can be used for WS up to 22 m s − 1 for the SMOS sea surface salinity retrieval is proposed. Our foam coverage model derived from SMOS data is now in much better agreement with other estimates derived from other sensors, although it predicts slightly lower coverage at all winds speeds, likely due to the longer wavelength of SMOS measurements.

Assimilation of SMOS soil moisture and brightness temperature products into a land surface model

Abstract The Soil Moisture and Ocean Salinity (SMOS) mission has the potential to improve the predictive skill of land surface models through the assimilation of its observations. Several alternate products can be distinguished: the observed brightness temperature (TB) data at coarse scale, indirect estimates of soil moisture (SM) through the inversion of the coarse-scale TB observations, and fine-scale soil moisture through the a priori downscaling of coarse-scale soil moisture. The SMOS TB products include observations over a large range of incidence angles at both H- and V-polarizations, which allows the merit of assimilating the full set of multi-angular/polarization observations, as opposed to specific sub-sets of observations, to be assessed. This study investigates the performance of various observation scenarios with respect to soil moisture and streamflow predictions in the Murray Darling Basin. The observations are assimilated into the Variable Infiltration Capacity (VIC) model, coupled to the Community Microwave Emission Modeling (CMEM) platform, using the Ensemble Kalman filter. The assimilation of these various observation products is assessed under similar realistic assimilation settings, without optimization, and validated by comparison of the modeled soil moisture and streamflow to in situ measurements across the basin. The best results are achieved from assimilation of the coarse-scale SM observations. The reduced improvement using downscaled SM is probably due to a lower number of observations, as a result of cloud cover effects on the downscaling method. The assimilation of TB was found to be a promising alternative, which led to improvements in soil moisture prediction approaching those of the coarse-scale SM assimilation.

Soil Moisture Retrieval Using Neural Networks: Application to SMOS

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures, $T_{b} $ 's) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using $T_{b} $ 's measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

Converting Between SMOS and SMAP Level-1 Brightness Temperature Observations Over Nonfrozen Land

The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.

New sea surface salinity retrieval methods based on SMOS data

The quality of brightness temperatures (TBs) provided by the Soil Moisture and Ocean Salinity (SMOS) satellite is assessed and validated by comparing them with simulated TBs. TB simulations are computed using the default transfer model implemented by the European Space Agency (ESA) level 2 ocean salinity processor, with auxiliary data taken from The European Centre for Medium Range Weather Forecasts (ECMWF). The ascending SMOS data of the western Pacific Ocean region (0 N–30 N, 120 E–150 E) in July and August 2012 are analysed, and biases of several kelvin are observed between the averaged SMOS TB and simulated TB, which strongly affect the accuracy of sea surface salinity (SSS) retrieval. Two methods are proposed in this article to deal with the biases. The first is that the biases are corrected, and the full range of SMOS multiangular TB is used for retrieval. The second is that the biases are not corrected, and only TB at angles of incidence in the range of 35–55 are used. Then all measurements of a given Stokes parameter in a pixel are regressed with respect to angles of incidence using a second-order polynomial fit in order to reduce noise in the TB. Finally, a least squares iterative process is used to retrieve SSS using the fitted TB. The accuracy of the retrieved SSS using the above two methods is estimated by comparing them with the Array for Real-Time Geostrophic Oceanography SSS and the ESA level 2 SSS.

Can SMOS Data be Used Directly on the 15-km Discrete Global Grid?

Radiometric observations from the Soil Moisture and Ocean Salinity (SMOS) mission are processed to Level 1 brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ) with ~ 42-km spatial resolution and reported on a 15-km hexagonal Discrete Global Grid (DGG). While these data should be used at the 42-km resolution which the oversampled DGG represents, this paper poses the question of whether they can be used directly at 15-km resolution without undertaking downscaling or implementing multiscale-type procedures when used in data assimilation. To assess the error associated with using the 42-km SMOS T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> data at 15-km resolution, this study employs 1-km Tb data from the Australian Airborne Cal/Val Experiment for SMOS (AACES). The study compares SMOS-like data derived from AACES at 42-km resolution with T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> values actually observed on the 15-km DGG. These 15-km DGG data are subsequently interpolated to a regular 12-km model grid and compared with actual observations at that resolution. The results show that the average root mean square differences in T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> between the 15- and 42-km footprints are 4.5 K and 3.9 K for horizontal (H) and vertical (V) polarizations, respectively, with a maximum difference of 12.9 K. The errors when interpolating the 42-km data onto the 12-km model grid were estimated to be 3.3 K for H polarization and 2.9 K for V polarization under the assumption of independence or 4.5 K and 3.9 K for H and V polarizations, respectively, with 4.0 K in H polarization and 3.6 K in V polarization from the 15- to 12-km interpolation process alone. An evaluation of the T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> differences for 42-km data assumed on the 15-km DGG found no correlation with vegetation based on leaf area index and only slight correlation with the spatial variance of SMOS data and topographic roughness. Given these differences and the noise that currently exists in SMOS T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> at 42 km, the 15-km DGG data can be used directly on the hexagonal grid or interpolated onto a regular grid of equivalent spatial resolution without further degrading the data quality.

Comparing soil moisture retrievals from SMOS and ASCAT over France

Abstract. The first products derived over France in 2010 from the L-band brightness temperatures (Tb) measured by the SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, were compared with the surface soil moisture (SSM) estimates produced by the C-band Advanced Scatterometer, ASCAT, launched in 2006 on board METOP-A. SMOS and ASCAT SSM products were compared with the simulations of the ISBA-A-gs model and with in situ measurements from the SMOSMANIA network, including 21 stations located in southern France. ASCAT tended to correlate better than SMOS with ISBA-A-gs. The significant anomaly correlation coefficients between in situ observations and the SMOS (ASCAT) product ranged from 0.23 to 0.48 (0.35 to 0.96). However, in wet conditions, similar results between the two satellite products were found. An attempt was made to derive SSM from regressed empirical logarithmic equations using a combination of SMOS Tb at different incidence angles and different polarizations, and the Leaf Area Index (LAI) modeled by ISBA-A-gs. The analysis of the intercept coefficient of the regression showed an impact of topography. A similar analysis applied to ASCAT and SMOS SSM values showed a more limited impact of topography on the intercept coefficient of the SMOS SSM product, while fewer residual geographic patterns were found for the ASCAT SSM.