Soil Moisture And Ocean Salinity Retrievals Research Articles

A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison

Passive microwave remote sensing at L-band (1.4 GHz) provides an unprecedented opportunity to estimate global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD), which are essential to monitor the Earth water and carbon cycles. Currently, only two space-borne L-band radiometer missions are operating: the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions in orbit since 2009 and 2015, respectively. This study presents a new mono-angle retrieval algorithm (called SMAP-INRAE-BORDEAUX, hereafter SMAP-IB) of SM and L-band VOD (L-VOD) from the dual-channel SMAP radiometric observations. The retrievals are based on the L-MEB (L-band Microwave Emission of the Biosphere) model which is the forward model of SMOS-IC and of the official SMOS retrieval algorithms. The SMAP-IB product aims at providing good performances for both SM and L-VOD while remaining independent of auxiliary data: neither modelled SM data nor optical vegetation indices are used as input in the algorithm. Inter-comparison with other SM and L-VOD products (i.e., MT-DCA, SMOS-IC, and the new versions of DCA and SCA-V extracted from SMAP passive Level 3 product) suggested that SMAP-IB performed well for both SM and L-VOD. In particular, SMAP-IB SM retrievals presented the higher scores (R = 0.74) in capturing the temporal trends of in-situ observations from ISMN (International Soil Moisture Network) during April 2015–March 2019, followed by MT-DCA (R = 0.71). While the lowest ubRMSD value was obtained by the new version of SMAP DCA (0.056 m3/m3), SMAP-IB SM retrievals presented best scores for R, ubRMSD (~ 0.058 m3/m3) and bias (0.002 m3/m3) when considering only products independent of optical vegetation indices (e.g., NDVI). L-VOD retrievals from SMAP-IB, MT-DCA, and SMOS-IC were well correlated (spatially) with aboveground biomass and tree height, with spatial R values of ~0.88 and ~ 0.90, respectively. All three L-VOD products exhibited a smooth non-linear density distribution with biomass and a good linear relationship with tree height, especially at high biomass levels, while the L-VOD datasets incorporating optical information in the algorithms (i.e., SCA-V and DCA) showed obvious saturation effects. It is expected that this new algorithm can facilitate the fusion of both SM and L-VOD retrievals from SMOS and SMAP to obtain long-term and continuous L-band earth observation products.

Assessment of SMOS and SMAP soil moisture products against new estimates combining physical model, a statistical model, and in-situ observations: A case study over the Huai River Basin, China

Soil moisture often governs the exchange of water between the land surface and the atmosphere and, as a result, has a profound effect on the global water and energy cycles. With the launch of the two new L-band satellite missions tasked with retrieving surface soil moisture (i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active/Passive (SMAP) missions), new possibilities exist for quasi-global soil moisture monitoring. Here, new soil moisture estimates (CLDAS-BPNN), generated by fusing a land surface model soil moisture product (CLDAS) using in-situ observations via a Back Propagation Neural Network (BPNN) method, are used to evaluate four satellite-derived soil moisture products (SMOS-L3, SMOS-IC, SMAP_L3_SM_P, and SMAP_L3_SM_P_E) over the highly irrigated Huai River Basin in China. Results indicate that the post-processing of CLDAS (to generate CLDAS-BPNN) reduces soil moisture errors (particularly bias) and preserves temporal correlations with in-situ observations. Due to extensive irrigation present in the basin, CLDAS-BPNN soil moisture time series have weak seasonal differences and the spatial distribution of their means does not reflect known precipitation patterns. Based on the validation of SMOS and SMAP soil moisture retrievals against CLDAS-BPNN, several key findings can be obtained. First, SMAP retrievals are recommended for locations possessing both valid SMOS and SMAP retrievals. However, SMOS provides generally better spatial support than SMAP. Second, the four satellite retrievals present no significant seasonal variation in time-varying errors, and larger soil moisture underestimation is observed in winter than in summer. Finally, all four satellite products exhibit degraded accuracy in mountainous and forested areas of the Huai River Basin relative to flatter terrain in the Huaibei plain.

Retrieving global surface soil moisture from GRACE satellite gravity data

Passive microwave radiometry from space through missions such as the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites is the most reliable means for mapping global surface soil moisture (SSM). Nonetheless, microwave SSM retrievals are uncertain over densely vegetated surfaces or areas with high radio frequency interference. This paper presents a new observationally driven approach to remote sensing of global SSM based on the terrestrial water storage anomaly (TWSA) data acquired from the Gravity Recovery and Climate Experiment (GRACE) satellite. This approach rests on a physically based, yet parsimonious, model based on the Richards’ equation and the assumption that the TWSA temporal rate of change (dS/dt) approximates the land surface net water flux (NWF) as the surface boundary condition. The GRACE-based SSM is found to be in a reasonable agreement with in-situ data and highly correlated with the SMAP and SMOS retrievals, especially over wet regions where the assumption of NWF ≈ dS/dt holds valid. The GRACE retrievals contain new SSM information relative to the microwave satellite data and provide a potential solution to improve the microwave data over densely vegetated surfaces or areas with high radio frequency interference.

Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models

Since 2010, SMOS (Soil Moisture and Ocean Salinity) retrievals of surface soil moisture (SM) and vegetation optical depth (VOD) have been produced through the inversion of the so-called Tau-Omega (TO) vegetation emission model. Tau-Omega is a 0th-order solution of the radiative transfer equations that neglects multiple scattering, conversely to 1st-order solutions as Two-Stream (2S). To date, very little is known about the compared retrieval performances of these emission models. Here, we inter-compared (SM, VOD) retrievals using the SMOS-IC algorithm running with the TO and 2S emission models. Retrieval performances obtained from TO and 2S were found to be relatively similar, except that a larger dry bias and a slightly lower SM unbiased RMSD were obtained with 2S and the VOD values of the two models vary over dense vegetation areas, both in terms of magnitude and seasonal variations. Considering this and the enhanced physical background of 2S that allows its implementation as a unified emission model for different applications, our study reveals the high interest of using Two-Stream in global retrieval algorithms at L-band.

Impact of temporal variations in vegetation optical depth and vegetation temperature on L-band passive soil moisture retrievals over a tropical forest using in-situ information

ABSTRACTThe Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide estimates of soil moisture (SM) at similar spatial resolutions using L-band brightness temperatures (). These missions meet the requirement of SM retrievals with an unbiased root-mean-square difference (ubRMSD) 0.04 (m m−3) compared to in-situ measurements over most of the ecosystems; however, their SM estimates over forests present an ubRMSD 0.10 (m m−3). In this paper, we compared the SM retrievals from the SMOS and SMAP SM products with in-situ SM over a tropical forest in Southern Mexico. The L-band passive SM retrievals were evaluated in terms of four statistical metrics: root-mean-square difference (RMSD), bias, ubRMSD, and correlation coefficient (). In-situ measurements of SM, soil and vegetation temperatures, precipitation, soil surface roughness, tree heights, diameters at the breast height of trunks, and forest cover fraction were collected during a field campaign from 6 January to 14 June 2015 in the Biosphere Reserve of Calakmul, Mexico, covering two areas of about 40 km 40 km each. The comparison between SM retrievals from SMOS and SMAP and in-situ SM showed an RMSD ranging from 0.107 to 0.322 (m m−3) and an ubRMSD from 0.049 to 0.128 (m m−3). Overall, the SMAP SM estimates showed higher values of and were closer to in-situ SM. Because the SMAP and SMOS radiometers performed very similar, these differences are due to the values assigned to the vegetation optical depth (), the scattering albedo (), and the representation of the dynamics in vegetation and soil temperatures in the SMOS and SMAP retrieval algorithm. Based on an optimization process, we estimated simultaneously optimal and values for the tropical forest by using observations from SMAP and SMOS radiometers.The optimal value of was 0.0655 for the tropical forest, and constant over the study period. In contrast, the optimal values of showed to be variant on time and ranging between 1.0 and 1.7, with an averaged value of 1.4 and a standard deviation of 0.24. When applying the optimal values of and and in-situ soil and vegetation temperatures, the SM retrievals showed an ubRMSD of 0.035–0.070 (m m), improving the SM retrievals about 45%. A sensitivity analysis was conducted to evaluate the effect of the uncertainties in , , and soil and vegetation temperatures on the estimates of . It was found that vegetation temperature () was the most sensitive parameter, with higher than 0.70 for both polarizations in . When comparing in-situ and surface temperature values used in the SMAP and SMOS SM retrieval algorithms, differences up to 10 K were observed, affecting the SM estimates. The results presented in this paper could be useful in the preparation of the SMAP Calibration/Validation Experiment 2019, aiming at improving SM retrievals over forests.

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, 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 HR=0.1, whereas values of ω=0.06 − 0.08 and HR=0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, 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, HR=0.4 and NRp=−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 HR 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=−0.019m3m−3, ubRMSE=0.062m3m−3 for the IGBP-based calibration; against R=0.54, bias=−0.034m3m−3 and ubRMSE=0.070m3m−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.

The Evaluation of Single-Sensor Surface Soil Moisture Anomalies over the Mainland of the People’s Republic of China

In recent years, different space agencies have launched satellite missions that carry passive microwave instruments on-board that can measure surface soil moisture. Three currently operational missions are the Soil Moisture and Ocean Salinity (SMOS) mission developed by the European Space Agency (ESA), the Advanced Microwave Scanning Radiometer 2 (AMSR2) developed by the Japan Aerospace Exploration Agency (JAXA), and the Microwave Radiation Imager (MWRI) from China’s National Satellite Meteorological Centre (NSMC). In this study, the quality of surface soil moisture anomalies derived from these passive microwave instruments was sequentially assessed over the mainland of the People’s Republic of China. First, the impact of a recent update in the Land Parameter Retrieval Model (LPRM) was assessed for MWRI observations. Then, the soil moisture measurements retrieved from the X-band observations of MWRI were compared with those of AMSR2, followed by an internal comparison of the multiple frequencies of AMSR2. Finally, SMOS retrievals from two different algorithms were also included in the comparison. For each sequential step, processing and verification chains were specifically designed to isolate the impact of algorithm (version), observation frequency or instrument characteristics. Two verification techniques are used: the statistical Triple Collocation technique is used as the primary verification tool, while the precipitation-based Rvalue technique is used to confirm key results. Our results indicate a consistently better performance throughout the entire study area after the implementation of an update of the LPRM. We also find that passive microwave observations in the AMSR2 C-band frequency (6.9 GHz) have an advantage over the AMSR2 X-band frequency (10.7 GHz) over moderate to densely vegetated regions. This finding is in line with theoretical expectations as emitted soil radiation will become masked under a dense canopy with stricter thresholds for higher passive microwave frequencies. Both AMSR2 and MWRI make X-band observations; a direct comparison between them reveals a consistently higher quality obtained by AMSR2, specifically over semi-arid climate regimes. Unfortunately, Radio Frequency Interference hampers the usefulness of soil moisture products for the SMOS L-band mission, leading to a significantly reduced revisit time over the densely populated eastern part of the country. Nevertheless, our analysis demonstrates that soil moisture products from a number of multi-frequency microwave sensors are credible alternatives for this dedicated L-band mission over the mainland of the People’s Republic of China.

SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone

Brightness temperatures at 1.4 GHz (L-band) measured by the Soil Moisture and Ocean Salinity (SMOS) Mission have been used to derive the thickness of sea ice. The retrieval method is applicable only for relatively thin ice and not during the melting period. Hitherto, the availability of ground truth sea ice thickness measurements for validation of SMOS sea ice products was mainly limited to relatively thick ice. The situation has improved with an extensive field campaign in the Barents Sea during an anomalous ice edge retreat and subsequent freeze-up event in March 2014. A sea ice forecast system for ship route optimisation has been developed and was tested during this field campaign with the ice-strengthened research vessel RV Lance. The ship cruise was complemented with coordinated measurements from a helicopter and the research aircraft Polar 5. Sea ice thickness was measured using an electromagnetic induction (EM) system from the bow of RV Lance and another EM-system towed below the helicopter. Polar 5 was equipped among others with the L-band radiometer EMIRAD-2. The experiment yielded a comprehensive data set allowing the evaluation of the operational forecast and route optimisation system as well as the SMOS-derived sea ice thickness product that has been used for the initialization of the forecasts. Two different SMOS sea ice thickness products reproduce the main spatial patterns of the ground truth measurements while the main difference being an underestimation of thick deformed ice. Ice thicknesses derived from the surface elevation measured by an airborne laser scanner and from simultaneous EMIRAD-2 brightness temperatures correlate well up to 1.5 m which is more than the previously anticipated maximal SMOS retrieval thickness.

Validation of SMOS sea ice thickness retrieval in the northern Baltic Sea

The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree well (mean deviations below 3.5 K). In a second comparison, we use high-resolution measurements of total ice thickness (sum of ice and snow thickness) from an electromagnetic (EM) sounding system to simulate brightness temperatures for 12 circular areas. While the SMOS observations and the simulations that use the EM modal ice thickness are highly correlated (r2=0.95), the simulated brightness temperatures are on average 12 K higher than observed by SMOS. This would correspond to an 8-cm overestimation of the modal ice thickness by the SMOS retrieval. In contrast, if the simulations take into account the shape of the EM ice thickness distributions (r2=0.87), the mean deviation between simulated and observed brightness temperatures is below 0.1 K.

Assimilation of SMOS soil moisture over the Great Lakes basin

Abstract The launch of European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite has opened up the new opportunities for land data assimilation. In this work, the one-dimensional version of the Ensemble Kalman filter (1D-EnKF) is applied to assimilate SMOS soil moisture retrievals (2010–2013) into a land surface-hydrological model, Modelisation Environmentale-Surface et Hydrologie (MESH), over the Great Lakes basin. A priori rescaling on the retrievals is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture's CDF. The SMOS retrievals, the open-loop soil moisture (no assimilation) and the assimilation estimates are validated against point-scale in situ measurements, respectively, in terms of the daily time series correlation coefficient (skill R ). The skill for SMOS retrievals typically decreases with increased canopy density. In contrast, the open-loop model typically provides higher soil moisture skill R for forest surfaces than for crop surfaces. The skill improvement Δ R A-M , defined as the skill for the assimilation soil moisture product minus the skill for the open-loop estimates, for both surface and root-zone soil moisture typically increases as the SMOS observation skill and decreases with increased open-loop skill, showing a strong linear relation to Δ R S-M , defined as the SMOS observation skill minus the open-loop surface soil moisture skill. Every time the SMOS skill is greater than or equal to the open-loop surface soil moisture skill, the assimilation is typically able to significantly improve the model soil moisture skill. The crop-dominated grids typically experience the largest Δ R A-M if the assimilated SMOS retrievals also come from crop surfaces (note that a model grid cell and the SMOS node mapped onto the grid are not exactly matched in space), consistent with a high satellite observation skill and a low open-loop skill, while Δ R A-M is usually weak or even negative for the forest-dominated grids when the SMOS retrievals also from forest surfaces are assimilated, due to the presence of a low observation skill and a high open-loop skill. The dependence of Δ R A-S , referred to as the skill for the surface soil moisture assimilation product minus the SMOS observation skill, upon the open-loop skill and the satellite observation skill is opposite to that for Δ R A-M . Overall our R metric of skill and the anomaly R metric as used in previous studies provide a consistent explanation for the vegetation modulation of the assimilation. This work offers further insight into the impact of the open-loop skill and the satellite observation skill on the assimilation.

Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm

The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The retrieval algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS retrieval algorithm converges to realistic soil moisture retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.

Reducing multiplicative bias of satellite soil moisture retrievals

Soil moisture is a principal component of the Earth's climate and hydrological systems that is difficult to monitor and model due to high variability, uncertainty in land surface characterization and uncertainty in soil moisture forcing. Satellite soil moisture retrievals and brightness temperature observations, such as those available from the Soil Moisture and Ocean Salinity (SMOS) mission, can be a valuable source of information for data assimilation and merging with other satellite retrieval datasets. To correct for biases in these data sets, bias correction methods such as cumulative distribution function (CDF) matching, linear rescaling and copulas are used to map satellite soil moisture climatology to that of in situ or model values. This study compared SMOS retrievals to soil moisture observations from the SCAN network for a calibration period of 2010–2011 and validation period of 2012–2013 before and after bias correction. The focus of the study was on the presence and removal of multiplicative bias when comparing SMOS retrievals to SCAN data. Additive bias between SMOS retrievals and SCAN observations was removed by standard bias correction techniques and a new resampling approach was found to reduce multiplicative biases. In addition, the new bias correction technique was found very competitive with the benchmark methods for both the calibration and validation periods.

EnOI Optimization for SMOS Soil Moisture Over West Africa

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.

Evaluation of SMOS soil moisture retrievals over the central United States for hydro-meteorological application

Soil moisture has been widely recognized as a key variable in hydro-meteorological processes and plays an important role in hydrological modelling. Remote sensing techniques have improved the availability of soil moisture data, however, most previous studies have only focused on the evaluation of retrieved data against point-based observations using only one overpass (i.e., the ascending orbit). Recently, the global Level-3 soil moisture dataset generated from Soil Moisture and Ocean Salinity (SMOS) observations was released by the Barcelona Expert Center. To address the aforementioned issues, this study is particularly focused on a basin scale evaluation in which the soil moisture deficit is derived from a three-layer Xinanjiang model used as a hydrological benchmark for all comparisons. In addition, both ascending and descending overpasses were analyzed for a more comprehensive comparison. It was interesting to find that the SMOS soil moisture accuracy did not improve with time as we would have expected. Furthermore, none of the overpasses provided reliable soil moisture estimates during the frozen season, especially for the ascending orbit. When frozen periods were removed, both overpasses showed significant improvements (i.e., the correlations increased from r=−0.53 to r=−0.65 and from r=−0.62 to r=−0.70 for the ascending and descending overpasses, respectively). In addition, it was noted that the SMOS retrievals from the descending overpass consistently were approximately 11.7% wetter than the ascending retrievals by volume. The overall assessment demonstrated that the descending orbit outperformed the ascending orbit, which was unexpected and enriched our knowledge in this area. Finally, the potential reasons were discussed.

The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau

This study evaluates SMOS (soil moisture and ocean salinity) soil moisture products against a newly established soil moisture network in the central Tibetan Plateau. Based on the results, the validity of assimilating the SMOS soil moisture retrievals into a land surface model is further evaluated. The ground truth is obtained by spatial upscaling from the network measurements within an area of approximately 10,000km2. Results show that both SMOS L2 and the preliminary version of L3 soil moisture products have large biases at the SMOS node scales (15 and 25km), but they can reflect the surface wetness conditions well when averaged at a 100-km scale during the unfrozen season (June to October). This finding indicates the applicability of SMOS retrievals is scale-dependent. Meanwhile, very few retrievals are available in winter due to the presence of frozen soil and snow cover, and the accuracy of ascending retrievals degrades during transition when diurnal freezing–thawing cycle occurs. Considering the SMOS L2 product has a better accuracy than that of L3, we assimilate it into a land surface model using a dual-pass land data assimilation scheme. The data assimilation estimate without in-situ tuning proves superior to either remote sensing or land surface modeling in estimating surface soil moisture for the unfrozen season, and its accuracy fulfills the SMOS measurement requirements (RMSD≤0.04m3m−3). Thus, the assimilation of SMOS retrievals holds promise to produce regional soil moisture dataset with acceptable accuracy for the Tibetan Plateau semi-arid region.

Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S.

As part of the Soil Moisture and Ocean Salinity (SMOS) validation process, a comparison of the skills of three satellites [SMOS, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) or Advanced Microwave Scanning Radiometer, and Advanced Scatterometer (ASCAT)], and one-model European Centre for Medium Range Weather Forecasting (ECMWF) soil moisture products is conducted over four watersheds located in the U.S. The four products compared in for 2010 over four soil moisture networks were used for the calibration of AMSR-E. The results indicate that SMOS retrievals are closest to the ground measurements with a low average root mean square error of 0.061 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for the morning overpass and 0.067 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for the afternoon overpass, which represents an improvement by a factor of 2-3 compared with the other products. The ECMWF product has good correlation coefficients (around 0.78) but has a constant bias of 0.1-0.2 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> over the four networks. The land parameter retrieval model AMSR-E product gives reasonable results in terms of correlation (around 0.73) but has a variable seasonal bias over the year. The ASCAT soil moisture index is found to be very noisy and unstable.

Initial Validation of SMOS Vegetation Optical Thickness in Iowa

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite mission provides microwave L-band measurements of vegetation optical thickness over the Earth. Optical thickness is related to water held in vegetation. The water content of crops varies over the growing season from a minimum during planting to a maximum during reproduction and back to a minimum during senescence. We found that in Iowa in 2010 the change in SMOS optical thickness over the growing season can be related to crop yields. However, there are inconsistencies in the optical thickness data, particularly high-frequency variation and unexpected changes outside of the growing season. We hypothesize that the unexpected changes during the dormant periods are due to changes in soil surface roughness caused by land management activities and show a relationship between changes in roughness and changes in optical thickness, which may be confusing the SMOS retrieval algorithm.

Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment, Western Denmark

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.

An Initial Assessment of SMOS Derived Soil Moisture over the Continental United States

The recently available Soil Moisture and Ocean Salinity (SMOS) 1.4 GHz based soil moisture retrievals for the year of 2010 and the first nine months of 2011 are assessed over the continental United States (CONUS) region, along with soil moisture retrievals produced at Princeton University based on the Advanced Microwave Scanning Radiometer (AMSR-E) 10.7 GHz channel using the Land Surface Microwave Emission Model (LSMEM) and in-situ measurements from the Natural Resource Conservation Service's (NRCS) Soil Climate Analysis Network (SCAN). The assessment is carried out using a performance metric developed by Crow (J. Hydromet., 2007), which calculates the ability of soil moisture estimates to correct errors in surface moisture predictions through a linear Kalman filter. Within the Crow framework, SMOS retrievals show the same level of skill as AMSR-E/LSMEM or SCAN when evaluated on the days where both are available. But the SMOS product is significantly less available than AMSR-E/LSMEM or SCAN, especially on rainy days, therefore it is less able to reproduce the rainfall-moisture dynamics and consequently achieves a lower performance metric if all available data are used from all products. Detailed analysis shows that, with uncertainties, the performance of both SMOS and AMSR-E/LSMEM generally decays with thicker vegetation and wetter climate but is not significantly influenced by topography. We expect SMOS to further improve its accuracy through validation studies and its availability under rainy conditions as well.

Radiometric Performance of the SMOS Reference Radiometers—Assessment After One Year of Operation

In this paper, we present an analysis of the radiometric performance of the three 1.4-GHz noise injection radiometers of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite. The units measure the antenna temperature, which contributes to the average brightness temperature level of SMOS retrievals. We assess the radiometric resolution of the receivers, the similarity between their measurements, and their thermal stability. For these purposes, we use SMOS measurement data gathered during the first year of the orbital operations of the satellite, which was launched in November 2009. The main results from the analysis are that the units meet the design requirements with a margin. Also, we present a new thermal model for the radiometers to further enhance their stability.