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This study examines the impact of ocean advection and surface freshwater flux on the non‐seasonal, upper‐ocean salinity variability in two climate model simulations with eddy‐resolving and eddy‐parameterized ocean components (HR and LR, respectively). We assess the realism of each simulation by comparing their sea surface salinity (SSS) variance with satellite and Argo float estimates. In the extratropics, the HR variance is about five times larger than that in LR and agrees with Argo. In turn, the extratropical satellite SSS variance is smaller than that from HR and Argo by about a factor of two, potentially caused by the insufficient resolution of radiometers to capture mesoscale features and their low sensitivity to SSS in cold waters. Using a simplified salinity conservation equation for the upper‐50‐m ocean, we find that the advection‐driven variance in HR is, on average, 10 times larger than the surface flux‐driven variance, reflecting the action of mesoscale processes. Plain Language Summary This study explores the importance of ocean currents, evaporation, and rainfall for driving changes in the salt concentration in the upper ocean (known as salinity) in two climate model simulations with differing ocean resolutions. The high‐resolution model (HR) simulates ocean currents with dimensions of tens of km, while the low‐resolution model (LR) can only simulate currents with hundreds of km in size. When comparing their simulated sea surface salinity variations with those captured by satellites and autonomous floats from the Argo array, the salinity variability in the high‐resolution model is similar to the Argo data at mid to high latitudes and about five times stronger than that in the low‐resolution model. The satellite data show a variability two times smaller than HR and Argo in the same regions, potentially due to their insufficient spatial resolution at higher latitudes and their low sensitivity to the surface salinity in cold waters. Using a simple equation describing the conservation of salinity in the upper ocean, we have shown that small‐scale ocean currents drive most of the salinity variability in HR, while in LR, ocean currents play a much smaller role. Key Points We investigate how advection and surface flux affect upper‐50‐m salinity variance in eddy‐resolving and eddy‐parameterized climate models The extratropical variance in the eddy‐resolving run matches Argo and is much larger than in the eddy‐parameterized run and satellite data The larger upper‐ocean salinity variance in the eddy‐resolving run is predominantly driven by mesoscale ocean processes

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson’s correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data.

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.

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation.

Synthetic Aperture Interferometric Radiometer (SAIR) as one of the most advanced instruments for Sea Surface Salinity (SSS) observation, has been in service on SMOS mission for years and is planned on the Chinese Ocean Salinity Satellite in the near future. However, a lot of Radio Frequency Interference (RFI) emissions are found in SMOS views, which contaminate the brightness temperature measurements of the SAIR instrument, and further impede the retrieval of SSS fields. Concerning SAIR’s operating mode, this study proposes an RFI mitigation method comprising two algorithms for co- and cross-polarization, respectively. First, RFI signatures are identified based on a series of thresholds defined by radiation theory, and then mitigated through a simple machine learning technique of Support Vector Regression (SVR), leveraging either SAIR’s multi-angle measurements or sea surface roughness descriptors, depending on the specific polarization mode. Finally, the outputs of all polarizations are merged and written back to the Level 1C brightness temperature product as the final result. Using the proposed method, the notable outliers arose from RFI contamination are attenuated, and the variation of standard deviations over nearby snapshots is smoothed, as expected on a homogeneous ocean. Furthermore, with the official L2OS software implementing the SSS retrieval procedure from the rewritten Level 1C brightness temperatures, the data re-gain of SSS fields is achieved in some places that are not attainable for the current SMOS Level 2 SSS products, with a reasonable error compared to WOA2009 SSS, confirming the validity of the proposed method. Hopefully, this work could provide a practical solution to current and future SAIR observing predicaments.

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m−3).

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.

Alterations to the global water cycle are of concern as Earth's climate changes. Although policymakers are mainly interested in changes to terrestrial rainfall—where, when, and how much it's going to rain—the largest component of the global water cycle operates over the ocean where nearly all of Earth's free water resides. Approximately 80% of Earth's surface freshwater fluxes occur over the ocean; its surface salinity responds to changing evaporation and precipitation patterns by displaying salty or fresh anomalies. The salinity field integrates sporadic surface fluxes over time, and after accounting for ocean circulation and mixing, salinity changes resulting from long-term alternations to surface evaporation and precipitation are evident. Thus, ocean salinity measurements can provide insights into water-cycle operation and its long-term change. Although poor observational coverage and an incomplete view of the interaction of all water-cycle components limits our understanding, climate models are beginning to provide insights that are complementing observations. This new information suggests that the global water cycle is rapidly intensifying.

In this work, a new methodology is proposed in order to derive vertical total electron content (VTEC) maps from the radiometric measurements of the Soil Moisture and Ocean Salinity (SMOS) mission as an alternative approach to those based on external databases and models. This approach uses spatiotemporal filtering techniques with optimized filters to be robust against the thermal noise and image reconstruction artifacts present in SMOS images. It is also possible to retrieve the Faraday rotation angle from the recovered VTEC maps in order to correct the effect that it causes in the SMOS brightness temperatures.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.