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Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products. [ABSTRACT FROM AUTHOR]

In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation. [ABSTRACT FROM AUTHOR]

Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O'Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water ("Angola water") forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally. [ABSTRACT FROM AUTHOR]

Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters. [ABSTRACT FROM AUTHOR]

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Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a τ - ω vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (τ , ω) and soil roughness ( S D ) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m3 for all stations, with a mean bias of 9.4 kg/m3. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (τ , ω , and S D ) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics. [ABSTRACT FROM AUTHOR]

Analysis of seasonal and interannual variations of the brightness temperature of the Yenisei, Pechora, and Khatanga estuaries is performed using the SMOS (Soil Moisture and Ocean Salinity) satellite MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) data for the period 2012–2020. It is shown that during the freezing period, with low river flow, Khatanga and Pechora bays are strongly influenced by the salty waters of the Laptev Sea and the Pechora Sea, respectively. In Yenisei Bay, the analysis reveals two characteristic areas for the winter period, delimited by a narrow strait between Sopochnaya Karga and Oshmarin capes. In the southern part of the bay, the water remains fresh or slightly salty, while in the northern part (north of the Sopochnaya Karga post), the water is always brackish, as this is the mixing zone of the fresh water of the Yenisei River and the salty water of the Kara Sea. The sea areas adjacent to the Yenisei and Pechora estuaries (the Kara Sea and Pechora Sea, respectively) are dynamic zones with brackish or salty water and constantly breaking ice under the influence of hydrological and climatic factors. The results show that SMOS MIRAS data can be used to estimate water salinity and movement of the transition zone under the ice in large bays and estuaries; to analyze the stability and dynamics of the Arctic sea-ice cover; and to determine the beginning of ice melt in large sea and freshwater areas. [ABSTRACT FROM AUTHOR]

Coastal waters off west Greenland are strongly influenced by the input of low salinity water from the Arctic and from meltwater from the Greenland Ice Sheet. Changes in freshwater content in the region can play an important role in stratification, circulation, and primary production; however, investigating salinity variability in the region is challenging because in situ observations are sparse. Here, we used satellite observations of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity mission produced by LOCEAN and by the Barcelona Expert Center (SMOS LOCEAN and SMOS BEC) and from the Soil Moisture Active Passive mission produced by the Jet Propulsion Laboratory (SMAP JPL) as well as by Remote Sensing Systems (SMAP RSS) to investigate how variability in a narrow coastal band off west Greenland is captured by these different products. Our analyses revealed that the various satellite SSS products capture the seasonal freshening off west Greenland from late spring to early fall. The magnitudes of the freshening and of coastal salinity gradients vary between the products however, being attenuated compared to historical in situ observations in most cases. The seasonal freshening off southwest Greenland is intensified in SMAP JPL and SMOS LOCEAN near the mouth of fjords characterized by large inputs of meltwater near the surface, which suggests an influence of meltwater from the Greenland Ice Sheet. Synoptic observations from 2012 following large ice sheet melting revealed good agreement with the spatial scale of freshening observed with in situ and SMOS LOCEAN data. Our analyses indicate that satellite SSS can capture the influence of meltwater input and associated freshwater plumes off coastal west Greenland, but those representations differ between products. [ABSTRACT FROM AUTHOR]

We investigate the Chukchi and the Beaufort seas in the Arctic Ocean, where salty and warm Pacific Water flows in through the Bering Strait and interacts with the sea ice, contributing to its summer melt. Thanks to in situ measurements recorded by two saildrones deployed during summer 2019 and to refined sea ice filtering in satellite L‐band radiometric data, we demonstrate the ability of satellite sea surface salinity (SSS) observed by Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive to capture SSS freshening induced by sea ice melt. We refer to these freshening events as meltwater lenses (MWL). The largest MWL observed by the saildrones during this period occupied a large part of the Chukchi shelf, with a SSS freshening reaching −5 practical salinity scale, persisting for up to 1 month. This MWL restricted the transfer of air‐sea momentum to the upper ocean, as illustrated by measured wind speed and vertical profiles of currents. With satellite‐based sea surface temperature, satellite SSS provides a monitoring of the different water masses encountered in the region during summer 2019. Using sea ice concentration and estimated Ekman transport, we analyze the spatial variability of sea surface properties after the sea ice edge retreat over the Chukchi and the Beaufort seas. The two MWL captured by the saildrones and the satellite measurements resulted from different dynamics. Over the Beaufort Sea, the MWL evolution followed the meridional sea ice retreat whereas, in the Chukchi Sea, a large persisting MWL was generated by advection and subsequent melting of a sea ice filament. Plain Language Summary: The Arctic Ocean is an area of large variations in salinity. Salinity is a main driver of ocean circulation as it determines (with seawater temperature) the seawater density. However, very little is known about salinity variations there, due to the paucity of measurements near ice and in river plumes where surface water is freshest. Here, we use surface salinity measurements from two autonomous vehicles, named saildrones, to show that satellite measurements can identify the evolution of freshwater lenses that result from sea ice melt. Over the Chukchi and the Beaufort seas, sea surface salinity exhibits large seasonal changes, partly because of the sea ice melting. In this region, water from the North Pacific Ocean enters the Arctic Ocean, resulting in large gradients of salinity and temperature. During summer 2019, the saildrones measured the surface salinity and temperature variability as the sea ice retreated northwards. By comparing these data with the measurements from satellites, we showed that satellites can detect these pools of fresh surface water in the Arctic Ocean, increasing the field of application of satellites to understand changes in conditions that determine the Arctic's role in climate change. Key Points: Saildrones and L‐band radiometers detect large sea surface salinity variability induced by sea ice over the Chukchi and the Beaufort SeaLow surface salinity due to sea ice melting decreases the vertical extent of momentum transfer, inhibiting it beyond 10 m depthMeltwater lenses may persist more than 1 month and reach a surface salinity 5 pss fresher than surrounding waters [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns of the time and length scales of SSS variability estimated from the three satellite missions. The temporal scales of SSS variability vary from more than 90 days in the tropics to ~15 days in the Southern Ocean. The very short temporal scales (close to the Nyquist period) in some parts of the ocean are probably due to the high level of noise in the satellite data or the high noise-to-signal ratio. The longest temporal scales are observed along the South Pacific Convergence Zone (SPCZ) and in the central and western tropical Pacific. These areas are also related to the strongest ENSO-related signal in SSS. The processes governing the SSS variability and distribution are also non-stationary, such that the scales determined over different observation periods may differ. Dominant spatial scales of SSS variability are generally the longest (up to 150 km) in the tropics and the shortest (<60 km) in the subpolar regions. The distribution of the dominant spatial scales is not simply latitudinal but exhibits a more complex spatial pattern. In the tropics, there is slight east-west and inter-hemispheric asymmetry observed in the Pacific but absent in the other two oceans. The analysis also reveals that the length scales of SSS variability are highly anisotropic in the tropics (the zonal scales are generally shorter than the meridional ones) and become more isotropic towards higher latitudes. Regional differences in the estimates of the scales from the three satellite SSS datasets may arise due to differences in the observation duration, spatial resolution and/or different level of noise. [ABSTRACT FROM AUTHOR]

Remote sensing soil moisture (SM) has a low spatial resolution. By contrast, to validate remote SM maps, point measurements gathered from scattered in situ stations are typically used. Therefore, a single representative SM value for the entire domain is required. The simplest approach is the arithmetic mean. Here, eight upscaling methods for in situ SM based in geostatistical interpolations and physical characteristics were tested and used to validate the Soil Moisture and Ocean Salinity mission observations. Comparisons showed that the simple mean performs well and is similar to the proposed upscaling methods, while overcoming the need for ancillary data. [ABSTRACT FROM AUTHOR]

To compare the accuracy of satellite salinity data of level-3 Soil Moisture Active Passive V4.0 (SSMAP) and debiased v5 CATDS level-3 Soil Moisture and Ocean Salinity (SSMOS) before and after tropical cyclones (TCs) in the Bay of Bengal (BoB), this study used the sea surface salinity of Argo (SArgo) to assess SSMAP and SSMOS before and after the passage of 10 TCs from 2015 to 2019. The results indicate that the SSMAP and SSMOS agreed well with SArgo before and after 10 TCs. It can be seen that the correlation between SSMAP and SArgo (before TCs: SSMAP = 0.95SArgo + 1.52, R2 = 0.83; after TCs: SSMAP = 0.87SArgo + 4.34, R2 = 0.79) was obviously higher than that of SSMOS and SArgo (before TCs: SSMOS = 0.68SArgo + 10.38, R2 = 0.62; after TCs: SSMOS = 0.88SArgo + 3.98, R2 = 0.58). The root mean square error (RMSE) was also significantly higher between SSMOS and SArgo (before TCs: 0.84 psu; after TCs: 0.78 psu) than between SSMAP and SArgo (before TCs: 0.58 psu; after TCs: 0.47 psu). In addition, this study compared SSMAP and SSMOS during two TCs that swept in nearshore and offshore waters, and the results show good agreement between SSMAP and SArgo in the nearshore and offshore waters of BoB. In the BoB, both SSMAP and SSMOS can retrieve sea surface salinity well, and SSMAP is overall better than SSMOS, but the SMOS salinity product can fill the gap of SMAP from 2010 to 2015. [ABSTRACT FROM AUTHOR]

In the Arctic, the sea surface salinity (SSS) plays a key role in processes related to water mixing and sea ice. However, the lack of salinity observations causes large uncertainties in Arctic Ocean forecasts and reanalysis. Recently the Soil Moisture and Ocean Salinity (SMOS) satellite mission was used by the Barcelona Expert Centre to propose an Arctic SSS product. In this study, we evaluate the impact of assimilating this data in a coupled ocean-ice data assimilation system. Using the Ensemble Kalman filter from July to December 2016, two assimilation runs assimilated two successive versions of the SMOS SSS product, on top of a pre-existing reanalysis run. The runs were validated against independent in situ salinity profiles in the Arctic. The results show that the biases and the Root Mean Squared Differences (RMSD) of SSS are reduced by 10% to 50% depending on areas and put the latest product to its advantage. The time series of Freshwater Content (FWC) further show that its seasonal cycle can be adjusted by assimilation of the SSS products, which is encouraging for its use in a long-time reanalysis to monitor the Arctic water cycle. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle. [ABSTRACT FROM AUTHOR]

• Multiple satellite formation flying reconfiguration for remote sensing application. • Low thrust manoeuvres via constrained convex optimal control problem. • Application of the control problem to a new mission concept for remote sensing. • Design of reconfiguration trajectories for remote sensing payload calibration. • Design of formation flying transition to safe mode for non-nominal operations. This paper presents a strategy for optimal manoeuvre design of multi-satellite formation flying in low Earth orbit environment, with the aim of providing a tool for mission operation design. The proposed methodology for formation flying manoeuvres foresees a continuous low-thrust control profile, to enable the operational phases. The design is performed starting from the dynamic representation described in the relative orbital elements, including the main orbital perturbations effects. It also exploits an interface with the classical radial-transversal-normal description to include the maximum delta-v limitation and the safety condition requirements. The methodology is applied to a remote sensing mission study, Formation Flying L-band Aperture Synthesis, for land and ocean application, such as a potential high-resolution Soil Moisture and Ocean Salinity (SMOS) follow-on mission, as part of a European Space Agency mission concept study. Moreover, the results are applicable to a wide range of low Earth orbit missions, exploiting a distributed system, and in particular to Formation Flying L-band Aperture Synthesis (FFLAS) as a follow-on concept to SMOS. [ABSTRACT FROM AUTHOR]

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyzes the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS, the Advanced SCATterometer on MetOp, the Global Land Data Assimilation System, and the WaterGAP Global Hydrology Model. In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission, and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems. [ABSTRACT FROM AUTHOR]

Microwave radiometry at L‐band is sensitive to sea ice thickness (SIT) up to ∼ 60 cm. Current methods to infer SIT depend on ice‐physical properties and data provided by the ESA's Soil Moisture and Ocean Salinity (SMOS) mission. However, retrieval accuracy is limited due to seasonally and regionally variable surface conditions during the formation and melting of sea ice. In this work, Arctic sea ice is segmented using a Bayesian unsupervised learning algorithm aiming to recognize spatial patterns by harnessing multi‐incidence angle brightness temperature observations. The approach considers both statistical characteristics and spatial correlations of the observations. The temporal stability and separability of classes are analyzed to distinguish ambiguous from well‐determined regions. Model uncertainty is quantified from class membership probabilities using information entropy. The presented approach opens up a new scope to improve current SIT retrieval algorithms, and can be particularly beneficial to investigate merged satellite products. Plain Language Summary: Remote sensing techniques are commonly used to provide maps of sea ice thickness (SIT). Methods to obtain these maps are based on the sea ice composition and on the signal measured by satellite. Sea ice Composition is spatially complex and changes during its formation and melting. Currently used data from observations of ESA's Soil Moisture and Ocean Salinity (SMOS) mission depend on several sea ice parameters, which hinders good estimation of almost any specific sea ice parameter. In this work, a new method to combine the information contained in SMOS brightness temperature data is investigated, with the aim to divide the Arctic region into a number of smaller areas – so called classes. Useful information about sea ice is contained in the spatial and statistical distribution of SMOS data, which are collected at different incidence angles. The relationship between the observations and the statistical properties of the obtained classes allow an assessment of its degree of separability and uncertainty. How classes change in time is used to estimate their temporal stability. The presented approach can be used to investigate the link between a variety of spatial datasets to improve current SIT products, and it can be applied in other scientific fields. Key Points: Retrieval algorithms to infer ice properties, such as sea ice thickness, exhibit high uncertainty due to limited knowledge of complexityAn unsupervised learning approach provides a synergistic framework which links data with the aim to recognize and analyze spatial patternsBayesian segmentation of Arctic sea ice from Soil Moisture and Ocean Salinity (SMOS) data reveals stable and separable classes while indicating model uncertainty [ABSTRACT FROM AUTHOR]

In this study, the hydrological dynamics of the Congo River Basin (CRB) have been analyzed. This is achieved using multiangular and dual‐polarization passive L‐band microwave signal from the Soil Moisture and Ocean Salinity (SMOS) satellite to estimate water surface extent dynamics from 2010 to 2017. The results provide new insights into the poorly characterized wetlands, peatlands, and floodplains dynamics of the CRB. We found that the mean flooded area for the entire CRB is equal to 89,408 ± 20,623 km2 corresponding to 2.39% of the entire basin. The results were compared with three land cover maps (European Space Agency‐Climate Change Initiative [ESA‐CCI], International Geosphere‐Biosphere Programme [IGBP], and Global Surface Water Occurrence [GSWO]) and the SWAMPS global dynamic water surfaces product. More inland waters were detected than the four previous products, except along the rivers. Floods and droughts during the last 10 years were also depicted. The knowledge about the CRB hydrological behavior was improved by analyzing the date of maximum floods, the time lag in days between precipitation, water surface extent, and river water height at the outlet of the nine major Congo subbasin. A lag of 67 ± 3 days between rainfall and inundated areas was found in the Upper Congo (r = 0.89). In the Kasai subbasin, no time lag between rainfall and inundation was found (r = 0.86). The contribution of each floodplain to the Congo discharge was also evaluated. In the future, the fusion of the current surface waters with water heights from the SWOT mission can provide further information to the water volumes of the CRB floodplains. Plain Language Summary: The Congo River Basin (CRB) has a vast flooded area, that is poorly characterized. We developed new flood extent maps for the CRB based on satellite data in the L‐band spectral band. This enabled an unprecedented insight into water dynamics of the wetlands, peatlands, and floodplains of the CRB through the detection of the dates of maximum floods, the lag with the rainfall peaks, and the duration of floods even in vegetated areas. Key Points: New knowledge of the Congo River Basin hydrology for the last decade through a new temporal and spatial dynamic water surface extent dataThe water extent in the Congo River Basin ranges from 78,602 to 99,225 km2 between 2011 and 2017The lags between rainfall and flood in the nine Congo subbasins has a high variability ranging from 0 to 67 days [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable in hydrological processes, bio-ecological processes, and biogeochemical processes. Long-term observations of soil moisture over large areas are critical to research on flooding and drought monitoring, water resource management, and crop yield forecasts. In this paper, Fengyun (FY3B and FY3C) SM products, Japan Aerospace Exploration Agency (JAXA) SM products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Land Parameter Retrieval Model (LPRM) AMSR2 L3 SM products, the Version 2 (v2) global land parameter data record (LPDR) of SM products, Soil Moisture Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3 SM products, the Soil Moisture Active Passive (SMAP) passive L3 SM products and the European Space Agency(ESA) Climate Change Initiative (CCI) SM products were evaluated using the ground-based observations in the Little Washita, Fort Cobb and Yanco networks. Long-time series comparison between measured and satellite products was conducted to evaluate the overall performance of FY3 series satellites SM products. Bias (mean bias), R (correlation coefficient), RMSE (root mean square error) and ubRMSE (unbiased root mean square error) were calculated to explore the agreement between satellite products and in-situ measurements. Taylor diagrams were used to compare the performance of various satellite products. The result showed that (1) FY3B, FY3C and LPRM AMSR2 ascending and descending products had an obvious overestimate with in-situ soil moisture in three networks. (2) JAXA AMSR2 ascending and descending products had considerable underestimation in three networks. (3) The validation result of SMOS ascending and descending products over the three networks was satisfactory, with a rather high correlation than other X-band products. (4) The validation result of SMAP ascending and descending products outperformed the other products over the Little Washita and Fort Cobb networks. (5) The ESA CCI product had the lowest values of RMSE and ubRMSE than the other products in the Yanco network, revealing the effectiveness of merging active and passive soil moisture products. (6)The LPDR descending SM products had better performance than the LPDR ascending SM products in the LW and FC networks. [ABSTRACT FROM AUTHOR]

In the Arctic, the sea surface salinity (SSS) plays a key role in processes related to water mixing and sea ice. However, the lack of salinity observations causes large uncertainties in Arctic Ocean forecasts and reanalysis. Recently the Soil Moisture and Ocean Salinity (SMOS) satellite mission was used by the Barcelona Expert Centre to propose an Arctic SSS product. In this study, we evaluate the impact of assimilating this data in a coupled ocean-ice data assimilation system. Using the Ensemble Kalman filter from July to December 2016, two assimilation runs assimilated two successive versions of the SMOS SSS product, on top of a pre-existing reanalysis run. The runs were validated against independent in situ salinity profiles in the Arctic. The results show that the biases and the Root Mean Squared Differences (RMSD) of SSS are reduced by 10% to 50% depending on areas and put the latest product to its advantage. The time series of Freshwater Content (FWC) further show that its seasonal cycle can be adjusted by assimilation of the SSS products, which is encouraging for its use in a long-time reanalysis to monitor the Arctic water cycle. [ABSTRACT FROM AUTHOR]

The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and nonlinear trends were observed in the global land areas that have experienced statistically significant changes. The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20% agreement, demonstrating the robustness and reliability of the observed trends. • Applying a polynomial-based temporal trend estimation method as an extension of linear regression. • Detecting global trends, encompassing linear and nonlinear (quadratic, cubic, concealed) patterns. • Analyzing the spatial distribution of the observed trends. • Assessing trend reliability with the Mann-Kendall non-parametric method. [ABSTRACT FROM AUTHOR]

In this study impact of assimilating Sea Surface Salinity (SSS) from multi-satellites (SMOS, Aquarius and SMAP) on numerical ocean model simulations in the north Indian Ocean has been analysed under the observing system experiment (OSE) framework. Daily data sets of Aquarius, SMAP and SMOS, which were available for a common period of April–May 2015, are used to constrain the ocean model using ensemble optimal interpolation technique. Apart from the control simulation in which satellite data were not assimilated, a total of seven assimilation experiments using different combinations of satellite SSS were conducted. The impact of assimilation experiments is analysed by comparing the model-simulated variables with in situ observations. Assimilating satellite SSS results in a reduction in Root Mean Square Error (RMSE) in SSS (∼ 54%) and also in subsurface salinity (∼ 21%) over the control run. The impact of assimilating SMAP observations is maximum on model simulations with the errors reducing by ∼ 54%. Subsurface salinity improvement is better with three satellites with ∼31% improvement in RMSE in the halocline region, which was ∼11% more than single satellite assimilation. Assimilation of SSS also resulted in improved simulations of the model surface, subsurface temperature and mixed layer depth. Model results show the ability of SSS observations to complement other ocean observation networks. One important observation from this study is that while the impact of assimilating SSS observations from a single satellite was on par with the impact of assimilating SSS observations from two or three satellites in correcting simulated surface salinity, assimilation from more than one satellite had a larger impact in the salinity of deeper layers of the ocean. • Ocean OSE has been performed for the Indian Ocean using SSS from Aquarius, SMAP and SMOS satellites. • Single satellite SSS assimilation is at par with 2 or 3 satellite SSS assimilation in improving surface salinity fields. • Upto 54% improvement at model surface salinity and 8% improvement at model MLD with SSS assimilation. • 2 or 3 satellite SSS data assimilation is more effective in correcting salinity fields in the deeper layers of ocean. [ABSTRACT FROM AUTHOR]

Remote sensing data are often measured or reported over wide spatial footprints with heterogeneous ground cover. Different types of vegetation, however, have unique signatures that evolve throughout the growing season. Without additional information, signals corresponding to individual vegetation types are unidentifiable from satellite measurements. In this paper, we propose a parametric mixture model to describe satellite data monitoring crop development in the US Corn Belt. The ground cover of each satellite footprint is primarily a mixture of corn and soybean. Using auxiliary data from multiple sources, we model the aggregate satellite signal, and identify the signatures of individual crop types, using nonlinear parametric functions. Estimation is performed using a Bayesian approach, and information from auxiliary data is incorporated into the prior distributions to identify distinct crop types. We demonstrate our parametric unmixing approach using data from the European Space Agency's Soil Moisture and Ocean Salinity satellite. Lastly, we compare our model estimates for the timing of key crop phenological stages to USDA ground-based estimates. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Remote sensing of soil moisture can provide important data for monitoring large-scale agricultural drought. Due to differences between the various sensors and inversion methods, remote sensing data from different sources are unsuitable for direct comparison and analysis. Data fusion has become an area of active research regarding the application of remote sensing data. Based on the principle of cumulative distribution function matching, this study proposed a continuous relationship establishment algorithm for multisource remote sensing soil moisture data. Using this new algorithm, soil Moisture and Ocean Salinity (SMOS) and Climate Change Initiative (CCI) satellite data from the Songnen Plain as test data were fused to a long time series product of real-time remote sensing soil moisture data. This application validation of this new method to SMOS and CCI indicated that this Lagrange interpolation continuous fusion algorithm could improve the fusion accuracy of multisource remote sensing soil moisture data significantly. The low-value region of the cumulative probability distribution curve is a crucial data segment for characterization of agricultural drought. Through implementation of the proposed continuous fusion algorithm, fused SMOS and CCI data were found to have high coincidence at each quantile in the low-value region of the curve. [ABSTRACT FROM AUTHOR]

As a dominant source of tropical variability, the Madden‐Julian oscillation (MJO) influences the ocean in many ways. One approach to observe the atmosphere‐ocean relationship is by examining sea surface salinity (SSS) due to direct freshening by MJO precipitation. The convectively enhanced (suppressed) phase of the MJO is associated with negative (positive) SSS anomalies that propagate eastward along the equatorial Indian and Pacific oceans. In this study, primary MJO events are identified, and their SSS signatures are compared for the first time across multiple satellite salinity products (the European Space Agency's Soil Moisture Ocean Salinity; the National Aeronautics and Space Administration's Aquarius and Soil Moisture Active Passive) from 2010 to 2017. While all satellite missions are capable of detecting MJO signals and primary events on an unprecedented observational scale, we find that the use of the combined active passive algorithm increases signal robustness, with the strongest signal response in Soil Moisture Active Passive and Soil Moisture Ocean Salinity (±0.2 psu) and the lowest in Aquarius (±0.1 psu). Plain Language Summary: The Madden‐Julian Oscillation (MJO) is the dominant form of intraseasonal variability in the tropics. It manifests as a pattern of eastward propagating enhanced and suppressed atmospheric convection over the equatorial Indian and Pacific Oceans. These atmospheric patterns can be easily observed in the surface ocean through sea surface salinity (SSS), as salinity is highly dependent on precipitation from convectively active events. Using satellite‐derived salinity products, we can observe how the convection and precipitation associated with the MJO influence SSS. In this study, we compare the different salinity satellite products and their associated processing algorithms in order to determine which product most adequately captures the MJO signal in SSS. MJO events can be further described as being either primary or successive events, based on their duration, timing, and strength. After identifying these primary events during strong MJO years, we compared the SSS response to these events using the satellite salinity products. We found that the best salinity product was SMAP, which has higher spatiotemporal resolution and could reach closer to coastlines, particularly around the Maritime Continent. MJO studies of salinity are crucial, as salinity is the best way to see how precipitation events and air‐sea interactions are translated into the ocean. Key Points: This is the first comparison of MJO‐induced SSS signals in all three satellite missions under various processing algorithmsThe MJO signal is more robust in CAP salinity products than in their non‐CAP counterparts of the same satellites (Aquarius and SMAP)Primary MJO events identified and detected in salinity are shown to be coupled with atmospheric forcing [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• The SMAP and SMOS soil moisture products were compared against in-situ observations. • Satellite soil moisture products were downscaled using thermal inertia theory. • A regression tree was developed for downscaling, based on in-situ soil moisture data. • Downscaled SMAP and SMOS products showed ubRMSEs of 0.07 and 0.05 cm3/cm3. • Downscaled airborne soil moisture retrievals showed an accuracy of 0.07 cm3/cm3. High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. This study investigates the feasibility of downscaling satellite soil moisture products to 1 km resolution. This study was undertaken in the semi-arid Goulburn River Catchment, located in south-eastern Australia. The Soil Moisture Active Passive (SMAP)-Enhanced 9 km (L3SMP-E) and Soil Moisture and Ocean Salinity (SMOS) 25 km gridded (SMOS CATDS L3 SM 3-DAY) radiometric products were compared with in-situ soil moisture observations and a regression tree model was developed for downscaling based on thermal inertia theory. Observations from a long-term soil moisture monitoring network were employed to develop a regression tree model between the diurnal temperature difference and the daily mean soil moisture for soils with different clay content and vegetation greenness. Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperatures were used to estimate the soil moisture at high spatial resolution by disaggregating the satellite soil moisture products through the regression model. The downscaled SMAP-Enhanced 9 km and SMOS 25 km gridded soil moisture products showed unbiased root mean square errors (ubRMSE) of 0.07 and 0.05 cm3/cm3, respectively, against the in-situ data. These ubRMSEs include errors caused by measuring instrument and the scale mismatch between downscaled products and in-situ data. An RMSE of 0.07 cm3/cm3 was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture. [ABSTRACT FROM AUTHOR]

Two L‐Band (1.4 GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) currently provide sea‐surface salinity (SSS) measurements. At this frequency, salinity is measured in the first centimetre below the sea surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS‐estimated negative RR due to measurement noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15 min apart (r = 0.7 at 1° × 1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time‐scale. Rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements. SMOS and SSMIS RR agree well when SMOS and SSMIS passes are less than 15 min apart (see an example on the figure below; r = 0.7 at 1° × 1° resolution). When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information to improve RR products based on morphing techniques. [ABSTRACT FROM AUTHOR]

The L-band (1.41 GHz) passive microwave remote sensing technique is the approach used by the first satellites dedicated to soil moisture measurement, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) mission developed by the National Aeronautics and Space Administration (NASA). These satellites aim to provide global soil moisture maps for the top ~5 cm layer of soil with an accuracy better than 0.04 m 3 /m 3 . However, with a passive microwave observing resolution of ~40 km, non-soil targets such as surface rock may possibly confound the brightness temperature observations and degrade the accuracy of retrievals for many SMOS and SMAP pixels across the world. Since the microwave contribution of rock is not well accounted for in current soil moisture retrieval algorithms, simply ignoring its existence may be detrimental to the performance of resultant soil moisture products. Using a combination of model simulations and airborne field campaign data from central Australia, this study has determined that a rock cover fraction threshold of up to 0.4 can be tolerated before the 0.04 m 3 /m 3 soil moisture target accuracy is potentially exceeded under extreme dry or wet conditions. However, this threshold reduces to 0.2 when assessed in terms of a brightness temperature impact >4 K. These rock fraction thresholds have subsequently been applied to the Ecoclimap rock cover map, identifying the SMOS and SMAP pixels globally that are likely to be adversely affected if rock is unaccounted for. The results show that approximately ~3.3% of all SMOS and SMAP pixels may have brightness temperature impacts exceeding 4 K from surface rock, with Asia being the most affected, having ~6.0% affected pixels. These values reduce to ~1.5% of SMOS and SMAP pixels globally, and ~3.1% for Asia, when assessed in terms of soil moisture errors expected to possibly exceed 0.04 m 3 /m 3 when not accounting for surface rock. [ABSTRACT FROM AUTHOR]

Global-scale surface soil moisture products are currently available from multiple remote sensing platforms. Footprint-scale assessments of these products are generally restricted to limited number of densely-instrumented validation sites. However, by taking active and passive soil moisture products together with a third independent soil moisture estimates via land surface modeling, triple collocation (TC) can be applied to estimate the correlation metric of satellite soil moisture products (versus an unknown ground truth) over a quasi-global domain. Here, an assessment of Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS) and Advanced SCATterometer (ASCAT) surface soil moisture retrievals via TC is presented. Considering the potential violation of TC error assumptions, the impact of active-passive and satellite-model error cross correlations on the TC-derived inter-comparison results is examined at in situ sites using quadruple collocation analysis. In addition, confidence intervals for the TC-estimated correlation metric are constructed from moving-block bootstrap sampling designed to preserve the temporal persistence of the original (unevenly-sampled) soil moisture time-series. This study is the first to apply TC to obtain a robust global-scale cross-assessment of SMAP, SMOS and ASCAT soil moisture retrieval accuracy in terms of anomaly temporal correlation. Our results confirm the overall advantage of SMAP (with a global average anomaly correlation of 0.76) over SMOS (0.66) and ASCAT (0.63) that has been established in several recent regional, ground-based studies. SMAP is also the best-performing product over the majority of applicable land pixels (52%), although SMOS and ASCAT each shows advantage in distinct geographic regions. [ABSTRACT FROM AUTHOR]

Salinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Kolodziejczyk et al. (2016) (hereafter K2016) developed a methodology to mitigate SMOS systematic errors in the vicinity of continents, that greatly improved the quality of the SMOS Sea Surface Salinity (SSS). Here, we find that SSS variability, however, often remained underestimated, such as near major river mouths. We revise the K2016 methodology with: a) a less stringent filtering of measurements in regions with high SSS natural variability (inferred from SMOS measurements) and b) a correction for seasonally-varying latitudinal systematic errors. With this new mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico. The standard deviation of the differences between SMOS and SMAP weekly SSS is <0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean. Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r 2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS SSS remains more impacted by RFI than SMAP SSS. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD ≥0 ). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD ≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data. [ABSTRACT FROM AUTHOR]

Recent studies have shown significant sea surface salinity (SSS) changes at scales ranging from regional to global. In this study, we estimate global salinity means and trends using historical (1950-2014) SSS data from the UK Met Office Hadley Centre objectively analyzed monthly fields and recent data from the SMOS satellite (2010-2014). We separate the different components (regimes) of the global surface salinity by fitting a Gaussian Mixture Model to the data and using expectation-maximization to distinguish the means and trends of the data. The procedure uses a non-subjective method (Bayesian information criterion) to extract the optimal number of means and trends. The results show the presence of three separate regimes: Regime A (1950-1990) is characterized by small trend magnitudes; Regime B (1990-2009) exhibited enhanced trends; and Regime C (2009-2014) with significantly larger trend magnitudes. The salinity differences between regime means were around 0.01. The trend acceleration could be related to an enhanced global hydrological cycle or to a change in the sampling methodology. Understanding past SSS changes can provide insight into future climate evolution by complementing the knowledge acquired in recent decades from long-term temperature record analyses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX ( Surface Externalisée )). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale. [ABSTRACT FROM AUTHOR]

L-band radiometer measurements collected over the Dome~C area from 2010 to 2015 indicated that the brightness temperature (T B ) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014–2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5° incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in the water and energy exchanges that occur in the terrestrial surface. Soil moisture can be retrieved at a larger scale by using the visible and InfraRed (IR) bands as well as through the microwave remote sensing. Because of very high importance of SM for variety of applications, there are now two dedicated microwave satellites in the Earth's orbit for soil moisture retrieval from space. The first, Soil Moisture and Ocean Salinity (SMOS) satellite has been launched by the European Space Agency in November 2009 and second is Soil Moisture Active and Passive (SMAP) launched by the National Aeronautics and Space Administration (NASA) in January 2015. In this review, brief background of soil moisture retrieval algorithms are presented with different applications in the area of water resources. The first section provides the introduction of the soil moisture, presents several in situ techniques for measurement of soil moisture and soil moisture retrieval algorithms from visible/IR and microwave remote sensing. Section 2 describes the satellite soil moisture applications in water resources. [ABSTRACT FROM AUTHOR]

The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image $T$ that models the Earth’s brightness temperature and an image $O$ modeling the RFIs. [ABSTRACT FROM PUBLISHER]

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 ( τ nad ) 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 ( Q r , H r , N rp , 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 N rp and H r on the SM and τ nad 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, Q r was set equal to zero and we assumed that N rH = N rV . The retrievals were performed by varying N rp from −1 to 2 by steps of 1 and H r 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. [ABSTRACT FROM AUTHOR]

Three multi-decadal satellite soil moisture (SM) products, obtained by merging two active and six passive, all-active-merged (CCI-ACT), all-passive-merged (CCI-PAS) and all-active-passive-merged (CCI-COMBINED), and Level-3 SM retrieved from Soil Moisture Ocean Salinity (SMOS) mission were evaluated over India. The evaluation strategy employed is twofold: (a) time series and correlation analysis of SM datasets with respect to the Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L) SM simulation and the India Meteorological Department (IMD) gridded rainfall; (b) investigate the spatial distribution of random error of the satellite products using Triple Collocation (TC) approach. The Pearson's correlation analysis showed that the performance of CCI-ACT and CCI-COMBINED are comparable to each other and they agree well with the MERRA-L simulated SM time series. They also had a good rank correlation with rainfall. The random error from TC is represented in terms of fractional Root Mean Square Error ( fRMSE TC ). It also represents the sensitivity of satellite retrievals to changes in true state. The analysis of fRMSE TC showed that descending swath of SMOS SM has a lower error than ascending for 71% of the pixels over India. CCI-ACT was found to have the most number of pixels with the lowest errors, having a mean fRMSE TC of 0.7188, compared to 0.7705 for CCI-COMBINED, 0.7828 for CCI-PAS and 0.8308 for SMOS-D. However, the error in CCI-ACT was highest in arid desert regions of western India. The error in CCI-COMBINED, CCI-PAS and SMOS-D grew with an increase in vegetation density. The fRMSE TC maps were analysed against the maps of the probability of occurrence of Radio Frequency Interference (RFI), Normalized Difference Vegetation Index (NDVI), soil texture (percentage of clay, sand, and silt) and modified Köppen-Geiger climate classification. The climate classification map was used to classify fRMSE TC against the different homogeneous climate classes. The analysis of the maps revealed that the inconsistency in SMOS is because of the RFI events over India. However, a multiple linear regression based attribution study showed that SMOS-D is the least affected by vegetation (4%) and the spatial distribution of CCI-ACT and CCI-COMBINED error showed more affinity towards soil texture than vegetation density. [ABSTRACT FROM AUTHOR]

The paper presents a detailed comparative study of three surface soil moisture datasets retrieved from Cosmic-Ray Soil Moisture Observing System (COSMOS) in situ neutron measurement, CATDS Soil Moisture Ocean Salinity (SMOS) satellite microwave observations, and modelled data of the Global Land Data Assimilation System (GLDAS). Subsurface datasets were also calculated from the in situ and satellite measurements by using an exponential filter and were compared with the GLDAS estimates. For these comparisons, the Triple Collocation (TC) method, Nash–Sutcliffe Efficiency (NSE) coefficients, trend charts, and scatterplots were used. The main goal of this work was to verify the concordance of cosmic-ray neutron measurements with low-resolution soil moisture data from the CATDS SMOS and GLDAS products. The second objective was to determine the possibility of obtaining comparable subsurface soil moisture products from the aforementioned soil moisture data source. The obtained results show that data from the COSMOS sensor, which assesses soil moisture in an area 600 m in diameter, agree reasonably well with the CATDS SMOS and GLDAS data having spatial resolution of about 25 km. These conclusions suggest that COSMOS Derlo measurements can be particularly useful for validation of low-resolution satellite soil moisture observations as well as modelled values. The results obtained by using the TC method also revealed satisfactory agreement among all studied surface soil moisture data. However, the performed analysis shows some preponderance of SMOS and COSMOS data over GLDAS products. Although GLDAS data show a noticeable smoothing effect, COSMOS and CATDS SMOS more effectively reveal temporal soil moisture changes. Thus, for some applications, the use of CATDS SMOS estimates rather than GLDAS products may be more appropriate. Results retrieved by using an exponential filter are significant and encouraging. In particular, subsurface soil moisture values calculated from CATDS SMOS show stronger correlation with COSMOS Derlo data than those of GLDAS. Furthermore, COSMOS Derlo data also respond more intensively to surface soil moisture changes. [ABSTRACT FROM AUTHOR]

Satellite measurements have largely improved sea surface salinity (SSS) observations in the recent decade, including Soil Moisture Ocean Salinity (SMOS), Aquarius/Satélite de Aplicaciones Científicas (SAC) D, and Soil Moisture Active-Passive (SMAP). This study evaluates the deviations between the satellite SSS products and the in-situ observations (ΔS) and analyses their relationships with the environmental factors, including sea surface temperature (SST), precipitation, and wind speed. The level 3 satellite SSS generally correlates well with Argo-observed SSS but differs in the middle and high latitudes and the tropical convergence zones. The smallest ΔS appears in the subtropical open oceans, where the sea surface features warm SST, rare precipitation, and low wind speed. The variations in mean |ΔS| for the three satellites show a robust dependence on SST, precipitation, and wind, with large ΔS (outside 5%–95% interval) in particular tending to occur in areas of cold SST, heavy rainfall, and high wind speeds. The most pronounced SSS deviations occur in the middle and high latitudes (beyond 40°) due to cold SST. In particular, heavy rainfall in the subpolar regions and strong westerly winds in the mid-latitudes also make the satellite SSS measurements less accurate. In the tropical convergence zones, large SSS deviations are mainly related to heavy rainfall, showing significant surface freshening. The fresh biases related to rainfall are more significant in Level 2 SSS than in Level 3, due to the short period of the convective rainfall. In addition, surface freshening is more obvious in low wind speed scenarios, showing that the fresh skin layer induced by rain is more common on the calm ocean surface. • The complicated environment in high latitudes leads to large SSS deviations. • The ideal area for satellite SSS calibration is in the subtropical open ocean. • Cold SST is the primary error source for Level 3 SSS products. • Considerable surface freshening is underestimated in Level 3 SSS products. [ABSTRACT FROM AUTHOR]

Recently, large efforts have been made to develop surface soil moisture (SSM) products based on observations from passive microwave L-band satellites at the global scale. Despite vast previous efforts to assess satellite SSM products based on in situ data, the performance of L-band SSM products remains still little known over the tropical areas including the rainforests. To close this knowledge gap, a comprehensive evaluation of five L-band SSM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, and MTDCA, as well as the recently developed SMAP INRAE-BORDEAUX (SMAP-IB) was conducted in tropical areas from 2015 to 2020. The investigation was implemented by using in situ observations, and expanded by the Triple Collocation Analysis (TCA) and double instrumental variable (IVd) methods. The results revealed that all the five L-band SSM products show a good capacity to estimate SSM in most moderately vegetated areas of the tropical areas while they exhibit some uncertainties in dense vegetation (e.g., rainforests). The SSM climatology of the five L-band products in rainforests agreed generally well with that of in situ measurements especially for SMOS-IC (R = 0.75), followed by SMAP-IB (R = 0.72). Notably, the newly developed SMAP-IB shows satisfactory performance in the tropics with the highest R of 0.73 and the smallest unbiased root mean square difference (ubRMSD) of 0.041 m3/m3 from the study based on in situ SM data, and R of >0.81 from the combined TCA/IVd method in most vegetation conditions. SMAP-DCA SSM demonstrates comparable performance to SMAP-SCA-V (R = 0.66) from the in situ based analysis. This study is expected to deepen our understanding on the skill of L-band SSM products in tropical areas and further promote possible upgrades in algorithms and applications in these regions. • Five L-band SSM products were evaluated over the tropics. • The newly developed SMAP-IB showed satisfying performances. • The combined TCA/IVd and in situ analyses led to consistent results. • L-band SSM shows acceptable climatology results in rainforests. [ABSTRACT FROM AUTHOR]

ESA's Soil Moisture Ocean Salinity (SMOS, since 2009) and NASA's Soil Moisture Active Passive (SMAP, since 2015) are the only two space-borne L-band radiometer missions currently in orbit, which provide key information on global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD). However, to date very few studies considered merging SMOS and SMAP for both SM and VOD retrievals simultaneously. This study presents the first global long-term and continuous SM and L-band VOD (L-VOD) dataset retrieved after merging the SMOS and SMAP brightness temperature (TB) observations, called the SMOS-SMAP-INRAE-BORDEAUX or SMOSMAP-IB product. We first developed a fitted SMOS TB dataset at a fixed incidence angle of 40°, and next applied a monthly linear rescaling of SMAP TB to SMOS TB for each polarization to produce a merged SMOS/SMAP TB (θ = 40°) dataset. The retrievals were then based on a mono-angular retrieval algorithm sharing a similar forward model with the SMOS-IC and the official SMOS retrieval algorithms. Results showed that the inter-calibration approach we used here could effectively remove the bias between the SMAP TB and fitted SMOS TB, with bias values reduced to 0.01 K (−0.02 K) compared to 3.45 K (1.65 K) for V (H) polarization before inter-calibration. The SMOSMAP-IB SM and L-VOD retrievals based on this new inter-calibrated SMOS/SMAP TB led to metrics that were equally good or better than those of other products (i.e., ESA CCI, SMOS-IC and the official SMAP products). When considering only long duration products, SMOSMAP-IB SM retrievals exhibited (i) the highest overall median R value of 0.72 with in-situ data from ISMN (International Soil Moisture Network) during 2013–2018, followed by SMOS-IC (R = 0.68) and CCI (R = 0.67), and (ii) the same smallest ub RMSD values as CCI (ub RMSD = 0.057 m3/m3 vs 0.061 m3/m3 for SMOS-IC). L-VOD retrievals from SMOSMAP-IB were found to have comparable spatial and temporal skills to SMOS-IC. Spatially, they both correlated well with aboveground biomass (R = 0.87), and temporally, they both showed a good representation of the short vegetation NDVI signal and of the forest area loss in the Brazilian Amazon from 2011 to 2019. Developing SMOSMAP-IB is a step forward towards building a time-continuous L-band SM and VOD products in response to the possible failure of one of the SMOS or SMAP sensors in the future. • The first SM and L-VOD product (SMOSMAP-IB) that fuses SMOS and SMAP is proposed. • SMOSMAP-IB SM correlates better with in-situ data than other retrieval products. • SMOSMAP-IB SM has similar ub RMSD skill scores as CCI SM. • SMOSMAP-IB L-VOD has comparable spatial and temporal skills as SMOS-IC L-VOD. [ABSTRACT FROM AUTHOR]

Currently, there are several space missions capable of measuring surface soil moisture, owing to the relevance of this variable in meteorology, hydrology and agriculture. However, the Plant Available Water (PAW), which in some fields of application could be more important than the soil moisture itself, cannot be directly measured by remote sensing. Considering the root zone as the first 50 cm of the soil, in this study, the PAW at 25 cm and 50 cm and integrated between 0 and 50 cm of soil depth was estimated using the surface soil moisture provided by the Soil Moisture Ocean Salinity (SMOS) mission. For this purpose, the Soil Water Index (SWI) has been used as a proxy of the root-zone soil moisture, involving the selection of an optimal T (T opt ), which can be interpreted as a characteristic soil water travel time. In this research, several tests using the correlation coefficient (R), the Nash-Sutcliffe score (NS), several error estimators and bias as predictor metrics were applied to obtain the T opt , making a comprehensive study of the T parameter. After analyzing the results, some differences were found between the T opt obtained using R and NS as decision metrics, and that obtained using the errors and bias, but the SWI showed good results as an estimator of the root-zone soil moisture. This index showed good agreement, with an R between 0.60 and 0.88. The method was tested from January 2010 to December 2014, using the database of the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) in Spain. The PAW estimation showed good agreement with the in situ measurements, following closely the dry-downs and wetting-up events, with R ranging between 0.60 and 0.92, and error values lower than 0.05 m 3 m −3 . A slight underestimation was observed for both the PAW and root-zone soil moisture at the different depths; this could be explained by the underestimation pattern observed with the SMOS L2 soil moisture product, in line with previous studies. Nevertheless, the results obtained in this research showed an encouraging improvement of the PAW estimation. Despite the need for more research on this issue, the results of this study show that this methodology can be useful for applications of great interest in agriculture and hydrology. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture products. SMOS operates at L-band (1.4 GHz) and, at this frequency, the signal not only depends on soil moisture and vegetation optical depth but is also significantly affected by surface effects and, in particular, by the soil roughness. However, when dense vegetation is present, the L-band signal is poorly sensitive to the surface effects. First, by using multiple regressions between soil moisture (SM) and brightness temperature (TB) at different incidence angles and polarizations, the SMOS sensitivity to the surface effects was evaluated. A global-scale map of SMOS sensitivity to the surface effects was computed and showed that, for 87% of the land surface, the SMOS observations were sensitive to these effects, while very low sensitivity to the surface effects was estimated over 13% of the land surfaces. For instance, over broadleaf evergreen forest (mainly the Amazon and Congo forests), SMOS was sensitive to the surface effects over only half of the pixels considered. In a second step, in L-MEB (L-band Microwave Emission of the Biosphere), the forward emission model of the SMOS algorithm, the vegetation and roughness effects were combined in a single parameter, referred to as TR in this study. By inverting L-MEB, SM and TR were retrieved at global scale from the SMOS Level 3 (L3) TB observations during 2011. Assuming a linear relationship between TR and the Leaf Area Index (LAI) obtained from MODIS data, the effects of roughness ( H r ) and vegetation were decoupled and a global map of soil roughness effects was estimated. It was found that the spatial pattern of the H r values could be related to the main vegetation types. Higher values of roughness ( H r = 0.32–0.39) were obtained for forests (broadleaf evergreen, deciduous and mixed coniferous) while lower values ( H r = 0.14–0.16) were obtained for deserts, shrubs and bare soils. Intermediate values ( H r = 0.20–0. 23) were obtained over grasslands, tundra and cultivated land. Over vegetation biomes composed of forests and wooded grasslands, the H r values were mainly correlated to the vegetation density ( r ~ 0.55). For deserts, shrubs and bare soils, the H r values were mainly correlated to the topography slopes ( r ~ 0.53). The global maps presented in this study could lead to improved retrievals of soil moisture and vegetation optical depth for present and future microwave remote sensing missions such as SMOS and Soil Moisture Active Passive (SMAP). [ABSTRACT FROM AUTHOR]

The study presents the validation of SMOS soil moisture satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for soil moisture in the first 5 cm of the soil surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile soil moisture measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the soil type, soil texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, soil type, and soil texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between soil moisture, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project http://assimo.meteoromania.ro [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important component of the Earth's surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth's surface SM using L-band radiometers: ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA's Terra/Aqua MODIS or ESA & EUMETSAT's MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011-2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM-LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ≈ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ≈ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ≈ -0.5 to -0.7; satellite R ≈ -0.4 to -0.7) indicating SM-LST coupling, than in winter (in situ R ≈ +0.3; satellite R ≈ -0.3) indicating SM-LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12m³/m³, stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM-LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status. [ABSTRACT FROM AUTHOR]

Near-surface salinity data from the Forecasting Ocean Assimilation Model (FOAM) system are used to understand various characteristics of satellite sea surface salinity (SSS) data from SMOS. The errors in the model fields are first estimated by comparing them to near-surface Argo salinity measurements, with RMS errors of less than 0.2 pss over most of the global oceans, except for regions of high variability in SSS such as boundary current regions and areas of large precipitation or river run-off. Regional biases are generally less than 0.05 pss but some regions such as the Antarctic Circumpolar Current and the region to the north of the Gulf Stream extension have larger biases. Various different processing versions of the SMOS data are assessed, including different temporal and spatial averaging, and the daily 1° resolution SMOS differences to FOAM are approximately 3–10 times larger than Argo-FOAM differences. The spatial information in the SMOS data is also assessed by comparing spatial gradients in the satellite SSS data with those calculated from other datasets including satellite sea surface temperature (SST) and satellite altimeter sea surface height (SSH) data, as well as with the model's gradients. The SMOS data contain information about the underlying ocean dynamics in the summer months, in agreement with the SSH data, which are not present in the satellite SST data or in the model's SSS fields. This demonstrates that the data contains useful information which could be used to correct the model through data assimilation. [ABSTRACT FROM AUTHOR]

Taking advantage of the spatially dense, multi-year time series of global Sea Surface Salinity (SSS) from two concurrent satellite missions, the spatial and temporal decorrelation scales of SSS in the Tropical Atlantic 30°N–30°S are quantified for the first time from SMOS and Aquarius observations. Given the dominance of the seasonal cycle in SSS variability in the region, the length scales are calculated both for the mean and anomaly (i.e. seasonal cycle removed) SSS fields. Different 7–10 days composite SSS products from the two missions are examined to explore the possible effects of varying resolution, bias corrections and averaging characteristics. With the seasonal cycle retained, the SSS field is characterized by strongly anisotropic spatial variability. Homogeneous SSS variations in the Tropics have the longest zonal scales of over ~ 2000 km and long temporal scales of up to ~ 70–80 days, as shown by both SMOS and Aquarius. The longest meridional scales, reaching over ~ 1000 km, are seen in the South Atlantic between ~ 10°–25°S, most discernible in Aquarius data. The longest temporal scales of SSS variability are reported by both satellites to occur in the North-West Atlantic region 15°–30°N, at the Southern end of the Sargasso Sea, with SSS persisting for up to 150–200 days. The removal of the seasonal cycle results in a noticeable decrease in the spatio-temporal decorrelation scales over most of the basin. Overall, with the exception of the differences in the South Atlantic, there is general agreement between the spatial and temporal scales of SSS from the two satellites and different products, despite differences in individual product calibration and resolution characteristics. These new estimates of spatio-temporal decorrelation scales of SSS improve our knowledge of the processes and mechanisms controlling the Tropical Atlantic SSS variability, and provide valuable information for a wide range of oceanographic and modelling applications. [ABSTRACT FROM AUTHOR]