Soil Moisture And Ocean Salinity Satellite Research Articles

A Soil Moisture Retrieval Method for Reducing Topographic Effect: A Case Study on the Qinghai–Tibetan Plateau With SMOS Data

The topography can be very important for passive microwave remote sensing of soil moisture due to its complex influence on the emitted brightness temperature observed by a satellite microwave radiometer. In this study, a methodology of using the first brightness Stokes parameter (i.e., the sum of vertical and horizontal polarization brightness temperature) observed by the Soil Moisture and Ocean Salinity (SMOS) was proposed to improve the soil moisture retrieval under complex topographic conditions. The applicability of the proposed method is validated using in-situ soil moisture measurements collected at four networks (Pali, Naqu, Maqu and Wudaoliang) on the Qinghai-Tibetan Plateau. The results over Pali, which is a typical mountainous area, showed that soil moisture retrievals using the first brightness Stokes parameter are in better agreement with the in-situ measurements (the correlation coefficient R >0.75 and unbiased root mean square error < 0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) compared with that using the single-polarization brightness temperature. At the other three networks with relatively flatter terrains, soil moisture retrievals using the first brightness Stokes parameter are found to be comparable to the single-polarization retrievals. On the contrary, the maximum bias of the retrieved soil moisture caused by topographic effects exceeds 0.1 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> when using vertical or horizontal polarization alone, which is far beyond the expected accuracy (0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) of SMOS satellite. In the regions on the Qinghai-Tibetan Plateau where the vegetation effect can be ignored, soil moisture retrieved using horizontal polarization brightness temperature is generally underestimated, overestimated when using vertical polarization brightness temperature. It is reasonable due to the polarization rotation effect (depolarization) caused by the topographic effects. It is concluded that the proposed method for soil moisture retrieval using the first brightness Stokes parameter has a great potential in reducing the influence of topographic effects.

A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data

Passive microwave remote sensing of soil moisture is an underdetermined problem, as observed microwave emission from the landscape is affected by a variety of unknown surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is proposed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VODp, p indicates the polarization with H for horizontal and V for vertical)), effective scattering albedo (ωpeff), soil surface roughness (Zps), and soil moisture (SMp). The advantage of the MTMA approach is that it does not need auxiliary data as inputs or constraints. SMOS polarization-dependent VOD are produced and compared at a global scale for the first time, and it is found that the polarization difference of vegetation effects should not be ignored in the SM retrieval algorithm. The MTMA VOD retrievals are found to have a reasonable global spatial distribution, which is generally consistent with the VOD retrievals obtained from the SMOS Level 3 (SMOS-L3) and SMOS-IC Version 2 (V2) (referred to as SMOS-IC), except for showing relatively lower values over densely vegetated areas compared with the other two SMOS products. The spatial distribution of retrieved ωpeff generally shows a dependence on both VOD and land cover types. In addition, the values of MTMA-ωVeff are higher than that of MTMA-ωHeff, indicating stronger microwave scattering of V-pol in the vegetation layer than that of H-pol. The retrieved surface roughness parameter (Zps) ranges from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP. The retrieved MTMA SM shows generally high correlations with in-situ measurements (11 dense observation networks) with overall correlation coefficients of > 0.75. The overall ubRMSE of MTMA-SMH and MTMA-SMV are < 0.055 m3/m3 and lower than that of SMOS-IC and SMOS-L3 products. SMOS-IC generally presents higher correlation coefficients compared to MTMA in most sites outside China; in China, RFI filtering is crucial and makes it very difficult when comparing algorithms based on different brightness temperature products. The number of effective retrievals of MTMA-SMH and MTMA-SMV ranges from 1409 to 1640 and 1104 to 1603 respectively, which is more than that from SMOS-IC (from 236 to 1358) over the selected 11 networks. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method of MTMA can be used to systematically retrieve SM, VOD and additional surface parameters (effective scattering albedo and surface roughness) with comparable or better performance of SM than that of SMOS-IC and SMOS-L3. Moreover, this paper for the first time produced a polarization-dependent SMOS VOD product at a global scale.

Uso de sensoriamento remoto e bancos de dados globais para estimativa de umidade do solo em diferentes profundidades no estado de Pernambuco

ABSTRACT The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.

Low- and Moderate-Level RFI Detection Using Point-Source Ripple for Synthetic Aperture Interferometric Radiometer

Finding out and turning off the radio-frequency interference (RFI) sources as many as possible is an important work for synthetic aperture interferometric radiometer (SAIR). While the impact of strong RFIs on reconstructed brightness temperature (BT) image is clearly observable, the presence of low- or moderate-level RFIs is often hard to be aware of, leaving the RFI detection and localization a difficult problem. In this article, an improved RFI detection method is proposed by making use of the point-source ripple to give prominence to the RFIs presented in the SAIR imagery. The method tests based on Soil Moisture and Ocean Salinity satellite (SMOS) visibility samples and the performance evaluations by numerical simulations validate the correctness and effectiveness of the developed method.

Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales.

Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product

Despite satellite imagery is being used to identify suitable areas for desert locust, there is a lack of automatized and operational procedures in Near Real Time (NRT). The aim of this study was to assess the capacity of Soil Moisture Near Real Time Neural Network Level 2 product (MIR_SMNRT2) from the Soil Moisture and Ocean Salinity satellite (SMOS) to predict nymphs of desert locust. We used soil moisture time series (between 2016 and 2019) to build 6 machine learning models (logistic regression model “glm”, eXtreme Gradient Boosting “xgbTree”, Weighted k-Nearest Neighbors “kknn”, Feed-Forward Neural Networks and Multinomial Log-Linear Models “nnet”, support vector machine radial “svmRadial”, and random forest “rf”) over the entire recession area. Model results proved that spatial and/or temporal constraints in data sampling conditioned the predictive capacity of the selected machine learning algorithms. Furthermore, we used a forward selection procedure to evaluate the impact that time series data exert on modelling. Our results suggest that soil moisture data retrieved between 95 and 12 days (before the sighting) provided sufficient information to achieve acceptable predictive performances. This methodology can improve current preventive and control operations, it is site-specific, and could be used to other pests.

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

A Matrix Completion Based Method for RFI Source Localization in Microwave Interferometric Radiometry

The Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency (ESA) is aimed to globally monitor the Earth surface moisture and ocean salinity. As the single payload of SMOS satellite, the Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) operates in the protected L-band. Nonetheless, the artificial sources emitting close to or/and fully in this band are contaminating the collected remote sensing data and deteriorating the performance of the SMOS mission. Identifying and localizing such sources is crucial to improve the quality of SMOS scientific products. In this article, we propose a method based on matrix completion (MC) for localization of radio frequency interference (RFI) sources. This method mainly exploits the low-rank property of the augmented covariance matrix (ACM) of the sparse array and addresses the ACM incompleteness (i.e., sampling data loss) due to inherent array geometry (e.g., the SMOS Y-shaped array) or potential hardware malfunction (e.g., the correlator failure). Validation results show that, compared with existing RFI localization approaches such as the discrete Fourier transformation (DFT) inversion and covariance-based direction-of-arrival (DOA) estimation, the proposed MC method possesses competitive localization accuracy, superior spatial resolution, and reduced artifacts.

Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection.

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

AbstractThe limited number of in situ stations of surface soil moisture (SM) in Africa creates a shortage in the validation of SM satellite products. Therefore, this study investigates the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and the H113 product from the Advanced Scatterometer (ASCAT) on the regional scale over Africa through these goals: (1) validate of satellite SM products against in situ stations and SM data from the ERA‐Interim atmospheric reanalysis product, (2) study the spatiotemporal variability of satellite SM products on the regional scale, and (3) evaluate the regional scale error patterns and investigate regions where the assimilation of satellites SM data may add improvement to ERA‐Interim. Standard statistical metrics, hovmöller diagrams, and the Triple Collocation (TC) model were used to achieve these goals. Land cover data, Normalized Difference Vegetation Index, and precipitation data were used to interpret results. The validation results based on statistical metrics and TC indicate that over the desert and shrub, passive products showed better performance than ASCAT, while over moderate vegetation areas (grassland), SMAP had the best among SM products. Over high densely vegetated regions, ASCAT showed a high comparatively performance than passive products. The potential regions for assimilation of satellite data sets were selected to be over savannas and grassland regions for ASCAT, and over shrub and grassland regions for SMAP. In particular, SMAP and ASCAT SM data sets are considered more stable than SMOS for data assimilation and capturing the spatial distribution of SM on the regional scale over Africa.

L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products.

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations.

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Measuring Sea‐Surface Salinity (SSS) from space is a relatively recent technique that relies on L‐band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40°N/S, in addition to other observing systems. The data are assimilated using a 3D‐Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite.The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root‐mean‐square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large‐scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation‐based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation.

An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa

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

A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data

Abstract. Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. Relative uncertainties associated with this method are large over thin ice regimes. Another retrieval method is based on the evaluation of surface brightness temperature (TB) in L-band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer-based method looses sensitivity for thick sea ice (&gt; 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0.7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes. However, in mixed first-year (FYI) and multiyear (MYI) ice regimes as in the Beaufort Sea, the CS2 retrieval shows the lowest bias.

Seasonal and interannual variability of the Eastern Tropical Pacific Fresh Pool

AbstractThe Eastern Pacific Fresh Pool (EPFP) is a large region of low sea surface salinity (SSS) defined by values lower than 34 practical salinity scale within (5°S–30°N, 75°W–180°W). The fresh pool dynamically responds to strong regional and seasonally varying ocean‐atmosphere‐land interactions (including monsoon rain, trade and gap winds, and strong currents). Using more than 5 years of Soil Moisture and Ocean Salinity (SMOS) satellite sea surface salinity (SSS) and complementary satellite wind, rain, currents, and sea surface temperature data together with a historical ensemble of in situ products, the present study explores the seasonal and interannual dynamics of the fresh pool over the period 2004–2015. An important interannual variability of the maximal surface extension of the EPFP over the past decade is revealed with two extreme events (2012, 2015) occurring during the SMOS satellite period. These extremes are found to be related to the El Niño‐Southern Oscillation (ENSO) phases and associated anomalies of precipitation, surface currents, and trade wind in the central Pacific. In 2012 (La Niña), stronger trade winds coupled with a deficit of precipitation induced a minimum extension of the pool during the rainy season. Whereas, during the strong El Niño 2014–2015, the EPFP extension reached an unprecedented maximum value. A modification of the atmospheric freshwater fluxes and ocean surface currents during winter 2014 is found to have favored the onset of this abnormal fresh event.

Statistical Models of Sea Surface Salinity in the South China Sea Based on SMOS Satellite Data

Study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef, and sea grass habitats. Three statistical methods applied without considering the physical effects of the input parameters are proposed to calculate SSS from soil moisture and ocean salinity (SMOS)-measured brightness temperature (TB) values and associated auxiliary data. Using these three statistical methods, named multiple linear regression (MLR) model, principal component regression (PCR) model, and quadratic polynomial regression (QPR) model, the first predictions of daily and monthly averaged SSS are made with $1 ^\circ\times1 ^\circ$ spatial resolution in the South China Sea (SCS, in the study area of 4°N-25 °N, 105°E-125°E) during the period between April and June 2013. Results are compared with the corresponding SMOS SSS products and Aquarius SSS products and validated using Argo measurements. Validation results show that the root-mean-squared error (RMSE) of the QPR model is around 0.46 practical salinity units (psu) compared to 0.58 psu for Aquarius daily SSS products. World Ocean Atlas (WOA13) SSS data are also used for validation in the SCS and the QPR model gives a 0.54-psu value of RMSE, which may be compared with 0.69 psu, 0.73 psu for SMOS and Aquarius Level-3 (L3) SSS products, respectively.