Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC

Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021

Salinity is a critical parameter in the Arctic Ocean, having potential implications for climate and weather. This study presents the first systematic analysis of 6 commonly used sea surface salinity (SSS) products from the National Aeronautics and Space Administration (NASA) Aquarius and Soil Moisture Active Passive (SMAP) satellites and the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, in terms of their consistency among one another and with in-situ data. Overall, the satellite SSS products provide a similar characterization of the time mean SSS large-scale patterns and are relatively consistent in depicting the regions with strong SSS temporal variability. When averaged over the Arctic Ocean, the SSS show an excellent consistency in describing the seasonal and interannual variations. Comparison of satellite SSS with in-situ salinity measurements along ship transects suggest that satellite SSS captures salinity gradients away from regions with significant sea-ice concentration. The root-mean square differences (RMSD) of satellite SSS with respect to in-situ measurements improves with increasing temperature, reflecting the limitation of L-band radiometric sensitivity to SSS in cold water. However, the satellite SSS biases with respect to the in-situ measurements do not show a consistent dependence on temperature. The results have significant implications for the calibration and validation of satellite SSS as well as for the modeling community and the design of future satellite missions.

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

<p>During the last 3<span> dec</span><span>ades, the Arctic rivers have increased their discharge around 10%, mainly due to the increase of the</span> <span>g</span><span>lobal atmospheric </span>temperature. The increase of the river discharge carries higher loads of dissolved organic matter (DOM) and suspended matter (SM) entering to the Arctic Ocean. This results in increased absorption of solar energy in the mixed layer, which can potentially contribute to the general sea ice retreat. Observation based studies (e.g. Bauch et al., 2013) showed correlation between river water discharge and local sea ice melting on the Laptev sea shelf due to the change on the ocean heat. Previous studies are based with a limited number of observations, both in space and in time.</p><p>Thanks to the ESA SMOS (Soil Moisture and Ocean Salinity) and NASA SMAP (Soil Moisture Active Passive) missions we have daily the sea surface salinity (SSS) maps from the Arctic, which permit to observe the salinity variations due to the river discharges. The Arctic sea surface salinity products obtained from SMOS measurements have been improved considerable by the Barcelona Expert Center (BEC) team thanks to the project Arctic+Salinity, funded by ESA. The new version of the product (v3) covers the years from 2011 up to 2018, have a spatial resolution of 25km and are daily maps with 9 day averages. The Arctic+ SSS maps provide a better description of the salinity gradients and a better effective spatial resolution than the previous versions of the Arctic product, so the salinity fronts are better resolved. The quality assessment of the Arctic+SSS product is challenging because, in this region, there are scarce number of in-situ measurements.</p><p>The high effective spatial resolution of the Arctic+ SSS maps will permit to study for the first time scientific physical processes that occurs in the Arctic. We will explore if a correlation between the Lena and Ob rivers discharge with the sea ice melting and freeze up is observed with satellite data, as already stated with in-situ measurements by Bauch et al. 2013. Salinity and sea ice thickness maps from SMOS and sea ice concentration from OSISAF will be used in this study.</p><p> </p><p>Bauch, D.,Hölemann, J. , Nikulina, A. , Wegner, C., Janout, M., Timokhov, L. and Kassens, H. (2013): Correlation of river water and local sea-ice melting on the Laptev Sea shelf (Siberian Arctic) , Journal of Geophysical Research C: Oceans, 118 (1), pp. 550-561 . doi: 10.1002/jgrc.20076</p>

<p>Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.<br>To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.<br>Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.<br>For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.</p>

<strong class="journal-contentHeaderColor">Abstract.</strong> Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are different, the European Space Agency established the Climate Change Initiative (CCI) program to harmonize the available multisource SM data, producing long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the superior SMAP dataset historically (named RF_SMAP), using the corresponding CCI data time-series. We assumed that the temporal changes in the SMAP dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal characteristics extracted from the CCI SM v05.2 data (coupled with three terrain characteristics and two location characteristics), which was migrated to the prediction of the RF_SMAP dataset. The available <em>in-situ</em> SM data and the real SMAP data from April 2015 to April 2016 were used as references to validate the predicted RF_SMAP data. It was shown that compared with the CCI dataset, the predicted RF_SMAP dataset is closer to the <em>in-situ</em> SM data and the real SMAP data. Moreover, the historical RF_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset in terms of average root mean square error (RMSE), bias (Bias), and Kling-Gutpa efficiency (KGE). Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset, with an unbiased root mean square error (ubRMSE) of 0.035. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.

The Soil Moisture Active Passive (SMAP) mission is designed to acquire L-band radiometer measurements for the estimation of soil moisture with 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> volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In regions near the coast or near inland bodies of water, the signal measured by the SMAP radiometer contains emissions from land and water, resulting in errors in the soil moisture estimation. In this article, the effort to extract the brightness temperature (TB) according to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements was addressed. A single pixel correction algorithm was applied and its performance was evaluated over simulated data. A data-driven approach for the estimation of land and water TB for data correction was developed. The correction algorithm was then applied to real data and its performance was assessed over the SMAP soil moisture retrievals. We showed that the single pixel algorithm is an effective and computationally efficient algorithm for removing land or water TB contamination from the SMAP data.

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018.&#13;\n&#13;\nCurrently, the data contains only horizontally polarised retrievals from SMOS's and SMAP's ascending and descending nodes, respectively.

In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%–18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20–40 m/s.

An important contribution of satellite Sea Surface Salinity (SSS) is the spatio-temporal monitoring of rivers fresh water plumes at mesoscale. In this paper, we detail a new correction for systematic errors in the Soil Moisture and Ocean Salinity (SMOS) measurements that is implemented in the Centre Aval de Traitement des Donnees SMOS (CATDS). With this new mitigation, the SMOS and Soil Moisture Active Passive (SMAP) SSS monitor very consistent features in most areas close to continents. The rms-difference between bi-weekly SMOS and SMAP SSS over 20 months and in selected coastal regions is about 0.3pss (once outliers are filtered out), rather consistent with the rms-difference between satellite and in situ SSS (on the order of 0.2pss). The coefficient of determination (r2) between SMOS and SMAP SSS is above than 0.8 in very fresh areas (river plumes). Over the open ocean, the rms difference between SMOS and ship SSS is 0.2pss.

The SMAP mission was designed to measure soil moisture globally for quantifying the water and carbon cyclces. The Brightness Temperature (TB) measurement by the SMAP L-band radiometer over ocean is also used for Sea Surface Salinity (SSS) retrieval. Considering the requirement of the uncertainty in the L-band TB measurement for SSS retrieval, the reflected galaxy correction is to be upgraded by using wind speed dependent galactic TB maps (the cosmic microwave background is included) for the radiometer TB data product (version 4) which is expected to be released in the summer 2018. We address the development of the galactic TB maps. In addition, a correction offset to the modeled ocean radiometric observation for horizontal polarization is described.

Deviations of satellite-measured sea surface salinity caused by environmental factors and their regional dependence

Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well‐established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in‐depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year‐to‐year seasonal variations over the 2010–2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r 2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS‐Aquarius period, when the representativity uncertainties are the largest, r 2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

• A data service system for the selected SMAP data and its derivative data products over CONUS is developed. • All map and geoprocessing services are implemented via the OGC standard interface. • The data service system is interoperable with web and desktop clients. • Monitoring and analyzing U.S. cropland soil moisture conditions are significantly simplified. Soil moisture is an essential parameter to understand crop conditions throughout the growing season. Collecting soil moisture data by field observation is labor-intensive, especially when attempting to obtain Conterminous United States (CONUS) geographic coverage. In addition, using soil moisture for assessing current and future crop conditions is best realized by combining soil moisture estimates with concurrent observations of crop conditions. However, until recently, this capability has not been available to the public. In this paper, we present an interoperable data service application system, the Crop Condition and Soil Moisture Analytics (Crop-CASMA) system, that facilitates the retrieval, analysis, visualization, and sharing of soil moisture data for the CONUS. This system delivers a variety of satellite remote sensing based data products that are derived from Soil Moisture Active Passive (SMAP) Level-4 data and SMAP Thermal Hydraulic disaggregation of Soil Moisture (THySM) data, as well as vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. To make services interoperable and reusable, all data products are disseminated via Open Geospatial Consortium (OGC) Web Map Service (WMS) and Web Coverage Service (WCS) interface standards. Additionally, a suite of geoprocessing operations, such as geospatial statistics, time-series profile generation, PDF map production, and image composition, has been implemented in the OGC Web Processing Service (WPS) interface standard. The implementation shows the proposed web service system can significantly simplify the mapping and quantitative analysis of soil moisture and crop condition over U.S. cropland. In addition, it is interoperable with GIS software and has been successfully integrated with web-based applications.