Soil Moisture Ocean Salinity Data Research Articles

Comparative Analysis between Sea Surface Salinity Derived from SMOS Satellite Retrievals and in Situ Measurements

Validating Sea Surface Salinity (SSS) data has become a key component of the Soil Moisture Ocean Salinity (SMOS) satellite mission. In this study, the gridded SMOS SSS products are compared with in situ SSS data from analyzed products, a ship-based thermosalinograph and a tropical moored buoy array. The comparison was conducted at different spatial and temporal scales. A regional comparison in the Baltic Sea shows that SMOS slightly underestimates the mean SSS values. The influence of river discharge overrides the temperature in the Baltic Sea, bringing larger biases near river mouths in warm seasons. The global comparison with two Optimal Interpolated (OI) gridded in situ products shows consistent large-scale structures. Excluding regions with large SSS biases, the mean ΔSSS between monthly gridded SMOS data and OI in situ data is −0.01 PSU in most open sea areas between 60°S and 60°N, with a mean Root Mean Square Deviation (RMSD) of 0.2 PSU and a mean correlation coefficient of 0.50. An interannual tendency of mean ΔSSS shifting from negative to positive between satellite SSS and in situ SSS has been identified in tropical to mid-latitude seas, especially across the tropical eastern Pacific Ocean. A comparison with collocated buoy salinity shows that on weekly and interannual scales, the SMOS Level 3 (L3) product well captures the SSS variations at the locations of tropical moored buoy arrays and shows similar performance with in situ gridded products. Excluding suspicious buoys, the synergetic analysis of SMOS, SMAP and gridded in situ products is capable of identifying the erroneous data, implying that satellite SSS has the potential to act as a real-time 27 Quality Control (QC) for buoy data.

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

AbstractThis global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting.

Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation

Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).

A Nomograph to Incorporate Geophysical Heterogeneity in Soil Moisture Downscaling

AbstractHydrological applications require robust and periodic spatially distributed soil moisture data. Radiometer‐based soil moisture (~30–60‐km resolution), after being appropriately downscaled (<5‐km resolution), can be a valuable resource for providing such data globally. However, the accuracy of available downscaling algorithms is severely affected by subgrid variability in geophysical factors and precipitation within a satellite footprint. In this work, we introduce a scaling nomograph that incorporates the scale and site specific dependence of soil moisture on geophysical heterogeneity and antecedent wetness conditions to overcome this limitation. We developed functional scaling relationships to estimate the semivariogram of downscaled soil moisture change without any available fine‐scale soil moisture data. The nomograph enables these relationships to be specific to the geophysical heterogeneity and antecedent wetness within a radiometer‐based satellite footprint through footprint specific heterogeneity and wetness indices. The heterogeneity index quantifies the subgrid scale variability and covariability of soil, vegetation, and topography within the footprint, and the wetness index is a measure of antecedent precipitation. The nomograph was developed for Arizona, Iowa, and Oklahoma and can enable downscaling to scales varying between 0.8 and 6.4 km. The true power of the nomograph is to enable the use of static dominant factors like soil to define dynamic scale specific scaling relationships for soil moisture for different kinds of land use and land cover in a data driven yet scientific approach, thus providing spatial transferability to the downscaling scheme. The spatial transferability of the nomograph was validated by downscaling Soil Moisture Ocean Salinity data in Manitoba, Canada.

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m3m−3 and 0.04 m3m−3, respectively. Those values are near to the expected 0.04 m3m−3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland

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 600m in diameter, agree reasonably well with the CATDS SMOS and GLDAS data having spatial resolution of about 25km. 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.

Calibration of Noah Soil Hydraulic Property Parameters Using Surface Soil Moisture from SMOS and Basinwide In Situ Observations

AbstractSoil hydraulic properties (SHPs) control infiltration and redistribution of moisture in a soil column. The Noah land surface model (LSM) default simulation selects SHPs according to a location’s mapped soil texture class. SHPs are instead estimated at seven sites in North America through calibration. A single-objective algorithm minimizes the root-mean-square difference (RMSD) between simulated surface soil moisture and observations from 1) a dense network of in situ probes, 2) Soil Moisture Ocean Salinity (SMOS) satellite retrievals, and 3) SMOS retrievals adjusted such that their mean equals that of the in situ network. Parameters are optimized in 2012 and validated in 2013 against the in situ network. RMSD and unbiased RMSD (ubRMSD) assess resulting surface soil moisture behavior. At all sites, assigning SHP parameters from a different soil texture than the one that is mapped decreases the RMSD by an average of 0.029 cm3 cm−3. Similar improvements result from calibrating parameters using in situ network data (0.031 cm3 cm−3). Calibrations using remotely sensed data show comparable success (0.029 cm3 cm−3) if the SMOS product has no bias. Calibrated simulations are superior to texture-based simulations in their ability to decrease ubRMSD at times of year when the default simulation is worst. Changes to both RMSD and ubRMSD are small when the default simulation is already good. Most calibrated simulations have higher runoff ratios than do texture-based simulations, a change that warrants further evaluation. Overall, parameter selection using SMOS data shows good potential where biases are low.

Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

Availability of accurate remotely-sensed sea surface salinity (SSS) measurements is crucial to investigating fundamental aspects of the global hydrological cycle, ocean dynamics and marine biogeochemistry. The European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) mission has been specifically designed for this aim. However, SMOS data display a high level of noise with respect to the signal they have to detect. Space–time averaging over relatively large sampling periods/areas is thus generally carried out to increase SSS accuracy, and further interpolation is required to fill in data gaps resulting from both mission geometry and other instrumental/physical limitations. Here, a daily, 1/4° nominal resolution, mesoscale-resolving SSS field product is obtained by using a multidimensional optimal interpolation (OI) algorithm combining SMOS salinity retrievals and satellite sea surface temperature data with in situ salinity measurements. The methodology has been developed in the framework of the ESA OSMOSIS (Ocean ecoSystem Modelling based on Observations from Satellite and In-Situ data) project and it is applied here to a wide zonal portion of the Southern Hemisphere (10°S–65°S). The interpolated SSS has been validated by looking at the differences with respect to fully independent in situ observations and by performing a wavenumber spectrum analysis. Despite minor progress being obtained in terms of root mean square of the differences with respect to in situ observations, a significant improvement in terms of effective spatial resolution is obtained with the new technique with respect to presently available SSS observation-based analyses.

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named GDOWN, is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2–3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between GDOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good indicator of the overall performance of DISPATCH. Especially, the sign of GDOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The GDOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

On the potential of data assimilation to generate SMOS-Level 4 maps of sea surface salinity

The Soil Moisture/Ocean Salinity (SMOS) satellite, launched in November 2009, measures visibilities at L-band, from which brightness temperatures are computed. This information is used to retrieve values of the sea surface salinity (SSS) and soil moisture; two variables whose observation is a key to better understand the oceanic component of the water cycle. A hierarchy of SSS products has been defined in the SMOS data processing chain. This work focuses on the so-called Level 3 (binned maps of SSS) and Level 4 (products combining SMOS data with any other source of information). The objective is to illustrate the feasibility of using data assimilation to produce Level 4 maps of sea surface salinity. The numerical model will increase the geophysical coherence of SMOS data as a dynamical interpolator. Here, the employment of data assimilation differs from its usual applications (improving model outputs for example). Indeed, the numerical model will interpolate the observations according to the general laws of fluid mechanics and, if possible, reduce the error contained in the original observations. The data assimilation method analyzed is a nudging algorithm. The domain of application for this feasibility study is the Northeast subtropical Atlantic gyre, a challenging region due to the presence of a large amount of noise that deteriorates the SMOS data. The main sources of errors are the vicinity of large landmasses that introduce a spurious bias, and the presence of a significant amount of artificial radio frequency interferences (RFI). While the Quality Controls already set up in the SMOS processing chain do filter the retrievals containing too large errors, wrong data are still present in Level 3 maps. Despite this difficulty, the results provide meaningful SMOS SSS Level 4 products in terms of their geophysical coherence (estimated using singularity analysis) and better agreement with in-situ data than Level 3 product.