Soil Moisture And Ocean Salinity L2 Research Articles

Assessment of 9 km SMAP soil moisture: Evidence of narrowing the gap between satellite retrievals and model-based reanalysis

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (∼10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(±0.15) for ERA5-Land vs. 0.60(±0.17) for SMAP-IB9 and 0.50(±0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(±0.18) for SMAP-IB9 vs. 0.66(±0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(±0.02) m3/m3 for both SMAP-E and ERA5-Land) and R (0.59(±0.16) for SMAP-IB9 vs. 0.61(±0.15) for ERA5-Land), respectively.

Soil moisture from remote sensing to forecast desert locust presence

Abstract Preventive control of desert locusts is based on monitoring recession areas to detect outbreaks. Remote sensing has been increasingly used in the preventive control strategy. Soil moisture is a major ecological driver of desert locust populations but is still missing in the current imagery toolkit for preventive management. By means of statistical analyses, combining field observations of locust presence/absence and soil moisture estimates at 1 km resolution from a disaggregation algorithm, we assess the potential of soil moisture to help preventive management of desert locust. We observe that a soil moisture dynamics increase of above 0.09 cm3/cm3 for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. We estimate the gains in early warning timing compared to using imagery from vegetation to be 3 weeks. We demonstrate that forecasting errors may be reduced by the combination of several types of indicators such as soil moisture and vegetation index in a common statistical model forecasting locust presence. Policy implications. Soil moisture estimates at 1 km resolution should be used to plan desert locust surveys in preventive management. When soil moisture increases in a dry area of potential habitat for the desert locust, field surveys should be conducted two months later to evaluate the need of further preventive actions. Remote sensing estimates of soil moisture could also be used for other applications of integrated pest management.

Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS

The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO2 fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO2. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands.

A Spatially Consistent Downscaling Approach for SMOS Using an Adaptive Moving Window

The European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) is the first spaceborne mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improve the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm for SMOS is proposed to obtain high-resolution (HR) SM maps at 1 km (L4), from the ∼40 km native resolution of the instrument. This algorithm introduces the concept of a shape adaptive moving window as an improvement of the current semi-empirical downscaling approach at SMOS Barcelona Expert Center, based on the “universal triangle”. Its inputs are as follows: the SMOS SM (L3 at ∼40 km spatial resolution), the vertical and the horizontal SMOS brightness temperatures (L1C at ∼40 km), and the HR normalized difference vegetation index and land surface temperature from optical-based sensors. The proposed method has the following advantages: HR SM maps are obtained while maintaining the dynamic range from the original L3 product; energy is conserved, because differences between aggregated L4 and L3 SM maps are negligible; and it can be applied to continental areas, even when they integrate different climates. A comparison of SMOS L3 and L4 products with in situ data for networks allocated in Spain and Australia shows good agreement in terms of correlation and root mean square error. The proposed method is shown to capture 1-km SM spatial variability while preserving the quality of SMOS at its native resolution.

Assimilation of near-real time data products into models of an urban basin

The goal of this study was to determine if assimilating a combination of various derived data products can help circumvent some of the difficulties associated with urban watershed modeling. Combinations of the SNODAS (Snow Data Assimilation System) snow water equivalent data, the SMOS (Soil Moisture and Ocean Salinity) L2 soil moisture, and streamflow observations were used for the data assimilation schemes. Combinations of these observation data sets were assimilated into lumped conceptual rainfall-runoff models of the highly-urbanized Don River basin (in southern Ontario) to determine if assimilation of geophysical variables will have a significant impact on simulations and forecasting in an urbanized watershed. The Ensemble Kalman Filter (EnKF) data assimilation method was used for these analyses, with various rainfall-runoff models that include GR4J, HyMod, MAC-HBV, and SAC-SMA models. The best data assimilation scheme for hydrologic modeling involved using a combination of streamflow, soil moisture, and snow water equivalent while performing both state and parameter updating. These results suggest that using a combination of soil moisture and snow water equivalent from the SMOS and SNODAS data products can improve simulations and ensemble forecasts in an urban basin.

The global SMOS Level 3 daily soil moisture and brightness temperature maps

Abstract. The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application (https://www.catds.fr/sipad/). The RE04 products, versions 300 and 310, used in this paper are also available at ftp://ext-catds-cpdc:catds2010@ftp.ifremer.fr/Land_products/GRIDDED/L3SM/RE04/.

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Northeast Brazil (NEB) has recently experienced one of its worst droughts in the last decades, with large losses on rainfed agriculture. Soil moisture is the main variable to monitor agricultural drought. The remote sensing approach for drought monitoring has been enriched with the launch of the Soil Moisture and Ocean Salinity (SMOS) in November 2009 by European Space Agency (ESA). In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture in the NEB. The SMOS-derived SWDI data (SWDIS) were evaluated against the atmospheric water deficit (AWD) calculated from in situ observations. Comparisons were made at seven-day and 0.25° scales, over the time-span of June 2010 to December 2013. It was found that the SWDIS has a reasonably good overall performance in terms of the drought-weeks detection (skill = 0.986) and capture of the upper soil moisture temporal dynamic (r = 0.652), implying that the SWDIS could be used to track agricultural droughts. Furthermore, SWDIS shows poor performance at sites located in mountains regions affected by severe droughts (−0.10 ≤ r ≤ 0.10). It is also noted that the vegetal cover/use, climate regime, and soil texture have little influence on the AWD-SWDIS coupling.

SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.

Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region.

Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°–25°N, 105°–125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index

Abstract Drought is a major cause of limited agricultural productivity and of crop yield uncertainty throughout the world. For that reason, agricultural drought research and monitoring are of increasing interest. Although soil moisture is the main variable to define and identify agricultural drought, the actual soil water content is rarely taken into account because this type of drought is commonly studied using methodologies based on either climatological data or hydrological modeling. Currently, it is possible to use remote sensing to obtain global and frequent soil moisture data that could be directly used for agricultural drought monitoring everywhere. For example, the SMOS (Soil Moisture and Ocean Salinity) satellite was launched in 2009 and provides global soil moisture maps every 1–2 days. In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture series in the REMEDHUS (Soil Moisture Measurement Stations Network) area (Spain) during the period 2010–2014. The satellite index was thus calculated using several approaches to obtain the soil water parameters and was compared with the SWDI obtained from in situ data. One approach was based directly on SMOS soil moisture time series (using the 5th percentile as an estimator for wilting point and the 95th percentile and the minimum of the maximum value during the growing season as estimators for field capacity). In this case, the results of the comparison were good, but the temporal distribution and the range of the index data were unrealistic. Other approaches were based on in situ data parameters and pedotransfer functions estimation. In this case, the results were better, and the satellite index was able to adequately identify the drought dynamics. Therefore, the final choice to apply the index in one particular site will depend on the availability of data. Finally, a comparison analysis was made with the SMOS SWDI and two indices (Crop Moisture Index, CMI, and Atmospheric Water Deficit, AWD) commonly used for agricultural drought monitoring and assessment. In both cases, the agreement was very good, and it was proven that SMOS SWDI reproduces well the soil water balance dynamics and is able to appropriately track agricultural drought.

Comparison of SMOS and AMSR-E vegetation optical depth to four MODIS-based vegetation indices

Abstract The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development.

Global-Scale Evaluation of Roughness Effects on C-Band AMSR-E Observations

Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF’s (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval process. In the first step, we retrieved a parameter (referred to as the parameter) accounting for the combined effects of roughness and vegetation. Then, global MODIS NDVI data were used to decouple the effects of vegetation from those of surface roughness. Finally, global maps of the Hr parameters were produced and discussed. Initial results showed that some spatial patterns in the values could be associated with the main vegetation types (higher values of were retrieved generally in forested regions, intermediate values were obtained over crops and grasslands, and lower values were obtained over shrubs and desert) and topography. For instance, over the USA, lower values of were retrieved in relatively flat regions while relatively higher values were retrieved in hilly regions.

SMOS soil moisture retrievals using the land parameter retrieval model: Evaluation over the Murrumbidgee Catchment, southeast Australia

The land parameter retrieval model (LPRM) is a methodology that retrieves soil moisture from low frequency dual polarized microwave measurements and has been extensively tested on C-, X- and Ku-band frequencies. Its performance on L-band is tested here by using observations from the Soil Moisture and Ocean Salinity (SMOS) satellite. These observations have potential advantages compared to higher frequencies: a low sensitivity to cloud and vegetation contamination, an increased thermal sampling depth and a greater sensitivity to soil moisture fluctuations. These features make it desirable to add SMOS-derived soil moisture retrievals to the existing European Space Agency (ESA) long-term climatological soil moisture data record, to be harmonized with other passive microwave soil moisture estimates from the LPRM. For multi-channel observations, LPRM infers the effective soil temperature (Teff) from higher frequency channels. This is not possible for a single channel mission like SMOS and therefore two alternative sources for Teff were tested: (1) MERRA-Land and (2) ECMWF numerical weather prediction systems, respectively. SMOS measures brightness temperature at a range of incidence angles, different incidence angle bins (45°, 52.5° and 60°) were tested for both ascending and descending swaths. Three LPRM algorithm parameters were optimized to match remotely sensed soil moisture with ground based observations: the single scattering albedo, roughness and polarization mixing factor. The soil moisture retrievals were optimized and evaluated against ground-based data from the Murrumbidgee Soil Moisture Monitoring Network (OzNet) in southeast Australia. The agreement with single-angle SMOS LPRM retrievals was close to the official SMOS L3 product, provided the three parameters were optimized for the OzNet dataset, with linear correlation of 0.70–0.75 (0.75–0.77 for SMOS L3), root-mean-square error of 0.069–0.085m3m−3 (0.084–0.106m3m−3 for SMOS L3) and small bias of −0.02–0.01m3m−3 (0.03–0.06m3m−3 for SMOS L3). These results suggest that the LPRM can be applied successfully to single-angle SMOS L-band observations, but further testing is required to determine if the same set of parameters can be used in other geographic areas.

EnOI Optimization for SMOS Soil Moisture Over West Africa

In land surface or numerical weather prediction (NWP) models, a soil moisture initialization scheme is important not to drift the prognostic variables to errors. We propose a novel approach for a stationary data assimilation scheme of ensemble optimal interpolation (EnOI) effective for soil moisture and ocean salinity (SMOS) soil moisture initialization. For the optimization of EnOI, the satellite retrieval error specification was conducted rather than ensemble evolution. As combining two ensembles generated from a satellite retrieval and a land surface model, this approach is termed as “two-step EnOI” in this study: (first step) the SMOS soil moisture retrieval ensembles (i.e., errors in brightness temperature, landscape, and geophysical parameters) were merged with SMOS L3 data; (second step) the data assimilation result from the first step was further used for the observations of the EnOI. This two-step EnOI was compared with a sequential ensemble Kalman filter (EnKF) evolving model state ensembles over time but assuming global constant a priori random errors for the SMOS observations. The point-scale comparison results showed that two-step EnOI was better matched with the field measurements than the SMOS L3 data and a sequential ensemble KF scheme. On meso-scale, a spatial average of two-step EnOI reached that of a sequential ensemble KF with the significantly reduced ensemble size. These results suggest that the performance of two-step EnOI is comparable to a sequential ensemble KF but computationally more effective. From this, it is illustrated that appropriate error specification of satellite retrieval is more important than a sequential evolution of model state ensembles, and brightness temperature ensemble mean can reduce the SMOS retrieval biases without sequential evolution.

The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau

This study evaluates SMOS (soil moisture and ocean salinity) soil moisture products against a newly established soil moisture network in the central Tibetan Plateau. Based on the results, the validity of assimilating the SMOS soil moisture retrievals into a land surface model is further evaluated. The ground truth is obtained by spatial upscaling from the network measurements within an area of approximately 10,000km2. Results show that both SMOS L2 and the preliminary version of L3 soil moisture products have large biases at the SMOS node scales (15 and 25km), but they can reflect the surface wetness conditions well when averaged at a 100-km scale during the unfrozen season (June to October). This finding indicates the applicability of SMOS retrievals is scale-dependent. Meanwhile, very few retrievals are available in winter due to the presence of frozen soil and snow cover, and the accuracy of ascending retrievals degrades during transition when diurnal freezing–thawing cycle occurs. Considering the SMOS L2 product has a better accuracy than that of L3, we assimilate it into a land surface model using a dual-pass land data assimilation scheme. The data assimilation estimate without in-situ tuning proves superior to either remote sensing or land surface modeling in estimating surface soil moisture for the unfrozen season, and its accuracy fulfills the SMOS measurement requirements (RMSD≤0.04m3m−3). Thus, the assimilation of SMOS retrievals holds promise to produce regional soil moisture dataset with acceptable accuracy for the Tibetan Plateau semi-arid region.

SMOS‐derived soil moisture anomalies and drought indices: a comparative analysis using in situ measurements

AbstractThe objective of this analysis is to demonstrate the feasibility of using a composite L2 Soil Moisture and Ocean Salinity (SMOS) soil moisture product for determining drought conditions by taking advantage of its spatial and temporal resolutions. The work investigates the potential relationships between soil moisture anomalies and two drought indices, the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index, both calculated on a ten‐day basis. As the two drought indices can be applied to different time scales for precipitation series, the influence of time scale on the drought definition is also studied. The anomalies were calculated both for the in situ soil moisture by REMEDHUS (Soil Moisture Measurement Stations Network, Spain) and from the SMOS L2 soil moisture product. In general, in situ anomalies exhibit higher correlation coefficients for the drought indices than those of SMOS, except for the shortest time scale. As expected, the short‐term remotely sensed anomalies have a high response to precipitation events. This effect may be due to the greater sensitivity of SMOS data to rainfall, as well as to the spatial averaged nature of its observations. The optimal time scale was 1 month for the SMOS values and ranged between 30 and 50 days for the in situ values. The use of evapotranspiration in the calculation of the indices did not improve the description of the anomalies. The relationship between indices and soil moisture conditions provides encouraging results. Indeed, this method generates preliminary but valuable insights for future satellite products. Copyright © 2014 John Wiley & Sons, Ltd.

Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment, Western Denmark

The Soil Moisture and Ocean Salinity (SMOS) satellite with a passive L-band radiometer monitors surface soil moisture. In addition to soil moisture, vegetation optical thickness $\tau_{\rm NAD}$ is retrieved (L2 product) from brightness temperatures ( $T_{B}$ , L1C product) using an algorithm based on the L-band Microwave Emission of the Biosphere (L-MEB) model with initial guesses on the two parameters (derived from ECMWF products and ECOCLIMAP Leaf Area Index, respectively) and other auxiliary input. This paper presents the validation work carried out in the Skjern River Catchment, Denmark. L1C/L2 data and the most sensitive algorithm parameters were analyzed by network and airborne campaign data collected within one SMOS pixel (44 km diameter). The SMOS retrieval is based on the prevailing low vegetation class. For the L1C comparison, $T_{B}$ 's were calculated from in situ soil moisture using L-MEB. Consistent with worldwide findings, the initial/retrieved SMOS soil moisture captures the in situ dynamics well but with significant wet/dry biases and too large amplitudes in case of the latter. While the initial $\tau_{\rm NAD}$ is in range with an in situ estimate for low agricultural vegetation, the retrieved $\tau_{\rm NAD}$ is too high with too pronounced temporal variability. A filter based on L2 criteria removed radio frequency interference (RFI) and improved the $R^{2}$ between retrieved and network soil moisture from 0.49 to 0.61, while the bias remained $(-0.092/-\!0.087 \hbox{m}^{3}/\hbox{m}^{3})$ . Likely error sources include the following: 1) still present RFI; 2) potential link between high retrieved $\tau_{\rm NAD}$ and other L-MEB parameters, e.g., low roughness parameter $(H_{R})$ ; 3) $\sim$ 18% lower sand and $\sim$ 8% higher clay fractions while $\sim\!\!0.35 \hbox{g/cm}^{3}$ lower bulk density in SMOS algorithm than in situ; and 4) caveats in the Dobson dielectric mixing model implemented in the L-MEB model. A previous study at the Danish validation site had revealed superior performance of the Mironov dielectric mixing model at the 2 $\times$ 2 km scale. Studies are ongoing to address the aforementioned issues, and the role of organic surface layers will be investigated.

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Abstract. Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB). The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and land surface temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in southern Germany. The brightness temperature root-mean-squared errors are between 6 K and 9 K. The L-MEB parameterisation is considered appropriate under local conditions even though it might possibly be further optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (R between 0.1–0.41). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for a considerable part of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles and the horizontal polarisation are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote-sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a longer term comparison.

Validation of SMOS Data Over Agricultural and Boreal Forest Areas in Canada

This study was conducted as part of the Soil Moisture and Ocean Salinity (SMOS) calibration and validation activities over agricultural and boreal forest sites located in Saskatchewan, Canada. For each site covering 33 km × 71 km (i.e., about two SMOS pixels), we examined the SMOS brightness temperature (L1c) and soil moisture (L2) products from May 1 to September 30, 2010. The consistency of these data with respect to theory and to the temporal variation of surface characteristics was first discussed at both sites. Then, the SMOS L1c (prototype 346) and L2 (prototypes 305-309) products were evaluated using the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) ground measurements and L-band passive microwave airborne measurements, in addition to AMSR-E soil moisture estimates and simulations from the zeroth order τ- ω radiative transfer model. For both study sites, the model underestimated SMOS brightness temperatures in V polarization, whereas an overestimation was observed in H polarization. The data sets showed that both the SMOS and AMSR-E soil moisture values were underestimated compared with ground measurements collected during CanEx-SM10 but less so for the AMSR-E estimates. The SMOS soil moisture product was underestimated with a RMSE varying from 0.15 to 0.18 m3/ m3. Furthermore, the overall results showed that errors in the soil moisture estimates increased with the absolute value of soil moisture.