[Show abstract][Hide abstract] 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 (τSMOS) on a global scale, to give a first indication of the potential of τSMOS 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 τSMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E (τAMSR-E). 16-day and annual average values of the passive microwave optical depth (τ) for the year 2010 were obtained from SMOS (1.4GHz) and AMSR-E (6.9GHz) 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 τSMOS and τAMSR-E from ascending orbits (Spearman's R=0.80). On global, annual scales, τSMOS showed higher correlations with τAMSR-E than with the VI's, while τAMSR-E was more highly correlated with VI's than with τSMOS. 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 τSMOS and τAMSR-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 τSMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development.
[Show abstract][Hide abstract] ABSTRACT: The FLuorescence EXplorer (FLEX) satellite mission, candidate of ESA's 8th Earth Explorer program, is explicitly optimized for detecting the sun-induced fluorescence emitted by plants. It will allow consistent measurements around the O2-B (687nm) and O2-A (760nm) bands, related to the red and far-red fluorescence emission peaks respectively, the photochemical reflectance index, and the structural-chemical state variables of the canopy. The sun-induced fluorescence signal, overlapped to the surface reflected radiance, can be accurately retrieved by employing the powerful spectral fitting technique. In this framework, a set of fluorescence retrieval algorithms optimized for FLEX are proposed in this study. Two main retrieval approaches were investigated: i) the optimization of the spectral fitting for retrieving fluorescence at the oxygen absorption bands; ii) the extension of the spectral fitting to a broader spectral window to retrieve the full fluorescence spectrum in the range from 670 to 780nm. The accuracy of the retrieval algorithms is assessed by employing atmosphere-surface radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes. The simulated dataset considers more realistic conditions because it includes directional effects, and the top-of-atmosphere radiance spectra are resampled to the current specifications of the FLuORescence Imaging Spectrometer (FLORIS) planned to serve as the primary instrument aboard FLEX. The retrieval accuracy obtained at the O2-A band is strongly affected by directional effects, and better performance is found in cases where directional effects are lower. However, the best performing algorithms tested provided similar performance, the RMSE (RRMSE) is 0.044mWm−2sr−1nm−1 (6.2%) at the O2-A band, 0.018mWm−2sr−1nm−1 (2.9%) at the O2-B band, and 6.225mWm−2sr−1 (6.4%) for the spectrally integrated fluorescence emission. The promising results achieved open new perspectives extending fluorescence studies not only in limited absorption bands, but its spectral behavior in relation to different plant species, photosynthetic rates and stress occurrences.
[Show abstract][Hide abstract] ABSTRACT: A methodology to retrieve soil moisture (SM) from SMOS data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness
temperatures, \Tb's) complemented with active microwaves (C-band ASCAT backscattering coefficients), and MODIS NDVI. The reference SM data used to train the NN are ECMWF model predictions.
The best configuration of SMOS data to retrieve SM using a NN is using \Tb's measured with both H and V polarizations for incidence angles from 25$^\circ$
to 60$^\circ$. The inversion of soil moisture can be improved by $\sim 10 \%$ by adding MODIS NDVI and ASCAT backscattering data and by an additional $\sim 5 \%$ by using local information on the maximum and minimum record of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN inverted SM is able to capture the temporal and spatial variability of the SM reference dataset. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's.
The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products.
The NN used in this study allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near real time processor and data assimilation in weather prediction models.
IEEE Transactions on Geoscience and Remote Sensing 11/2015; in press(11). DOI:10.1109/TGRS.2015.2430845 · 3.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m3/m3 to 0.046 m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.
[Show abstract][Hide abstract] ABSTRACT: A synthetic study was performed to determine the potential to retrieve dry-snow density and ground permittivity from multiangular L-band brightness temperatures. The thereto employed emission model was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS) coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. The restriction to L-band made it possible to avoid scattering and absorption in the snow volume, leading to a rather simple formulation of our emission model. Parametric model studies revealed L-band signatures related to the mass density of the bottom layer of a dry snowpack. This gave rise to the presented analysis of corresponding retrieval performances based on measurements synthesized with the developed emission model. The question regarding the extent to which random noise translates into retrieval uncertainties was investigated. It was found that several classes of snow densities could be distinguished by retrievals based on L-band brightness temperatures with soil moisture and ocean salinity (SMOS)-typical data quality. Further synthetic retrievals demonstrated that propagation effects must be taken into account in dry snow even at L-band when retrieving permittivity of the underlying ground surface. Accordingly, current SMOS-based retrievals seam to underestimate actual ground permittivity by typically 30% as dry snow is wrongly considered as “invisible.” Although experimental validation has not yet been performed, the proposed retrieval approach is seen as a promising step toward the full exploitation of L-band brightness temperatures available from current and future satellite Earth observation missions, especially over the cold regions of the Northern Hemisphere.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 08/2015; 8(8):1-14. DOI:10.1109/JSTARS.2015.2422998 · 3.03 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Soil moisture (SM) represents less than 1/10000 of the total water of our planet but it plays an important role as it
affects the water and energy exchanges at the land surface/atmosphere interface and it is the reservoir of water for
agriculture and vegetation in general. SM has been endorsed by the Global Climate Observing System (GCOS) as
an Essential Climate Variable. In order to use SM information for climate modeling, SM datasets spanning long
time periods are needed. In the context of the European Space Agency (ESA) Climate Change Initiative (CCI)
several strategies have been evaluated to merge SM datasets from different microwave sensors . These strategies
consist typically in merging a posteriori several SM datasets computed with different algorithms applied to data from
different sensors. In addition, they do not include data from the Soil Moisture and Ocean Salinity (SMOS) satellite
, which is the first mission specifically designed to retrieve SM from space. In the context of an ESA funded
project, we have studied several approaches to add SMOS data to long term SM datasets. In a first phase, three
different approaches are tested to merge ESA SMOS and NASA/JAXA Advanced Scanning Microwave Radiometer
(AMSR-E): (i) applying the LPRM algorithm to SMOS data (ii) using SMOS SM as reference to determine simple
regression equations linking AMSR-E brightness temperatures to SMOS SM and recomputing a SM dataset from
AMSR-E observations (iii) using neural networks (NNs) trained with ECMWF numerical weather prediction models
reanalysis to recompute new SM datasets coherent by construction using as input data from SMOS or AMSR-E. This
paper is mainly devoted to the third approach.
[Show abstract][Hide abstract] ABSTRACT: Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll and (ii) the functional status of actual photosynthesis and vegetation stress. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
Global Change Biology 07/2015; DOI:10.1111/gcb.13017 · 8.04 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the va riable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.
IEEE Transactions on Geoscience and Remote Sensing 06/2015; 53(6):3507-3521. DOI:10.1109/TGRS.2014.2378913 · 3.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure soil
moisture (hereafter SM) from space. The instrument on-board SMOS is a L-band aperture synthesis radiometer,
with full-polarization and multi-angular capabilities (Mecklenburg et al. 2012). The operational SM retrieval algo-
rithm is based on a physical model (Kerr et al. 2012). In addition, Rodriguez-Fernandez et al. (2014) have recently
implemented an inverse model based in neural networks using the approach of Aires & Prigent (2006), which con-
sists in training the neural networks with numerical weather prediction models (ECMWF, Balsamo et al. 2009). In
the context of an ESA funded project (de Jeu et al, this conference, session CL 5.7), we have studied this neural
network approach to create a consistent soil moisture dataset from 2003 to 2014 using NASA/JAXA Advanced
Scanning Microwave Radiometer (AMSR-E) and ESA SMOS radiometers as input data. Two neural networks al-
gorithms have been defined and optimized using AMSR-E or SMOS as input data in the periods 2003-Oct 2011
and 2010-2014, respectively. The two missions overlapping period has been used to demonstrate the consistency
of the SM dataset produced with both algorithms by comparing monthly averages of SM and by comparing with
time series of in situ measurements at selected locations and other SM products such as the SMOS operational SM,
ECMWF model SM, and AMSR-E LPRM SM (Owe et al. 2008). Finally, the long time series of SM obtained with
neural networks will be compared to in-situ measurements and ECMWF ERA-Interim SM at selected locations.
This long-term soil moisture dataset can be used for hydrological and climate applications and it is the first step
towards a longer dataset which will include additional sensors.
European Geophysical Union General Assembly 2015, Viena, Austria; 04/2015
[Show abstract][Hide abstract] ABSTRACT: The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010-11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.
Journal of Hydrometeorology 02/2015; 16(3):150223132350009. DOI:10.1175/JHM-D-14-0052.1 · 3.65 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree
[Show abstract][Hide abstract] ABSTRACT: Remote estimation of sun-induced chlorophyll fluorescence emitted by terrestrial vegetation can provide an unparalleled opportunity to track spatio-temporal variations of photosynthetic efficiency. Here we provide the first direct experimental evidence that the two peaks of the chlorophyll fluorescence spectrum can be accurately mapped from high-resolution radiance spectra and that the signal is linked to variations in actual photosynthetic efficiency. Red and far-red fluorescence measured using a novel airborne imaging spectrometer over a grass carpet treated with an herbicide known to inhibit photosynthesis was significantly higher than the corresponding signal from an equivalent untreated grass carpet. The reflectance signal of the two grass carpets was indistinguishable, confirming that the fast dynamic changes in fluorescence emission were related to variations in the functional status of actual photosynthesis induced by herbicide application. Our results from a controlled experiment at the local scale illustrate the potential for the global mapping of terrestrial photosynthesis through space-borne measurements of chlorophyll fluorescence.
[Show abstract][Hide abstract] ABSTRACT: Microwave radiometry L-band SMOS SMAP Radiative transfer Snow Soil freeze/thaw Passive L-band (1–2 GHz) observables are sensitive to surface soil moisture and ocean salinity, which is the core of the "soil moisture and ocean salinity" (SMOS) mission of the European Space Agency (ESA). This work investigates microwave emission processes that are important to link L-band brightness temperatures with soil freeze/ thaw states and the presence and the state of snow. To this end, a ground snow radiative transfer (GS RT) model has been developed on the basis of the "Microwave Emission Model of Layered Snowpacks" (MEMLS). Our model sensitivity study revealed that L-band emission of a freezing ground can be affected significantly by dry snow, which has been mostly disregarded in previous studies. Simulations suggest that even dry snow with mostly negligible absorption at the L-band can impact L-band emission of winter landscapes significantly. We found that impedance matching and refraction caused by a dry snowpack can increase or decrease L-band emission depending on the polarization and the observation angle. Based on the performed sensitivity study, these RT processes can be compensatory at vertical polarization and the observation angle of 50°. This suggests the use of vertical polarized brightness temperatures measured at around 50° in order to achieve segregated information on soil-frost. Furthermore, our simulations demonstrate a significant sensitivity of L-band emission at horizontal polarization with respect to the density of the lowest snow layer as the result of refraction and impedance matching by the snowpack.
[Show abstract][Hide abstract] ABSTRACT: The launch of the SMOS mission 2-Nov-2009 marked a milestone in remote sensing for it was the first time a radiometer capable of acquiring wide field of view images at every single snapshot, a unique feature of the synthetic aperture technique, made it to space. The technology behind such an achievement was developed thanks to the effort of a community of researchers and engineers in different groups around the world. It was only because of their joint work that SMOS finally became a reality. The fact that the European Space Agency, together with CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnológico e Industrial), managed to get the project through should be considered a merit and a reward for that entire community. This paper is an invited historical review that, within a very limited number of pages, tries to provide insight into some of the developments which, one way or another, are imprinted in the name of SMOS.
[Show abstract][Hide abstract] ABSTRACT: We present a novel algorithm for detecting seasonal soil freezing processes using L-band microwave radiometry. L-band is the optimal choice of frequency for the monitoring of soil freezing, due to the inherent high contrast of the microwave signature between the frozen and thawed states of the soil medium. Dual-polarized observations of L-band brightness temperature at a range of observation angles were collected from a tower-based instrument, and evaluated against ancillary information on soil and snow properties over four winter seasons. During the first three winter periods the measurement site was located over mineral soil on a forest clearing, for the fourth winter the instrument was moved to a wetland site. Both sites are located in Sodankylä, Northern Finland. The test sites represent two environments typical for the northern boreal forest zone. The data were applied to derive an empirical relation between the onset and progress of soil freezing and the observed passive L-band signature. A retrieval algorithm was developed using the observations at the forest opening site. The algorithm exploits the perceived change in brightness temperature and the change in the relative difference between the signatures at horizontal and vertical polarization. With the collected experimental dataset, these features were linked optimally to the progress of soil freezing by choice of observation angle, polarization and temporal averaging. The wetland site observations provided the first opportunity for demonstrating the developed algorithm over a different soil type, giving a first estimate of the algorithm performance over larger heterogeneous targets. The future objective is to adapt the algorithm to L-band satellite observations. The present study is highly relevant for the development of freeze–thaw algorithms from current and future L-band satellite missions such as SMOS and SMAP.
[Show abstract][Hide abstract] ABSTRACT: In a recent paper, Leroux et al.  compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2- 3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable”. In this clarification, the analysis of Leroux et al. is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux et al. is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.
IEEE Transactions on Geoscience and Remote Sensing 05/2014; 52(3):1901-1906. DOI:10.1109/TGRS.2013.2282172 · 3.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS)
mission it has been shown that brightness temperatures at a low
microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice
properties. In a first demonstration study, sea ice thickness has been
derived using a semi-empirical algorithm with constant tie-points. Here
we introduce a novel iterative retrieval algorithm that is based on a
sea ice thermodynamic model and a three-layer radiative transfer model,
which explicitly takes variations of ice temperature and ice salinity
into account. In addition, ice thickness variations within a SMOS
footprint are considered through a statistical thickness distribution
function derived from high-resolution ice thickness measurements from
NASA's Operation IceBridge campaign. This new algorithm has been used
for the continuous operational production of a SMOS based sea ice
thickness data set from 2010 on. This data set is compared and validated
with estimates from assimilation systems, remote sensing data, and
airborne electromagnetic sounding data. The comparisons show that the
new retrieval algorithm has a considerably better agreement with the
validation data and delivers a more realistic Arctic-wide ice thickness
distribution than the algorithm used in the previous study.
The Cryosphere Discussions 12/2013; 7(6):5735-5792. DOI:10.5194/tcd-7-5735-2013
[Show abstract][Hide abstract] ABSTRACT: The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9 K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures' dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r2 = 0.58.
The Cryosphere Discussions 11/2013; 7(6). DOI:10.5194/tc-7-1971-2013