Soil Moisture And Ocean Salinity Pixel Research Articles

Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models.

Impact of Urban Cover Fraction on SMOS and SMAP Surface Soil Moisture Retrieval Accuracy

Both the European Space Agency's soil moisture and ocean salinity (SMOS) mission and the National Aeronautics and Space Administration's soil moisture active passive (SMAP) mission employ L -band (1.413 GHz) radiometers to observe brightness temperatures at ∼40-km spatial resolution to subsequently derive global soil moisture every two to three days with a target accuracy of 0.04 m3/m3. However, the man-made structures that dominate urban areas in many of the SMOS and SMAP radiometers pixels may confound the interpretation of their radiometric observations if not taken into account, and thus, degrade the soil moisture retrieval accuracy. This paper investigates the effect that urban areas are expected to have on the SMOS and SMAP soil moisture retrieval accuracy using experimental data from the Australian airborne field campaigns performed over the past six years. Taking the total radiometric error budgets for the SMOS (3.95 K) and the SMAP (1.3 K) missions as conservative benchmarks for radiometric “error” that can be tolerated to achieve the 0.04 m3/m3 target accuracy, urban fraction thresholds of 6.6% and 2.2% were obtained for the SMOS and SMAP pixels, respectively, under warm dry (soil moisture < 0.15 m3/m3) conditions, increasing to 16.8% and 5.2% under cold and/or wet conditions. These results have been extrapolated globally, assuming that the microwave behavior of the cities analyzed here is representative of those elsewhere, to identify the SMOS and SMAP pixels that are expected to be adversely affected by urban areas if not explicitly taken into account in retrieval algorithms.

Proxy Indicators for Mapping the End of the Vegetation Active Period in Boreal Forests Inferred from Satellite-Observed Soil Freeze and ERA-Interim Reanalysis Air Temperature

Triggered by decreases in photoperiod and temperature, evergreen needle-leaved trees in the boreal region downregulate photosynthetic activity and enter dormancy in autumn. Accompanying changes in canopy structure and chlorophyll content are small and precede the cessation of photosynthetic activity. Low solar elevation and cloud cover during this period pose additional challenges for the use of optical satellite instruments. Alternatively, environmental variables that correlate with photosynthesis, such as soil freeze, can be detected from satellite microwave observations independent of weather and illumination conditions. We tested for the first time the usability of satellite-observed soil freeze from the Soil Moisture and Ocean Salinity (SMOS) instrument as a proxy indicator for the end of vegetation active period (VAPend) at six eddy covariance sites in Finland and Canada. The time when soil freeze commenced over the large SMOS pixel can be employed to estimate VAPend (R2 = 0.84, RMSE = 7.5 days), defined as the time when the photosynthetic capacity of the forest drops below 10% of the growing season maximum. In comparison to satellite-based soil freeze timing, an air temperature-based proxy from ERA-Interim reanalysis data showed better performance (R2 = 0.92, RMSE = 5.2 days). VAPend was mapped in the boreal forest zone in Finland and Canada from both indicators based on linear regression models.

Surface rock effects on soil moisture retrieval from L-band passive microwave observations

The L-band (1.41 GHz) passive microwave remote sensing technique is the approach used by the first satellites dedicated to soil moisture measurement, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) mission developed by the National Aeronautics and Space Administration (NASA). These satellites aim to provide global soil moisture maps for the top ~5 cm layer of soil with an accuracy better than 0.04 m3/m3. However, with a passive microwave observing resolution of ~40 km, non-soil targets such as surface rock may possibly confound the brightness temperature observations and degrade the accuracy of retrievals for many SMOS and SMAP pixels across the world. Since the microwave contribution of rock is not well accounted for in current soil moisture retrieval algorithms, simply ignoring its existence may be detrimental to the performance of resultant soil moisture products. Using a combination of model simulations and airborne field campaign data from central Australia, this study has determined that a rock cover fraction threshold of up to 0.4 can be tolerated before the 0.04 m3/m3 soil moisture target accuracy is potentially exceeded under extreme dry or wet conditions. However, this threshold reduces to 0.2 when assessed in terms of a brightness temperature impact >4 K. These rock fraction thresholds have subsequently been applied to the Ecoclimap rock cover map, identifying the SMOS and SMAP pixels globally that are likely to be adversely affected if rock is unaccounted for. The results show that approximately ~3.3% of all SMOS and SMAP pixels may have brightness temperature impacts exceeding 4 K from surface rock, with Asia being the most affected, having ~6.0% affected pixels. These values reduce to ~1.5% of SMOS and SMAP pixels globally, and ~3.1% for Asia, when assessed in terms of soil moisture errors expected to possibly exceed 0.04 m3/m3 when not accounting for surface rock.

SMOS optical thickness changes in response to the growth and development of crops, crop management, and weather

Abstract The Soil Moisture and Ocean Salinity (SMOS) remote sensing satellite was launched by the European Space Agency in 2009. The L-band brightness temperature observed by SMOS has been used to produce estimates of both soil moisture and τ, the optical thickness of the land surface. Although τ should theoretically be proportional to the amount of vegetation present within a SMOS pixel, several initial investigations have not been able to confirm this expected behavior. However, when the noise in the SMOS τ product is removed, τ in the U.S. Corn Belt, a region of extensive row-crop agriculture, has a distinct shape that mirrors the growth and development of crops. We find that the peak value of SMOS τ occurs at approximately 1000 °C day (base 10 °C) growing degree days after the mean planting date of maize (corn). We can explain this finding in the following way: τ is directly proportional to the water column density of vegetation; maize contributes the most to growing season changes in τ in the Corn Belt; and maize reaches its maximum water column density at its third reproductive stage of development, at about 1000 °C day growing degree days. Consequently, SMOS τ could be used to monitor the phenology of crops in the Corn Belt at a spatial resolution similar to a U.S. county and a temporal frequency on the order of days. We also examined the magnitude of the change in SMOS τ over the growing season and hypothesized it would be related to the amount of accumulated solar radiation, but found this not to be the case. On the other hand, the change in magnitude was smallest for the year in which the most precipitation fell. These findings are rational since SMOS τ at the satellite scale is in fact a function of both vegetation and soil surface roughness, and soil surface roughness is reduced by precipitation. To fully explain changes in SMOS τ in the Corn Belt it appears that it will be necessary to use in situ and remotely-sensed observations along with agro-ecosystem models to account for land management decisions made by farmers that affect changes in soil surface roughness and all of the relevant biophysical processes that affect the growth and development of crops.

Snow density and ground permittivity retrieved from L-band radiometry: Application to experimental data

Abstract The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between − 6 kg m − 3 and 15 kg m − 3 for the forest clearing site, and between 37 kg m − 3 and 90 kg m − 3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈ 45 km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.

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.

Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Validation of SMOS Brightness Temperatures During the HOBE Airborne Campaign, Western Denmark

The Soil Moisture and Ocean Salinity (SMOS) mission delivers global surface soil moisture fields at high temporal resolution which is of major relevance for water management and climate predictions. Between April 26 and May 9, 2010, an airborne campaign with the L-band radiometer EMIRAD-2 was carried out within one SMOS pixel (44 × 44 km) in the Skjern River Catchment, Denmark. Concurrently, ground sampling was conducted within three 2 × 2 km patches (EMIRAD footprint size) of differing land cover. By means of this data set, the objective of this study is to present the validation of SMOS L1C brightness temperatures <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> of the selected node. Data is stepwise compared from point via EMIRAD to SMOS scale. From ground soil moisture samples, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's are pointwise estimated through the L-band microwave emission of the biosphere model using land cover specific model settings. These <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's are patchwise averaged and compared with EMIRAD <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's. A simple uncertainty assessment by means of a set of model runs with the most influencing parameters varied within a most likely interval results in a considerable spread of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's (5-20 K). However, for each land cover class, a combination of parameters could be selected to bring modeled and EMIRAD data in good agreement. Thereby, replacing the Dobson dielectric mixing model with the Mironov model decreases the overall RMSE from 11.5 K to 3.8 K. Similarly, EMIRAD data averaged at SMOS scale and corresponding SMOS <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's show good accordance on the single day where comparison is not prevented by strong radio-frequency interference (RFI) (May 2, avg. RMSE = 9.7 K). While the advantages of solid data sets of high spatial coverage and density throughout spatial scales for SMOS validation could be clearly demonstrated, small temporal variability in soil moisture conditions and RFI contamination throughout the campaign limited the extent of the validation work. Further attempts over longer time frames are planned by means of soil moisture network data as well as studies on the impacts of organic layers under natural vegetation and higher open water fractions at surrounding grid nodes.

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.

A proposed extension to the soil moisture and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40–50 km. Uncertainty in the retrieval of soil moisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soil moisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops), with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40–60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE, −3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, −0.3%v/v bias) in pixels characterised by a critical amount of forest (40–60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, based on an aperture synthesis L-band radiometer was successfully launched in November 2009. In the context of a validation campaign for the SMOS mission, intensive airborne and in situ observations were performed in southwestern France for the SMOS CAL/VAL, from April to May 2009 and from April to July 2010. The CAROLS (Cooperative Airborne Radiometer for Ocean and Land Studies) bi-angular (34°–0°) and dual-polarized (V and H) L-band radiometer was designed, built and installed on board the French ATR-42 research aircraft. During springs of 2009 and 2010, soil moisture observations from the SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) network of Météo-France were complemented by airborne observations of the CAROLS L-band radiometer, following an Atlantic–Mediterranean transect in southwestern France. Additionally to the 12 stations of the SMOSMANIA soil moisture network, in situ measurements were collected in three specific sites within an area representative of a SMOS pixel. Microwave radiometer observations, acquired over southwestern France by the CAROLS instrument were analyzed in order to assess their sensitivity to surface soil moisture ( w g ). A combination of microwave brightness temperature ( T b) at either two polarizations or two contrasting incidence angles was used to retrieve w g through regressed empirical logarithmic equations with good results, depending on the chosen configuration. The regressions derived from the CAROLS measurements were applied to the SMOS T b and their retrieval performance was evaluated. The retrievals of w g showed significant correlation ( p-value < 0.05) with surface measurements for most of the SMOSMANIA stations (8 of 12 stations) and with additional field measurements at two specific sites, also. Root mean square errors varied from 0.03 to 0.09 m 3 m − 3 (0.06 m 3 m − 3 on average).

Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

Abstract. In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS1database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km2). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km2) requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66%) but the precipitation occurrences are in general retrieved (about 67%). To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT) model – ISBA (Interactions between Soil Biosphere Atmosphere) is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E). We show that the PERSIANN database provides useful information at temporal and spatial scales in the context of soil moisture retrieval. 1Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System – http://chrs.web.uci.edu/persiann

Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Abstract. The main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the Science preparation for SMOS, the Valencia Anchor Station (VAS) experimental site, in Spain, was chosen to be one of the main test sites in Europe for Calibration/Validation (Cal/Val) activities. In this framework, the paper presents an approach consisting in accurately simulating a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over the wide VAS surface (50×50 km2). Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface) - module ISBA (Interactions between Soil-Biosphere-Atmosphere) to simulate the spatial and temporal distribution of surface soil moisture. The calibration as well as the validation of the ISBA model are performed using in situ soil moisture measurements. It is shown that a good consistency is reached when point comparisons between simulated and in situ soil moisture measurements are made. Actually, an important challenge in remote sensing approaches concerns product validation. In order to obtain an representative soil moisture mapping over the Valencia Anchor Station (50×50 km2 area), a spatialization method is applied. For verification, a comparison between the simulated spatialized soil moisture and remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E) and from the European Remote Sensing Satellites (ERS-SCAT) is performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-SCAT soil moisture products are also used to be compared with the simulated spatialized soil moisture. However, the lack of soil moisture data from the ERS-SCAT sensor over the area (45 observations for one year) prevented capturing the soil moisture variability.

Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency

A deterministic approach for downscaling ∼ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.