Ahmad Al Bitar

Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network

The Soil Moisture and Ocean Salinity (SMOS) satellite has opened the era of soil moisture products from passive L-band observations. In this paper, validation of SMOS products over continental U.S. is done by using the Soil Climate Analysis Network (SCAN)/SNOwpack TELemetry (SNOTEL) soil moisture monitoring stations. The SMOS operational products and the SMOS reprocessing products are both used and compared over year 2010. First, a direct node-to-site comparison is performed by taking advantage of the oversampling of the SMOS product grid. The comparison is performed over several adjacent nodes to site, and several representative couples of site-node are identified. The impact of forest fraction is shown through the analysis of different cases across the U.S. Also, the impact of water fraction is shown through two examples in Florida and in Utah close to Great Salt Lake. A radiometric aggregation approach based on the antenna footprint and spatial description is used. A global comparison of the SCAN/SNOTEL versus SMOS is made. Statistics show an underestimation of the soil moisture from SMOS compared to the SCAN/SNOTEL local measurements. The results suggest that SMOS meets the mission requirement of 0.04 m3/m3 over specific nominal cases, but differences are observed over many sites and need to be addressed.

Disaggregation of SMOS Soil Moisture in Southeastern Australia

Disaggregation based on Physical And Theoretical scale Change (DisPATCh) is an algorithm dedicated to the disaggregation of soil moisture observations using high-resolution soil temperature data. DisPATCh converts soil temperature fields into soil moisture fields given a semi-empirical soil evaporative efficiency model and a first-order Taylor series expansion around the field-mean soil moisture. In this study, the disaggregation approach is applied to Soil Moisture and Ocean Salinity (SMOS) satellite data over the 500 km by 100 km Australian Airborne Calibration/validation Experiments for SMOS (AACES) area. The 40-km resolution SMOS surface soil moisture pixels are disaggregated at 1-km resolution using the soil skin temperature derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and subsequently compared with the AACES intensive ground measurements aggregated at 1-km resolution. The objective is to test DisPATCh under various surface and atmospheric conditions. It is found that the accuracy of disaggregation products varies greatly according to season: while the correlation coefficient between disaggregated and in situ soil moisture is about 0.7 during the summer AACES, it is approximately zero during the winter AACES, consistent with a weaker coupling between evaporation and surface soil moisture in temperate than in semi-arid climate. Moreover, during the summer AACES, the correlation coefficient between disaggregated and in situ soil moisture is increased from 0.70 to 0.85, by separating the 1-km pixels where MODIS temperature is mainly controlled by soil evaporation, from those where MODIS temperature is controlled by both soil evaporation and vegetation transpiration. It is also found that the 5-km resolution atmospheric correction of the official MODIS temperature data has a significant impact on DisPATCh output. An alternative atmospheric correction at 40-km resolution increases the correlation coefficient between disaggregated and in situ soil moisture from 0.72 to 0.82 during the summer AACES. Results indicate that DisPATCh has a strong potential in low-vegetated semi-arid areas where it can be used as a tool to evaluate SMOS data (by reducing the mismatch in spatial extent between SMOS observations and localized in situ measurements), and as a further step, to derive a 1-km resolution soil moisture product adapted for large-scale hydrological studies.

Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S.

As part of the Soil Moisture and Ocean Salinity (SMOS) validation process, a comparison of the skills of three satellites [SMOS, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) or Advanced Microwave Scanning Radiometer, and Advanced Scatterometer (ASCAT)], and one-model European Centre for Medium Range Weather Forecasting (ECMWF) soil moisture products is conducted over four watersheds located in the U.S. The four products compared in for 2010 over four soil moisture networks were used for the calibration of AMSR-E. The results indicate that SMOS retrievals are closest to the ground measurements with a low average root mean square error of 0.061 m3·m-3 for the morning overpass and 0.067 m3·m-3 for the afternoon overpass, which represents an improvement by a factor of 2-3 compared with the other products. The ECMWF product has good correlation coefficients (around 0.78) but has a constant bias of 0.1-0.2 m3·m-3 over the four networks. The land parameter retrieval model AMSR-E product gives reasonable results in terms of correlation (around 0.73) but has a variable seasonal bias over the year. The ASCAT soil moisture index is found to be very noisy and unstable.

SMOS Retrieval Results Over Forests: Comparisons With Independent Measurements

This paper shows results obtained by using SMOS Level 2 retrieval algorithm, run at prototype stage, over forests. For each SMOS pixel, the algorithm estimates the soil moisture (SM) and the vegetation optical depth (τ). Average τ values retrieved in 4 days of July 2011 over forest pixels are shown and compared against forest height estimated by GLAS Lidar on board ICEsat satellite. Results of the comparison show a significantly increasing trend of τ with respect to forest height. For each 1-m interval of forest height estimated by Lidar, the standard deviation of optical depth is slightly higher than 0.1. The analysis is made again considering forest τ retrieved in 4 days of February, May, and November 2011, and it is observed that seasonal effects over optical depth are low. As an insight, it is shown that the increasing trend is still observed after subdividing world forests into Coniferous, Deciduous Broadleaf, and Evergreen Broadleaf. Comparisons with independent information about biomass are also shown at regional level for the U.S. The increasing trend is still observed, but with a reduced range of values. For SM, 14 nodes of the SCAN/SNOTEL network in the U.S. are considered. Over 2 years of data, retrieved values of SM are compared against ground measurements. Obtained values of correlation coefficient, rms error, and bias are reported.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm

The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The retrieval algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS retrieval algorithm converges to realistic soil moisture retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.

Copula-Based Downscaling of Coarse-Scale Soil Moisture Observations With Implicit Bias Correction

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 variable 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 applications, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

An Analytical Model of Evaporation Efficiency for Unsaturated Soil Surfaces with an Arbitrary Thickness

Analytical expressions of evaporative efficiency over bare soil (defined as the ratio of actual to potential soil evaporation) have been limited to soil layers with a fixed depth and/or to specific atmospheric conditions. To fill the gap, a new analytical model is developed for arbitrary soil thicknesses and varying boundary layer conditions. The soil evaporative efficiency is written [0.5 – 0.5 cos(πθL/ θmax)]^P with θL being the water content in the soil layer of thickness L, θmax the soil moisture at saturation and P a function of L and potential soil evaporation. This formulation predicts soil evaporative efficiency in both energy-driven and moisture-driven conditions, which correspond to P 0.5 respectively. For P = 0.5, an equilibrium state is identified when retention forces in the soil compensate the evaporative demand above the soil surface. The approach is applied to in situ measurements of actual evaporation, potential evaporation and soil moisture at five different depths (5, 10, 30 and 60/100 cm) collected in summer at two sites in southwestern France. It is found that (i) soil evaporative efficiency cannot be considered as a function of soil moisture only, since it also depends on potential evaporation, (ii) retention forces in the soil increase in reaction to an increase of potential evaporation and (iii) the model is able to accurately predict soil evaporation process for soil layers with an arbitrary thickness up to 100 cm. This new model representation is expected to facilitate the coupling of land surface models with multi-sensor (multi-sensing-depth) remote sensing data.

An Initial Assessment of SMOS Derived Soil Moisture over the Continental United States

The recently available Soil Moisture and Ocean Salinity (SMOS) 1.4 GHz based soil moisture retrievals for the year of 2010 and the first nine months of 2011 are assessed over the continental United States (CONUS) region, along with soil moisture retrievals produced at Princeton University based on the Advanced Microwave Scanning Radiometer (AMSR-E) 10.7 GHz channel using the Land Surface Microwave Emission Model (LSMEM) and in-situ measurements from the Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN). The assessment is carried out using a performance metric developed by Crow (J. Hydromet., 2007), which calculates the ability of soil moisture estimates to correct errors in surface moisture predictions through a linear Kalman filter. Within the Crow framework, SMOS retrievals show the same level of skill as AMSR-E/LSMEM or SCAN when evaluated on the days where both are available. But the SMOS product is significantly less available than AMSR-E/LSMEM or SCAN, especially on rainy days, therefore it is less able to reproduce the rainfall-moisture dynamics and consequently achieves a lower performance metric if all available data are used from all products. Detailed analysis shows that, with uncertainties, the performance of both SMOS and AMSR-E/LSMEM generally decays with thicker vegetation and wetter climate but is not significantly influenced by topography. We expect SMOS to further improve its accuracy through validation studies and its availability under rainy conditions as well.

DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence

To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three-dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-D RB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.