Mahta Moghaddam

The Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Councils Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earths land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

Vegetation characteristics and underlying topography from interferometric radar

This paper formulates and demonstrates methods for extracting vegetation characteristics and underlying ground surface topography from interferometric synthetic aperture radar (INSAR) data. The electromagnetic scattering and radar processing, which produce the INSAR observations, are modeled, vegetation and topographic parameters are identified for estimation, the parameter errors are assessed in terms of INSAR instrumental performance, and the parameter estimation is demonstrated on INSAR data and compared to ground truth. The fundamental observations from which vegetation and surface topographic parameters are estimated are (1) the cross-correlation amplitude, (2) the cross-correlation phase, and (3) the synthetic aperture radar (SAR) backscattered power. A calculation based on scattering from vegetation treated as a random medium, including the effects of refractivity and absorption in the vegetation, yields expressions for the complex cross correlation and backscattered power in terms of vegetation characteristics. These expressions lead to the identification of a minimal set of four parameters describing the vegetation and surface topography: (1) the vegetation layer depth, (2) the vegetation extinction coefficient (power loss per unit length), (3) a parameter involving the product of the average backscattering amplitude and scatterer number density, and (4) the height of the underlying ground surface. The accuracy of vegetation and ground surface parameters, as a function of INSAR observation accuracy, is evaluated for aircraft INSAR, which is characterized by a 2.5-m baseline, an altitude of about 8 km, and a wavelength of 5.6 cm. It is found that for ≈0.5% accuracy in the INSAR normalized cross-correlation amplitude and ≈5° accuracy in the interferometric phase, few-meter vegetation layer depths and ground surface heights can be determined from INSAR for many types of vegetation layers. With the same observational accuracies, extinction coefficients can be estimated at the 0.1-dB/m level. Because the number of parameters exceeds the number of observations for current INSAR data sets, external extinction coefficient data are used to demonstrate the estimation of the vegetation layer depth and ground surface height from INSAR data taken at the Bonanza Creek Experimental Forest in Alaska. This demonstration shows approximately 5-m average ground truth agreement for vegetation layer depths and ground-surface heights, with a clear dependence of error on stand height. These errors suggest refinements in INSAR data acquisition and analysis techniques which will potentially yield few-meter accuracies. The information in the INSAR parameters is applicable to a variety of ecological modeling issues including the successional modeling of forested ecosystems.

Bistatic scattering from three-dimensional layered rough surfaces

An analytical method to calculate the bistatic-scattering coefficients of a three-dimensional layered dielectric structure with slightly rough interfaces is presented. The interfaces are allowed to be statistically distinct, but possibly dependent. The waves in each region are represented as a superposition of an infinite number of up- and down-going spectral components whose amplitudes are found by simultaneously matching the boundary conditions at both interfaces. A small-perturbation formulation is used up to the first order, and the scattered fields are derived. The calculation intrinsically takes into account multiple scattering processes between the boundaries. The formulation is then validated against known solutions to special cases. New results are generated for several cases of two- and three-layer media, which will be directly applicable for modeling of the signals from radar systems and subsequent estimation of a layered medium subsurface properties, such as moisture content and layer depths

Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery

Characterization of boreal forests in ecosystem models requires temporal and spatial distributions of water content and biomass over local and regional scales. The authors report on the use of a semi-empirical algorithm for deriving these parameters from polarimetric synthetic aperture radar (SAR) measurements. The algorithm is based on a two layer radar backscatter model that stratifies the forest canopy into crown and stem layers and separates the structural and biometric attributes of forest stands. The structural parameters are estimated by training the model with SAR image data over dominant coniferous and deciduous stands in the boreal forest such as jack pine, black spruce, and aspen. The algorithm is then applied on AIRSAR images collected during the Boreal Ecosystem Atmospheric Study (BOREAS) over the boreal forest of Canada. The results are verified using biometry measurements during BOREAS-intensive field campaigns. Field data relating the water content of tree components to dry biomass are used to modify the coefficients of the algorithm for crown and stem biomass. The algorithm was then applied over the entire image generating biomass maps. A set of 18 test sites within the imaged area was used to assess the accuracy of the biomass maps. The accuracy of biomass estimation is also investigated by choosing different combinations of polarization and frequency channels of the AIRSAR system. It is shown that polarimetric data from P-band and L-band channels provide similar accuracy for estimating the above-ground biomass for boreal forest types. In general, the use of P-band channels can provide better estimates of stem biomass, while L-band channels can estimate the crown biomass more accurately.

Nonlinear two-dimensional velocity profile inversion using time domain data

An iterative algorithm is developed to solve the nonlinear inverse scattering problem for two-dimensional lossless dielectric inhomogeneities using time-domain scattering data. The method is based on performing Born-type iterations on a volume integral equation and, hence, successively calculating higher-order approximations to the unknown object profile. Both the full-angle and the limited-angle problems are considered. Solutions are obtained for cases where the first-order Born approximation is severely violated. Wideband time-domain scattered field measurements make it possible to use sparse data sets and thus reduce experimental complexity and computation time. Several examples are given to show the ability of this method to invert arbitrarily shaped permittivity profiles using few transmitters and receivers. The high-resolution capability of the algorithm is also demonstrated. >

The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

Mapping vegetated wetlands of Alaska using L-band radar satellite imagery.

Wetlands act as major sinks and sources of important atmospheric greenhouse gases and can switch between atmospheric sink and source in response to climatic and anthropogenic forces in ways that are poorly understood. Despite their importance in the carbon cycle, the locations, types, and extents of northern wetlands are not accurately known. We have used two seasons of L-band synthetic aperture radar (SAR) imagery to produce a thematic map of wetlands throughout Alaska. The classification is developed using the Random Forests decision tree algorithm with training and testing data compiled from the National Wetlands Inventory (NWI) and the Alaska Geospatial Data Clearinghouse (AGDC). Mosaics of summer and winter Japanese Earth Resources Satellite 1 (JERS-1) SAR imagery were employed together with other inputs and ancillary datasets, including the SAR backscatter texture map, slope and elevation maps from a digital elevation model (DEM), an open-water map, a map of proximity to water, data collection dates, and geographic latitude. The accuracy of the resulting thematic map was quantified using extensive ground reference data. This approach distinguished as many as nine different wetlands classes, which were aggregated into four vegetated wetland classes. The per-class average error rate for aggregate wetlands classes ranged between 5.0% and 30.5%, and the total aggregate accuracy calculated based on all classified pixels was 89.5%. As the first high-resolution large-scale synoptic wetlands map of Alaska, this product provides an initial basis for improved characterization of land-atmosphere CH4 and CO2 fluxes and climate change impacts associated with thawing soils and changes in extent and drying of wetland ecosystems.

Estimating Subcanopy Soil Moisture with RADAR

The subcanopy soil moisture of a boreal old jack pine forest stand is estimated using polarimetric L and P band airborne synthetic aperture radar (AIRSAR) data. Model simulations have shown that for this stand the principal scattering mechanism responsible for radar backscatter is the double-bounce mechanism between the tree trunks and the ground. The data to be used here were acquired during five flights from June to September 1994 as part of the Boreal Ecosystem-Atmosphere Study (BOREAS) project. The dielectric constants, or equivalently moisture contents, of the trunks and soil can change significantly during this period. To estimate these dynamic unknowns, parametric models of radar backscatter for the double-bounce mechanism are developed using a series of simulations of a numerical forest scattering model. A nonlinear optimization procedure is used to estimate the dielectric constants. Ground measurements of soil and trunk moisture content are used to validate the results. The trunk moisture content measurements are used to gain confidence that the respective estimation results are accurate enough not to corrupt the soil moisture estimation, which is the main focus of this paper. After conversion of the trunk moisture measurements to dielectric constants it is found that the estimated values are within 14% of the measurements. Owing to possible calibration uncertainties in the soil moisture measurements on the ground as well as in AIRSAR data, the variations rather than the absolute levels of the estimated soil moisture are considered. The results indicate that the estimated variations closely track the measurements. The worst case average estimated change differs by <1% volumetric soil moisture from that measured on the ground.

Study of some practical issues in inversion with the Born iterative method using time-domain data

The performance of the Born iterative method of nonlinear two-dimensional profile inversion is examined for the reconstruction of large objects and in the presence of measurement noise. Time-domain data are used. It is shown that objects at least as large as about nine wavelengths can be inverted without any convergence problems. The algorithm is shown to perform well in the presence of 10% noise, or 20-dB signal-to-noise ratio. The simultaneous inversion of permittivity and conductivity profiles is formulated and solved using the Born iterative method. Objects with various loss tangents are reconstructed, and the limits of applicability of the algorithm are investigated. >

Microwave scattering from mixed-species forests, Queensland, Australia

The potential of synthetic aperture radar (SAR) data for retrieving the above-ground and component (e.g., branch, trunk) biomass of mixed-species forests (including woodlands) typical to subtropical Queensland, Australia, was evaluated using a wave scattering model based on that of Durden et al. (1989). The model was parameterized using field data collected for nine forest types, which were selected through combined analysis of 1 : 4000 aerial photographs and light detection and ranging data. The simulated SAR backscatter data demonstrated a good correspondence at most frequencies and polarizations with Airborne SAR data. Analysis of scattering mechanisms revealed dominance of C-band horizontal-vertical (HV) volume scattering and increases with small-branch/foliage biomass, dominance of L- and P-band HH trunk-ground scattering and increases with trunk biomass, and dominance of L-band HV volume (branch) scattering and increases with large-branch biomass. The study concluded that above-ground biomass estimated using empirical relationships with selected SAR channels will be more reliable for forests of similar structural form due to dominance of microwave interaction with particular biomass components and the strength and consistency of relationships between these and the affiliated components that represent the total. In mixed-species forests, retrieval will be compromised by interaction with a greater diversity of structures and variability in relationships between structural components. Although empirical relationships with selected combinations of channels (e.g., L-band HH/HV) might allow retrieval of component and total biomass of forests containing trees of similar form (e.g., as mapped using Landsat sensor data), the use of SAR inversion models was considered a more appropriate route for retrieving the biomass of forests containing a mix of structural forms.