Mihai A. Tanase

The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission

NASAs Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.

Sensitivity of X-, C-, and L-Band SAR Backscatter to Burn Severity in Mediterranean Pine Forests

Synthetic aperture radar (SAR) data at X-, C-, and L-bands have been investigated to determine the relationship between backscatter and forest burn severity over three sites in Spain. The dependence of SAR backscatter on local incidence angle and environmental conditions has been analyzed. At HH and VV polarizations, the backscatter increased with burn severity for X- and C-bands, whereas it decreased for L-band. Cross-polarized (HV) backscatter decreased with burn severity for all frequencies. Determination coefficients were used to quantify the relationship between radar backscatter and burn severity for given intervals of local incidence angle. For X- and C-band copolarized data, higher determination coefficients were observed for slopes oriented toward the sensors, whereas for cross-polarized data, the determination coefficients were higher for slopes oriented away from the sensor. At L-band, the association strength of cross-polarized data to burn severity was high for all local incidence angles. C- and L-band cross-polarized backscatter showed better potential for burn severity estimation in the Mediterranean environment when the local incidence angle is accounted for. The small dynamic range observed for X-band data could hinder its use in forests affected by fires.

Evaluation of IEM, Dubois, and Oh Radar Backscatter Models Using Airborne L-Band SAR

The backscatter predicted by three common surface scattering models (the Integral Equation Model (IEM), the Dubois, and the Oh models) was evaluated against fully polarized L-band airborne observations. Before any site-specific calibration, the Oh model was found to be the most accurate among the three, with mean errors between the simulated and the observed backscatter of 1.2 dB ( ±2.6 dB standard deviation of the error) and -0.4 dB ( ±2.4 dB) for HH and VV polarizations, respectively, while the IEM and Dubois presented larger errors, with a maximum of 4.5 dB ( ±2 dB) for the IEM in VV polarization. The backscatter errors were observed to be related to surface roughness, another major factor determining the electromagnetic scattering at the soil surface. An existing semiempirical calibration of the surface roughness correlation length was therefore applied to improve the mismatch between modeled and observed backscatters. The application of the semiempirical calibration led to a significant improvement of the backscatter prediction for the IEM. After calibration, the IEM outperformed the Oh model, resulting in a mean backscatter error of -0.3 dB ( ±1.1 dB) and -0.2 ( ±1.2 dB) for HH and VV polarizations, respectively. To test the robustness of the semiempirical calibration, calibration functions derived from an independent data set were applied and shown to also improve the (uncalibrated) IEM performance, suggesting that the calibration procedure is relatively robust for global application.

Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment

Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragon (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels.

TerraSAR-X Data for Burn Severity Evaluation in Mediterranean Forests on Sloped Terrain

TerraSAR-X (TSX) dual-polarized synthetic aperture radar (SAR) data from a test site in Spain have been investigated to determine the relationship between forest burn severity and SAR backscatter. The role of the local incidence angle on the backscatter coefficient has been also studied. Burn severity was estimated by means of composition burn index plots and the remotely sensed differenced normalized burn ratio index. To infer the potential of the TSX data for burn severity assessment, the determination coefficients obtained from linear regression analysis have been used. At horizontal transmit horizontal receive (HH) polarization, backscatter increased for slopes oriented toward the sensor and areas affected by high burn severity, whereas, at horizontal transmit vertical receive (HV) polarization, higher backscatter occurred for slopes oriented away from the sensor in areas of low burn severity. The dependence of the backscatter coefficient on topography for areas affected by forest fire has been confirmed. The HH backscatter presented a clear descending trend with the increase in local incidence angle, whereas the HV backscatter presented an ascending trend. The determination coefficients showed that, at HH polarization, better estimates of burn severity are obtained at low local incidence angles, whereas, for HV polarization, the best estimates are obtained at high local incidence angles. The dual-polarized X-band SAR data showed potential for burn severity estimation in the Mediterranean environment if local incidence angle is accounted for.

Sensitivity of L-Band Radar Backscatter to Forest Biomass in Semiarid Environments: A Comparative Analysis of Parametric and Nonparametric Models

This paper investigated the effectiveness of frequently used parametric and nonparametric models for biomass retrieval from L-band radar backscatter. Two areas, one in Spain and one in Australia, characterized by different tree species, forest structure, and field sampling designs were selected to demonstrate that retrieval error metrics are similar for different local conditions and sampling characteristics. A mixed-model retrieval strategy was proposed to reduce the overall (i.e., across the entire biomass range) as well as by-biomass-interval errors. Significant relationships were found between aboveground biomass and radar backscatter with most of the backscatter dynamic range being limited to a fairly low range of biomass values ( t/ha) in both study areas. Biomass retrieval errors were largely similar for all parametric and nonparametric models tested. However, some parametric models consistently provided lower correlation between the observed and the predicted biomass while nonparametric models generally provided an unbiased estimation. A mixed-model retrieval strategy was shown to reduce biomass estimation errors by up to 15%. Biomass retrieval errors were highly variable within the L-band sensitivity interval, suggesting that overall accuracy estimates should be used with care, particularly for low biomass intervals ( t/ha) where surface scattering could dominate the total backscatter. Despite exhibiting the highest dynamic range, low biomass areas were characterized by the highest estimation errors (in excess of 80%). Conversely, relative estimation errors were as low as 20%-35% for the 30-75 t/ha biomass intervals, while at higher biomass levels, the estimation error increased due to signal saturation.

Polarimetric Properties of Burned Forest Areas at C- and L-Band

Fully polarimetric C- and L-band synthetic aperture radar (SAR) data have been investigated to determine the relationship between polarimetric target decomposition components and forest burn severity over two sites located in a Mediterranean environment. The dependence of the polarimetric decomposition metrics on SAR acquisition geometry and environmental conditions was also analyzed at C-band. Multiple linear regression models with interactions (i.e., the incidence angle was included as a predictor variable and its interaction with the radar metrics was accounted for as a multiplicative effect) were used to quantify burn severity retrieval accuracy. According to our experiment, we found that for steep SAR acquisition geometries C-band polarimetric components related to surface scattering mechanisms had increased sensitivity to burn severity levels, while for datasets acquired with more grazing geometries the polarimetric components related to volume scattering and dihedral scattering mechanisms were more correlated with burn severity levels. At L-band only volume and dihedral scattering related decomposition components provided significant relationships with burn severity levels. Relatively low burn severity estimation errors (less than 20% of burn severity range) were obtained for all datasets, with L-band data presenting the highest sensitivity to fire effects. Using a single regression model provided sufficient accuracy for burn severity estimation when taking into account the local incidence angle. The use of fully polarimetric data improved the estimation accuracy of forest burn severity with respect to backscatter intensities by a small margin for our study sites. However, since backscatter intensity metrics already provide high retrieval accuracies, whatever improvement was bound to be low.

Effective LAI and CHP of a Single Tree From Small-Footprint Full-Waveform LiDAR

This letter has tested the canopy height profile (CHP) methodology as a way of effective leaf area index (LAIe) and vertical vegetation profile retrieval at a single-tree level. Waveform and discrete airborne LiDAR data from six swaths, as well as from the combined data of six swaths, were used to extract the LAIe of a single live Callitris glaucophylla tree. LAIe was extracted from raw waveform as an intermediate step in the CHP methodology, with two different vegetation-ground reflectance ratios. Discrete point LAIe estimates were derived from the gap probability using the following: 1) single ground returns and 2) all ground returns. LiDAR LAIe retrievals were subsequently compared to hemispherical photography estimates, yielding mean values within ±7% of the latter, depending on the method used. The CHP of a single dead Callitris glaucophylla tree, representing the distribution of vegetation material, was verified with a field profile manually reconstructed from convergent photographs taken with a fixed-focal-length camera. A binwise comparison of the two profiles showed very high correlation between the data reaching R2 of 0.86 for the CHP from combined swaths. Using a study-area-adjusted reflectance ratio improved the correlation between the profiles, but only marginally in comparison to using an arbitrary ratio of 0.5 for the laser wavelength of 1550 nm.

An Examination of the Effects of Spatial Resolution and Image Analysis Technique on Indirect Fuel Mapping

The high number of forest fires occurring every year constitutes one of the major degradation factors of ecosystems, especially in the Mediterranean region. The ability of satellite sensors to cover wide areas makes them valuable tools for the prevention, detection and mapping of wildfires and fire related properties of the ecosystems. However, increased spatial resolution, coupled with a lower number of available spectral channels, makes image analysis difficult. Thus, users need to employ additional image analysis techniques in order to achieve their objectives. The aim of this work was to assess to what extent potential fuels can be discriminated within different image analysis techniques and satellite imagery products using the indirect mapping method. The main hypothesis was that very high-resolution data, coupled with object-oriented image analysis (OOIA), would improve the ability to differentiate among potential fuels. To test this hypothesis, fuel types were extracted from high and very high spatial resolution satellite imagery using object-oriented and pixel-based image analyses, respectively. The general conclusion of this study was that the use of object-oriented image analysis not only produces more accurate results, but also allows differentiation among the largest number of potential fuels. Object-oriented analysis allows better discrimination of potential fuels using very high-resolution imagery and the indirect mapping method. Six different potential fuels were classified using the object-oriented approach compared with only four when using pixel-based image analysis. The overall accuracy achieved reached approximately 80%.

Forest Biomass Estimation at High Spatial Resolution: Radar Versus Lidar Sensors

This letter evaluates the biomass-retrieval error in pine-dominated stands when using high-spatial-resolution airborne measurements from fully polarimetric L-band radar and airborne laser scanning sensors. Information on total above-ground biomass was estimated through allometric relationships from plot-level field measurements. Multiple-linear-regression models were developed to model relationships between biomass and radar/lidar data. Overall, lidar data provided lower estimation errors (17.2 t·ha-1, 28% relative) when compared with radar data (30.3 t·ha-1, 61% relative). However, for the 30-100 t·ha-1 biomass range, the relative error from radar-based models was only 9% higher than that from lidar-based models. This suggests that high-spatial-resolution radar data could provide fundamentally similar results to lidar for some biomass intervals. This is an important finding for large-scale biomass estimation that needs to rely upon satellite data, as there are no lidar satellites planned for the foreseeable future.