A two-step algorithm to delineate urban targets in PolSAR data was studied. First, we used an eigenvalue- and eigenvector-based decomposition algorithm without the azimuth symmetry assumption to extract three types of scattering components of radar targets. The odd scattering of urban targets was weaker than that of non-urban targets. The cross scattering in urban areas with small azimuth orientation (AO) angles was smaller than that in urban areas with large AO angles. Then, considering the relative importance of the odd and cross scatterings, urban targets were delineated. The algorithm was verified using PALSAR2 L-band data, San Francisco, California (CA), USA, where four types of radar targets, including urban targets with small and large AO angles, water surface, and vegetated areas, were studied. Urban targets of a wide range of AO angles have been effectively identified.

Trees in flat and wet forest floors and urban targets with small orientation angles can produce strong double scattering. Then, misclassification of trees as urban targets occurs in the PolSAR data decomposition. This study proposed an improved decomposition algorithm for the urban and forest targets classification. First, strong double scattering between forest and urban targets was differentiated, and then forest targets were treated as azimuthally symmetric radar targets in the PolSAR decomposition algorithm. The improved decomposition algorithm was verified by two L-band PolSAR datasets, a UAVSAR dataset northwest New Bern, NC, USA, and one ALOS PALSAR dataset San Francisco, CA, USA. In the datasets, the double scattering was dominant for forested areas. The double scattering dominated some urban areas, but other urban areas had strong volume scattering. Nevertheless, the revised decomposition algorithm correctly delineated both types of targets and extended the usability of the original decomposition algorithm.

Urban targets with large azimuth orientation angles and vegetation targets have strong volume scattering in polarimetric synthetic aperture radar (PolSAR) data. Their separation is a challenge. The real parts of ShhShv* and SvvSvh* correlate to the azimuthal symmetry of radar targets in the PolSAR data. The values are close to zero for azimuthally symmetric vegetation targets and much away from zero for azimuthally asymmetric urban targets. Also, in the urban area with large azimuth orientation angles, the real part values of ShhShv* are negative, but the real part values of SvvSvh* positive. Thus, the sum of the real parts of ShhShv* and -SvvSvh* are studied as a new separation descriptor, Sd, to delineate the urban targets from vegetation targets. The descriptor is evaluated by using the 2009 PALSAR San Francisco PolSAR data. The Sdvalues are negative in the urban area where targets with large azimuth orientation angles exist but close to zero in the vegetated area. Correct separation rates between the urban and vegetation targets are 92.44%-99.5%, with an empirical threshold. Thus, the descriptor can effectively differentiate urban targets with large azimuth orientation angles from vegetation targets.

With a moving SAR platform, the HV (H transmitted and V received) and VH (V transmitted and H received) components in the polarimetric synthetic aperture radar (PolSAR) data are obtained at different positions along the azimuth direction. The HV and VH datasets' correlation coefficient varies whether a ground radar target is stationary or not. A building is standing, but a tree canopy may not be due to the surrounding air movement. Therefore, based on the correlation between the HV and VH components in one single PolSAR dataset, a stationary descriptor,$S_{D}$, was studied to separate the volume scattering from a stationary urban target with a large azimuth orientation angle or a non-stationary vegetation canopy. The separability was evaluated using two PALSAR PolSAR datasets. The correct separation rates were 86.2% for urban targets with large azimuthal orientation angles and 93.9% or higher for vegetation targets. Thus,$S_{D}$effectively differentiates urban targets with large azimuth orientation angles from vegetation targets.

The tree height estimation using the simplified three-stage algorithm and HH and HV polarizations or dual-polarization data was studied. First, we considered (HH+2×HV) as VV data obtaining the simulated quad-polarization data. Then, the polarization interference coefficients were computed. To reduce the combined effort of the HH and HV polarizations on the interference coefficients, we revised the coefficients. Both sets of coefficients were used in the estimation of tree heights. The simulated mean tree height was 18 m. The estimation was improved when the revised coefficients were used. The mode of heights changed from 20.0 m to 18.4 m before and after the revision. Thus, the estimated tree heights should be acceptable. The required input of the quad-pol data to the three-stage algorithm was simplified with the input of the dual-pol data.

After revaluating the validity of the assumption made in the existing decomposition algorithms for the quad-pol SAR datasets, we proposed a two-step algorithm to resolve the invalid assumption. The algorithm was assessed using the quad-pol ALOS/PALSAR and NASA/JPL UAVSAR datasets. The results were satisfactory. The algorithm should be valid.

An algorithm to remove the impact of man-made targets on tree height estimation using the three-stage algorithm and PolInSAR data was studied. The data were the German/DLR E-SAR L-band PolInSAR data near Oberpfaffenhofen, Germany. Within the data, there were forested areas and man-made targets such as runways and buildings with variable azimuth angles. The runways with smooth surface or buildings with large azimuth angles were sequentially removed by the mask of surface and non-surface scattering components derived from the PolSAR decomposition algorithm, and the mask of high and low coherence coefficient. Then, tree heights were estimated. Results were satisfactory. Therefore, the developed algorithm was valid, and the applicability of the three-stage algorithm has been extended.

Coupled with Landsat-8 and SMAP brightness temperature datasets and using the HP model, an algorithm to estimate soil moisture in November of 2015 at Zoige alpine wetland, China was developed. The algorithm was verified using soil moisture data downloaded at NASA Earthdata web site. The spatial patterns of two datasets were similarly to each other with the maximum value occurring near the center but the minimum value in the eastern region. The spatial correlation coefficient of both datasets was 0.6832. The preliminary findings should be very encouraging in the pursuing to produce soil moisture data with high spatial resolution.

Soil moisture of Zoige wetland, China from 2007 to 2009 was estimated using PALSAR radiometrically terrain-corrected (RTC) and Landsat-5 surface reflectance data coupled with water cloud model (WCM) radar backscattering model. Soil moisture derived from L-HH polarization RTC data was suitable for assessing the soil moisture in Zoige wetland. In 2007, the soil moisture in the growing season had similar spatiotemporal patterns and trends, and the intra-annual difference of soil moisture was very small. However, the inter-annual soil moisture increased from 2007 to 2009 substantially. Variations of temperature and precipitation were attributed to the causes.

A new four component decomposition algorithm for urban targets delineation from PolSAR imagery was studied. First, a new correlation coefficient was introduced to describe characteristics of urban targets. The coefficient was the linear combination of two elements of a coherent matrix. Then, the coefficient was used to modify the volumetric scattering model component in Yamaguchi et al.'s four-component decomposition algorithm with the azimuth deorientation. Thus, a new four-component decomposition algorithm was established. With the algorithm, the percentage of double-bounced scattering increased but the percentage of volumetric scattering decreased in urban areas. Thus, the urban targets whose azimuth angles are 0° or non-zero° were effectively identified.

The influence of the azimuth angle and water surface roughness determined by wind speeds on the SAR imaging of a metallic bridge over water was simulated after the identification of the double- and triple-bounced radar returns. Different bounced returns were caused by interactions of the bridge surface and water surface. In the simulation, the radar wavelength was L-band. The azimuth angle was between 0 and 45°. The wind speed was from 0 to 40m/s. The Pierson-Moskowitz spectra were used to model the spatial spectral energy distribution of the sea surface. The Kirchhoff approximation algorithm was used to compute the backscatter. With the noise level of a SAR system at -30dB, three regions were delineated. In region I, both double- and triple-bounced returns existed. There were only double-bounced returns in region II. None of the double- and triple-bounced returns was in region III.

Monthly methane concentration (CH4) level over Zoige alpine wetland, China from October of 2002 to December of 2006 was analyzed based on the SCIAMACHY data. The level was low and might decrease from January to March then increase until August (except for 2006), and then decreased until fall or early winter. The linkage of the CH4, air temperature, precipitation, and soil temperature and moisture content was then explored by using meteorological and GLDAS datasets. The CH4emission and concentration level in the vertical column over the wetland could be affected more by the temperature than the precipitation.

A normalized function was proposed as the framework to study the heterogeneity of urban radar targets. The function consisted of three types of scales, the physical size of a target, heterogeneity in dielectric constant, and target geometry. The azimuth direction or azimuth scale, one component of the target geometry was exampled to investigate the influence of azimuth orientations of buildings on radar backscattering. The results should advance SAR application and theory.