Ground-based C-band tomographic profiling of a conifer forest stand

We provide a demonstration of the new tomographic profiling (TP) technique, here applied to forestry for the first time. The portable ground-based synthetic aperture radar (GB-SAR) system was used to capture profiles of the vertical polarimetric backscattering patterns through a ～7 m tall stand of Norway spruce trees. The TP scheme collects data as for normal SAR imaging, but with the antennae aligned in the along-track direction. Adaptive post-processing meant that each TP scan simultaneously captured along-track image transects over the incidence angle range 0°–60°. An important feature of the derived image products is that incidence angle is constant at every point within an image. The measured HH–VV height backscatter profiles were very similar, whilst the cross-/co-polarization ratio showed very little variation with height through the stand. Backscattering profiles showed closest agreement with the branch biomass distribution through the canopy, rather than with trunk or branch + trunk biomasses. Equivalent interferometric tree heights were estimated from the centre of mass of the backscatter-height distribution, which displayed increasing height with increasing incidence angle. There was no significant vertical separation between the cross- and co-polarization returns.     

Towards a detection of grassland cutting practices with dual polarimetric TerraSAR-X data

In this study, polarimetric synthetic aperture radar (SAR) parameters are analysed and compared with in situ measurements in order to develop a methodology for detecting cutting practices within grassland areas. The grasslands were monitored with TerraSAR-X radar imaging in dual polarization HH/VV mode and are located near the banks of the Kasari River, close to the Baltic Sea coast of Estonia. The parameters analysed include HH, VV, HH + VV, and HH – VV backscatter, HH/VV polarimetric coherence magnitude and phase, T₁₂ polarimetric coherence magnitude and phase, and also dual polarimetric entropy, alpha, and alpha dominant parameters. Using these parameters derived from the dual polarimetric TerraSAR-X data set, it was virtually impossible to distinguish tall grass (height >30 cm) from short grass (height <30 cm). On the other hand, it proved feasible to detect areas where grass had been cut and left on the ground. Several parameters showed specific behaviour for the state of grassland and the most notable change was found in the dual polarimetric dominant scattering alpha angle. This angle changed from 10° to 25° after tall grass had been cut and left on the ground. This behaviour of the dominant scattering alpha angle can effectively be described using a particle scattering model for vegetation backscattering.     

Development of polarization ratio model for sea surface wind field retrieval from TerraSAR-X HH polarization data

In this article, the polarization ratio (PR) of TerraSAR-X (TS-X) vertical–vertical (VV) and horizontal–horizontal (HH) polarization data acquired over the ocean is investigated. Similar to the PR of C-band synthetic aperture radar (SAR), the PR of X-band SAR data also shows significant dependence on incidence angle. The normalized radar cross-section (NRCS) in VV polarization data is generally larger than that in HH polarization for incidence angles above 23°. Based on the analysis, two PR models proposed for C-band SAR were retuned using TS-X dual-polarization data. A new PR model, called X-PR hereafter, is proposed as well to convert the NRCS of TS-X in HH polarization to that in VV polarization. By using the developed geophysical model functions of XMOD1 and XMOD2 and the tuned PR models, the sea surface field is retrieved from the TS-X data in HH polarization. The comparisons with in situ buoy measurements show that the combination of XMOD2 and X-PR models yields a good retrieval with a root mean square error (RMSE) of 2.03 m s^–1 and scatter index (SI) of 22.4%. A further comparison with a high-resolution analysis wind model in the North Sea is also presented, which shows better agreement with RMSE of 1.76 m s^–1 and SI of 20.3%. We also find that the difference between the fitting of the X-PR model and the PR derived from TS-X dual-polarization data is close to a constant. By adding the constant to the X-PR model, the accuracy of HH polarization sea surface wind speed is further improved with the bias reduced by 0.3 m s^–1. A case acquired at the offshore wind farm in the East China Sea further demonstrates that the improvement tends to be more effective for incidence angles above 40°.     

Fully polarimetric airborne SAR and ERS SAR observations of snow: implications for selection of ENVISAT ASAR modes

Snow cover has a substantial impact on processes involved in the interaction between atmosphere and surface, and the knowledge of snow parameters is important in both climatology and weather forecasting. With the upcoming launch of Advanced Synthetic Aperture Radar (ASAR) instruments on Envisat, enhanced snow-mapping capabilities are foreseen. In this paper fully polarimetric C- and L-band airborne SAR data, ERS SAR and auxiliary data from various snow conditions in mountainous areas are analysed in order to determine the optimum ASAR modes for snow monitoring. The data used in this study are from the Norwegian part of the snow and ice experiment within the European Multi-sensor Airborne Campaign (EMAC'95) acquired in the Kongsfjellet area, located in Norway, 66°?N, 14°?E. Fully polarimetric C- and L-band SAR data from ElectroMagnetic Institute SAR (EMISAR), an airborne instrument operated by the Danish Center for Remote Sensing (DCR), were acquired in March, May, and July 1995. In addition, several ERS SAR, airborne photos, field and auxiliary data were acquired.

A larger separation between wet snow and bare ground in EMISAR C-VV polarisation data was found at high incidence angle (55°) compared to lower incidence angle (45°). Cross-polarized observations from bare ground, dry and wet snow in the incidence angle range 35° to 65° are below the specified Envisat ASAR noise floor of –20–22 dB. The backscattering angular dependency for wet snow and bare ground derived from EMISAR C-VV and ERS SAR data corresponds well, and agrees to some extent with volume and surface scattering model results. The C-band is more sensitive to variation in snow properties than the L-band.     

Simulated and observed L-HH radar backscatter from tropical mangrove forests

Abstract

We applied the Santa Barbara canopy backscatter model to model radar backscatter from mangrove forest stands in the Ganges delta of southern Bangladesh, and assessed the feasibility of delineating flooding boundaries within the stands. Modelled L-band (0-235 m wavelength) HH backscatter showed that canopy volume scattering dominated for stands under nonflooded ground surface. Double bounce trunk-ground term were enhanced by the presence of water under trees. For flooded mangrove forest, the trunk-ground term was dominant at small radar incidence angles; the trunk-ground term dominancy reduced as the incidence angle increased. Shuttle Imaging Radar (SIR-B) data and model results showed that for the mangrove forest, radar data with small incidence angles should be used to delineate the flooding boundaries.     

COSMO-SkyMed high resolution fully polarimetric synthetic aperture radar exploitation - the ‘strip-spot’ approach

This focused study aimed to generate a fully polarimetric synthetic aperture radar (SAR) data set of 1 m resolution based on the spotlight and stripmap COSMO-SkyMed (CSK) satellite data fusion. The results show the feasibility of overcoming the limitation of the nominal 3 m resolution generated by a series of multi-temporal stripmap SAR data observed in all the polarisations. The CSK satellite system does not allow the observation of cross-polar data in the spotlight acquisition mode because only co-polar data are available. In this work, a fully polarimetric scattering matrix is estimated by using two spotlights in the horizontal horizontal (HH) and vertical vertical (VV) polarisations and two stripmaps in the horizontal vertical (HV) and vertical horizontal (VH) polarisations. The stripmaps were resampled and super-resolved by using the amplitude and phase estimation of sinusoids (APES) filter to achieve the spotlight resolution. The results show that the proposed strip-spot approach has immediate operative applications.     

On the influence of canopy structure on the radar backscattering of mangrove forests

This paper is the third of a series which aims to evaluate the effects of canopy structure on the polarimetric radar response of mangrove forests. It complements the experimental and theoretical study of closed canopies presented in the previous papers by analysing two different mangrove stands of equal biomass but which greatly differ in their structure. For the three considered frequencies (C-, L- and P-band), experimental observations show that the back-scattering from the open declining stand is higher than that of the closed forest. The corresponding enhancement factor increases with wavelength and shows maximum values for the HH polarization. The identification of the scattering mechanisms occurring between the incident radar wave and the forest components was performed with the assistance of a polarimetric scattering model based on a radiative transfer approach. For the co-polarizations, results of the simulation study confirm that the backscatter enhancement is mainly due to an increase of either the surface scattering or the interaction component. For the cross-polarization HV at L- and P-bands, the increase of the volume component, originating from a stronger interaction with bigger branches, is found to be responsible for the observed enhancement. These findings confirm the large effect of the canopy structure on the forest backscatter and give rise to two important applications. First, the mapping of open declining mangrove stands appears feasible by using either the backscattering coefficient values, especially at P-HH and P-HV, or the HH-VV phase difference at P-band. Second, the use of the σ °-biomass statistical relationships must be restricted to homogeneous closed canopies.     

Application of a three-component scattering model over snow-covered first-year sea ice using polarimetric C-band SAR data

In this study we examine the utility of a three-component scattering model to quantify the sensitivity of radar incidence angle over snow-covered landfast first-year sea ice (FYI) during the late winter season. This three-component scattering model is based on (1) surface scattering contributed from the snow-covered FYI (smooth-ice (SI), rough-ice (RI), and deformed-ice (DI) types); (2) volume scattering contributed from snow layers which consist of enlarged snow grains, elevated brine volume, and preferential orientation of snow grains relative to radar look direction, as well as the underlying sea ice; and (3) double-bounce scattering contributed from ice ridges and ice fragments. This study uses RADARSAT-2 C-band polarimetric synthetic aperture radar (POLSAR) data acquired on 15 and 18 May 2009 for Hudson Bay, near Churchill, during late winter with surface air temperatures ≤?8°C at two different incidence angles (29° and 39°). The three-component scattering model is used to discriminate between snow-covered smooth, rough, and deformed FYI. The model shows enhanced discrimination at an incidence angle of 29°, compared with an incidence angle of 39°. The model is then used to quantify the sensitivity of radar incidence angle to each of the three scattering contributors. The results show that the relative fraction of surface scattering dominates for all three FYI types (SI ≈ 77.3%; RI ≈ 66.0%; and DI ≈ 61.1%) at 29° and decreases with increasing incidence angle and surface roughness. Volume scattering is found to be the second dominant mechanism (SI ≈ 19.1%, RI ≈ 32.2%, and DI ≈ 37.4% at 29° and SI ≈ 28.3%, RI ≈ 41.0%, and DI ≈ 49.5% at 39°) over snow-covered FYI and it increases with incidence angle and surface roughness. The double-bounce scattering contribution is low for all FYI types at both incidence angles.     

Relationships between physical characteristics and polarimetric radar backscatter for Corsican pine stands in Thetford Forest,U.K.

Global monitoring of forest extent and changes of extent with time are vital not just to provide a continuous assessment of a renewable resource essential to mankind but also to give an input to models of the global carbon cycle and of climate change. Microwave remote sensing is a potential means to accomplish this objective. However, to determine the optimum characteristics of a satellite synthetic aperture radar (SAR) for reliable forest monitoring it is first necessary to validate modelled microwave backscatter relationships in studies of relatively small areas using data from aircraft SAR systems and from detailed ground survey.

The Maestro 1 Campaign provided fully polarimetric SAR data in three wavelengths together with a large set of ground data for Thetford Forest. Calibrated microwave responses were averaged for more than 300 stands, primarily of Corsican pine. A georefercnccd database for the test site was built up from these mean responses combined with information on planting date, species and, for those stands fully sampled, characteristics such as height and timber volume. The radar backscatter at P and L bands, expecially the cross-polarized responses. correlate well with volume.

An analysis is made of the accuracy with which regression models can be used to predict timber volume over larger areas around the test site. The precision of this prediction can be improved by fuller use of the information contained within the measured data, specifically in the correlation between the HH and VV returns, and by the fuller use of theoretical models, which consider more comprehensively the complete polarimetric backscatter mechanisms relevant to the response of stands.     

Multi-incidence angle RISAT-1 Hybrid Polarimetric SAR data for large area mapping of maize crop – a case study in Khagaria district,Bihar, India

Analysis of hybrid polarimetric synthetic aperture radar data has gained importance in the last couple of years with the availability of spaceborne data from Radar Imaging Satellite-1 (RISAT-1). RISAT-1 provides right circular transmit and linear receive data in Fine Resolution Stripmap-1 (FRS-1) mode with a swath of 25 km approximately covering 625 km² areas. But an administrative unit, like a district, in India cannot be covered in single FRS-1 acquisition. In this article, the possibility of acquisition of multi-incidence angle FRS-1 data to cover a larger area in three consecutive days over Khagaria district of Bihar State, India, for maize crop discrimination and mapping was investigated. It was assumed that the difference of 3 days in imaging does not affect the backscatter response from maize crop as there will not be much change in the maize crop characteristics in 3 days. The backscatter response of maize crop, which is in maximum vegetative stage, was studied at three incidence angles (viz. 28°, 42°, and 52°). The analysis was carried out for the discrimination of maize crop at each incidence angle in Raney derived hybrid decomposition parameters viz. Odd bounce, Double bounce, and Volume scattering mechanisms. The result shows that there is a slight difference in the backscatter response from maize crop due to the changes in incidence angle from 28° to 42° and has not shown any significant difference from 42° to 52°. However, the maize crop got well discriminated in the scatter plots of volume and double bounce scattering at both 28° and 42° and with odd and volume scattering combinations at 52°. The classification of the multi-incidence angle data resulted in 47,732 ha of maize cropped area in Khagaria district during rabi (winter season), 2014–15 with the producer’s accuracy of 92.00%.     

Classification of low-salinity sea ice types by ranging scatterometer

Abstract

A helicopter-borne 8-channel ranging scatterometer HUTSCAT (Helsinki University of Technology Scatterometer) was used to investigate the backscattering behaviour of low-salinity sea ice at 5-4GHz and 9-8 GHz. The measurements were conducted during the BEPERS-88 Sea Ice Campaign in the Gulf of Bothnia, 6-12 March 1988. The backscattering properties of several sea ice types were examined at the two frequencies, using HH, VV, HV, and VH polarization modes. An incidence angle of 23° off nadir was used in order to investigate the feasibility of the ESA ERS-I (launched in July 1991) SAR for sea ice mapping. The capability of the new scatterometer to identify sea ice types was examined using the following radar output products at 8 channels: (a) the backscattering coefficient and (b) the characteristics of the radar return versus range spectrum (range resolution 65 cm).     

Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat

Increasing studies have been conducted to investigate the potential of polarimetric synthetic aperture radar (SAR) in crop growth monitoring due to the capability of penetrating the clouds, haze, light rain, and vegetation canopy. This study investigated the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) of corn and wheat. The in-situ measured crop height and FVC were collected from 29 April to 30 September 2013, at the study site in southwest Ontario, Canada. A total of 10 Radarsat-2 polarimetric SAR images were acquired throughout the same growing season. It was observed that at the early growing stage, the corn height was strongly correlated with the SAR parameters including HV (R² = 0.88), HH-VV (R² = 0.84), and HV/VV (R² = 0.80), and the corn FVC was significantly correlated with HV (R² = 0.79) and HV/VV (R² = 0.92), but the correlation became weaker at the later growing stage. The sensitivity of the SAR parameters to wheat variables was very low and only HV and Yamaguchi helix scattering showed relatively good but negative correlations with wheat height (R² = 0.57 and R² = 0.39) at the middle growing stage. These findings indicated that Radarsat-2 polarimetric SAR (C-band) has a great potential in crop height and FVC estimation for broad-leaf crops, as well as identifying the changes in crop canopy structures and phenology.     

Anisotropic analysis of polarimetric scattering and case studies with UAVSAR images

This article analyses the anisotropy of polarimetric scattering changing with azimuth incidence angle using a multi-look processed synthetic aperture radar (SAR) image. First, three canonical scattering models were developed to simulate the migration tracks on the Cameron polarimetric space. The migration tracks indicate that these polarimetric parameters have anisotropic property. Second, unmanned aerial vehicle synthetic aperture radar (UAVSAR) data are used to validate the simulated results. The Cameron scattering-type parameter z and the orientation angle calculated by SAR data are consistent with the simulated results by small perturbation method (SPM) double-scattering. Finally, based on the anisotropic analysis, a new method of extracting polarimetric information is proposed. Using this method, six parameters were obtained and two additional parameters, Purity and Stability, were derived. These parameters contain specific physical meaning and are useful in the recognition of the scattering mechanism. Purity can be used to recognize the simple structure scatterers with zero orientation. Stability has the potential to describe the dynamic property of scatterers.     

Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

ABSTRACT

The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band ~ 3 cm, C-band ~ 6 cm, and L-band ~ 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34°) and TerraSAR-X (HH, 22° and 53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.     

L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest

At present, the greatest source of uncertainty in the global carbon cycle is in the terrestrial ecosystems. In order to reduce these uncertainties it is necessary to provide consistent and accurate global estimates of the world forest biomass. One of the most promising methods for obtaining such estimates is through polarimetric SAR backscatter measurements at low frequencies. In this paper, the relation between polarimetric SAR backscatter at L- and P-bands and forest biomass is investigated using data acquired within the BioSAR-I campaign in southern Sweden during 2007. Methods for estimating biomass on stand level using these data are developed and evaluated, and the results for the two frequency bands are compared. For L-band data, the best results were obtained using HV-polarized backscatter only, giving estimation errors in terms of root mean square errors (RMSE) between 31% and 46% of the mean biomass for stands with biomass ranging from 10 to 290 t/ha, and an (adjusted) coefficient of determination (R²) between 0.4 and 0.6. For P-band data, the results are better than for L-band. Models using HV- or HH-polarized P-band backscatter give similar results, as does a model including both HV and HH. The RMSEs were between 18 and 27%, and the R² values were between 0.7 and 0.8.     

Multi-polarization Envisat-ASAR images as a function of leaf area index (LAI) of White Poplar and Desert Date plantations

The relationship between leaf area index (LAI) of plantations and multi-polarization Synthetic Aperture Radar (SAR) data (Envisat-ASAR) was investigated for White Poplar (Populus tomentosa Carr) and Desert Date (Elaeagnus angustifolius) in Heihe district, northwest china. The study showed that, for homogeneous White Poplar plantations, HH and HV polarization data (where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization) were sensitive to LAI and the r² (logistic relationship fits) values between HH polarization and LAI, 0.56 and 0.58 on 25 and 28 June images respectively, was much higher than that for the other polarizations of VV, VH and HV. For Desert Date plantations, the heterogeneity of the forests results in a more complex backscattering than that for White Poplar. Incidence angle also plays an important role in SAR backscattering, so a suitable SAR mode should be chosen to avoid scattering saturation when the LAI and incidence angle exceed certain values. The logistic polarization ratios of HH/HV and VV/VH showed varying correlation with LAI over White Poplar plantations, probably due to incidence angle.     

Predicting winter wheat condition,grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images

Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all‐weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi‐temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C‐HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C‐HH backscatter (r = 0.65, 0.67 and 0.70, respectively), and soil water content at 5 cm, 10 cm and 20 cm depths were correlated significantly with C‐VV backscatter (r = 0.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C‐HH backscatter (r = ?0.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C‐HH backscatter. Furthermore, C‐HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi‐spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C‐HH & C‐VV backscatter at winter wheat booting stage was observed (r = ?0.73 and ?0.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C‐HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C‐HH backscatter at milking stage (r = ?0.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain‐filling stage (r = 0.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C‐HH backscatter and SIPI data.     

Land deformation monitoring using coherent target-neighbourhood networking method combined with polarimetric information: a case study of Shanghai,China

In this article, an advanced approach for land deformation monitoring using synthetic aperture radar (SAR) interferometry combined with polarimetric information is presented. The linear and nonlinear components of the deformation, the error of the digital elevation model (DEM) and the atmospheric artefacts can be achieved by a coherent target (CT)-neighbourhood networking approach. In order to detect recent land deformation in Shanghai, China, 12 ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization images acquired from January 2006 to August 2008 are employed for deformation analysis. Over a 2.5-year period, two deformation velocity fields from HH and VV modes over Shanghai are derived using the CT-neighbourhood networking SAR interferometry (InSAR), then integrated into a final deformation map by a fusion scheme. It is found that the annual subsidence rates in the study area range from??20 to 10 mm year^?1 and the average subsidence rate in the downtown area reaches??7.5 mm year^?1, which is consistent with the local government statistics published in 2007.     

Evaluation of different orientation angle distributions within the X-Bragg scattering model for bare soil moisture estimation

In this paper, the applicability of three different orientation angle distributions of surface facets within the extended Bragg (X-Bragg) scattering model is investigated for estimation of soil moisture over bare surfaces using both Eigen-based and model-based polarimetric synthetic aperture radar (PolSAR) decomposition techniques. The three distributions considered for investigation in the X-Bragg model are uniform, half cosine, and the Lee distributions. In order to understand the sensitivity of the model using the three orientation angle distributions, key polarimetric parameters, such as scattering entropy (H), scattering anisotropy (A), scattering mechanism (α), cross-pol power (T₃₃), linear T₁₂ coherence (|γ_{(HH+VV)(HH–VV)}|), are simulated and analysed for various widths of distributions. The analysis of the simulated polarimetric parameters show that the Lee distribution has a reduced roughness validity range compared with the uniform and half cosine distributions. DLR E-SAR L-band data from the AgriSAR’2006 campaign over the Demmin test site in Northern Germany are inverted for soil moisture over bare surfaces. The inverted soil moisture from the physics-based X-Bragg model is compared with in situ measured TDR (time domain reflectometry) soil moisture values. The inversion results using the Eigen-based decomposition reveal similar root mean square error (RMSE = 14 vol.%) and inversion rates for three distributions. The model-based decomposition inversion results obtained at various fixed widths of distributions reveal that the Lee distribution shows less RMSE of 8 vol.% and high inversion rates for moderate surface roughness (ks = 0.5) as compared with half cosine and uniform distributions.     

Investigation of the effect of the incidence angle on land cover classification using fully polarimetric SAR images

Incidence angle is one of the most important imaging parameters that affect polarimetric SAR (PolSAR) image classification. Several studies have examined the land cover classification capability of PolSAR images with different incidence angles. However, most of these studies provide limited physical insights into the mechanism how the variation of incidence angle affects PolSAR image classification. In the present study, land cover classification was conducted by using RADARSAT-2 Wide Fine Quad-Pol (FQ) images acquired at different incidence angles, namely, FQ8 (27.75°), FQ14 (34.20°), and FQ20 (39.95°). Land cover classification capability was examined for each single-incidence angle image and a multi-incidence angle image (i.e., the combination of single-incidence angle images). The multi-incidence angle image produced better classification results than any of the single-incidence angle images, and the different incidence angles exhibited different superiorities in land cover classification. The effect mechanisms of incidence angle variation on land cover classification were investigated by using the polarimetric decomposition theorem that decomposes radar backscatter into single-bounce scattering, double-bounce scattering and volume scattering. Impinging SAR easily penetrated crops to interact with the soil at a small incidence angle. Therefore, the difference in single-bounce scattering between trees and crops was evident in the FQ8 image, which was determined to be suitable for distinguishing between croplands and forests. The single-bounce scattering from bare lands increased with the decrease in incidence angles, whereas that from water changed slightly with the incidence angle variation. Consequently, the FQ8 image exhibited the largest difference in single-bounce scattering between bare lands and water and produced the fewest confusion between them among all the images. The single- and double-bounce scattering from urban areas and forests increased with the decrease in incidence angles. The increase in single- and double-bounce scattering from urban areas was more significant than that from forests because C-band SAR could not easily penetrate the crown layer of forests to interact with the trunks and ground. Therefore, the FQ8 image showed a slightly better performance than the other images in discriminating between urban areas and forests. Compared with other crops and trees, banana trees caused stronger single- and double-bounce scattering because of their large leaves. As a large incidence angle resulted in a long penetration path of radar waves in the crown layer of vegetation, the FQ20 image enhanced the single- and double-bounce scattering differences between banana trees and other vegetation. Thus, the FQ20 image outperformed the other images in identifying banana trees.