The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

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.     

Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields

The objective of this investigation is to analyze the sensitivity of ASAR (Advanced Synthetic Aperture Radar) data to soil surface parameters (surface roughness and soil moisture) over bare fields, at various polarizations (HH, HV, and VV) and incidence angles (20°-43°). The relationships between backscattering coefficients and soil parameters were examined by means of 16 ASAR images and several field campaigns. We have found that HH and HV polarizations are more sensitive than VV polarization to surface roughness. The results also show that the radar signal is more sensitive to surface roughness at high incidence angle (43°). However, the dynamics of the radar signal as a function of soil roughness are weak for root mean square (rms) surface heights between 0.5 cm and 3.56 cm (only 3 dB for HH polarization and 43° incidence angle). The estimation of soil moisture is optimal at low and medium incidence angles (20°-37°). The backscattering coefficient is more sensitive to volumetric soil moisture in HH polarization than in HV polarization. In fact, the results show that the depolarization ratio σ_HH⁰/σ_HV⁰ is weakly dependent on the roughness condition, whatever the radar incidence. On the other hand, we observe a linear relationship between the ratio σ_HH⁰/σ_HV⁰ and the soil moisture. The backscattering coefficient ratio between a low and a high incidence angle decreases with the rms surface height, and minimizes the effect of the soil moisture.     

Assessing relationships between Radarsat-2 C-band and structural parameters of a degraded mangrove forest

Relationships were assessed between mangrove structural data (leaf area index (LAI), stem density, basal area, diameter at breast height (DBH)) collected from 61 stands located in a black mangrove (Avicennia germinans)-dominated forest and both single polarized ultra-fine (3 m) and multipolarized fine beam (8 m) Radarsat-2 C-band synthetic aperture radar (SAR) data. The stands examined included representatives from the four types of mangroves that typify this degraded system, specifically: predominantly dead mangrove, poor-condition mangrove, healthy dwarf mangrove, and tall healthy mangrove. The results indicate that the selection of the spatial resolution (3 m vs. 8 m) of the incidence angle (27–39°) and the polarimetric mode greatly influence the relationship between the SAR and mangrove structural data. Moreover, the extent of degradation, i.e. whether dead stands are considered, also determines the strength of the relationships between the various SAR and mangrove parameters.

When dead stands are included, the strongest overall relationships between the ultra-fine backscatter (incidence angle of ～32°) and the various structural parameters were found using the horizontal-horizontal (HH) polarization/horizontal-vertical (HV) polarization ratio. However, if the dead stands are not included, then significant relationships with the ultra-fine data were only calculated with the HH data. Similar results were observed using the corresponding incidence angle (～33°) of the fine beam data. When a shallower incidence angle was considered (～39°), fewer and weaker relationships were calculated. Moreover, no significant relationships were observed if the dead stands were excluded from the sample at this incidence angle. The highest correlation coefficients using the steepest incidence (～27°) were found with the co-polarized (HH, vertical-vertical (VV) polarization) modes. Several polarimetric parameters (entropy, pedestal height, surface roughness, alpha angle) based on the decomposition of the scattering matrix of the fine beam mode at this incidence angle were also found to be significantly correlated to mangrove structural data. The highest correlation (R = 0.71) was recorded for entropy and LAI. When the dead stands were excluded, volume scattering was found to be the most significant polarimetric parameter. Finally, multiple regression models, based on texture measures derived from both the grey level co-occurrence matrix (GLCM) and the sum and difference histogram (SADH) of the ultra-fine data, were developed to estimate mangrove parameters. The results indicate that only models derived from the HH data are significant and that several of these were strong predictors of all but stem density.     

Assessment of polarimetric SAR data for discrimination between wet versus dry soil moisture conditions

Radarsat-2 imagery from extreme dry versus wet conditions are compared in an effort to determine the value of using polarimetric synthetic aperture radar (SAR) data for improving estimation of fuel moisture in a chronosequence of Alaskan boreal black spruce ecosystems (recent burns, regenerating forests dominated by shrubs, open canopied forests, moderately dense forest cover). Results show strong distinction between wet and dry conditions for C-HH and C-LR polarized backscatter, and Freeman–Durden and van Zyl surface bounce decomposition parameters (35–65% change for all but the dense spruce site). These four SAR variables have high potential for evaluation of within site surface soil moisture, as well as for relative distinction between wet and dry conditions across sites for lower biomass and sparse canopy forested sites. However, for any given test site except the shrubby regrowth site, van Zyl volume, surface, and double bounce scattering all result in similar percentage increases from dry to wet soil condition. This indicates that for most of these test sites/cases moisture enhances the magnitude of the return for all scattering mechanisms evaluated. Thus, differences in scattering from the interaction of biomass, surface roughness, and moisture condition across sites remains an issue and backscatter due to surface roughness or biomass cannot be uniquely estimated. In contrast, the Cloude–Pottier C-band decomposition variables appear invariant to soil moisture, but may instead account for variations in ecosystem structure and biomass. Further investigation is needed, as results warrant future research focused on evaluation of multiple polarimetric parameters in algorithm development.     

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.     

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.     

Model-based target decomposition with the π/4 mode compact polarimetry dataπ/4模式简缩极化基于模型的目标分解

In this paper, model-based (surface, dihedral and volume scattering) target decomposition technique is proposed to decompose the π/4 mode compact polarimetric radar data. A general relationship between fully polarimetric coherence matrix and the Stokes vector of the π/4 mode compact polarimetric data is first established. Based on the Stokes vector, a proposed algorithm to retrieve the power of three scattering mechanisms is given in details. We validate this algorithm with L-band AIRSAR, San Francisco Bay, and results of decomposition are discussed and assessed in detail by being compared with the quad-pol Freeman-Durden decomposition results. Finally, the π/4 mode decomposition is compared with the CTLR (circular transmitting and linear reeving) mode, and with the π/4 mode m ? δ targets decomposition. The comparison results are analyzed and discussed in detail.     

Measurement of radar backscatter from rough soil surfaces using linear and circular polarizations†

Radar backscatters from loam with a dry bulk density of 0·6g/cm³ have been measured at 9·9 GHz using both linear and circular polarizations. The sensitivity of radar return to soil moisture content has been obtained at five polarization combinations, HH, VV, HV, LR and LL (L and R denote the left-circular and the right-circular polarizations, respectively). Comparison of the moisture sensitivities shows that the sensitivity of HV is the highest among five polarizations and the sensitivity of LL is slightly higher than that of HH, VV and LR. Surface scatter theories are discussed in relation to the moisture sensitivities of five polarizations.     

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.     

A new empirical model to retrieve soil moisture and roughness from C-band radar data

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of three experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne (SIR-C, RADARSAT) or helicopter borne (ERASME) sensors, operating in different configurations of polarization (HH or VV) and incidence angle. Simultaneously to radar acquisitions, a complete ground truth data base was built up with different surface condition measurements of the mean standard deviation (rms) height s, the correlation length l, and the volumetric surface moisture M_v. This algorithm is more specifically developed using the radar cross-section σ⁰ (HH polarization and 39° incidence angle off nadir), namely, σ_0HH,39, and the differential (HH polarization) radar cross-section Δσ₀=σ_0,23°−σ_0,39° in terms of an original roughness parameter, Z_s, namely Z_s=s²/l, and M_v. A good agreement is observed between model outputs and backscattering measurements over different test fields. Eventually, an inversion technique is proposed to retrieve Z_s and M_v from radar measurements.     

卫星微波遥感结合可见光遥感估算黄河源区土壤湿度研究

利用欧洲环境卫星(ENVISAT)搭载的高级合成孔径雷达ASAR(Advanced Synthesis Aperture Radar)交叉极化模式(APP)2009年8月9日和10月6日的数据对青藏高原东北部玛曲地区土壤湿度进行了估算。对于裸土区域采用表层微波后向散射几何光学模型GOM(Geometry Optics Model),对于植被覆盖度大的区域利用“水-云”模型处理植被层对后向散射系数的影响,取得了较好的结果:遥感估算的土壤湿度值和地面实测值之间的均方根误差RMSE<0.05,决定系数R²>0.82,表明该方法适合反演玛曲地区的土壤水分。从遥感估算的总体结果可以看出:山谷和陡峭山坡的反演结果相对较差,而在相对平坦的地区反演结果较好,估算的土壤湿度值在0.20~0.50 m³/m³之间。     

Development of a knowledge based decision tree classifier using hybrid polarimetric SAR observables

ABSTRACT

Fast, efficient and accurate classification of the land cover using Synthetic aperture radar (SAR) observables extracted from hybrid polarimetric SAR data is achieved in this research. The proposed knowledge-based tree classifier utilizes little apriori real-time field survey information along with just four features, namely, backscattering coefficient (σ_rh+σ_rv), scattering mechanism (α), diffuse scattering and odd bounce (surface) scattering feature from m- α decomposition method in a sequential manner. We have exploited the Separability Index (SI) criterion for identifying these 4 features among the available 16 features. The overall accuracy (OA), user accuracy (UA), producer accuracy (PA), kappa coefficient (κ), precision (P), recall (R) and score (F) of the proposed classifier underscores its merits. Further, for the sake of fair comparison with the existing approaches, we have built the layer stacked images using these four features and applied them to the supervised maximum likelihood estimator classifier (MLE) as well as the unsupervised k-means classifier. It is found that the proposed classifier has better performance in terms of OA, UA, PA and κ on different SAR data sets consisting of different areas.     

Seasonal and inter-annual variation in surface energy fluxes and forcing parameters in agro-climatic regions of India

Understanding changes in monsoon variability over a decade requires thorough knowledge of the seasonal and inter-annual variability in surface energy flux and its forcing parameters (land surface and meteorology) in response to climate change. In the present study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua climate model gridded global products (0.05° × 0.05° spatial resolution) of land surface temperature (LST; T_s), normalized difference vegetation index (NDVI), and surface albedo (α) were used to generate seasonal (June–September) and inter-annual (2003–2012) variation in surface energy flux and its forcing parameters over different agro-climatic regions (ACRs) of India. Energy fluxes were retrieved using a single-source surface energy balance model (here vegetation and soil is considered as a single unit). Energy flux observations over different ACRs allowed comparison of the seasonal transition of latent heat flux (LE), net radiation (R_n), soil heat flux (G), available energy (Q = R_n – G), and evaporative fraction (EF) as terrestrial links to the atmosphere. The seasonal and inter-annual variation in EF was investigated by plotting against the soil moisture information retrieved from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) global monthly data product (1° × 1° spatial resolution). Decadal and seasonal analysis showed that energy fluxes vary widely in time and space due to variability in surface radiation parameters (T_s, α), vegetation cover, soil moisture, and air temperature (T_a), which influence the seasonal transition of monsoon through LE and EF. Among the ACRs, LE and EF were found lowest in the Western Dry Region (WDR) and highest in the Western Himalayan Region (WHR). The spatiotemporal depiction of MODIS LE and MODIS EF over a span of 10 years can identify the hotspots and monsoon intensity over different ACRs. Climatic parameters that are susceptible to changes resulting from climate change are thoroughly studied in the present analysis.     

Simple modelling of radar backscattering coefficient over a bare soil: variation with incidence angle,frequency and polarization

As a basis for inversion algorithms, there is a need for the development of simple backscattering soil models which can account for the variations of incidence angle observed in the same picture or in multiangle systems. A correction factor for the variations of incidence angle is therefore coupled with a classical linear model of the variation of backscattering coefficient with surface soil moisture in a four-parameter model. The correction factor is based on the cosine-type behaviour of the backscattering coefficient as a function of incidence angle, which is observed for rough agricultural surfaces. This simple model is tested on radar measurements performed over a large range of radar configurations. The model is shown to reproduce correctly the observed variations of the radar signal with incidence angle and soil moisture. Its parameters have a physical sense and vary as expected, from literature, with frequency and polarization. When tested on data simulated by the analytical Integral Equation Model, the results of the cosine model are confirmed, as well as the variation of its calculated parameters with frequency and polarization. The inversion of the model with the angular correction factor shows that the cosine model allows the retrieval of soil moisture with a precision of about 20 per cent of the value at C band and at HV and HH polarization.     

Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-band polarimetric synthetic aperture radar data

Biomass has a direct relationship with agricultural production and may help to predict crop yield. Earth observation technology can contribute significantly to monitoring given the availability of temporally frequent and high-resolution radar or optical satellite data. Polarimetric Synthetic Aperture Radar (PolSAR) has several advantages for operational monitoring given that at these longer wavelengths atmospheric and illumination conditions do not affect acquisitions and considering the sensitivity of microwaves to the structural properties of targets. Therefore, SARs are a promising source of data for crop mapping and monitoring. With increasing access to SARs the development of robust methods to monitor crop productivity is timely.

In this paper, we examine the use of machine learning and artificial intelligence approaches to analyze a time series of Polarimetric parameters for crop biomass estimation. In total, 14 polarimetric parameters from a time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne L-band data were used for biomass estimation for an intensively cropped site in western Canada. Then, Multiple linear regression (MR) and artificial neural network (ANN) models were developed and evaluated to estimate the biomass for canola, corn, and soybeans. According to the experimental results, the ANN provided more accurate biomass estimates compared to MR.

Canola biomass, in general, showed less sensibility to almost all the polarimetric parameters. Nevertheless, Freeman-Double combined with vertical-vertical backscattering (VV) delivered the correlation coefficient (r) of 0.72, and the root mean square error (RMSE) of 56.55 g m^?2of canola biomass. For corn, the highest correlation was observed between a pairing of horizontal- horizontal backscattering (HH) with Entropy (H) for biomass estimation yielding an r of 0.92 and RMSE of 196.71 g m^?2. Horizontal-vertical backscattering (HV) and Yamaguchi-Surface (O_Y) delivered the highest sensitivity for soybeans (r of 0.82 and RMSE of 13.48 g m^?2). If all crops are pooled, H combined with O_Y provided the most accurate estimates of biomass (r of 0.89 and RMSE of 135.31 g m^?2). These results demonstrated that models which make use of polarimetric parameters that characterize the multiple sources of scattering typical of vegetation canopies can be used to estimate crop biomass accurately. Such results bode well for agricultural monitoring considering the increasing number of satellite SAR sensors with various frequencies, imaging modes and revisit times. As such, the time series analysis and methods proposed in this study could be used to monitor crop development and productivity using SAR space technologies.     

Surface soil moisture estimation over dense crop using Envisat ASAR and Landsat TM imagery: an approach

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m³ m^?3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model.     

Compression of multi-polarimetric SAR intensity images based on 3D-matrix transform

The algorithms for multi-polarimetric synthetic aperture radar (SAR) intensity image compression are investigated. First, the multi-polarimetric SAR intensity images (HH, HV and VV) are considered as a 3D-matrix unit, and then a 3D-matrix transform is adopted to remove the redundancies, which includes 1D discrete cosine transform (DCT) in the polarimetric channels and 2D discrete wavelet transform (DWT) in each polarimetric SAR image plane. After the 3D-matrix transform, two methods are proposed to encode the 3D mixed coefficients. One is a bit allocation encoding based on differential entropy and the other is a 3D set partitioning in hierarchical trees (SPIHT) encoding which is an improvement of the conventional SPIHT. The two methods can remove not only the redundancies of the image inside but also the redundancies among the polarimetric channels, because they do not process every channel image separately. Both the theory and experimental results show that the proposed methods are efficient.     

Monitoring soybean growth using L-, C-, and X-band scatterometer data

A ground-based fully polarimetric scatterometer operating at multiple frequencies was used to continuously monitor soybean growth over the course of a growing season. Polarimetric backscatter data at L-, C-, and X-bands were acquired every 10 min. We analysed the relationships between L-, C-, and X-band signatures, and biophysical measurements over the entire soybean growth period. Temporal changes in backscattering coefficients for all bands followed the patterns observed in the soybean growth measurements (leaf area index (LAI) and vegetation water content (VWC)). The difference between the backscattering coefficients for horizontally transmitted horizontally received (HH) and vertically transmitted vertically received (VV) polarizations at the L-band was apparent after the R2 stage (DOY 224) due to the double-bounce scattering effect. Results indicated that L-, C-, and X-band radar backscatter data can be used to detect different soybean growth stages. The results of correlation analyses between the backscattering coefficient for specific bands/polarizations and soybean growth data showed that L-band HH-polarization had the highest correlation with the vegetation parameters LAI (r = 0.98) and VWC (r = 0.97). Prediction equations for estimation of soybean growth parameters from the L-HH were developed. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. These results provide a basis for developing a method to retrieve crop biophysical properties and guidance on the optimum microwave frequency and polarization necessary to monitor crop conditions. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive (SMAP) satellite.     

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°.