Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields

The sensitivity of TerraSAR-X radar signals to surface soil parameters has been examined over agricultural fields, using HH polarization and various incidence angles (26°, 28°, 50°, 52°). The results show that the radar signal is slightly more sensitive to surface roughness at high incidence (50°–52°) than at low incidence (26°–28°). The difference observed in the X-band, between radar signals reflected by the roughest and smoothest areas, reaches a maximum of the order of 5.5 dB at 50°–52°, and 4 dB at 26°–28°. This sensitivity increases in the L-band with PALSAR/ALOS data, for which the dynamics of the return radar signal as a function of soil roughness reach 8 dB at HH38°. In the C-band, ASAR/ENVISAT data (HH and VV polarizations at an incidence angle of 23°) are characterised by a difference of about 4 dB between the signals backscattered by smooth and rough areas.Our results also show that the sensitivity of TerraSAR-X signal to surface roughness decreases in very wet and frozen soil conditions. Moreover, the difference in backscattered signal between smooth and rough fields is greater at high incidence angles. The low-to-high incidence signal ratio (Δσ° = σ_26°–28°/σ_50°–52°) decreases with surface roughness, and has a dynamic range, as a function of surface roughness, smaller than that of the backscattering coefficients at low and high incidences alone. Under very wet soil conditions (for soil moistures between 32% and 41%), the radar signal decreases by about 4 dB. This decrease appears to be independent of incidence angle, and the ratio Δσ° is found to be independent of soil moisture.     

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.     

Integration of multitemporal/polarization C‐band SAR data sets for land‐cover classification

This paper investigates the potential of multitemporal/polarization C‐band SAR data for land‐cover classification. Multitemporal Radarsat‐1 data with HH polarization and ENVISAT ASAR data with VV polarization acquired in the Yedang plain, Korea are used for the classification of typical five land‐cover classes in an agricultural area. The presented methodologies consist of two analytical stages: one for feature extraction and the other for classification based on the combination of features. Both a traditional SAR signal property analysis‐based approach and principal‐component analysis (PCA) are applied in the feature extraction stage. Special concerns are in the interpretation of each principal component by using principal‐component loading. The tau model applied as a decision‐level fusion methodology can provide a formal framework in which the posteriori probabilities derived from different sensor data can be combined. From the case study results, the combination of PCA‐based features showed improved classification accuracy for both Radarsat‐1 and ENVISAT ASAR data, as compared with the traditional SAR signal property analysis‐based approach. The integration of PCA‐based features based on multiple polarization (i.e. HH from Radarsat‐1, and both VV and VH from ENVISAT ASAR) and different incidence angles contributed to a significant improvement of discrimination capability for dry fields which could not be properly classified by using only Radarsat‐1 or ENVISAT ASAR data, and thus showed the best classification accuracy. The results of this case study indicate that the use of multiple polarization SAR data with a proper feature extraction stage would improve classification accuracy in multitemporal SAR data classification, although further consideration should be given to the polarization and incidence angle dependency of complex land‐cover classes through more experiments.     

SAR applications in agriculture A comparison of steep and shallow mode (30° and 53° incidence angles) data

Abstract

The multi-date data set discussed in this paper was acquired over an agricultural site near Melfort, Saskatchewan, in 1983. The Synthetic Aperture Radar (SAR) data are C-band (5.26GHz) with vertical polarization and two incidence angles, 53° and 30°. A comparison between the data at the two incidence angles shows that the pixel statistics of the image data are different for the two angles. Examination of the statistical distribution shows that the 53° data are more influenced by vegetation than the 30° angle data. The classification accuracy of the 53° data was higher than that obtained with the 30° data. In addition, the classification accuracies obtained using multi-date combinations with the 53° data were superior to accuracies obtained using multi-angle combinations (53°+ 30°). An overall improvement in classification accuracy was obtained using a post segmentation field classifier.     

Microwave electromagnetic modelling of Sahelian grassland

In this paper radar scattering models based on coherent and incoherent formulations for an African grassland (Sahelian) are examined. The coherent model is used to account for the structure of the grass plants and the results are compared with the same model assuming random placement and orientation of scatters, and the radiative transfer model. The validity of the three models applied to grass vegetation is determined by comparing the model predictions with Envisat Advanced Synthetic Aperture Radar (ASAR) data gathered in 2005 over Sahelian grassland. The Agoufou site, as defined in the African Monsoon Multidisciplinary Analysis (AMMA) project, is selected as the test target and a set of ground data was collected during 2004 and 2005. Through a comprehensive data comparison, it is shown that the coherent scattering model with a generator considering botanical information is the best model to predict the backscattering data that matches Envisat measurements well (correlation?=?0.92). At low incidence angles (<30°), the radar backscatter shows a strong dependence on soil moisture variations. The analysis of the different contributions leads to a study of the main scattering mechanisms. For high incidence angles, the backscattering coefficient at HH polarization shows a marked seasonal variation associated with grass presence.     

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.     

Multi‐frequency scatterometer measurements on water surfaces agitated by artificial and natural rain

Images of rain events over the ocean acquired by a multi‐frequency/multi‐polarization Synthetic Aperture Radar (SAR) show different radar contrasts at different frequencies and polarizations. In order to better understand these effects, field and laboratory experiments were performed at different rain rates and wind speeds with scatterometers working at different radar frequencies, polarizations, and incidence angles. Our results show that the dominant scattering mechanism on a rain‐roughened water surface, observed at VV polarization, at all incidence angles is Bragg scattering from ring waves. At HH polarization the radar backscatter is caused by both ring waves and non‐propagating splash products, with the dominating effect depending on incidence angle. The reduction and enhancement of the surface roughness by ring waves and sub‐surface phenomena, respectively, result in a transition wavenumber between reduction of the radar backscattering and its enhancement of about 100 rad m^?1. We assume that this transition wavenumber depends on the drop‐size distribution of the rain. Taking into consideration the different dependencies of the radar backscatter at different frequencies and polarizations on rain rate, we suggest a method to estimate rain rates by calculating the ratio of the radar cross‐sections at L band, VV polarization and at C band, HV polarization. Provided an availability of SAR data at the respective frequency–polarization combinations, this method allows for investigating the nature of small‐scale (convective) rain events over the ocean.     

Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data

Hydrology is the single most important abiotic factor in the formation and functioning of a wetland. Many limitations still exist to accurately characterizing wetland hydrology over large spatial extents, especially in forested wetlands. Imaging radar has emerged as a viable tool for wetland flood mapping, although the limitations of radar data remain uncertain. The influence of incidence angle on the ability to detect flooding in different forest types was examined using C-HH Radarsat-1 data (23.5°, 27.5°, 33.5°, 39.0°, 43.5°, and 47.0°) during the leaf-off and leaf-on seasons. The ability to detect flooding under leaf-on conditions varied much more according to incidence angle while forest type (open canopy tupelo-cypress, tupelo-cypress, and bottomland hardwood) had a greater effect during the leaf-off season. When all forest types were considered together, backscatter generally decreased with increasing incidence angle under all conditions (2.45 dB between 23.5° and 47.0° flooded, leaf-off; 2.28 dB between 23.5° and 47.0° not flooded, leaf-off; 0.62 between 23.5° and 43.5° flooded, leaf-on; 1.73 dB between 23.5° and 43.5° not flooded, leaf-on; slope was not constant between incidence angles), but the distinction between flooded and non-flooded areas did not decline sharply with incidence angle. Differentiation of flooded and non-flooded forests was similar during the leaf-off and leaf-on seasons. The ability to detect inundation under forest canopies was less than expected at smaller incidence angles and greater than expected at larger incidence angles, based on the results of previous studies. Use of a wider range of incidence angles during the entire year increases the temporal resolution of imagery which may, in turn, enhance mapping of inundation beneath forest canopies.     

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.     

Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.     

ASAR polarimetric, multi-incidence angle and multitemporal characterization of Doñana wetlands for flood extent monitoring

Doñana National Park wetlands, in South West Spain, undergo yearly cycles of inundation and drying out. During the hydrological year 2006-2007, 43 ASAR/Envisat images of Doñana, mostly in HH and VV polarizations, were acquired with the aim to monitor the flood extent evolution during an entire flooding cycle. The images were ordered in the seven ASAR incidence angles, also referred to as swaths, to achieve high observation frequency.In this study, backscattering temporal signatures of the main land cover types in Doñana were obtained for the different incidence angles and polarizations. Plots showing the σ⁰HH/σ⁰VV ratio behavior were also produced. The signatures were analyzed with the aid of miscellaneous site data in order to identify the effect of the flooding on the backscattering. Conclusions on the feasibility to discriminate emerged versus flooded land are derived for the different incidence angles, land cover types and phenological stages: intermediate incidence angles (ASAR IS3 and IS4) came up as the most appropriate single swaths to discriminate open water surface from smooth bare soil in the marshland deepest areas. Flood mapping in pasture lands, the most elevated regions, is feasible at steep to mid incidence angles (ASAR IS1 to IS4). In the medium elevation zones, colonized by large helophytes, shallow incidence angles (ASAR IS6 and IS7) enable more accurate flood delineation during the vegetation growing phase.Since Doñana land covers require different observation swaths for flood detection, the composition of different incidence angle images close in time provides the optimum flood mapping. Such composition is possible four times per ASAR 35-day orbit cycle, using pairs of 12-h apart IS1/IS6 and IS2/IS5 Doñana images.     

基于ENVISAT/ASAR的神经网络反演人工林叶面积指数研究

对我国西北黑河地区的人工林,进行了基于ENVISAT/ASAR数据构造神经网络的反演杨树林叶面积指数研究。首先,分析了白杨树林、沙枣树林的叶面积指数(LAI)与ENVISAT/ASAR不同极化后向散射系数的相关关系,研究表明人工林的空间分布均一性是影响雷达后向散射和LAI关系的首要因素,其次,不同的入射角对后向散射也具有明显的差异。基于上述分析,通过神经网络算法,利用不同时相、不同入射角的ENVISAT/ASAR雷达影像对白杨树林LAI进行了反演研究,对验证样本、训练样本、所有样本实测值与预测值进行了比较验证,其决定系数R²分别为0.61\,0.91和0.82,表明基于ENVISAT/ASAR雷达数据利用神经网络算法反演人工林叶面积指数的可行性。     

Influence of topography and sensor view angles on NIR/red ratio and greenness vegetation indices of wheat

Abstract

Reflectance factors of winter wheat were measured with aground-based radiometer to determine the effect of topography and sensor view angle on the diurnal behaviour of two spectral vegetation indices. Data are presented for fields with 10^° slopes in a topographically complex area of central Italy. The ratio of reflectances in near-infrared (NIR) (0.78 to 0.89 μm) to red (0.63 to 0.69 μm) was less sensitive to field aspect than greenness. However, when nadir and off-nadir view angles were compared for the same aspect, greenness displayed less variability. Field aspect and view angle had less effect on both indices when solar zenith angles were small.     

A coding optimized polarization conversion metasurface in broadband and wide‐angle

In this article, a coding optimized metasurface is proposed for linear polarization conversion in broadband and wide‐angle. Using an improved coding method, the geometry of the metallic structure and the size of the unit cell are encoded with a code sequence. The code sequence is optimized by genetic algorithm, considering bandwidth, efficiency, and angular stability. In this way, a coding optimized metasurface was obtained for polarization conversion in broadband and wide‐angle. It can convert linear polarized incidence to its orthogonal polarized reflection in 8 to 18.9 GHz and 0° to 45°, with the polarization conversion ratio larger than 80%. Both simulated and measured results demonstrated the effectiveness of the proposed metasurface. Hence, our method offers an effective strategy to design high‐performance polarization conversion metasurfaces.     

Effect of different handgrip angles on work distribution during hand cycling at submaximal power levels

The effect of different handle angles on work distribution during hand cycling was determined. Able-bodied subjects performed hand cycling at 20% of maximum power level (mean (SD) power level: 90.0 (25.8) W) at a cadence of 70 rpm using handle angles of ±30°, ±15° and 0°. The handle angle had a significant effect on work during the pull down (p < 0.001) and lift up (p = 0.005) sector, whereby the highest work was performed with handle angles of +30° and ?15° respectively. The cycle sector had a significant effect on work (p < 0.001) and significantly (p = 0.002) higher work was performed in the pull down sector (25% higher than mean work over one cycle) as compared to the lift up sector (30% lower than mean work over one cycle). Therefore, a fixed handle angle of +30° is suggested to be optimal for power generation. The results of this study help to optimise the handbike–user interface. A more pronated handle angle compared to the one conventionally used was found to improve the performance of hand cycling and thereby the mobility of disabled people.     

The use of a microwave backscatter model for retrieving soil moisture over bare soil

Abstract

Most attempts at predicting soil moisture from C-band microwave backscattering coefficients for bare soil are made by fitting experimental calibration relations obtained for limited ranges of incidence angle and soil surface roughness. In this paper, a more general approach is discussed using an inversion procedure to extend the use of a single experimental calibration relation to a wider range of incidence angle and surface roughness. A correcting function is proposed to normalize the backscattering coefficients to the conditions (incidence angle and surface roughness) of the calibration relation. This correcting function was derived from simulated data using the physical optics or KirchhofTs scatter model using the scalar approximation. Before discussing the inversion procedure, the backscattering coefficients calculated by the model have been compared with experimental data measured in the C-band, HH polarization and three incidence angles (Θ= 15°, 23°, 50°) under a wide range of surface soil moisture conditions (0.02H_v  0.35cm³ cm^-3) and for a single quite smooth soil surface roughness (0–011 _s  OOI4/n)m. The model was found to be experimentally validated from 15° to 23° of incidence and for surface soil moistures higher than 0-I0cm³cm^-3. For the inversion procedure, it is assumed to have a wider range of validity (15°  Θ 35° ) for ihc incidence angle. A sensitivity analysis of the model to errors on roughness parameter and incidence angle was performed in order to assess the feasability and suitability of the described inversion procedure.     

Wide‐angle scanning circular polarization phased array based on polarization rotation technology

A wide‐beam circular polarization (CP) antenna and a wide‐angle scanning phased array based on novel polarization rotation reflective surface (PRRS) are proposed. The CP wide‐beam pattern is obtained by the combination of the radiation wave from the patch antenna and the orthogonal reflected wave from the PRRS with a 90° phase difference. The proposed CP wide‐beam antenna obtains the patterns with the 3‐dB beamwidth more than 136° and the axial ratio (AR) beamwidth more than 132° in the xoz‐plane. Furthermore, an eight‐element phased array based on the wide‐beam CP antenna element is also developed. The measured results show that the main beam of the array can scan from ?65° to 65° with a gain fluctuation less than 3 dB and the ARs at every scanning angle less than 3 dB.     

Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island

Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different radar parameters (wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest.Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on radar wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°).The radar backscattering coefficients (σ°) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the radar signal. This decrease in radar signal was of the same order as the decrease in radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the radar wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the radar signal at these wavelengths to surface roughness of bare soils. Indeed, sometimes the radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band).Concerning the influence of radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.     

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.     

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°.