行播玉米不同生长期空隙率模型研究

以垄行结构玉米农作物为例,通过构建玉米平均理念植株进行玉米植株的参数化,解决了不同生长期玉米冠层叶片的空间分布变化问题,构建了适用于全生长期玉米冠层方向孔隙率的计算模型.模型将不同生长期的行播作物的冠层孔隙率计算归纳到同一个近似体质下,以Boolean原理为基础计算冠层的各向孔隙率.通过与RGM模型对不同生长期冠层方向孔隙率计算结果的对比,验证了本模型的计算精度.     

基于Bi-Mimics模型的GNSS-R农作物生物量监测理论研究

GNSS-R(GNSS-Reflectometry)遥感从机理上讲属于双站雷达,在一阶辐射传输方程Mimics(Michigan Microwave Canopy Scattering)模型的基础上,将其修改为双站散射模型Bi-Mimics(Bistatic\|Mimics);将模型中的树干层去掉,保留树冠层和地表间的散射机制,发展了适用于农作物的Bi-Mimics模型。利用该模型,模拟分析在GNSS-R工作的L波段农作物的散射特性;根据GNSS-R设置模拟分析了镜像散射系数与农作物生物量之间的关系;并根据双站雷达理论公式,模拟了农作物生物量与接收机信号之间的关系。结果表明GNSS-R从理论上用来研究和监测农作物的生物量存在可行性,但研究工作有待进一步深入。     

水云模型于L波段SAR和中国北方森林的适用性分析

水云模型假定来自植被体的体散射是主导散射机制,二面角散射可以忽略;这一假定是否适用于穿透性较强的L波段SAR和中国东北森林有待研究。本文以黑龙江省逊克县森林和ALOS PALSAR全极化数据为基础,分析典型地物的Yamaguchi极化分解散射分量的直方图,研究中国东北典型森林在L波段的散射机制,以确定水云模型的适用性。结果表明,体散射是该地区森林的主导散射机制,树干-地面的二面角散射可以忽略,水云模型的假设条件满足,可以应用于L波段SAR和中国东北森林。     

水面船只SAR探测的极化方式研究

利用SIR-C SAR的C和L波段全极化数据,分析水面船只的极化散射特性和船只与背景海面雷达后向散射的信噪比特性,研究水面船只SAR探测的最优极化方式。结果显示,二面角散射是水面船只SAR成像的主要机理。线性极化中,HV极化具有最大的船只与背景海面雷达后向散射信噪比。与线性极化相比,圆极化的雷达后向散射信噪比更优。C波段和L波段的水面船只的极化散射特性存在较大的差异,L波段的信噪比大于C波段的信噪比。水面船只的雷达后向散射特性表明,L波段的圆极化是水面船只探测的最优极化方式。     

植被在L至X波段的散射和辐射测量

为了测量L至X波段之间在七个频带上的主动与被动微波信号，我们己将宽频带响应的H与V极化辐射仪和噪声传送机连接在一起组成一种合成的辐射-散射仪，这种仪器安装在车载升降台上进行测量操作。在1984年及1985年的农作物生长季节，我们使用该仪器，并根据各种农作物的生长发育特点，对甜菜、小麦、玉米等作物进行了散射辐射测量，同时.根据所记录的作物类型，植株间距及植物冠高等内容，对植被覆盖的几何结构也进行了描述，对土壤层的特征则是用湿度、温度剖面及介电常数来刻画，此外，植物水份含量的季节性变化也作为一个变化因子而予以考虑。相应于微波信号的这些参量可以揭示土攘与植被之间的散射和辐射过程之间的相互作用，而辐射与散射特性之间的信号差异可以从己测量的植物类型中识别出来。     

降雨影响散射计测风的初步研究

给出了降雨影响C、Ku波段微波散射计测量海面风速的初步结果。研究结果显示,海面风速为25～30 m/s时,雨速为15 mm/hr的降雨会使这两个波段的微波散射计测量的风速偏低10 m/s。
     

双基地海底散射模型仿真研究

研究声纳探测系统优化的问题。潜艇的隐身能力越来越强,要求声纳的探测技术不断提高。为提高声纳探测系统的性能,建立了双基地海底散射模型。模型既考虑了海水和海底之间的粗糙界面引起的界面散射,又考虑了海底不均匀沉积层引起的体积散射。确定了散射强度与双基地各角度之间的关系;讨论了海底的反向散射、平面内散射和三维散射模型,通过仿真,得出散射强度随散射角度变化的特点,为双基地海底混响的预报提供了科学依据。     

基于微波植被离散散射模型的小麦双站散射和辐射特征研究

J.M.Stile于1996年提出了草类植被的一阶离散相干散射模型,模型虽然从双站散射的角度出发,但是Stile仅对模型的后向散射形式进行了详细的讨论.本文的研究重点是该模型的双站散射形式,首先经过公式推导得到了双站散射模型的具体表达式,然后通过能量守恒定律发展了基于该草类植被散射模型的辐射模型,最后模拟了小麦的双站散射系数和亮度温度,并对模拟结果进行了分析.     

从受严重散射影响的红外图象中提取植被信息

在分析了各类散射对彩红外图象影响的基础上,针对植物的光谱反射特性及其在彩红外图象中的色彩特征,提出了一个从受严重散射影响的彩红外图象中撮以植被信息的方法。该方法是将图象的色饱和度调整到一定范围,并按比例曲线增强近红外波段,衰减其它波段,实验结果表明该方法是有效的。     

基于宽带扫频的导弹RCS测量与分析

针对导弹的典型几何外形结构,基于高频散射机理对其散射特性进行了初步分析,包括散射源分布及其散射类型、各散射源散射特性随导弹姿态的变化趋势等;接着,利用宽带扫频RCS测量系统对导弹L、X波段RCS和X波段一维距离像进行了测量,得到了导弹在不同技术条件下的RCS曲线和一维距离像;最后,对测量结果进行了初步分析;测量结果表明:导弹头锥仓与进气道腔体存在较大散射,尤其是进气道壁与弹体之间连接缝隙的散射较为突出;导弹在X波段下的RCS较L波段小,且受姿态变化的影响更为敏感;另外,导弹RCS峰值点并未出现在迎头向,而是偏离迎头向一定角度,尤以X波段下最为明显,偏离角度达20°左右;测量结果为导弹在试验中的应用,包括试验设计、试验结果分析与评估、等效推论等提供了重要的技术依据。     

Modelling temporal variations in microwave backscattering from reed marshes

A full understanding of radar backscattering characteristics and their seasonal variations is one of the important ways to analyse the growth conditions in wetlands. This research simulated seasonal C-band and L-band synthetic aperture radar (SAR) backscattering from reed marshes using a refined version of the electromagnetic (REM) model, which was first validated by time series of multimode SAR observations at the experimental site used. Then, two factors including sensor parameters and vegetation structure, which influence the temporal evolution of the radar response from reed marshes, were assessed. The results demonstrate that the radar response is closely related to growth processes in the reed marsh. At the early growth stage when reed marshes are sparse, the double-bounce mechanism was dominant at all the incidence angles of C-band radar, but for L-band radar, strong specular reflection was produced from the smooth water if the incidence angle is lower than 25°. It was also found that the sensitivity to the density and height of reed marshes is greater for L-band radar than at the C-band, indicating that L-band backscattering may be useful for reed marsh biomass retrieval.     

Properties of X-, C- and L-band repeat-pass interferometric SAR coherence in Mediterranean pine forests affected by fires

Synthetic Aperture Radar (SAR) data has been investigated to determine the relationship between burn severity and interferometric coherence at three sites affected by forest fires in a hilly Mediterranean environment. Repeat-pass SAR images were available from the TerraSAR-X, ERS-1/2, Envisat ASAR and ALOS PALSAR sensors. Coherence was related to measurements of burn severity (Composite Burn Index) and remote sensing estimates expressed by the differenced normalized burn ratio (dNBR) index. In addition, the effects of topography and weather on coherence estimates were assessed. The analysis for a given range of local incidence angle showed that the co-polarized coherence increases with the increase of burn severity at X- and C-band whereas cross-polarized coherence was practically insensitive to burn severity. Higher sensitivity to burn severity was found at L-band for both co- and cross-polarized channels. The association strength between coherence and burn severity was strongest for images acquired under stable, dry environmental conditions. When the local incidence angle is accounted for the determination coefficients increased from 0.6 to 0.9 for X- and C-band. At L-band the local incidence angle had less influence on the association strength to burn severity.     

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.     

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.     

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.     

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%.     

A first try at identification of sea ice using the three beam scatterometer of ERS-1

Regional-scale behaviour of backscattering at C-band is investigated using data of the ERS-1 three-beam scatterometer, the AMI-WIND, during November 1991, over the Arctic ocean. The normalized radar cross-section appears as a linear function of incidence angle, whose two parameters vary considerably between zones of first-year and multi-year sea ice. Once determined the slope parameter of the AMI-WIND data, measured normalized radar cross-sections are corrected to bring them to a single reference incidence angle. False-colour mapping of this variable displays first-year and multi-year sea ice zones as determined previously from several passive micro-wave sensors (ESMR. SSM,/I)     

Assessing the influence of vegetation cover on soil-moisture signatures in fire-disturbed boreal forests in interior Alaska: Modelled results

A microwave backscattering model for shrub clumps was presented. The modelling approach was to treat the clumps as scatterers and attenuators. Three major model components were defined: surface backscattering, clump volume scattering, and multiple path interactions between clumps and ground. Total backscatter was computed by incoherent summation of the components. We then used the model to study the effects of variations in surface and willow properties (soil moisture content, and surface roughness rms height and correlation length, and willow ground coverage, clump height, and stem density) on backscatter from willows in Alaskan boreal forest region. We examined the sensitivity to variations of the six parameters combined and to variation of each parameter alone from willows of three clump sizes representing different stages of vegetation regrowth after fire. Modelled C-band backscatter was more sensitive to the variations of the surface and willow parameters than L-band backscatter at incidence angles between 20° and 60°. At incidence angles of 20-60°, C-HH and C-VV backscatter was sensitive to the variations of the three surface parameters. L-HV and L-VV backscatter were only sensitive to the moisture variation. Among the three willow parameters, change of willow ground coverage produced more sensitive cases than variations of clump height and stem density combined at C- and L-band.     

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.     

Terrestrial laser scanner intensity correction for the incidence angle effect on surfaces with different colours and sheens

Laser scanning intensity captures information about the reflectance of target-surfaces and is used for a variety of applications such as data registration, classification, object detection and recognition. To enhance its utility, intensity values often undergo a correction process, which reduces the influence of nuisance parameters on recorded intensity (often range and incidence angle). This study applies and tests the Torrance-Sparrow model to correct intensity for the incidence angle effect in terrestrial laser scanning. Main components of the Torrance-Sparrow model are the geometrical attenuation (G) and microfacet distribution functions (D). Four models of geometrical attenuation and microfacet distribution functions are evaluated, namely, (i) Beckmann, (ii) Trowbridge-Reitz, (iii) GGX, and (iv) shifted gamma distribution (SGD). These models provide different derivations of the functions G and D, which estimate parameters necessary for the Torrance-Sparrow model. Target-surfaces scanned from various incidence angles are used for the assessment. These are painted with eight different colours (white, yellow, red, green, blue, grey, brown, and black) and two sheens (flat and semi-gloss), which create different reflection characteristics (diffuse and specular). Numerical and visual evaluations show that all four models manage to model the specular reflection component of the semi-gloss sheen target-surfaces for all tested colours. However, in flat-sheen surfaces, the Beckmann and SGD models show inferior modelling than GGX and Trowbridge-Reitz for brown, grey, and black colours. In addition, a relative comparison of the roughness parameters and Fresnel factors showed that only the Trowbridge-Reitz model produced reasonable values, based on encountered surface characteristics. Application of the Trowbridge-Reitz model in independent point-cloud data shows how intensity values can be corrected for the incidence angle effect in real cases.