无论是多角度遥感的发展、 还是偏振、 高光谱遥感的发展, 它们有一个相同的目的, 即利用电磁波的种种特性、 以及空间特性来对地球表面的一切地物进行精确的识别。 任何单一的方法和手段不可能完整地描述和反映地物的所有特征。 偏振测量是目标测量识别技术中不可缺少的技术之一, 并且成为近年来全世界目标识别领域中的研究热点。 由于定量遥感的反射强度对植被遥感的影响不可忽视, 反射辐射信号呈现饱和或过弱都不能被检测到。 而偏振是植被定量遥感的重要手段, 因而有必要开发一种克服由反射强度强弱引起的植被反演误差的方法, 这也是我们目前的研究目标。 如果反射的辐射信号太强或太弱, 都会影响遥感的准确性, 而来自植被的偏振光可以提供有用的信息, 特别是当反射的辐射信号饱和时, 使得传感器不能获得足够有用的非偏振信息。 本研究采用基于地面的偏振成像光谱仪系统, 开发了一种偏振方法来克服反射强度过强过弱引起的植被反演误差。 利用FISS-P偏振成像光谱仪系统研究了反射强度对遥感植被NDVI和DoLP效用的影响, 实验地点在北京市中国科学院奥林匹克科技园。 在对目标采样时对反射率强, 反射率弱以及反射率适中的植被分别测量, 同时对目标植被的不同波段(470, 555, 670, 864 nm)的DoLP进行计算与分析。 地基成像光谱仪系统(FISS-P)提供了具有偏振信息的高空间分辨率图像, 我们可以确定在阴影和强反射区域中单个像素的光谱偏振特性。 在成像光谱信息的基础上, 利用光的偏振性来对地物的物理特性进行分析。 本文使用斯托克斯分量来表征反射光的各个偏振分量, 使用线偏振度(DoLP)表征反射光偏振程度。 信号饱和度和阴影效应导致归一化植被指数(NDVI)植被密集程度非常低, 造成严重的反演误差, 然而强反射对线偏振度(DoLP)的影响不大。 研究结果表明, 反射辐射信号饱和时, 偏振效应可以通过适当的频带提高植被的反演精度, 平均NDVI的相对误差为33.8％, 而DoLP(670 nm)的相对误差仅为6.3％, 而其他波段的DoLP(555 nm, 864 nm)的相对误差要大很多。 这项研究结果表明, 在植被识别时可以忽略强反射, 然而, 阴影(弱反射)效应是不容忽视的。 FISS-P偏振成像光谱仪是用于计算具有不同反射强度的样品类型的偏振和非偏振参数的有效工具, 同时发现在识别植被时, 强烈的反射可以忽略不计, 但是植被的阴影(弱反射)效应不容忽视。 与非偏振方法相比, 偏振效应可以提高反射辐射信号饱和时的植被反演精度。 这项研究分析了使用偏振法强弱反射强度引起的误差减少。 为了进一步揭示植被的阴影(弱反射)效应与DoLP之间的关系, 还有一些问题需要解决。

Whether the development of multi angle remote sensing, or the development of polarization and hyperspectral remote sensing all have the same purpose. They use the characteristics and spatial characteristics of electromagnetic waves to accurately identify all the surface of the earth’s object. Any single method and means cannot fully describe and reflect all the features of the ground. Polarization measurement is one of the indispensable technologies in target recognition and recognition technology, and has become a research hotspot in the field of target recognition in the world in recent years. Since the effects of strong and weak reflection intensity on vegetation remote sensing cannot be ignored in quantitative remote sensing inversion, which renders the reflected radiant signal as either saturated or too weak to be detected. Polarization is an important method for the quantitative remote sensing of vegetation. Consequently, it is necessary to develop a method to overcome the vegetation inversion error caused by strong and weak reflection intensities, which is the goal of our present research. If the reflected radiant signal is either too strong or too weak, it will affect the accuracy of remote sensing. Polarized light from vegetation can provide useful information, especially when the reflected radiant signal is saturated so that the sensor cannot obtain enough useful non-polarization information. This study developed a polarization method to overcome the vegetation inversion error caused by strong or weak reflection intensity using a ground-based polarized field imaging spectrometer system. The FISS-P polarization imaging spectrometer system was used to study the effect of reflection intensity on the utility of remote sensing vegetation NDVI and DoLP. The experiment was conducted at the Olympic Science and Technology Park of Chinese Academy of Sciences in Beijing. When targets are sampled, the vegetation with strong reflectivity, low reflectivity and moderate reflectivity is measured respectively. Meanwhile, the DoLP of target vegetation’s different bands (470, 555, 670, 864 nm) are calculated and analyzed. The degree of vegetation density (NDVI) is very low due to signal saturation and shadow effect, resulting in severe inversion error. However, strong reflection has little effect on DoLP. As the ground-based field imaging spectrometer system (FISS-P) provides high-spatial-resolution images with polarization information, we can determine the spectrum-polarization characteristics of single pixels in shaded and strong reflection areas. On the basis of the imaging spectral information, the physical properties of the ground objects are analyzed by using the polarization of light. In this paper, the Stokes component is used to characterize the polarization components of the reflected light, and the degree of polarization of the reflected light is characterized by linear polarization (DoLP). Signal saturation and shadow effects result in very low values for dense vegetation on the Normalized Difference Vegetation Index (NDVI), causing serious inversion error. However, strong reflection has few effects on the degree of linear polarization (DoLP). This study showed that polarization can improve vegetation inversion accuracy by using the appropriate band when the reflected radiant signal is saturated, and the relative error of the average NDVI is 33.8%, while that of DoLP (670 nm) is only 6.3%,the relative errors of DoLP (555, 864 nm) in other bands are much larger. The results of this study show that strong reflection can be ignored when identifying vegetation, however, the shadow (weak reflection) effects could not be ignored. FISS-P images are an effective tool for calculating polarization and non-polarization parameters for sample types with different reflection intensities. In conclusion, the polarization effect can improve the vegetation inversion accuracy when the reflected radiant signal is saturated compared with non-polarization methods. This study analyzed the reduction in error caused by strong and weak reflection intensities using a polarization method. And there are still some problems need to be solved in order to further reveal the relationship between the shadow (weak reflection) effects and DoLP of vegetation.