ASAR数据和TM影像协同反演土壤水分方法研究

发布时间：2018-06-06 10:39

本文选题：协同反演 + 后向散射系数　；参考：《成都理工大学》2017年硕士论文

【摘要】：土壤水分是陆地表面过程中非常重要的参数之一,对全球水循环、气候变化监测以及能量平衡有着重要的意义。不仅如此,在人类的生产生活中,土壤水分不仅是农作物生长发育的基本条件,同时还影响着能量的转换与消耗,在地表径流等过程中扮演着重要角色。因此,建立一套准确有效、实时动态的土壤水分监测系统对农业生产、防旱减灾以及生态环境评价有着重要的科学意义。本文针对农业用地等较为低矮植被覆盖区域,建立了适合的土壤水分的方法。该方法以土壤散射模型和植被散射模型为理论依据,利用水云模型模拟植被的后向散射以及植被对土壤后向散射的双程衰减,利用被动光学遥感数据模拟的叶面积指数作为植被冠层结构参数输入水云模型,使该模型能够适用于大尺度的植被后向散射模拟,再利用Oh模型对土壤的后向散射部分进行模拟,从而建立了一种主被动遥感协同反演土壤水分的新方法。由于水云模型中原有的土壤后向散射模拟项为简单的线性方程,通过利用Oh模型替换掉原始水云模型中的土壤后向散射模拟项,增加考虑了土壤粗糙度对后向散射的影响,使模型对土壤后向散射的描述更加准确,从而提高模型的模拟精度。最后本文选取甘肃省黑河流域中游张掖地区的盈科绿洲农业灌区为研究区域,以主动微波遥感Envisat ASAR数据、被动光学遥感Landsat-5 TM数据以及研究区实地观测数据为基础,对模型的可行性以及反演精度进行了对比验证。研究结果表明,通过利用研究区的实地观测数据对模型的模拟结果进行检验,改进的水云模型模拟得到的土壤水分值相比于传统水云模型的模拟结果具有更高的精度。同时也发现改进后的模型在对土壤较为湿润条件下的土壤水分模拟结果相比于干燥条件下的精度有所降低,但相比于传统水云模型而言整体有所改善。所以,今后的研究工作还需进一步对植被覆盖相对密集和土壤水分变化较大的区域进行。
[Abstract]:Soil moisture is one of the most important parameters in the process of land surface, which is of great significance to global water cycle, climate change monitoring and energy balance. Moreover, in human life, soil moisture is not only the basic condition for the growth and development of crops, but also affects the energy conversion and consumption, and plays an important role in the process of surface runoff. Therefore, it is of great scientific significance to establish a set of accurate and effective, real-time and dynamic soil moisture monitoring system for agricultural production, drought prevention and disaster reduction and ecological environment evaluation. In this paper, a suitable method of soil moisture was established for low vegetation cover areas such as agricultural land. Based on the soil scattering model and vegetation scattering model, the water cloud model is used to simulate the backscattering of vegetation and the two-way attenuation of backscattering by vegetation. The leaf area index simulated by passive optical remote sensing data is used as the parameters of vegetation canopy structure to input into the water cloud model, which makes the model suitable for large-scale vegetation backscattering simulation. The backscattering part of soil was simulated by Oh model, and a new method of soil moisture retrieval by active and passive remote sensing was established. Because the original soil backscattering simulation term in the water cloud model is a simple linear equation, by replacing the soil backscattering simulation term in the original water cloud model with Oh model, the effect of soil roughness on backscattering is considered. The model is more accurate to describe the backscattering of soil, and the simulation accuracy of the model is improved. Finally, this paper selects Yingke Oasis Agricultural Irrigation area in the middle reaches of Heihe River Basin of Gansu Province as the research area, based on active microwave remote sensing Envisat ASAR data, passive optical remote sensing Landsat-5 TM data and field observation data. The feasibility and inversion accuracy of the model are compared and verified. The results show that the simulation results of the improved water cloud model are more accurate than those of the traditional water cloud model by using the field observation data of the study area. At the same time, it is also found that the precision of the improved model is lower than that of the dry model, but the overall improvement is compared with the traditional water cloud model. Therefore, further research work should be carried out in areas where vegetation cover is relatively dense and soil moisture changes greatly.
【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S152.7

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