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表面粗糙度是有效解释雷达后向散射系数和微波辐射亮度温度的关键参数之一。表面粗糙度参数的测量精度受到测量方法、测量仪器、数据预处理等的影响,如何获取到表面粗糙度的“真”值是地表粗糙度测量急需解决的问题,并且有助于提高利用微波遥感技术反演地表参数的能力。本文利用激光扫描仪的二维高度数据和蒙特卡罗方法模拟的一维表面高度数据,分析了重复采样次数、采样间隔、采样剖面长度、空间自相关函数类型和大尺度结构(数据倾斜和农田垄行结构)对表面粗糙度精度的影响,研究表明:在大于20次重复采样、小于10mm的采样间隔、200倍相关长度的剖面长度的条件下,农田表面粗糙度参数的测量精度约为80%;分形相关函数与实测农田表面的空间自相关系数的吻合性要高于高斯函数和指数函数;数据倾斜和农田垄行结构严重影响表面粗糙度参数的结果,在进行表面粗糙度参数的计算之前,需从剖面高度分布数据中去除以上两个因素的影响。
Surface roughness is one of the key parameters to effectively explain the radar backscattering coefficient and microwave radiation brightness temperature. How to get the “true” value of the surface roughness is an urgent problem to be solved in the measurement of surface roughness, and it is helpful to improve the measurement accuracy of the surface roughness parameter by measuring method, measuring instrument and data pretreatment. Microwave Remote Sensing Retrieval of Surface Parameters. In this paper, we use the two-dimensional height data of laser scanner and the Monte Carlo method to simulate the one-dimensional surface height data, analyzed the number of repetitive sampling, sampling interval, sampling profile length, spatial autocorrelation function type and large-scale structure (data tilt and farmland Ridge structure) on the accuracy of surface roughness. The results show that the measurement accuracy of farmland surface roughness is about 80 under the condition of more than 20 repeated samplings, less than 10mm sampling interval and 200 times the correlation length. %. The agreement between the fractal correlation function and the spatial autocorrelation coefficient of farmland surface is higher than that of Gaussian function and exponential function. The data slope and farmland ridge structure seriously affect the results of surface roughness parameters. After calculating the surface roughness parameters Previously, the impact of these two factors was removed from the profile height distribution data.