Microwave remote sensing measurements are sensitive to surface parameters, and can be used for quantitative estimation of soil moisture, surface roughness and vegetation information. The ground-based true values of surface roughness parameters are important for the validation and accuracy evaluation of inversion results, and also vital in microwave soil moisture inversion algorithm for calibrating the constants in the model. For a long time, there is a lack of a simple, fast, high-precision method to obtain surface roughness parameters. Firstly, 2 commonly used parameters that characterize surface roughness i.e. root mean square height and correlation length, are described in this paper. Then, the concrete process of surface roughness measurement by close-range photogrammetry is introduced. We also analyze the factors which influence the measurement accuracy of close-range photogrammetry. In addition, close-range photogrammetry results are compared with the traditional pin-profiler method, and the measurement results of the root mean square height and correlation length of the 2 methods are analyzed. It is shown that the mean square root height error measured by the traditional pin-profiler method can reach is 12%-35.1%, and the correlation length error is 19.6%-62.4%. The advantages and disadvantages of the 2 methods are analyzed from the aspects of applicability and accuracy. The accuracy required for microwave inversion of surface roughness parameters is also analyzed. Result shows the way by close-range photogrammetry can improve the accuracy of surface roughness measurement effectively, and meet the requirements of inversion research of soil surface roughness. The influence of sampling interval on roughness measurement accuracy is also analyzed by changing the sampling interval of close-range photogrammetry. This article uses Agisoft Photoscan software to reconstruct point cloud. Specifically, a non-measuring camera is used to obtain multi-angle overlapping image of the study area. Secondly, SIFT (scale-invariant feature transform) approach is used to detect correspondences across the photos. Then bundle-adjustment algorithm is used to solve camera intrinsic and extrinsic orientation parameters and a multi-view approach is utilized to reconstruct dense cloud. Finally, the effect of non-contact measurement can be achieved by measuring the generated dense point cloud. This article also presents the requirements of hardware and image acquisition operations that need paid attention during the measurement of surface roughness using close-range photogrammetry. In this paper, the method of measuring surface roughness by close-range photogrammetry has the following advantages: The accuracy of surface roughness measurement is greatly improved, and the problem of certain surface which is extremely rough and can't be measured is also solved. And the sampling interval of close-range photogrammetry is quantitatively analyzed. Close-range photogrammetric measurement can easily adjust the sampling angle and describe the anisotropy of surface roughness accurately. The number of sampling can be easily increased so as to solve the measurement error caused by the heterogeneity of the surface. At the same time, it is concluded that the measurement result is stable and can be regarded as the true value when the number of measurements is greater than 12. With the advantages of non-contact measurement, high precision, adjustable sampling interval and frequency, the close-range photogrammetric method provides an effective solution for soil surface roughness measurement.%微波遥感观测量对地表参数敏感,可用于对土壤水分、地表粗糙度和植被信息的定量提取.地表粗糙度参数实测值,是遥感反演结果验证、精度评价,以及各类遥感反演算法中的重要参量,但一直缺少一种简便、快捷、高精度的获取手段.该研究将近景摄影测量方法和微波遥感领域传统地表粗糙度测量的针板法进行对比,分析2种方法均方根高度及相关长度的测量结果,表明近景摄影测量法可以有效提高粗糙度实地测量精度;以近景摄影测量法全角度粗糙度参数的均值作为实测真实值,表明传统针板法测量的均方根高度误差达到12%~35.1%,相关长度误差最大能达到19.6%~62.4%;分析了采样间隔和采样数目对粗糙度测量的影响,表明针板法采用1 cm的间隔是实际地表较好的选择,但仅采用2个方向均值的测量方法必然带来很大误差,同时得出测量次数大于12时,测量结果趋于稳定并可认定为真值的结论;此外,研究表明,在一些传统针板法无法提取粗糙度的情况,近景摄影测量法依然有效.该研究表明,近景摄影测量法的非接触式、高精度、采样间隔及采样频率可调节等特征,为微波遥感工作提供了有效的地表粗糙度地面实测手段.
展开▼
