Please wait a minute...
Remote Sensing for Natural Resources    2023, Vol. 35 Issue (1) : 115-122     DOI: 10.6046/zrzyyg.2021461
A method for soil roughness measurement based on UAV point cloud data
ZHANG Tian1,2,3(), ZHOU Zhongfa1,2,3(), WANG Lingyu1,2,3, ZHAO Xin1,2,3, ZHANG Wenhui1,2,3, ZHANG Shu1,2,3, WANG Yu1,2,3
1. School of Geography and Environmental Science, Institute of Karst Science, Guizhou Normal University, Guiyang 550001,China
2. State Key Laboratory Incubation Base for Mountain Ecology Environment of Guizhou Province, Guiyang 550001,China
3. State Engineering Technology Institute for Karst Desertification Control, Guiyang 550001, China
Download: PDF(3601 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

The soil roughness of cultivated land is an important element affecting the monitoring of agricultural information, such as soil moisture, microwave remote sensing observation, and plant growth. Soil roughness is generally interpreted according to field photos. However, such interpretation suffers some shortcomings such as low efficiency and anthropogenic effects on processing results. UAV low-altitude remote sensing is sensitive to surface relief. To explore the precision of the soil roughness determined using UAV data, this study employed UAV photogrammetry to photograph the surface and then compared the photogrammetry results with the data obtained using a gauging plate for soil roughness. The results show that the close-range photogrammetry had mean absolute errors of mainly 0.4~1.2 cm, a mean relative error of 6.16%, and a root mean square error of 0.40 cm. Therefore, UAV-based point cloud photogrammetry could be effectively applied to the measurement of surface roughness, and a smaller sampling area is associated with more accurate soil roughness.

Keywords soil roughness      point cloud data      close-range photogrammetry      three-dimensional modeling     
ZTFLH:  P237  
Issue Date: 20 March 2023
E-mail this article
E-mail Alert
Articles by authors
Zhongfa ZHOU
Lingyu WANG
Wenhui ZHANG
Cite this article:   
Tian ZHANG,Zhongfa ZHOU,Lingyu WANG, et al. A method for soil roughness measurement based on UAV point cloud data[J]. Remote Sensing for Natural Resources, 2023, 35(1): 115-122.
URL:     OR
Fig.1  Data sampling diagram
类别 参数 参数值
无人机参数 无人机质量/g 907(起飞重量)
最大上升速度/(m/s) 5
最大下降速度/(m/s) 3
相机参数 相机型号 L1D-20C_10.3
焦距/mm 10
相片ISO曝光范围 100~3 200(自动)
相机分辨率/像素 5 472×3 648
飞行参数 飞行高度/m 10
航向/旁向重叠度/% 75/75
拍摄模式 悬停拍摄
地面分辨率/cm 0.43
照片数量/张 94
Tab.1  UAV, camera and flight parameters
Fig.2  Technical roadmap
Fig.3  Schematic diagram of block sampling
测量板法 无人机点云摄影测量法
平均值 1.08 44.04 1.09 42.40
方差 0.06 53.00 0.05 41.41
最大值 1.39 55.00 1.48 49.00
最小值 0.80 23.00 0.60 24.00
Tab.2  Comparison of accuracy verification of surface roughness parameter measurement results (cm)
Fig.4  Result comparison between measuring boards method and close-range photogrammetry
Fig.5  Comparison of the same soil transect obtained by different measurement methods
Fig.6  UAV photogrammetry calculation results
RMSH/cm ACL/cm 与上位测量结果之差
RMSH/cm ACL/cm
100×100 0.16 1.44
80×80 0.13 1.00 0.03 0.44
60×60 0.11 0.53 0.02 0.47
40×40 0.07 0.89 0.04 -0.36
20×20 0.04 0.26 0.03 0.63
10×10 0.02 0.14 0.02 0.12
Tab.3  Measurement results of soil roughness parameters under different sampling area
Fig.7  Soil surface models of different sampling area
方法 外业数据
测量板法 60 10 测量板 1.08 2D
无人机点云摄影测量法 2 20 无人机 0.32 3D
Tab.4  Comparison of the required time and accuracy of the two methods
[1] 徐元柳. 基于裸露地表辐射传输模型的粗糙度反演与地形校正[D]. 北京: 中国地质大学(北京), 2009:45-53.
[1] Xu Y L. Roughness retrieval and topographic correction based on bare surface radiative transfer model[D]. Beijing: China University of Geosciences(Beijing), 2009:45-53.
[2] 江冲亚, 方红亮, 魏珊珊. 地表粗糙度参数化研究综述[J]. 地球科学进展, 2012, 27(3):292-303.
[2] Jiang C Y, Fang H L, Wei S S. Review of land surface roughness parameterization study[J]. Advances in Earth Science, 2012, 27(3):292-303.
[3] 姜灵海. 基于哨兵数据的土壤水分反演算法研究[D]. 成都: 电子科技大学, 2020.
[3] Jiang L H. Research on soil moisture retreval based on Sentinel data[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[4] 梁勇, 杨磊, 吴秋兰, 等. 地表粗糙度影响下的GNSS-R土壤湿度反演仿真分析[J]. 武汉大学学报(信息科学版), 2018, 43(10):1546-1552.
[4] Liang Y, Yang L, Wu Q L, et al. Simulation of soil roughness impact in GNSS-R soil moisture retrieval[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10):1546-1552.
[5] Alvarez-Mozos J, Verhoest N E, Larranaga A, et al. Influence of surface roughness spatial variability and temporal dynamics on the retrieval of soil moisture from SAR observations[J]. Sensors (Basel), 2009, 9(1):463-489.
doi: 10.3390/s90100463 url:
[6] 陈世超, 林剑辉, 孙宇瑞, 等. 基于土壤表面粗糙度预测降雨影响下的表层土壤孔隙度[J]. 北京林业大学学报, 2013, 35(2):69-74.
[6] Chen S C, Lin J H, Sun Y R, et al. Predicting topsoil porosity using soil surface roughness under rainfall influence[J]. Journal of Beijing Forestry University, 2013, 35(2):69-74.
[7] Darboux F, Gascuelodoux C, Davy P. Effects of surface water storage by soil roughness on overland-flow generation[J]. Earth Surface Processes and Landforms, 2010, 27(3):223-233.
doi: 10.1002/esp.v27:3 url:
[8] García Moreno R, et al.Díaz Álvarez M C,Saa Requejo A, Multifractal analysis of soil surface roughness[J]. Vadose Zone Journal, 2008, 7(2):512-520.
doi: 10.2136/vzj2007.0016 url:
[9] Thomsen L M, Baartman J E M, Barneveld R J, et al. Soil surface roughness:Comparing old and new measuring methods and application in a soil erosion model[J]. Soil, 2015, 1(1):399-400.
doi: 10.5194/soil-1-399-2015 url:
[10] Riegler-Nurscher P, Moitzi G, Prankl J, et al. Machine vision for soil roughness measurement and control of tillage machines during seedbed preparation[J]. Soil and Tillage Research, 2020, 196:104351.
doi: 10.1016/j.still.2019.104351 url:
[11] Robichaud P R, Molnau M. Measuring soil roughness changes with an ultrasonic profiler[J]. Transactions of the ASAE, 1990, 33(6):1851-1858.
doi: 10.13031/2013.31549 url:
[12] 陶浩然, 陈权, 李震, 等. 近景摄影测量提高裸露地表粗糙度测量精度[J]. 农业工程学报, 2017, 33(15):162-167.
[12] Tao H R, Chen Q, Li Z, et al. Improvement of soil surface roughness measurement accuracy by close-range photogrammetry[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(15):162-167.
[13] 蔡祥, 孙宇瑞, 林剑辉, 等. 基于激光反射的土壤表面粗糙度测量装置设计与试验[J]. 农业机械学报, 2010, 41(1):68-69.
[13] Cai X, Sun Y R, Lin J H, et al. Design of a laser scanner for characterizing soil surface roughness[J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(1):68-69.
[14] 王章琼, 白俊龙, 张明. 数码摄影测量及三维建模在岩体结构面粗糙度教学中的应用[J]. 实验技术与管理, 2020, 37(3):45-47.
[14] Wang Z Q, Bai J L, Zhang M. Application of digital photogrammetry and 3D modeling in teaching of rock discontinuity surface roughness[J]. Experimental Technology and Management, 2020, 37(3):45-47.
[15] 王洪晓, 张光辉, 朱平宗, 等. 3种地表随机糙率测量方法比较[J]. 水土保持研究, 2020, 27(4):72-76.
[15] Wang H X, Zhang G H, Zhu P Z, et al. Comparison of three metho-ds to measure surface random roughness[J]. Journal of Soil and Water Conservation, 2020, 27(4):72-76.
[16] 张胜宾, 黄培奎, 赵祚喜. 土壤表面粗糙度检测方法研究[J]. 农机化研究, 2016, 38(11):257-262.
[16] Zhang S B, Huang P K, Zhao Z X. A research on soil surface roughness measurement methods[J]. Journal of Agricultural Mechanization Research, 2016, 38(11):257-262.
[17] 李红阳. 基于无人机摄影测量技术的土地调查方法研究[D]. 唐山: 华北理工大学, 2018.
[17] Li H Y. The research method of land survey based on UAV photogrammetry techology[D]. Tangshan: North China University of Science and Technology, 2018.
[18] 李德仁, 李明. 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报(信息科学版), 2014, 39(5):505-512.
[18] Li D R, Li M. Research progress and application prospect of UAV remote sensing system[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5):505-512.
[19] Warner W S. Mapping a three-dimensional soil surface with hand-held 35 mm photography[J]. Soil and Tillage Research, 1995, 34(3):187-197.
doi: 10.1016/0167-1987(95)00462-2 url:
[20] 许元红. 低空无人机技术在贵州山区大比例尺DLG制作中的应用研究[D]. 贵阳: 贵州师范大学, 2018:1-8.
[20] Xu Y H. The application of low-altitude drone thchnology in the production of large scale DLG in Guizhou mountain area[D]. Guiyang: Guizhou Normal University, 2018:1-8.
[21] 胡玉祥, 张洪德, 熊文辉, 等. 基于三维激光点云的地表竣工规划验收测绘级工程应用[J]. 测绘通报, 2020(s1):53-58.
[21] Hu Y X, Zhang H D, Xiong W H, et al. Survey and engineering application of metro completion plan acceptance based on 3D laser point cloud[J]. Bulletin of Surveying and Mapping, 2020(s1):53-58.
[22] 尹林江, 周忠发, 李韶慧, 等. 基于无人机可见光影像对喀斯特地区植被信息提取与覆盖度研究[J]. 草地学报, 2020, 28(6):1664-1672.
doi: 10.11733/j.issn.1007-0435.2020.06.021
[22] Yin L J, Zhou Z F, Li S H, et al. Research on vegetation extraction and fractional vegetation cover of Karst area based on visible light image of UAV[J]. Acta Agrestia Sinica, 2020, 28(6):1664-1672.
[23] 冯静静. 基于LiDAR点云数据与高分辨率影像的单木参数提取[D]. 北京: 北京林业大学, 2017:1-5.
[23] Feng J J. Single tree parameter extraction based on LiDAR point cloud data and high resolution image[D]. Beijing: Beijing Forestry University, 2017:1-5.
[24] 赵勋, 岳彩荣, 李春干, 等. 基于机载LiDAR点云数据森林郁闭度估测[J]. 遥感技术与应用, 2020, 35(5):1136-1145.
[24] Zhao X, Yue C R, Li C G, et al. Canopy density estimation based on airborne LiDAR point cloud data[J]. Remote Sensing Technolo-gy and Application, 2020, 35(5):1136-1145.
[25] 姜云, 王军, 陈方圆. 黑土区大田尺度下LiDAR点云数据农作物种植区提取[J]. 测绘工程, 2020, 29(4):32-37.
[25] Jiang Y, Wang J, Chen F Y. Extraction of paddy planting area based on LiDAR point cloud data at field-scale in black soil area[J]. Engineering of Surveying and Mapping, 2020, 29(4):32-37.
[26] 段平, 李佳, 李海坤, 等. 无人机影像点云与地面激光点云匹配的三维建模方法[J]. 测绘工程, 2020, 29(4):44-47.
[26] Duan P, Li J, Li H K, et al. 3D modeling method of UAV image point cloud matching with ground laser point cloud[J]. Engineering of Surveying and Mapping, 2020, 29(4):44-47.
[27] 刘洁, 杨晓菊, 廖宜涛, 等. 基于线结构激光传感器的土壤表面粗糙度测量方法研究[J]. 农业工程学报, 2019, 35(3):134-138.
[27] Liu J, Yang X J, Liao Y T, et al. Study on soil surface roughness measurement method based on linear laser sensor[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(3):134-138.
[28] 罗达, 林杭生, 金钊, 等. 无人机数字摄影测量与激光雷达在地形地貌与地表覆盖研究中的应用及比较[J]. 地球环境学报, 2018, 10(3):215-222.
[28] Luo D, Lin H S, Jin Z, et al. Application and comparison of UAV digital photogrammetry and LiDAR in topography and land cover research[J]. Journal of Earth Environment, 2018, 10(3):215-222.
[29] Verhoeven G. Taking computer vision aloft-archaeological three-dimensional reconstructions from aerial photographs with PhotoScan[J]. Archaeological Prospection, 2011, 18(1):67-73.
doi: 10.1002/arp.v18.1 url:
[30] Remondino F, El-Hakim S. Image-based 3D modelling:A review[J]. Photogrammetric Record, 2010, 21(115):269-291.
doi: 10.1111/(ISSN)1477-9730 url:
[31] Tavani S, Granado P, Corradetti A, et al. Building a virtual outcrop,extracting geological information from it,and sharing the results in Google Earth via OpenPlot and Photoscan:An example from the Khaviz Anticline (Iran)[J]. Computers and Geosciences, 2014, 63:44-53.
doi: 10.1016/j.cageo.2013.10.013 url:
[32] 柴子为, 康峻, 王力, 等. 基于无人机影像的山地人工林景观DEM构建[J]. 遥感技术与应用, 2015, 30(3):504-509.
[32] Cai Z W, Kang J, Wang L, et al. Construction of mountain plantation landscape DEM based on UAV image[J]. Remote Sensing Technology and Application, 2015, 30(3):504-509.
[33] 行敏锋, 黄方, 官雨薇. 基于图像处理的土壤表面粗糙度测量实验研究[J]. 实验科学与技术, 2019, 17(6):1-3.
[33] Xing M F, Huang F, Guan Y W. Experimental study on measurement of soil surface roughness based on image processing[J]. Experiment Science and Technology, 2019, 17(6):1-3.
[34] 刘立超, 魏国梁, 张青松, 等. 基于激光雷达的农业耕作微地貌测量装置设计与实验[J]. 农业机械学报, 2019, 50(7):85-91.
[34] Liu L C, Wei G L, Zhang Q S, et al. Design and experiment of agricultural microgeomorphology measuring device based on LiDAR[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(7):85-91.
[35] 雷志斌. 基于高分三号雷达数据的作物覆盖区土壤水分反演研究[D]. 北京. 中国地质大学(北京), 2020:18-20.
[35] Lei Z B. Research on soil moisture retrieval of crop covering area based on GaoFen3 Radar data[D]. Beijing: China University of Geosciences(Beijing), 2020:18-20.
[36] 李俐, 王荻, 潘彩霞, 等. 基于神经网络与决策树的土壤粗糙度测量[J]. 农业工程学报, 2015, 31(14):132-138.
[36] Li L, Wang D, Pan C X, et al. Soil surface roughness measuring method based on neural network and decision tree[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(14):132-138.
[37] 张恒瑞, 段喜明, 魏征, 等. 基于无人机多光谱遥感的华北地区夏玉米LAI监测[J]. 山西农业科学, 2021, 49(5):608-614.
[37] Zhang H R, Duan X M, Wei Z, et al. LAI monitoring of summer maize in North China based on UAV multi-spectral remote sensing[J]. Journal of Shanxi Agricultural Sciences, 2021, 49(5):608-614.
[38] 郑兴明, 赵凯, 李晓洁. 农田表面粗糙度参数的测量与精度分析[J]. 地球信息科学学报, 2013, 15(5):753-755.
[38] Zheng X M, Zhao K, Li X J. Accuracy analysis of agriculture soil surface roughness parameter[J]. Journal of Geo-Information Science, 2013, 15(5):753-755.
[39] Kim S B, Moghaddam M, Tsang L, et al. Models of L-band Radar backscattering coefficients over global rain for soil moisture retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2):1381-1396.
doi: 10.1109/TGRS.2013.2250980 url:
[1] CUI Ziwei, XU Wenxue, LIU Yanxiong, GUO Yadong, MENG Xiangqian, JIANG Zhengkun. Current status of the acquisition and processing of airborne laser sounding data[J]. Remote Sensing for Natural Resources, 2023, 35(3): 1-9.
[2] LIAN Huiqing, MENG Lu, HAN Ruigang, YANG Yi, YU Biao. Geological information extraction based on remote sensing of unmanned aerial vehicle: Exemplified by Liujiang Basin[J]. Remote Sensing for Land & Resources, 2020, 32(3): 136-142.
[3] MA Jing, LIU Xianglei. Application of 3D visualization to the reconstruction of urban old districts[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 170-174.
[4] CHENG Xiao-qian, FAN Liang-xin, ZHAO Hong-qiang. Filtering of Airborne LiDAR Data for Cityscapes Based on Segmentation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 29-32.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech