空气动力学粗糙度研究进展

doi:10.12118/j.issn.1000-6060.2022.371

Abstract

Abstract:

Aerodynamic roughness is defined as the height at which the wind speed becomes zero under neutral and stable conditions. It is an important parameter for measuring the momentum and energy exchange between the underlying surface and atmosphere, and it is critical for investigating various surface processes and climate change. However, it has always been difficult to estimate aerodynamic roughness accurately at the regional scale, and there is no unified estimation model presently. Therefore, the parameterization of aerodynamic roughness is a topic worthy of further study. As a long-range monitoring method, remote sensing technology has the advantages of macroscopic and rapid acquisition of ground feature information, and its ability to achieve dynamic monitoring at the regional scale or a larger scale in estimating the aerodynamic roughness of vegetation-covered surfaces. Therefore, using remote sensing technology to estimate aerodynamic roughness has become a hot issue at home and abroad in recent years. In this study, the progress of research on aerodynamic roughness at home and abroad in recent years is systematically described. The estimation methods are divided into two categories: one is based on measured data, and the other is the remote sensing method, which is rapidly advancing. This study primarily introduces the method of estimating the aerodynamic roughness of the underlying surface of vegetation by remote sensing technology. Methods based on measured data include the canopy height fixed ratio method, field experiment method, and wind tunnel method; remote sensing methods include vegetation index, LIDAR, and multisource remote sensing synergistic methods. In addition, the advantages and disadvantages of the different methods are summarized at the end of each section. Finally, this study analyses the influence of meteorological factors and morphological characteristics of surface roughness elements on aerodynamic roughness and discusses the development trends and problems of remote sensing techniques in estimating aerodynamic roughness, aiming to provide ideas for subsequent research on remote sensing monitoring of aerodynamic roughness.

Key words: aerodynamic roughness, remote sensing, research status, influencing factors

LI Xinyu,WANG Jingpu,WANG Zhoulong. Research progress on aerodynamic roughness[J].Arid Land Geography, 2023, 46(3): 407-417.

Figures/Tables 1

References 67

[1]	Dong Z B, Liu X P, Wang X M. Aerodynamic roughness of gravel surfaces[J]. Geomorphology, 2002, 722(1-2): 17-31.
[2]	Miles E S, Steiner J F, Brun F. Highly variable aerodynamic roughness length (z₀) for a hummocky debris-covered glacier[J]. Journal of Geophysical Research, 2017, 122(16): 8447-8466.
[3]	Sun G H, Hu Z Y, Wang J M, et al. Upscaling analysis of aerodynamic roughness length based on in situ data at different spatial scales and remote sensing in north Tibetan Plateau[J]. Atmospheric Research, 2016, 176-177: 231-239.
[4]	Liu J Q, Kimura R, Miyawaki M, et al. Effects of plants with different shapes and coverage on the blown-sand flux and roughness length examined by wind tunnel experiments[J]. Catena, 2021, 197: 104976, doi: 10.1016/j.catena.2020.104976. doi: 10.1016/j.catena.2020.104976
[5]	Abbas M R, Rasib A W B, Abbas T R, et al. Assessment of aerodynamic roughness length using remotely sensed land cover features and MODIS[J]. IOP Conference Series: Earth Environmental Science, 2021, 722(1): 012015, doi: 10.1088/1755-1315/722/1/012015. doi: 10.1088/1755-1315/722/1/012015.
[6]	Van der Graaf S C, Kranenburg R, Segers A J, et al. Satellite-derived leaf area index and roughness length information for surface-atmosphere exchange modelling: A case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0[J]. Geoscientific Model Development, 2020, 13(5): 2451-2474. doi: 10.5194/gmd-13-2451-2020
[7]	贾立, 王介民. 卫星遥感结合地面资料对区域表面动量粗糙度的估算[J]. 大气科学, 1999, 23(5): 632-640.
	[ Jia Li, Wang Jiemin. Estimation of area roughness length for momentum using remote sensing data and measurements in field[J]. Chinese Journal of Atmospheric Sciences, 1999, 23(5): 632-640. ]
[8]	朱彩英, 张仁华, 王劲峰, 等. 运用SAR图像和TM热红外图像定量反演地表空气动力学粗糙度的二维分布[J]. 中国科学: 地球科学, 2004, 34(4): 385-393.
	[ Zhu Caiying, Zhang Renhua, Wang Jinfeng, et al. Quantitative inversion of two-dimensional distribution of surface aerodynamic roughness using SAR images and TM thermal infrared images[J]. Scientia Sinica (Terrae), 2004, 34(4): 385-393. ]
[9]	冯健武, 刘辉志, 王雷, 等. 半干旱区不同下垫面地表粗糙度和湍流通量整体输送系数变化特征[J]. 中国科学: 地球科学, 2012, 42(1): 24-33.
	[ Feng Jianwu, Liu Huizhi, Wang Lei, et al. Seasonal and inter-annual variation of surface roughness length and bulk transfer coefficients in a semiarid area[J]. Scientia Sinica (Terrae), 2012, 42(1): 24-33. ]
[10]	Allen R G, Tasumi M, Trezza R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model[J]. Journal of Irrigation Drainage Engineering, 2007, 133: 395-406. doi: 10.1061/(ASCE)0733-9437(2007)133:4(395)
[11]	薛晶, 侯占峰, 刘海洋, 等. 草原灌木带空气动力学粗糙度研究[J]. 干旱地区农业研究, 2016, 34(6): 253-256.
	[ Xue Jing, Hou Zhanfeng, Liu Haiyang, et al. Study on the aerodynamic roughness of grassland shrub belt[J]. Agricultural Research in the Arid Areas, 2016, 34(6): 253-256. ]
[12]	莎日娜, 于明含, 丁国栋, 等. 沙质农田油沙豆保护性耕作优化模式的风洞模拟试验[J]. 干旱区资源与环境, 2022, 36(4): 87-94.
	[ Sha Rina, Yu Minghan, Ding Guodong, et al. Shelter efficiency of conservation tillage model in Cyperus esculentus in sandy farmland[J]. Journal of Arid Land Resources and Environment, 2022, 36(4): 87-94. ]
[13]	鞠英芹, 刘寿东, 马德栗, 等. 农田与草地下垫面空气动力学粗糙度的研究[J]. 科技通报, 2016, 32(8): 5-11, 16.
	[ Ju Yingqin, Liu Shoudong, Ma Deli, et al. Study on aerodynamic roughness length on crop and grass underlying surfaces[J]. Bulletin of Science and Technology, 2016, 32(8): 5-11, 16. ]
[14]	赵永来, 麻硕士, 陈智, 等. 残茬覆盖地表空气动力学粗糙度变化规律[J]. 农业机械学报, 2013, 44(4): 118-122.
	[ Zhao Yonglai, Ma Shuoshi, Chen Zhi, et al. Variational rule of aerodynamic roughness under crop stubble coverage[J]. Journal of Agricultural Machinery, 2013, 44(4): 118-122. ]
[15]	张杰, 黄建平, 张强. 稀疏植被区空气动力学粗糙度特征及遥感反演[J]. 生态学报, 2010, 30(11): 2819-2827.
	[ Zhang Jie, Huang Jianping, Zhang Qiang. Retrieval of aerodynamic roughness length character over sparse vegetation region[J]. Acta Ecologica Sinica, 2010, 30(11): 2819-2827. ]
[16]	Graf A, Boer A V D, Moene A, et al. Intercomparison of methods for the simultaneous estimation of zero-plane displacement and aerodynamic roughness length from single-level eddy-covariance data[J]. Boundary-Layer Meteorology, 2014, 151(2): 373-387. doi: 10.1007/s10546-013-9905-z
[17]	张春来, 邹学勇, 董光荣, 等. 耕作土壤表面的空气动力学粗糙度及其对土壤风蚀的影响[J]. 中国沙漠, 2002, 22(5): 473-475.
	[ Zhang Chunlai, Zou Xueyong, Dong Guangrong, et al. Aerodynamic roughness of cultivated soil and its influence on soil erosion by wind in a wind tunnel[J]. Journal of Desert Research, 2002, 22(5): 473-475. ]
[18]	于名召. 空气动力学粗糙度的遥感方法及其在蒸散发计算中的应用研究[D]. 北京: 中国科学院大学, 2018.
	[ Yu Mingzhao. Research on remote sensing methods for the aerodynamic roughness length and its application in evapotranspiration calculation[D]. Beijing: University of Chinese Academy of Sciences, 2018. ]
[19]	Liu Y, Guo W D, Huang H L, et al. Estimating global aerodynamic parameters in 1982—2017 using remote-sensing data and a turbulent transfer model[J]. Remote Sensing of Environment, 2021, 260: 112428, doi: 10.1016/j.rse.2021.112428. doi: 10.1016/j.rse.2021.112428
[20]	Yuan Y W, Wang X S, Fang Y, et al. Examination of the quantitative relationship between vegetation canopy height and LAI[J]. Advances in Meteorology, 2013, 2013(3): 1-6.
[21]	Gupta R K, Prasad T S, Vijayan D. Estimation of roughness length and sensible heat flux from WiFS and NOAA AVHRR data[J]. Advances in Space Research, 2002, 29(1): 33-38. doi: 10.1016/S0273-1177(01)00624-X
[22]	Xing Q, Wu B F, Yan N N, et al. Evaluating the relationship between field aerodynamic roughness and the MODIS BRDF, NDVI, and wind speed over grassland[J]. Atmosphere, 2017, 8(1): 1-11. doi: 10.3390/atmos8010001
[23]	Hammond D S, Chapman L, Thornes J E. Roughness length estimation along road transects using airborne LIDAR data[J]. Meteorological Applications, 2012, 19(4): 420-426. doi: 10.1002/met.273
[24]	Li A H, Zhao W G, Mitchell J J, et al. Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland[J]. Photogrammetric Engineering Remote Sensing, 2017, 83(6): 415-427. doi: 10.14358/PERS.83.6.415
[25]	Colin J, Faivre R. Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe Basin in China[J]. Hydrology Earth System Sciences, 2010, 14(12): 2661-2669. doi: 10.5194/hess-14-2661-2010
[26]	Santos C, Lorite I J, Allen R G, et al. Aerodynamic parameterization of the satellite-based energy balance (METRIC) model for ET estimation in rainfed olive orchards of Andalusia, Spain[J]. Water Resources Management, 2012, 26(11): 3267-3283. doi: 10.1007/s11269-012-0071-8
[27]	Masseroni D, Facchi A, Gandolfi C. Estimation of zero-plane displacement height and aerodynamic roughness length on rice fields[J]. Italian Journal of Agrometeorology, 2015, 20(1): 67-75.
[28]	Yang R, Friedl M A. Determination of roughness lengths for momentum and heat over boreal forests[J]. Boundary-Layer Meteorology, 2003, 107(3): 581-603. doi: 10.1023/A:1022880530523
[29]	Toure A A, Rajot J L, Garba Z, et al. Impact of very low crop residues cover on wind erosion in the Sahel[J]. Catena, 2011, 85(3): 205-214. doi: 10.1016/j.catena.2011.01.002
[30]	邢丽玮. 基于MODIS和GLAS数据反演多时序中国陆表植被空气动力学粗糙度[D]. 北京: 首都师范大学, 2012.
	[ Xing Liwei. Retrieval of aerodynamic roughness of land surface vegetation in China with multiple time series based on MODIS and GLAS data[D]. Beijing: Capital Normal University, 2012. ]
[31]	周艳莲, 孙晓敏, 朱治林, 等. 几种典型地表粗糙度计算方法的比较研究[J]. 地理研究, 2007, 26(5): 887-896.
	[ Zhou Yanlian, Sun Xiaomin, Zhu Zhilin, et al. Comparative research on four typicalcc surface roughness length calculation methods[J]. Geographical Research, 2007, 26(5): 887-896. ]
[32]	Yu M Z, Wu B F, Zeng H W, et al. The impacts of vegetation and meteorological factors on aerodynamic roughness length at different time scales[J]. Atmosphere, 2018, 9(4): 149, doi: 10.3390/atmos9040149. doi: 10.3390/atmos9040149
[33]	Zhang C L, Yuan Y X, Zou X Y, et al. A comparison of the aerodynamic characteristics of four kinds of land surface in wind erosion areas of northern China[J]. Catena, 2022, 212: 106112, doi: 10.1016/j.catena.2022.106112. doi: 10.1016/j.catena.2022.106112
[34]	Yu M Z, Wu B F, Yan N N, et al. A method for estimating the aerodynamic roughness length with NDVI and BRDF signatures using multi-temporal Proba-V data[J]. Remote Sensing, 2017, 9(1): 6, doi: 10.3390/rs9010006. doi: 10.3390/rs9010006
[35]	卢俐, 刘绍民, 孙敏章, 等. 大孔径闪烁仪研究区域地表通量的进展[J]. 地球科学进展, 2005, 20(9): 932-939. doi: 10.11867/j.issn.1001-8166.2005.09.0932
	[ Lu Li, Liu Shaomin, Sun Minzhang, et al. Advances in the study of areal surface flux with large aperture scintillometer[J]. Advances in Earth Science, 2005, 20(9): 932-939. ] doi: 10.11867/j.issn.1001-8166.2005.09.0932
[36]	赵永来, 麻硕士, 陈智. 植被覆盖地表的空气动力学粗糙度及对土壤风蚀的影响[J]. 农机化研究, 2007(2): 36-39.
	[ Zhao Yonglai, Ma Shuoshi, Chen Zhi. Aerodynamic roughness of vegetation coverage surface and its influence on soil erosion by the wind tunnel[J]. Journal of Agricultural Mechanization Research, 2007(2): 36-39. ]
[37]	王佳庭, 于明含, 杨海龙, 等. 乌兰布和沙漠典型植物群落土壤风蚀可蚀性研究[J]. 干旱区地理, 2020, 43(6): 1543-1550.
	[ Wang Jiating, Yu Minghan, Yang Hailong, et al. Soil erosibility of typical plant communities in Ulan Buh Desert[J]. Arid Land Geography, 2020, 43(6): 1543-1550. ]
[38]	刘艳萍, 刘铁军, 蒙仲举. 草原区植被对土壤风蚀影响的风洞模拟试验研究[J]. 中国沙漠, 2013, 33(3): 668-672.
	[ Liu Yanping, Liu Tiejun, Meng Zhongju. Wind tunnel simulation test on the influence factors of wind erosion in grassland areas[J]. Journal of Desert Research, 2013, 33(3): 668-672. ]
[39]	Minvielle F, Marticorena B, Gillette D A, et al. Relationship between the aerodynamic roughness length and the roughness density in cases of low roughness density[J]. Environmental Fluid Mechanics, 2003, 3(3): 249-267. doi: 10.1023/A:1022830119554
[40]	Lu L, Liu S, Xu Z, et al. The characteristics and parameterization of aerodynamic roughness length over heterogeneous surfaces[J]. Advances in Atmospheric Sciences, 2009, 26(1): 180-190. doi: 10.1007/s00376-009-0180-3
[41]	Maurer K D, Hardiman B S, Vogel C S, et al. Canopy-structure effects on surface roughness parameters: Observations in a great lakes mixed-deciduous forest[J]. Agricultural Forest Meteorology, 2013, 177: 24-34. doi: 10.1016/j.agrformet.2013.04.002
[42]	Abbas M R, Hason M M, Ahmad B B, et al. Surface roughness distribution map for Iraq using satellite data and GIS techniques[J]. Arabian Journal of Geosciences, 2020, 13(17): 839, doi: 10.1007/s12517-020-05802-z. doi: 10.1007/s12517-020-05802-z
[43]	Bastiaanssen W G M, Menenti M, Feddes R A. A remote sensing surface energy balance algorithm for land (SEBAL)[J]. Journal of Hydrology, 1998, 212-213: 198-212.
[44]	Fang H, Baret F, Plummer S, et al. An overview of global leaf area index (LAI): Methods, products, validation, and applications[J]. Reviews of Geophysics, 2019, 57: 739-799. doi: 10.1029/2018RG000608
[45]	Schaudt K J, Dickinson R E. An approach to deriving roughness length and zero-plane displacement height from satellite data, prototyped with BOREAS data[J]. Agricultural Forest Meteorology, 2000, 104(2): 143-155. doi: 10.1016/S0168-1923(00)00153-2
[46]	Alekseychik P K, Korrensalo A, Mammarella I, et al. Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed-species boreal mire complex[J]. Geophysical Research Letters, 2017, 44(11): 5836-5843. doi: 10.1002/2017GL073884
[47]	Cho J, Miyazaki S, Yeh J F, et al. Testing the hypothesis on the relationship between aerodynamic roughness length and albedo using vegetation structure parameters[J]. International Journal of Biometeorology, 2012, 56(2): 411-418. doi: 10.1007/s00484-011-0445-2 pmid: 21562788
[48]	杨阿强, 孙国清, 卢立新, 等. 基于MODIS资料的中国东部时间序列空气动力学粗糙度和零平面位移高度估算[J]. 气象科学, 2011, 31(4): 516-524.
	[ Yang Aqiang, Sun Guoqing, Lu Lixin, et al. Deriving aerodynamic roughness length and zero-plane displacement height from MODIS product for eastern China[J]. Journal of the Meteorological Sciences, 2011, 31(4): 516-524. ]
[49]	Jasinski M F, Crago R D. Estimation of vegetation aerodynamic roughness of natural regions using frontal area density determined from satellite imagery[J]. Agricultural and Forest Meteorology, 1999, 94(1): 65-77. doi: 10.1016/S0168-1923(98)00129-4
[50]	Xing Q, Wu B F, Yan N N, et al. Sensitivity of BRDF, NDVI and wind speed to the aerodynamic roughness length over sparse Tamarix in the downstream Heihe River Basin[J]. Remote Sensing, 2018, 10(1): 56, doi: 10.3390/rs10010056. doi: 10.3390/rs10010056
[51]	Paul-Limoges E, Christen A, Coops N C, et al. Estimation of aerodynamic roughness of a harvested douglas-fir forest using airborne LIDAR[J]. Remote Sensing of Environment, 2013, 136: 225-233. doi: 10.1016/j.rse.2013.05.007
[52]	Greeley R, Lancaster N, Sullivan, et al. A relationship between radar backscatter and aerodynamic roughness: Preliminary results[J]. Geophysical Research Letters, 1988, 15(6): 565-568. doi: 10.1029/GL015i006p00565
[53]	Tian X, Li Z Y, Van der Tol C, et al. Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data[J]. Remote Sensing of Environment, 2011, 115(9): 2330-2341. doi: 10.1016/j.rse.2011.04.033
[54]	Ferhat Bingöl. A simplified method on estimation of forest roughness by use of aerial LIDAR data[J]. Energy Science & Engineering, 2019, 7(6): 3274-3282.
[55]	Streutker D R, Glenn N F. LiDAR measurement of sagebrush steppe vegetation heights[J]. Remote Sensing of Environment, 2006, 102(1-2): 135-145. doi: 10.1016/j.rse.2006.02.011
[56]	Trepekli K, Friborg T. Deriving aerodynamic roughness length at ultra-high resolution in agricultural areas using UAV-Borne LiDAR[J]. Remote Sensing, 2021, 13(17): 3538, doi: 10.3390/rs13173538. doi: 10.3390/rs13173538
[57]	吴炳方, 熊隽, 闫娜娜, 等. 基于遥感的区域蒸散量监测方法——ETWatch[J]. 水科学进展, 2008, 19(5): 671-678.
	[ Wu Bingfang, Xiong Jun, Yan Nana, et al. ETWatch for monitoring regional evapotranspiration with remote sensing[J]. Advances in Water Science, 2008, 19(5): 671-678. ]
[58]	Meier R, Davin E L, Bonan G B, et al. Impacts of a revised surface roughness parameterization in the community land model 5.1[J]. Copernicus GmbH, 2021, 15(6): 2365-2393.
[59]	张雅静, 申向东. 植被覆盖地表空气动力学粗糙度与零平面位移高度的模拟分析[J]. 中国沙漠, 2008, 28(1): 21-26.
	[ Zhang Yajing, Shen Xiangdong. Simulation analysis of vegetation covered surface’s aerodynamics roughness length and zero displacement[J]. Journal of Desert Research, 2008, 28(1): 21-26. ]
[60]	周艳莲, 孙晓敏, 朱治林, 等. 几种不同下垫面地表粗糙度动态变化及其对通量机理模型模拟的影响[J]. 中国科学: 地球科学, 2006, 36(增刊1): 244-254.
	[ Zhou Yanlian, Sun Xiaomin, Zhu Zhilin, et al. Dynamic variation of surface roughness on several different underlying surfaces and its influence on flux mechanism model simulation[J]. Scientia Sinica (Terrae), 2006, 36 (Suppl.1): 244-254. ]
[61]	Li J, Okin G S, Herrick J E, et al. Evaluation of a new model of aeolian transport in the presence of vegetation[J]. Journal of Geophysical Research: Earth Surface, 2013, 118(1): 288-306. doi: 10.1002/jgrf.v118.1
[62]	Nakai T, Sumida A, Daikoku K, et al. Parameterisation of aerodynamic roughness over boreal, cool-and warm-temperate forests[J]. Agricultural and Forest Meteorology, 2008, 148(12): 1916-1925. doi: 10.1016/j.agrformet.2008.03.009
[63]	崔珂军, 李生宇, 范敬龙, 等. 蒙古国中部草原地区风蚀沙漠化的风沙活动特征——以乔伊尔市为例[J]. 干旱区地理, 2022, 45(3): 792-801.
	[ Cui Kejun, Li Shengyu, Fan Jinglong. Aeolian sand activity characteristics of wind erosion and desertification in the grassland area of central Mongolia: A case of Choir City[J]. Arid Land Geography, 2022, 45(3): 792-801. ]
[64]	Basu S, Chakraborty P K, Nath R. Aerodynamic properties of green gram sown in different environments in Indo-Gangetic plains of west Bengal[J]. Journal of Agrometeorology, 2018, 20(2): 122-125. doi: 10.54386/jam.v20i2
[65]	刘啸然, 李茂善, 胡文斌. 藏北高原那曲地区不同下垫面地表粗糙度的变化特征研究[J]. 高原气象, 2019, 38(2): 428-438. doi: 10.7522/j.issn.1000-0534.2018.00083
	[ Liu Xiaoran, Li Maoshan, Hu Wenbin. Variations of surface roughness on different underlying surface at Nagqu Area over the Qinghai-Tibetan Plateau[J]. Plateau Meteorology, 2019, 38(2): 428-438. ] doi: 10.7522/j.issn.1000-0534.2018.00083
[66]	Zeng Z, Piao S, Li L Z X, et al. Climate mitigation from vegetation biophysical feedbacks during the past three decades[J]. Nature Climate Change, 2017, 7(6): 432-436. doi: 10.1038/nclimate3299
[67]	谭锦, 吴秀芹, 阮永健, 等. 油莎豆(Cyperus esculentus)耕作区作物残茬对农田风蚀的影响[J]. 干旱区地理, 2022, 45(2): 546-556.
	[ Tan Jin, Wu Xiuqin, Ruan Yongjian. Effects of crop residues on farmland wind erosion in Cyperus esculentus planting area[J]. Arid Land Geography, 2022, 45(2): 546-556. ]

指数	植被类型	表达式	参考文献
NDVI	农田	z₀=exp(-5.5+5.8NDVI)	Gupta等^[21]
	林地、草地	z₀=0.0206e^7.6978NDVI	Abbas等^[5]
	草地	z₀=0.0203NDVI^0.9547	Xing等^[22]
	玉米、小麦	z₀（x, y）=exp[7.13+9.33NDVI(x, y)]	贾立等^[7]
	春玉米	z₀=0.2255NDVI+0.0087	Yu等^[34]
	冬小麦	z₀=0.2476NDVI+0.0615
	夏玉米	z₀=0.2858NDVI+0.1017
	紫花苜蓿	z₀=e^-5.5+5.3NDVI	Van der Graaf等^[6]
LA	林地	z₀=0.3299Lp^1.5+2.1713	Schaudt等^[45]
	莎草	LAI_s=6.51z₀-0.17	Alekseychik等^[46]
	玉米	z₀=z₀’+0.3h(C_d×LAI)^1/2	Lu等^[40]
	短高山草	z₀=exp[-2.225-0.938/(LAI-0.205)]	Sun等^[3]
	短高山草	z₀=exp[2.613-0.173/(LAI-0.287)]	Sun等^[3]
BRDF_R	草地	z₀=0.0013BRDF_R-0.65	Xing等^[22]
FAI	宽叶林、针叶林	z₀/h=0.0537×FAI^0.51[1.0-exp(-10.9FAI^0.874)]+0.00368	Schaudt等^[45]
HDVI	春玉米	z₀=0.2236HDVI-0.0279	Yu等^[34]
	冬小麦	z₀=0.2695HDVI+0.0688
	夏玉米	z₀=0.2113HDVI+0.0391
NDVI，u	稀疏柽柳	z₀=0.5307u^NDVI-0.3952	Xing等^[50]
CI	草地	z₀=0.0078CI-0.493	Xing等^[22]
NDVI，α	棉花	z₀=exp[0.26(NDVI/α)-2.21]	Allen等^[10]

Research progress on aerodynamic roughness

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 1

References 67

Related Articles 15

Metrics

Comments

Recommended 0

[1]	JIANG Yuekun, SHI Pengjuan. Spatiotemporal evolution and its affecting factors of urban-rural income gap at the city-level scale in China [J]. Arid Land Geography, 2024, 47(1): 147-157.
[2]	LI Shiyi, GUAN Quanli. Influence of farmers’ irrigation behavior goals on irrigation water efficiency: A case of Xayar County [J]. Arid Land Geography, 2024, 47(1): 48-57.
[3]	YANG Yu, SONG Futie, ZHANG Jie. Spatial structure characteristics and influencing factors of financial network of China based on geodetectors [J]. Arid Land Geography, 2023, 46(9): 1524-1535.
[4]	LI Jianhui, CHEN Lin, DANG Zheng. Spatial pattern and influencing factors of patriotic education bases in the Yellow River Basin [J]. Arid Land Geography, 2023, 46(9): 1536-1544.
[5]	TANG Taibin, ZHOU Bao, JIN Xiaomei, WEI Sailajia, MA Tao, ZHANG Yongyan. Change of surface temperature in the source area of the Yellow River in summer [J]. Arid Land Geography, 2023, 46(8): 1250-1259.
[6]	ZHANG Hao, HAN Zenglin, QIAO Guorong, WANG Hui, WANG Hongye, DUAN Ye. Patterns and influencing factors of tourism economic linkages between cities in the Yellow River Basin [J]. Arid Land Geography, 2023, 46(8): 1344-1354.
[7]	BAI Yang,HU Jingxuan,CHEN Chunyan,LU Wen. Regional differences and influencing factors of efficiency of tourism aid for Xinjiang: Based on three-stage DEA and Tobit model [J]. Arid Land Geography, 2023, 46(8): 1366-1375.
[8]	WU Haijuan, ZHENG Fang, YI Jieyan. Residential satisfaction and its influencing factors in ecological immigrant villages and towns [J]. Arid Land Geography, 2023, 46(8): 1387-1396.
[9]	CHENG Shuo, LI Yanzhong, XING Yincong, YU Zhiguo, WANG Yuangang, HUANG Manjie. Simulation performance of remote sensing precipitation products on hydrological drought characteristics in the source region of the Yellow River [J]. Arid Land Geography, 2023, 46(7): 1063-1072.
[10]	YANG Xuewen, WANG Ninglian, LIANG Qian, CHEN An’an. Glacier changes on the north slope of Tianshan Mountains in recent 60 years [J]. Arid Land Geography, 2023, 46(7): 1073-1083.
[11]	LI Shiyao, CONG Shixiang, WANG Rongrong, YU Hailong, HUANG Juying. Monitoring of maize canopy SPAD value under drought stress based on UAV multi-spectral remote sensing [J]. Arid Land Geography, 2023, 46(7): 1121-1132.
[12]	TIAN Liulan, WANG Shanshan, WU Zhaopeng. Construction of ecological security pattern in Urumqi based on multi-temporal remote sensing data [J]. Arid Land Geography, 2023, 46(7): 1155-1165.
[13]	KONG Deming, HAO Lisha, XIA Siyou, LI Hongbo. Food security in the argo-pastoral ecotone of northern China from the perspective of grain yield [J]. Arid Land Geography, 2023, 46(5): 782-792.
[14]	DONG Jiefang, ZHANG Kaili, QU Xueshu, RUAN Zheng. Measurement and influencing factors of ecological well-being performance of cities in Yellow River Basin [J]. Arid Land Geography, 2023, 46(5): 834-845.
[15]	JIANG Leipeng,DING Jianli,BAO Qingling,GE Xiangyu,LIU Jingming,WANG Jinjie. Runoff estimation with low altitude remote sensing and satellite images [J]. Arid Land Geography, 2023, 46(3): 385-396.