空气动力学粗糙度研究进展

doi:10.12118/j.issn.1000-6060.2022.371

摘要/Abstract

摘要：

空气动力学粗糙度是衡量地球表面与大气之间动量和能量交换的重要参数，对于研究各种地表过程和气候变化至关重要。遥感技术作为远距离监测手段，研究空气动力学粗糙度时其优势在于高时效、高经济效益，能实现区域或大空间尺度的动态监测，因此利用遥感技术估算空气动力学粗糙度成为热点问题。通过系统阐述近年来国内外空气动力粗糙度研究进展，重点介绍了利用遥感技术估算植被下垫面空气动力学粗糙度的方法，对各种估算方法的优势和不足进行了总结，分析了气象因素和地表粗糙元形态特征因素对空气动力学粗糙度的影响，进而对遥感技术在该领域的应用做出展望，旨在为空气动力学粗糙度遥感监测的研究提供思路。

关键词: 空气动力学粗糙度, 遥感, 研究现状, 影响因素

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

李鑫玉,王静璞,王周龙. 空气动力学粗糙度研究进展[J]. 干旱区地理, 2023, 46(3): 407-417.

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

图/表 1

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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]