被动微波反演土壤水分的L波段新发展及未来展望

doi:10.18306/dlkxjz.2018.02.003

摘要/Abstract

摘要：

土壤水分是陆—气交互作用的重要边界条件,在全球水循环和能量循环中扮演着关键角色,直接影响降水、径流、下渗与蒸散发等水文循环过程,并能反映洪涝和干旱的程度。随着第一颗采用被动微波干涉成像技术的SMOS(Soil Moisture and Ocean Salinity)卫星的发射成功,L波段被动微波遥感技术逐渐成为大尺度土壤水分监测的主要手段,促进了“射频干扰的检测与抑制”、“植被光学厚度反演与植被影响校正”以及“土壤粗糙度参数化方案”等关键问题的研究。本文梳理了“基于微波植被指数的L波段多角度数据反演土壤水分算法研究”项目的最新研究成果,同时评述了围绕以上关键技术问题所取得的国内外研究进展,并对土壤水分微波遥感的未来发展进行了展望。

关键词: 被动微波遥感, 土壤水分, 植被光学厚度, 地表粗糙度, L波段

Abstract:

Soil moisture is an important boundary condition of land-atmosphere interactions and plays a major role in the Earth's water and energy cycles. It directly affects the hydrological processes such as precipitation, runoff, infiltration, and evapotranspiration, and can provide direct information for flood and drought monitoring. Accompanied by the continuous development of space science and technology, especially the successful launching of the first L-band satellite mission of Soil Moisture and Ocean Salinity (SMOS) using passive microwave interference imaging technology, L-band passive microwave remote sensing has become a key tool in large-scale soil moisture mapping. New issues regarding L-band application including "detection and mitigation of radio frequency interference", "vegetation optical depth retrieval and vegetation effects correction", and "soil roughness parameterization" have been studied extensively. In this article, we summarize the latest research results of the project "Vegetation effects on soil moisture estimation using multi-angle observations at L-band" funded by the National Natural Science Foundation of China, and review the research progress made regarding the above issues. The future development of soil moisture microwave remote sensing is also prospected. The review of the research progress and the prospect of the cutting-edge issues will be helpful for the demonstration and implementation of China's future satellite missions, and promote the microwave remote sensing of soil moisture and application in eco-hydrology studies at the global and regional scales.

Key words: passive microwave remote sensing, soil moisture, vegetation optical depth, soil roughness, L-band

赵天杰. 被动微波反演土壤水分的L波段新发展及未来展望[J]. 地理科学进展, 2018, 37(2): 198-213.

Tianjie ZHAO. Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects[J]. PROGRESS IN GEOGRAPHY, 2018, 37(2): 198-213.

图/表 6

图1

图2

图3

图4

表1

适用于L波段的土壤粗糙度参数化方案及其默认参数设置(受权引用自Peng et al, 2017)"

类别	名称	参数化方案	参数设置	文献
基于试验数据	C79	$r s, p = Γ s, h H p$ $H p = exp - hco s N p (θ i)$	$h = 2 kσ) 2$ , $N p = 2$	Choudhury等(1979)
	W83		$h = 2 kσ) 2$ , $N p = 0$	Wang等(1983)
	W01		$h = 1.3972 m 0.5879$ , $N p = 0$ , $m = σ L c$	Wigneron等(2001)
	W01sm		$h = 0.5761 w s - 0.3475 m 0.4230$ , $N p = 0$ , $m = σ L c$	Wigneron等(2001)
	E07		$h = 2 kσ) 2$ , $N p = 1 p = h - 1 p = v$	Escorihuela等(2007)
	E07sm		$N p = 2 kσ) 2 - 4.4 w s - w FC, if w s ≤ w FC 2 kσ) 2, if w s ≤ w FC$ , $N p = 1 p = h - 1 p = v$	Escorihuela等(2007)
	W11		$h = 0.9437 σ 0.8865 σ + 2.2913 6$ , $N p = 0$	Wigneron等(2011)
	SMOS		$h = 0.10, if w s ≤ w t 0.10 - 0.05 (w s - w t) w FC - w t, if w t ≤ w s ≤ w FC 0.05, if w s ≥ w FC$ , $N p = 1 p = h - 1 p = v$	Kerr等(2012)
	W99	$r s, h = Γ s, h H h$ $r s, v = r s, h co s N θ i$ $H h = exp - h 0.1 cos θ i$	$h = kσ$ , $N = 0.655, for θ i ≤ 60$	Wegmuller等(1999)
	S06	$r s, h = Γ s, h H p$ $H p = exp (- h ? G (ε r, θ i, p))$	$h = σ λ 2 2$ , $G (ε r, θ i, p) = a - 2 (ε r, θ i, p)$ , $a (ε r, θ i, p) = k 1 θ i, p + k 2 θ i, p ε r$	Schwank等(2006)
	L13	$r s, p = 1 - Q Γ s, p + Q Γ s, q H p$ $H p = exp - hco s N p (Q i)$	$h = 2.265 1 - exp - Z s 2.023, Z s ≤ 1.253 1.046, Z s > 1.253$ , $Z s = σ 2 L c$ $Q = 0.253 h$ , $N v = 0.999 h - 0.54$ , $N h = N v + ? N$ $? N = 2.029 - 0.7457 Z s$	Lawrence等(2013)
	G14	$T b p = T s, eff + a eff, p T a - T s, eff + r s, p (T b DN - T s, eff)$ $r s, p = 1 - Q) Γ s, p G p + Q Γ s, q G q$ $a eff, p = 1 - Q A s, p + Q A s, Q$	$G p = exp - δ R 2 (c 0 + c 1 Γ s, p) co s c 2 (θ i) 103$ $Q = 0.5 1.0 - sin (2 β R) 2 β R)$ $A s, p = 1.0 - exp - δ R 1.5 (c 3 + c 4 Γ s, p) co s c 5 (θ i) 102$	Goodberlet等(2014)
基于理论模型	S02	$r s, p = Γ s, p ? exp - 2 kσ 2 cso θ i 2 + A p Γ s, p B p$	$A p or B p = exp a θ i, p, ρ + b θ i, p, ρ ? log kσ + c θ i, p, ρ ? kσ + d θ i, p, ρ ? W$ $a, b, c, d θ i, p, ρ = e p, ρ + g p, ρ θ i + h (p, ρ) θ i 2$	Shi等(2002)
	C10	$r s, p = A p m B p Γ s, p$	$A p, B p, C p = a p θ i 3 + b p θ i 2 + c p θ i + d p$ , $m = σ L c$	Chen等(2010)
	Z15	$r s, p = Γ s, p H p$ $H p = A p exp (B p Z s 2 + C p Z s)$	$A p, B p, C p = a p θ i 2 + b p θ i + c p,$ $Z s = σ 2 L c$	Zhao, Shi et al. (2015b)

表1

图5

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