基于土壤水分的农业干旱监测研究进展

doi:10.18306/dlkxjz.2020.10.014

Abstract

Abstract:

Soil moisture (SM) is a key variable for assessing agricultural drought. For a long time, due to the lack of large-scale, high-precision, continuous SM measurements, SM-based agricultural drought monitoring has been limited in application. With the recent development of remote sensing technologies, the spatial-temporal coverage and accuracy of SM data have been improved significantly. Agricultural drought monitoring indicators using SM have received increasing attention. This study systematically summarized the main characteristics of the SM data sets from multiple sources including in-situ measurements and microwave remote sensing, and further reviewed the three categories of agricultural drought indicators based on SM: 1) drought indicators based on long-term SM; 2) drought indicators based on both SM and soil hydraulic parameters; and 3) integrated drought indicators based on multiple variables including SM. Finally, with the aim of providing some reference for future research, we envisioned the challenges and opportunities for agricultural drought monitoring from the perspectives of 1) the development of SM data; 2) the strengthening of agricultural drought mechanism research; and 3) the improvement of agricultural drought monitoring systems.

Key words: agricultural drought, soil moisture, drought monitoring indicators

WU Zemian, QIU Jianxiu, LIU Suxia, MO Xingguo. Advances in agricultural drought monitoring based on soil moisture[J].PROGRESS IN GEOGRAPHY, 2020, 39(10): 1758-1769.

Figures/Tables 4

Tab.1

Fig.1

Tab.2

Agricultural drought monitoring indicators based on long-term soil moisture data"

干旱指数	公式或计算思路	参考文献
SMA	$SMA = S M i - S M i ˉ$	[72]
SMP	假设土壤水分为Beta分布,使用L-矩法拟合其分布,进而从不同的百分位确定不同干旱程度的阈值	[73]
SMDI	$S D i = S M i - median (S M i) median (S M i) - min (S M i) × 100, S M i ≤ median (S M i) S M i - median (S M i) max (S M i) - median (S M i) × 100, S M i > median (S M i)$ $SMD I i = 0.5 SMD I i - 1 + S D i 50$	[7]
NSM	$NS M i = S M i - S M i ˉ σ i$	[75]
SSI	与SPI的计算思路一致,先确定土壤水分的概率密度函数,再将其转换为标准正态分布	[31]

Tab.2

Fig.2

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卫星	传感器	产品名称	波段	产品空间分辨率/km	数据起始时间	参考文献
MetOp-A/B	ASCAT		C	25	2007-01	[53]
SMOS	MIRAS	SMOS-L3	L	25	2009-11	[28]
		SMOS-IC	L	25		[54]
GCOM-W1	AMSR2	LPRM	X	25	2012-07	[55]
		JAXA	X	25		[56]
SMAP	Radiometer	L3_SM_P	L	36	2015-03	[29]
		L3_SM_P_E	L	9
SMAP/Sentinel-1	Radiometer/SAR	L2_SM_SP	L/C	3	2015-03	[57]
FY-3B/C	MWRI		X	25	2011-07	[58]

Advances in agricultural drought monitoring based on soil moisture

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