Agricultural drought monitoring:Progress, challenges, and prospects

doi:10.1007/s11442-016-1297-9

Abstract

Abstract:

In this paper, we compared the concept of agricultural drought and its relationship with other types of droughts and reviewed the progress of research on agricultural drought monitoring indices on the basis of station data and remote sensing. Applicability and limitations of different drought monitoring indices were also compared. Meanwhile, development history and the latest progress in agricultural drought monitoring were evaluated through statistics and document comparison, suggesting a transformation in agricultural drought monitoring from traditional single meteorological monitoring indices to meteorology and remote sensing-integrated monitoring indices. Finally, an analysis of current challenges in agricultural drought monitoring revealed future research prospects for agricultural drought monitoring, such as investigating the mechanism underlying agricultural drought, identifying factors that influence agricultural drought, developing multi-spatiotemporal scales models for agricultural drought monitoring, coupling qualitative and quantitative agricultural drought evaluation models, and improving the application levels of remote sensing data in agricultural drought monitoring.

Key words: agricultural drought, drought monitoring, research progress, research prospect

Xianfeng LIU, Xiufang ZHU, Yaozhong PAN, Shuangshuang LI, Yanxu LIU, Yuqi MA. Agricultural drought monitoring:Progress, challenges, and prospects[J].Journal of Geographical Sciences, 2016, 26(6): 750-767.

Figures/Tables 7

Figure 1

Figure 2

Table 1

Main meteorological and agricultural drought monitoring indices"

Indices	Proposed time	Main author	Indices description
PA	1906	Henry (1906)	Drought occurs when precipitation during 21 days or a longer period is equal to or less than 30% of the normal precipitation.
PDSI	1965	Palmer (1965)	Water deficit of actual water supply continues to be less than the local climate water supply in a period.
CMI	1968	Palmer (1968)	Mainly used for agricultural drought monitoring and analyzing conditions of crop drought on the basis of a water balance model.
CWSI	1988	Jackson (1988)	Determines crop water deficit by considering the relationship between soil moisture and farmland evapotranspiration on the basis of the water and energy balance principle.
Z	1990	Me (1990)	Assumes that rainfall conforms to Person III distribution, and through precipitation normalization to determine drought index.
SPI	1993	McKee (1993)	Reflects the probability of precipitation occurring during a certain period, which is suitable for monthly or even longer-scale drought monitoring.
WDI	1994	Moran (1994)	This index is established by a combination of the differences between air and land surface temperature and vegetation index, considering the nearly linear relationship between vegetation cover and the most theoretical parameter in the crop water stress index.
VCI	1995	Kogan (1996)	Overcomes the shortage of anomaly vegetation and normalized vegetation index, which can effectively monitor the spatiotemporal variation in drought.
NDWI	1996	Gao (1996)	This index can effectively detect water content in vegetation canopy and respond promptly when vegetation undergoes water stress by introducing shortwave infrared bands.
CI	1998	Zhang (1998)	Integrates the standardized precipitation index and relative humidity index, which is suitable for near real-time and historical meteorological drought.
TVDI	2002	Sandholt (2002)	Characterizes crop water stress through the dry and wet equation determined by vegetation cover and surface temperature.
VSWI	2004	Haboudane	This index, combined with the land surface temperature index and vegetation index, is used for agricultural drought monitoring.
SC-PDSI	2004	Wells (2004)	This index is self-calibrated PDSI, which can determine model calibration parameters according to local climate characteristics.
K	2007	Wang(2007)	This index, used for meteorological and agricultural droughts, is defined as the ratio of the relative variability in seasonal rainfall and relative variability in evaporation.
VegDRI	2008	Brown (2008)	This is a synthesized drought index that includes information on vegetation, meteorology, and soil water capacity by using data mining.
SPEI	2010	Vicente-Serrano (2010)	This index is a modified SPI, which introduces evapotranspiration data for calculating drought.

Table 1

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Figure 5

Figure 6

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