Journal of Geographical Sciences >
Agricultural drought monitoring:Progress, challenges, and prospects
*Corresponding author: Pan Yaozhong (1965-), PhD and Professor, specialized in statistics and disaster remote sensing research. E-mail: pyz@bnu.edu.cn
Author: Liu Xianfeng (1986-), PhD Candidate, specialized in resource and environmental remote sensing and disaster remote sensing. E-mail: liuxianfeng7987@163.com
Received date: 2015-11-10
Accepted date: 2015-12-15
Online published: 2016-06-15
Supported by
Major Project of High-resolution Earth Observation System
Copyright
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.
LIU Xianfeng , ZHU Xiufang , PAN Yaozhong , LI Shuangshuang , LIU Yanxu , MA Yuqi . Agricultural drought monitoring:Progress, challenges, and prospects[J]. Journal of Geographical Sciences, 2016 , 26(6) : 750 -767 . DOI: 10.1007/s11442-016-1297-9
Figure 1 Drought transfer processes and interactions |
Figure 2 Development process of drought monitoring indices |
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. |
Figure 3 Statistics of issued and citation literatures in agricultural drought monitoring during 1990-2014 |
Figure 4 Statistics of issued literature relevant to drought in different subjects |
Figure 5 Key research directions of agricultural drought monitoring in the future |
Figure 6 Current and future satellite missions relevant to drought monitoring |
The authors have declared that no competing interests exist.
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