Crops ›› 2016, Vol. 32 ›› Issue (1): 162-168.doi: 10.16035/j.issn.1001-7283.2016.01.030

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The Use of Vegetation Supply Water Index (VSWI) based on Different Vegetation Indices in the Spring Drought Monitoring in Henan Province

Li Qi1,2,Sun Xiaoyu1,2,Wang Lianxi1,2,Miao Miao1,2,Wu Dongli3   

  1. 1 Jiangsu Key Laboratory of Agricultural Meteorology,Nanjing 210044,Jiangsu,China
    2 College of Applied Meteorology,Nanjing University of Information Science & Technology,Nanjing 210044,Jiangsu,China;
    3 Meteorological Observation Centre of China Meteorological Administration,Beijing 100081,China
  • Received:2015-11-13 Revised:2015-12-15 Online:2016-02-15 Published:2018-08-26

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Abstract:

As a common natural disaster, drought seriously threatens the agricultural production, especially the spring drought frequently happens in Henan province. This paper presented a case study of different indices of Vegetation Supply Water Index (VSWI) in Henan drought monitoring in spring. NDVI, EVI and MSAVI were selected to build different VSWI models, which were analyzed the development trend of spring drought. The relationship of three indices and long time series of precipitation were discussed on the meteorological stations. The results showed that,in some extent, there was a positive correlation between three indices and the soil moisture. VSWIE and VSWIM were more suitable for monitoring surface soil moisture. Overall speaking, VSWIE was the best for monitoring spring drought. Furthermore, by comparing the spring precipitation anomaly percentage in Henan province, we found that the year of 2000 was the most severe spring drought year of the recent 30 years. Therefore, VSWI model based on EVI was suitable to monitor the spring drought in Henan province.

Key words: Vegetation indices, Vegetation Supply Water Index (VSWI), Spring drought monitoring, Remote sensing application

Fig.1

The changes of NDVI, EVI and MSAVI over time"

Table 1

Correlation between VSWIN, VSWIE and VSWIM（2012）model and soil moisture of different depth"

监测时段
Monitoring time		 VSWIN与RSM相关系数
VSWIN with RSM
correlation coefficient		 VSWIE与RSM相关系数
VSWIE with RSM
correlation coefficient		 VSWIM与RSM相关系数
VSWIM with RSM
correlation coefficient		 样本量
Sample
size
10cm 20cm 10cm 20cm 10cm 20cm
3月5日-3月12日 0.1801 0.1017 0.3295* 0.211 0.3382* 0.2203 14
3月13日-3月20日 - - - - - - -
3月21日-3月28日 0.0120 0.1153 0.2983* 0.2504 0.3033* 0.2481 15
3月29日-4月5日 0.0420 0.1007 0.1578 0.1749 0.1619 0.1755 15
4月6日-4月13日 0.0008 0.2086 0.0586 0.3369* 0.0436 0.3386* 15
4月14日-4月21日 0.0054 0.0191 0.0521 0.1316 0.0238 0.0642 12
4月22日-4月29日 0.0120 0.0558 0.0180 0.0944 0.0173 0.0195 15
4月30日-5月7日 -0.0035 0.0045 0.0006 0.0231 0.0004 0.0194 15
5月8日-5月15日 0.0028 -0.0131 -0.0014 0.0009 -0.0020 -0.0001 15
5月16日-5月23日 -0.0178 -0.0069 -0.0310 -0.0168 -0.0339 -0.0173 15
5月24日-5月31日 -0.0011 -0.0016 -0.0190 -0.0422 -0.0259 -0.0443 15

Table 2

Sub regional spring precipitation anomaly percentage of 2000 and 2001 %"

年份
Year		 豫北Yubei 豫中Yuzhong 豫东Yudong 豫西
Yuxi		 豫南Yunan
2000 -63.86 -69.52 -70.87 -60.32 -62.95
2001 -88.80 -86.78 -86.64 -48.91 -61.83

Fig.2

Typical station VSWIE and corresponding time accumulated precipitation of eight days in the spring of 2012"

Table 3

Spring drought remote sensing monitoring statistics of Henan province in 2000 (pixel percentage ) %"

监测时段
Monitoring time		 干旱等级 Drought grade
适宜 Suitable 轻旱 Light drought 中旱 Medium drought 重旱 Severe drought
3月5日-3月12日 11.29 19.36 32.19 37.13
3月13日-3月20日 8.45 16.26 20.15 55.12
3月21日-3月28日 9.86 22.35 18.81 48.96
3月29日-4月5日 4.65 20.27 22.17 52.88
4月6日-4月13日 5.96 15.36 21.27 57.40
4月14日-4月21日 14.82 25.12 20.53 39.52
4月22日-4月29日 10.42 30.82 28.86 29.90
4月30日-5月7日 13.07 30.81 25.03 31.09
5月8日-5月15日 16.17 28.33 21.89 33.61
5月16日-5月23日 7.73 16.04 18.17 58.06
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