基于多源遥感数据的甘肃省农业干旱研究

doi:10.12118/j.issn.1000-6060.2022.165

Abstract

Abstract:

Drought is the main environmental stressor to crop growth and development and a pivotal natural constraint to high agricultural yield and harvest. Agricultural drought monitoring is typically based on meteorological observations, which, to some extent, cannot reflect the agricultural drought conditions on a regional scale. Taking the Gansu Province of China as the study area, the compositive meteorological drought index (CMDI) was constructed using a random forest regression model and the spatiotemporal pattern and change rule of drought during the crop-growing season (April to September) from 2011 to 2019 in the Gansu Province were analyzed based on MODIS, TRMM, ESA CCI, and other remote sensing data products as well as meteorological station data. The results are as follows: (1) the coefficient of determination (R²) of the CMDI and the measured value were all greater than 0.634 in each month; the CMDI has a high spatial correlation with the standardized precipitation evapotranspiration index, showing that the CMDI can reflect the occurrence and development of agricultural drought. (2) The agricultural drought in the Gansu Province exhibited an obvious pattern of regional differentiation, with the drought degree gradually increasing from southeast to northwest. Most parts of the Hexi region were in special and severe drought, the Longzhong region was in severe (medium) drought, and the Longdong, Longnan, and Gannan regions were in fluctuating drought. (3) From 2011 to 2019, the agricultural drought in the Gansu Province exhibited a significant fluctuating trend on both annual and monthly scales, and the drought degree was most moderate in 2012 and most severe in 2017. Agricultural drought has been reduced in the Gannan region in April-September, most areas in April and June, the Longdong region in May, and the Longnan region in September. However, the remaining regions experienced an increasing trend of drought during the crop-growing season from 2011 to 2019.

Key words: drought, agricultural, remote sensing, random forest, Gansu Province

ZHANG Juan, YAO Xiaojun, LI Jing, WANG Xiaoyan. Agricultural drought research based on multi-source remote sensing data in Gansu Province[J].Arid Land Geography, 2023, 46(1): 11-22.

Figures/Tables 10

Fig. 1

Tab. 1

Calculation method of drought index"

干旱指数	计算公式	符号含义
植被状态指数（VCI）	$V C I = (N D V I - N D V I m i n) / (N D V I m a x - N D V I m i n)$	NDVI为归一化植被指数;NDVI_max、NDVI_min分别为某月归一化植被指数的最大值和最小值。
温度状态指数（TCI）	$T C I = (L S T - L S T m i n) / (L S T m a x - L S T m i n)$	LST为地表温度即地面的温度（℃）;LST_max、LST_min分别为某月地表温度的最高值和最低值（℃）。
降水状态指数（PCI）	$P C I = (T R M M - T R M M m i n) / (T R M M m a x - T R M M m i n)$	TRMM为卫星降水数据，表征降水量的大小（mm·h^-1）;TRMM_max、TRMM_min分别为某月降水量的最大值和最小值（mm）
土壤湿度状态指数（SMCI）	$S M C I = (S M - S M m i n) / (S M m a x - S M m i n)$	SM为土壤湿度;SM_max、SM_min分别为某月土壤湿度的最大值和最小值。
温度植被干旱指数（TVDI）	$T V D I = L S T - L S T m i n N D V I / L S T m a x N D V I - L S T m i n N D V I$	$L S T m a x N D V I$ 、 $L S T m i n N D V I$ 分别为某月NDVI对应的最高和最低地表温度（℃）。
微波综合干旱指数（MIDI）	$M I D I = α P C I + β T C I + (1 - α - β) S M C I$	α、β分别为PCI、TCI的权重值。

Tab. 1

Tab. 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

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等级	类型	综合气象干旱指数	标准化降水蒸散发指数
1	无旱	-0.6<CMDI	-0.5<SPEI
2	轻旱	-1.2<CMDI≤-0.6	-1.0<SPEI≤-0.5
3	中旱	-1.8<CMDI≤-1.2	-1.5<SPEI≤-1.0
4	重旱	-2.4<CMDI≤-1.8	-2.0<SPEI≤-1.5
5	特旱	CMDI≤-2.4	SPEI≤-2.0

Agricultural drought research based on multi-source remote sensing data in Gansu Province

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