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

摘要/Abstract

摘要：

干旱是农作物生长发育的主要环境胁迫因子，也是制约农业丰产丰收的关键自然要素。农业干旱监测通常基于气象站点观测数据，这在一定程度上难以反映区域尺度的农业干旱状况。以甘肃省为研究区，基于MODIS、TRMM、ESA CCI等遥感数据产品和气象站点数据，利用随机森林回归模型构建综合气象干旱指数（CMDI），并对甘肃省2011—2019年农作物生长季（4—9月）旱情时空格局及变化规律进行分析。结果表明：（1） CMDI与实测值的决定系数（R²）在各月均高于0.634，且与标准化降水蒸散发指数（SPEI）在空间上具有一定的相关性，表明该指数可反映农业干旱的发生发展过程。（2）甘肃省农业干旱呈现明显的地域分异规律，干旱程度由东南向西北逐渐加重，其中河西地区多为特旱区和重旱区，陇中地区为重（中）旱区，陇南、陇东、甘南地区为干旱-无旱波动变化区。（3） 2011—2019年甘肃省农业干旱在年、月尺度上均呈现较大的波动趋势，其中2012年干旱程度最轻，2017年则最为严重;甘肃省大部分地区在4月和6月，陇东、陇南地区分别在5月和9月以及甘南地区4—9月农业旱情有所减轻外，其余地区在农作物生长季的旱情呈加重趋势。

关键词: 干旱, 农业, 遥感, 随机森林, 甘肃省

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

张娟, 姚晓军, 李净, 王晓燕. 基于多源遥感数据的甘肃省农业干旱研究[J]. 干旱区地理, 2023, 46(1): 11-22.

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.

图/表 10

图1

表1

干旱指数计算方法"

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。

表1

表2

图2

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图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