融合GPM降水数据的土壤干旱遥感监测

doi:10.13866/j.azr.2021.02.23

Abstract

Abstract:

Global warming has aggravated the drought trend, producing a severe threat to agriculture and animal husbandry in the arid and semi-arid regions of northwest China. The existing remote sensing methods for monitoring drought mostly only consider vegetation and temperature, ignoring the role of precipitation. Additionally, most research is limited to the classification of relative drought levels. Our research starts from the typical landform features of the Loess Plateau in Shenmu City, Shaanxi Province, and the severity of drought stress in the Hongjiannao wetland. Considering the decisive role of precipitation on drought, especially in complex geomorphic background areas, we apply higher-precision GPM precipitation data to conduct a comprehensive drought model, which can truly reveal the relative humidity of the soil. The results show that the soil drought remote sensing monitoring model (SMMI=0.384VCI+0.769TCI+0.640PCI-0.022), which comprehensively considers vegetation, temperature, and precipitation, can accurately predict the relative humidity within 10 cm of the soil surface in Shenmu. The equal weights of temperature and precipitation in the model indicate that drought in this area is primarily determined by temperature-led evapotranspiration and the water supply of precipitation. The overall crop growth season in Shenmu is in a state of light drought, and the average frequency of drought occurrence is 64.44% (moderate drought > mild drought > severe drought > extraordinary drought). The intensity and frequency of drought present a spatial differentiation characteristic of being higher in northwest and lower in the southeast. Although the frequency of extreme drought is low, the geographical difference is significant. The frequency of mild drought is high, but with a less evident geographical difference. The weakening of the winter monsoon caused a decrease in precipitation. Therefore, the drought intensity of Shenmu gradually developed from a light drought in April to a severe drought between late May and early June. Affected by the southeast monsoon in July, the rainfall increases, and the severity of drought decreases. After mid-August, Shenmu enters into a state dominated by the absence of drought, and into the end of October, there is no drought at all. In terms of seasons, the spring drought is severe, and is characterized by the highest frequency of moderate drought. Although the frequency of summer drought is quite low, the frequency of extreme drought is higher than that of spring. The autumn drought was of the lowest frequency and a light drought dominated. Over 19 years, the drought trend in Shenmu showed a slight reduction, but that of Hongjiannao wetland showed an increase due to the significant increase in annual evaporation.

Key words: global precipitation measurement, moderate-resolution imaging spectroradiometer, drought, relative soil humidity, Shenmu City

TAN Huizhi,YIN Qian,JI Liwen,LU Qian,LU Xiaoning,CUI Linlin,XIA Zhiye,XU Weixin,CHEN Jun. Remote sensing monitoring of soil drought in Shenmu City, Shaanxi Province integrating GPM precipitation data[J].Arid Zone Research, 2021, 38(2): 513-525.

Figures/Tables 17

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气象站点	Pearson相关系数	气象站点	Pearson相关系数
神木国家站	0.702^**	锦界	0.672^**
大保当	0.685^**	栏杆堡	0.611^**
大柳塔	0.589^**	乔岔滩	0.742^**
店塔	0.562^**	沙峁	0.747^**
红碱淖	0.539^**	万镇	0.685^**

期号	日序	对应季节	期号	日序	对应季节
1	第97~112 d	春季	8	第209~224 d	夏季
2	第113~128 d	春季	9	第225~240 d	夏季
3	第129~144 d	春季	10	第241~256 d	秋季
4	第145~160 d	春季	11	第257~272 d	秋季
5	第161~176 d	夏季	12	第273~288 d	秋季
6	第177~192 d	夏季	13	第289~304 d	秋季
7	第193~208 d	夏季

土壤表层/cm	植被状态指数
土壤表层/cm	VCI	TCI	PCI
10	0.307^*	0.510^**	0.364^*
20	0.155	0.469^**	0.210

干旱等级	简称	土壤相对湿度
特大干旱	特旱	0.00~0.25
严重干旱	重旱	0.25~0.35
中度干旱	中旱	0.35~0.45
轻度干旱	轻旱	0.45~0.55
无旱	无旱	0.55~1.00

期号	SMMI	干旱等级	期号	SMMI	干旱等级
1	0.475	轻旱	8	0.513	轻旱
2	0.407	中旱	9	0.575	无旱
3	0.389	中旱	10	0.584	无旱
4	0.349	重旱	11	0.629	无旱
5	0.376	中旱	12	0.633	无旱
6	0.404	中旱	13	0.664	无旱
7	0.465	轻旱

Remote sensing monitoring of soil drought in Shenmu City, Shaanxi Province integrating GPM precipitation data

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