干旱区研究

干旱区研究 ›› 2018, Vol. 35 ›› Issue (3): 722-727.doi: 10.13866/j.azr.2018.03.27

• 其他 • 上一篇    下一篇

辽西北地区玉米干旱的识别方法

刘晓静1, 马东来2, 张继权3, 陈鹏1   

  1. 1.吉林师范大学旅游与地理科学学院,吉林 四平 136000;
    2.筑波大学系统与信息工程研究生院,日本 筑波 3050006;
    3.东北师范大学环境学院,吉林 长春 130024
  • 收稿日期:2017-09-13 修回日期:2017-11-01 出版日期:2018-05-15 发布日期:2025-11-17
  • 作者简介:刘晓静(1985-),女,讲师,博士,主要从事自然灾害风险评价与管理方面的教学和科研工作.E-mail:liuxj@jlnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41501559,41571491,41501557)共同资助

Identification Method of Maize Drought in Northwest Liaoning Province

LIU Xiao-Jing1, MA Dong-lai2, ZHANG Ji-quan3, CHEN Peng1   

  1. 1. College of Tourism and Geographic Science,Jilin Normal University,Siping 136000,Jilin,China;
    2. University of Tsukuba Graduate School of System and Information Engineering,Tsukuba 3050006,Japan;
    3. College of Environment Science,Northeast Normal University,Changchun 130000,Jilin,China
  • Received:2017-09-13 Revised:2017-11-01 Published:2018-05-15 Online:2025-11-17

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摘要： 以辽宁省西北地区为例,以玉米为研究对象,通过利用MOD09A1数据计算研究区玉米各生育阶段的农田浅层土壤湿度指数(cropland soil moisture index,CSMI),分析各生育阶段CSMI与相应实测0~50 cm平均土壤湿度的相关性,二者相关系数绝对值均大于等于0.5,且达到0.01的置信水平,表明农田浅层土壤湿度指数(CSMI)能够很好地表达辽西北地区玉米不同生育阶段土壤水分状况。以2009年严重夏旱为例,计算研究区玉米6月下旬至8月中旬的CSMI,利用自然断点分级法将两个生长阶段的指数划分为4个干旱等级,即轻旱、中旱、重旱和特旱,并借助GIS技术绘制干旱等级图。结果表明该研究方法在实现研究区玉米干旱的识别上是可行的,可为农业干旱灾害风险评价与管理提供技术支撑。

关键词: 玉米, 生育阶段, MODIS, 农田浅层土壤湿度指数(CSMI), 土壤湿度, 干旱识别, 辽宁

Abstract: Identification of agricultural drought is a prerequisite for agricultural drought risk assessment and management.The key indicator for identifying agricultural drought is soil moisture content because the crop growth depends on moisture in soil in arid area.In this paper,a case study on maize drought in northwest Liaoning Province was carried out,and the whole growth period of maize was divided into six growing stages based on the information of FAO (Food and Agriculture Organization of the United Nations) and the observational data in northwest Liaoning Province during the growth period of maize for many years.The Cropland Soil Moisture Indexes (CSMIs) at different growing stages of maize were calculated using the MOD09A1 data.The correlation between CSMIs and the corresponding measured soil moisture content at 0-50 cm depth was analyzed.The results showed that all the absolute values of the correlation coefficients were higher than or equal to 0.5,and the confidence level reached 0.01.Taking the severe summer drought in northwest Liaoning Province in 2009 as an example,the CSMI indexes of maize in late-June and mid-August and at jointing and heading stages were calculated.The indexes at two growing stages were divided into four drought levels,i.e.the light drought,medium drought,heavy drought and severe drought by using the Natural Breakpoint Classification method.The drought level maps were drawn by using GIS (Geographical Information System) means.The results showed that the drought levels of maize at jointing and heading stages in northwest Liaoning Province in 2009 were mainly the light drought,medium drought and heavy drought.The area of medium drought was the largest compared with that at other two growing stages,and the area of heavy drought enlarged significantly at heading stage.Spatially,medium drought of maize occurred in Zhangwu County,Fuxin county,Kaiyuan City and north Shenyang,and heavy drought in Changtu County,Jianchang County,Beipiao City,Yi County and Linghai City,respectively.The distribution of maize drought levels in the most counties and cities in the study area was basically in line with the information of actual drought disaster situation in the same period in 2009.But there was a regional difference,for example,the drought in Chaoyang County and Jianping County might be caused by different regional climatic conditions and local disaster prevention and mitigation.The results of this study showed that the research method was feasible in identifying maize drought in northwest Liaoning Province,and could provide the technical support for the evaluation and management of agricultural drought disaster risk.

Key words: maize, grow stage, MODIS, CSMI, soil moisture content, drought identification, Liaoning Province