自然资源学报 ›› 2020, Vol. 35 ›› Issue (10): 2553-2568.doi: 10.31497/zrzyxb.20201019

• 其他研究论文 • 上一篇    

基于TRMM降尺度和MODIS数据的综合干旱监测模型构建

余灏哲1,2,3, 李丽娟2, 李九一2   

  1. 1.陕西理工大学历史文化与旅游学院, 汉中 723000;
    2.中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室,北京 100101;
    3.中国科学院大学资源与环境学院,北京 100049
  • 收稿日期:2019-06-27 修回日期:2019-11-24 出版日期:2020-10-28 发布日期:2020-12-28
  • 通讯作者: 李丽娟(1961- ),女,吉林吉林人,博士,研究员,博士生导师,研究方向为水文与水资源。E-mail: lilj@igsnrr.ac.cn
  • 作者简介:余灏哲(1992- ),男,陕西汉中人,博士,讲师,研究方向为水文水资源与区域可持续发展。E-mail: yuhaozhe1992@126.com
  • 基金资助:
    国家重点研发计划项目(2016YFC0401402-04,2016YFC0401307)

Establishment of comprehensive drought monitoring model based on downscaling TRMM and MODIS data

YU Hao-zhe1,2,3, LI Li-juan2, LI Jiu-yi2   

  1. 1. School of History Culture and Tourism, Shaanxi University of Technology, Hanzhong 723000, Shaanxi, China;
    2. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-06-27 Revised:2019-11-24 Online:2020-10-28 Published:2020-12-28

摘要: 京津冀地区是我国优质冬小麦的主产区之一,但在全球气候变暖影响下,该地区干旱灾害频发,因此准确监测京津冀地区旱情既能为区域农业生产提供科学指导,又起到保障国家粮食安全等重大战略作用。综合考虑干旱发生过程中大气降水—植被生长—土壤水分盈亏等致旱因子,首先利用GWR(Geographically Weighted Regression)模型对TRMM(Tropical Rainfall Measuring Mission)3B43数据进行降尺度处理,得到1 km分辨率的降水状态参数(Precipitation Condition Index,PCI);再结合MODIS(Moderate-Resolution Imaging Spectroradiometer)数据,得到植被状态指数(Vegetation Condition Index,VCI)、温度状态指数(Temperature Condition Index,TCI),最后基于多元回归模型构建综合干旱指数(Comprehensive Drought Index,CDI),以实现对京津冀地区干旱时空监测评价。结果表明:(1)基于GWR模型与占比系数法得到的1 km空间分辨率TRMM年数据、月尺度数据,不仅在空间分辨率上相比原始TRMM数据得到很大的提升,并且数据精度也通过了检验,表明降尺度分析提高了TRMM数据对研究区降水时空特征的描述能力;(2)监测模型结果与京津冀地区所经历的干旱历程等实际旱情基本一致,并且CDI指数与标准化降水指数(Standardized Precipitation Index,SPI)做相关分析,其相关系数R在0.45~0.85之间,与作物受旱面积做相关分析,相关系数R介于在-0.81~-0.86之间,与作物标准化单产进行相关分析,其相关系数R均大于0.6,并且均通过P<0.05的显著性检验,表明本文所构建的综合干旱监测模型在京津冀地区是适用的。

关键词: TRMM, 统计降尺度, 多源遥感数据, 综合干旱监测模型, 京津冀地区

Abstract: The Beijing-Tianjin-Hebei region is one of the main producing areas of high-quality winter wheat in China, but drought disasters frequently occur in this region under the influence of global warming. Accurate monitoring of drought in the Beijing-Tianjin-Hebei region can not only provide scientific guidance for regional agricultural production, but also play an important strategic role in guaranteeing national food security. Therefore, in this study, drought-causing factors such as precipitation, vegetation growth, soil moisture gain and loss were considered comprehensively. Firstly, the GWR (Geographical Weighted Regression) model was used to downscale TRMM (Tropical Rainfall Measuring Mission) 3B43 data, and the Precipitation Condition Index (PCI) with a 1-km resolution was obtained. Combining MODIS (Moderate-Resolution Imaging Spectroradiometer) data, the Vegetation Condition Index (VCI), Temperature Condition Index (TCI) were obtained. Finally, a comprehensive drought index (CDI) was constructed based on the multiple regression model to achieve spatial and temporal monitoring and evaluation. The results show that: (1) The annual and monthly data of the 1-km spatial resolution TRMM based on the GWR model and proportion coefficient method have been greatly improved in spatial resolution compared with the original TRMM data, and the accuracy of the data has also passed the test, which shows that the downscaling analysis improves the description ability of TRMM data to the spatial and temporal characteristics of precipitation in the study area. (2) The results of the monitoring model are basically consistent with the drought process. The correlation coefficient (R value) between CDI and Standard Precipitation Index (SPI) was 0.45-0.85, and the correlation coefficient between CDI and drought area of crops ranged from -0.81 to -0.86, and all of them passed the very significant test of P<0.01, and the R value was greater than 0.6 between the CDI and standardized unit yield of crop (P<0.05), which indicated that the comprehensive drought monitoring model constructed by this research was applicable in the Beijing-Tianjin-Hebei region.

Key words: TRMM, statistical downscaling, multi-source remote sensing data, comprehensive drought monitoring model, Beijing-Tianjin-Hebei region