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中国沙漠 ›› 2017, Vol. 37 ›› Issue (1): 132-139.DOI: 10.7522/j.issn.1000-694X.2015.00181

• 生物与土壤 • 上一篇    下一篇

温度植被干旱指数(TVDI)在陇东土壤水分监测中的适用性

沙莎, 郭铌, 李耀辉, 胡蝶, 王丽娟   

  1. 中国气象局兰州干旱气象研究所 甘肃省干旱气候与减灾重点实验室/中国气象局干旱气候与减灾重点实验室, 甘肃 兰州 730020
  • 收稿日期:2015-09-16 修回日期:2015-11-30 出版日期:2017-01-20 发布日期:2017-01-20
  • 作者简介:沙莎(1985-),女,辽宁沈阳人,助理研究员,硕士,主要从事GIS、遥感的气象应用研究。E-mail:nuist_shasha@126.com
  • 基金资助:
    甘肃省气象局科研项目(2015-13);国家公益性行业(气象)科研专项(GYHY201006023,GYHY201506001-5);中国气象局兰州干旱气象研究所2014年基本科研业务费项目(KYYWF201410)

Applicability of TVDI in Monitoring Drought in Longdong Area of Gansu, China

Sha Sha, Guo Ni, Li Yaohui, Hu Die, Wang Lijuan   

  1. Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Arid Climate Change and Disaster Reduction, Institute of Arid Meteorology, CMA, Lanzhou 730020, China
  • Received:2015-09-16 Revised:2015-11-30 Online:2017-01-20 Published:2017-01-20

摘要: 温度植被干旱指数(TVDI)是利用光学遥感进行干旱监测常用的遥感指数。但前人更多的是利用单时次的遥感数据计算TVDI,这使不同时间TVDI的可比性不高。利用历史遥感数据构建了NDVI-LSTLST,地表温度)、EVI(增强植被指数)-LST、SAVI(土壤调整植被指数)-LST 3种特征空间,讨论了TVDI方法在甘肃省陇东地区的适用性。结果表明:(1)特征空间法可用于甘肃省陇东地区土壤水分的监测,EVI-LST特征空间构建的TVDI与土壤相对湿度RSM的相关性更高;(2)DEM(数字高程模型)对特征空间法有一定的改进作用,LST经过DEM订正后,一方面特征空间干、湿边的拟合程度提高,另一方面TVDIRSM的相关性得到一定程度的提高;(3)利用历史遥感数据建立的特征空间提高了TVDI的时空可比性:TVDI能够较好的指示出研究区每年RSM的空间分布特征及不同年份间RSM的差异。

关键词: TVDI, DEM, MODIS

Abstract: Clear principle and concise physical meanings make Temperature Vegetation Dryness Index (TVDI) be a wildly used method in drought monitoring. It was not temporal comparable that building feature space by using single time remote sensing data in most former studies. In this paper, NDVI-LST, EVI-LST and SAVI-LST feature space is building by history MODIS data and the applicability is discussed. The results showed that: (1) In over all, TVDI method can be used to monitoring the Relative Soil Moisture (RSM) status in Longdong area. The correlation between TVDI calculated by EVI-LST space and RSM is highest. (2) Land Surface Temperature (LST) corrected by DEM improved TVDI method. In one hand, goodness of fitting of the dry and wet edge is improved. On the other hand, the correlation between TVDI and RSM is improved in a manner. (3) Building feature space though history remote sensing data is a reasonable and feasible method. Temporal and spatial comparable are enhanced by this method. TVDI indicated the history RSM spatial distribution every single year well and the variance of RSM in different year well too.

Key words: TVDI, DEM, MODIS

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