• 资源研究方法 •

### 基于FY-3A/VIRR和TERRA/MODIS数据藏北干旱监测对比

1. 1. 成都信息工程大学资源环境学院,成都 610225;
2. 西藏高原大气环境科学研究所,拉萨 850000;
3. 重庆市气象科学研究所,重庆 401147
• 收稿日期:2016-07-12 修回日期:2016-11-14 出版日期:2017-08-02 发布日期:2017-08-02
• 通讯作者: 冯文兰（1979- ）,女,四川成都人,教授,主要从事资源环境遥感和城市地理研究。E-mail:fwl@cuit.edu.cn
• 作者简介:王凤杰（1989- ）,女,河南新乡人,硕士研究生,主要从事3S集成与气象应用研究。E-mail:ewangfengjie5006@163.com
• 基金资助:
国家自然科学基金项目（41465006和41301653）; 重庆市科委项目（cstc2016shmszx00006）

### The Comparison of FY-3A/VIRR and TERRA/MODIS Data for Drought Monitoring

WANG Feng-jie1, FENG Wen-lan1, Zha xiyangzong2, NIU Xiao-jun2, LIU Zhi-hong1, WANG Yong-qian1, 3

1. 1. College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China;
2. Tibet Institute of Plateau Atmospheric and Environmental Science, Lhasa 850000, China;
3. Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
• Received:2016-07-12 Revised:2016-11-14 Online:2017-08-02 Published:2017-08-02
• Supported by:
National Natural Science Foundation of China, No. 41465006 and 41301653Chongqing Science and Technology Committee, No. cstc2016shmszx00006.

Abstract: Drought is the key factor to restrict the development of agriculture and animal husbandry in northern Tibet. Temperature Vegetation Drought Index (TVDI) is one of the commonly used remote sensing methods for monitoring drought, which couples surface temperature (Ts) and vegetation index (VI ). The TERRA/MODIS L1B, MODIS-LST, FY/VIRR L1B and FY/VIRR-LST, at 1 km spatial resolution, are used for the monitoring and analysis. The monitoring period is from 25 July to 4 August 2015. The space of VI-Ts for the whole study area is typically triangular, from which a linear regression analysis is conducted to get the equations of the dry and wet line. TVDI for northern Tibet is extracted. Then, the measured soil moisture data and cumulative total precipitation data in the same period are used to verify the accuracy of TVDI to monitor drought by comparing TVDIE (E represents EVI) and TVDIN (N represents NDVI). The result shows that noise and number of pixels effect monitoring precision, and the precision is better after removing the noise. Small number of fitting pixels will lower their correlation with the equations of the dry and wet line, which affect the accuracy of drought monitoring. There is a significant linear correlation between TVDI and measured soil moisture (P < 0.05), and the coefficients between MODIS-TVDIE, MODIS-TVDIN, FY/VIRR-TVDIE, FY/VIRR-TVDIN and measured soil moisture were 0.611, 0.581, 0.420 and 0.386 respectively. Correlation between MODIS-TVDI and measured soil moisture is higher than that between FY/VIRR-TVDI and measured soil moisture. The correlation between TVDIE and measured soil moisture is also higher than that of TVDIN and measured soil moisture. The coefficients between MODIS-TVDIE, MODIS-TVDIN, FY/VIRR-TVDIE, FY/VIRR-TVDIN and cumulative total precipitation were 0.370, 0.336, 0.275 and 0.171 respectively (P < 0.05). The correlations are consistent with the correlations between TVDI and measured soil moisture. The result suggests that the TVDI based on MODIS and FY/VIRR data are both feasible for drought monitoring in the study area, and TVDIE is better than TVDIN to monitor drought. The monitoring precision of MODIS-TVDI is higher than that of FY/VIRR-TVDI, but FY/VIRR data is also a reliable product for monitoring drought.

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