• •

### 多时间尺度温度-植被指数特征空间旱情监测的差异性

1. 1.中国气象局农业气象保障与应用技术重点开放实验室,河南 郑州 450003
2.中国林业科学研究院荒漠化研究所,北京 100091
3. 中国气象局国家气候中心,北京 100081
• 收稿日期:2013-05-24 修回日期:2013-08-26 出版日期:2014-08-10 发布日期:2014-08-10
• 作者简介:

作者简介：闫 峰（1973&amp;#x02013;）,男,江苏连云港人,博士,主要从事环境遥感和灾害学研究。E-mail: njuyf@163.com

• 基金资助:
中国气象局农业气象保障与应用技术重点开放实验室开放基金项目(AMF201204)和国家自然科学基金项目(41301458)资助

### Diversities of Drought Monitoring Based on Temperature-vegetation Index Featured Spaces with Multi-time Scale

Feng YAN1,2(), Yan-jiao WANG3(), Bo WU2

1. 1. Key Laboratory of Agrometeorological Support and Applied Technique, China Meteorological Administration, Zhengzhou,Henan 450003, China
2. Institute of Desertifieation Studies, Chinese Academy of Forestry, Beijing 100091, China
3. National Climate Center, China Meteorological Administration, Beijing 100081, China
• Received:2013-05-24 Revised:2013-08-26 Online:2014-08-10 Published:2014-08-10

Abstract:

Agricultural drought is one of the most serious meteorological disasters in China. Temperature (Ts)-vegetation index (VI) featured space is an important method for soil moisture estimation and drought monitoring. In this paper, multi-time scales AQUA- MODIS products such as MYD11A1 with the day of year (DOY)148, MYD09GA (DOY:148), MYD11A2 (DOY:145), MYD09A1 (DOY:145) and MYD13A2 (DOY:137) acquired on May 28, 2011 were used to establish Ts-enhanced vegetation index (EVI) featured space with 1 d, 8 d and 16 d time scales. Besides, 36 in situ relative soil moisture (RSM) on May 28, 2011 and ASTER-GDEM2 data in Hebei Province were also used to study the diversity of drought monitoring. Results showed that: 1) Correlations between temperature vegetation drought index (TVDI) derived from multi-time scale Ts-EVI featured spaces and RSM varied significantly in different soil depths. Among them, the correlation of RSM20-TVDI was the highest, correlation of RSM10-TVDI was higher and that of RSM50-TVDI was the lowest. Relationships between RSM20 and TVDI derived from Ts-EVI featured spaces with multi-time scales showed that coefficients of determination of RSM-TVDI with Ts148-EVI148 and Ts148-EVI145 scales were relatively higher and those with Ts148-EVI137, Ts145-EVI145 and Ts145-EVI137 scales were relatively lower. 2) Among the 36 in situ RSM samples, 10 samples were used as test samples and the rest ones were used to establish the soil moisture estimation model RSM20-TVDI. Moreover, mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were used as indices to evaluate RSM estimation abilities of the RSM20-TVDI model. Error tests showed that the maximums of MAE, MRE and RMSE of the RSM20-TVDI models from different time scales featured spaces were no more than 4.85%, 8.33% and 0.054, respectively. So the RSM20-TVDI model could estimate soil moisture successfully. 3) The spatial distributions of drought retrieved from Ts with 1d (Ts148) and 8d (Ts145) scales and EVI with 1 d (EVI148), 8 d (EVI145) and 16 d (EVI137) scales, respectively, showed good consistencies. Drought monitoring on May 28, 2011 showed that the mild drought regions mainly distributed in the northern parts of Langfang, northern of Cangzhou, southern of Hengshui, middle and southern of Xingtai and northern of Handan. Moderate drought regions mainly located in southwestern of Cangzhou and eastern of Xingtai. Severe and extreme drought regions mainly distributed in the southwestern parts of Baoding, southern of Shijiazhuang and the middle and northern of Handan. 4) Diversity existed in drought areas derived from Ts-EVI featured spaces with multi-time scales. For drought monitoring based on Ts-EVI featured space with MODIS products, on the condition that the data were available and with ideal quality, Ts and EVI with 1 d scale should be the first choice, then Ts with 1 d scale and EVI with 8 d scale, both Ts and EVI with 8 d scale, and Ts with 1 d or 8 d and EVI with 16 d scales should be the last choice.

• TP79