TVDI用于干旱区农业旱情监测的适宜性

张喆; 丁建丽; 李鑫; 鄢雪英

doi:10.7522/j.issn.1000-694X.2013.00454

中国沙漠 >

2015 , Vol. 35 >Issue 1: 220 - 227

DOI: https://doi.org/10.7522/j.issn.1000-694X.2013.00454

天气与气候

TVDI用于干旱区农业旱情监测的适宜性

张喆 ,
丁建丽 ,
李鑫 ,
鄢雪英

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新疆大学资源与环境科学学院/绿洲生态教育部重点实验室, 新疆乌鲁木齐 830046

张喆(1988-),女,甘肃武威人,硕士研究生,主要从事干旱区资源环境及遥感应用研究.Email: zhangzhe_0110@yeah.net

收稿日期: 2013-10-17

修回日期: 2013-12-09

网络出版日期: 2015-01-20

基金资助

国家自然科学基金项目(U1303381,41261090,41161063);新疆维吾尔自治区青-科技创新人才培养工程(2013711014);教育部新世纪优秀人才支持计划(NCET-12-1075);霍英东青-教师基金项目(121018)

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Suitability of TVDI Used to Monitor Agricultural Drought in Arid Area

Zhang Zhe ,
Ding Jianli ,
Li Xin ,
Yan Xueying

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College of Resources and Environment Science/Key Laboratory of Oasis Ecosystem of Ministry of Education, Xinjiang University, Urumqi 830046, China

Received date: 2013-10-17

Revised date: 2013-12-09

Online published: 2015-01-20

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摘要

基于地表温度/植被指数(T_s/VI)特征空间建立的温度植被干旱指数(TVDI)受诸多因素的影响,其中一个重要的影响因素是植被指数,该指数在高、低植被覆盖时的敏感性不同,从而导致TVDI对旱情监测的准确度不同.针对这一问题,以新疆塔里木盆地北缘渭干河-库车河三角洲绿洲为研究区,选择2011年4月、8月两景TM影像,利用归一化植被指数(NDVI)和比值植被指数(RVI)分别建立T_s/VI特征空间,线性拟合特征空间的上、下边界,计算得到两种温度植被干旱指数(TVDI-NDVI、TVDI-RVI).用TVDI与同期野外实测的土壤含水量数据进行回归分析.结果表明:(1)植被指数、地表温度、土壤水分之间有显著互动关系,以不同植被指数计算得到的两种TVDI与表层土壤水分相关性较好,均能够反映区域土壤干旱状况;(2)由于植被指数对植被探测的敏感性,在4月低植被覆盖时,TVDI-NDVI与表层土壤水分的相关性较高,为0.4299,8月高植被覆盖时,TVDI-RVI与表层土壤水分的相关性较高,达到0.5791;(3)在低植被覆盖区域,NDVI较RVI敏感,而在高植被覆盖区域,RVI敏感性较高.RVI适用于高植被覆盖时反演土壤湿度,NDVI则更适用于中、低植被覆盖时.

关键词： 归一化植被指数; 比值植被指数; 地表温度; 温度植被干旱指数

本文引用格式

张喆 , 丁建丽 , 李鑫 , 鄢雪英 . TVDI用于干旱区农业旱情监测的适宜性[J]. 中国沙漠, 2015 , 35(1) : 220 -227 . DOI: 10.7522/j.issn.1000-694X.2013.00454

Abstract

Vegetation index has different vegetation detection capability which could result in the different sensitivity on temperature vegetation dryness index. Therefore, two sets of image in April and August of 2011 were chosen to construct the Surface Temperature (T_s)/Vegetation Index (VI) feature space which used Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI) and LST in the Delta Oasis of the Weigan and Kuqa Rivers in Xinjiang. In the establishment of the T_s/VI space, linear regression functions were used to approximately fit the lower and upper boundaries of the T_s/VI space so that the dry and wet edges of the space could be determined. Two T_s/VI spaces were established based on the T_s/NDVI and T_s/RVI and the difference between the two spaces were investigated. Temperature Vegetation Dryness Index (TVDI) was calculated from the T_s/NDVI and the T_s/RVI spaces. Make use of TVDI and contemporaneous field measured soil gravimetric water moistures data carrying through regress analysis and establish experienced models of TVDI to assess soil moisture. The main results were as follows:(1) It was significantly associated among VI, T_s and soil moisture. TVDI-NDVI and TVDI-RVI could be used to predict the near surface soil water moisture. (2) TVDI-NDVI had a higher correlation (0.4299) with the soil surface moisture in April (low vegetation coverage month) but TVDI-RVI had a higher correlation (0.5791) with the soil surface moisture in August (high vegetation coverage month). (3) Analyzing and comparing NDVI and RVI, it was showed that NDVI was sensitive in the low vegetation coverage and RVI was sensitive in the high vegetation coverage. The conclusion was the T_s/RVI feature space was more suitable for inversing soil moisture in high vegetation coverage and the T_s/NDVI feature space was more suitable for inversing soil moisture in low vegetation coverage.

Key words： NDVI; RVI; LST; TVDI

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