TVDI用于干旱区农业旱情监测的适宜性

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

摘要/Abstract

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

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

中图分类号:

S152.7

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.

Zhang Zhe, Ding Jianli, Li Xin, Yan Xueying. Suitability of TVDI Used to Monitor Agricultural Drought in Arid Area[J]. JOURNAL OF DESERT RESEARCH, 2015, 35(1): 220-227.

参考文献

[1] 赵庆云,张武,王式功,等. 西北地区东部干旱半干旱区极端降水事件的变化[J].中国沙漠,2005,25(6):904-909.
[2] 闫峰,覃志豪,李茂松,等.农业旱灾监测中土壤水分遥感反演研究进展[J].自然灾害学报,2006,15(6):114-121.
[3] 张平,杨德保,尚可敬,等.2002年春季中国沙尘天气与物理量场的相关分析[J].中国沙漠,2003,23(6):675-680.
[4] 李静,孙虎,邢东兴等.西北干旱半干旱区湿地特征与保护[J].中国沙漠,2003,23(6):670-674.
[5] 刘卫国,王曼,丁俊祥,等.帕默尔干旱指数在天山北坡典型绿洲干旱特征分析中的适用性[J].中国沙漠,2013,33(1):249-257.
[6] 张学艺,李剑萍,秦其明,等.几种干旱监测模型在宁夏的对比应用[J].农业工程学报,2009,25(8):18-23.
[7] 杨秀海,卓嘎,罗布.基于MODIS数据的青藏高原旱情监测研究[J].中国沙漠,2014,34(2):527-534.
[8] 于敏,高玉中,张洪玲.地表温度-植被指数特征空间干旱监测方法的改进[J].农业工程学报,2010,26(9):243-250.
[9] 伍漫春,丁建丽,王高峰.基于地表温度-植被指数特征空间的区域土壤水分反演[J].中国沙漠,2012,32(1):148-154.
[10] 李红军,郑力,雷玉平,等.植被指数-地表温度特征空间研究及其在旱情监测中的应用[J].农业工程学报,2006,22(11):170-174.
[11] 高志海,李增元,魏怀东,等.干旱地区植被指数(VI)的适宜性研究[J].中国沙漠,2006,26(2):243-248.
[12] 彭道黎,滑永春.几种植被指数探测低盖度植被能力的研究[J].福建林学院学报,2009,29(1):11- 16.
[13] Huete A,Didan K,Shimabokuro Y,et al.Regional Amazon Basin and global analysis of MODIS vegetation indices:early results and comparisons with AVHRR[J].Proceeding of IGARSS,2000,6(2):536-538.
[14] Wang C Y,Luo C F,Qi S H,et al.A method of land cover classification for China based on NDVI-T_s space[J].Journal of Remote Sensing,2005,9(1):93-99.
[15] Wang Z X,Liu C,Huete A R.From AVHRR-NDVI to MODIS-EVI:Advances in vegetation index research[J].Acta Ecologica Sinica,2003,23(5):979-987.
[16] 覃志豪,Li W J,Zhang M H.单窗算法的大气参数估计方法[J].国土资源遥感,2003,14(2):37-43.
[17] Sobrino J A,Jim M C,Leonardo P.Land surface temperature retrieval from LANDSAT TM5[J].Remote Sensing of Environment,2004,90(4):434-440.
[18] 李正国,王仰麟,吴健生,等.基于植被/温度特征的黄土高原地表水分季节变化[J].生态学报,2007,27(11):4563- 4575.
[19] Lambin E F,Ehrlich D.Combing vegetation indices and surface temperature for land-cover mapping at broad spatial scales[J].International Journal of Remote Sensing,1995,16(3):573-579.
[20] 江东,王乃斌,杨小唤,等.植被指数-地面温度特征空间的生态学内涵及其应用[J].地理科学进展,2001,20(2):146-152.
[21] Sandholt I,Rasmussen K,Andersen J.A simple interpretation of the surface temperature vegetation index space for assessment of surface moisture status[J].Remote Sensing of Environment,2002,79(2):213-224.
[22] Jordan C F.Derivation of leaf area index from quality of light on the forest floor[J].Ecology,1969,50:663-666.