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国土资源遥感  2019, Vol. 31 Issue (2): 172-179    DOI: 10.6046/gtzyyg.2019.02.24
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
MODIS数据在陕西省干旱监测中的应用
刘英1, 岳辉1, 侯恩科2
1.西安科技大学测绘科学与技术学院,西安 710054
2.西安科技大学地质与环境学院,西安 710054
Drought monitoring based on MODIS in Shaanxi
Ying LIU1, Hui YUE1, Enke HOU2
1.College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China;
2.School of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China;
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摘要 

以MODIS归一化差异植被指数(normalized difference vegetation index,NDVI)和陆地表面温度(land surface temperature,Ts)数据为基础,构建双抛物线型NDVI-Ts特征空间,利用实测土壤湿度对其进行验证,并基于该特征空间的温度植被干旱指数(temperature vegetation dryness index,TVDI)监测和分析了2000—2016年间陕西省旱情时空分布特征和规律。结果表明,NDVI-Ts特征空间呈双抛物线型,基于该特征空间的TVDI与10 cm深土壤湿度呈显著负相关关系(P<0.05)。空间上,2000—2016年间陕西省旱情主要分布在陕北西北部、北部以及关中北部、东部地区; 时间上,2000年陕西省受旱面积占比为31.95%,2016年为27.65%。榆林市北部大部分地区、延安市中部部分地区、关中地区中部以及陕南零散地区旱情得到显著缓解,约占14.45%,而全省84.48%地区旱情虽发生了变化,但变化不显著; 全省97.62%地区变异系数较小,位于00.8之间,主要分布在陕北北部和关中南部,表明全省旱情较稳定。全省23.74%地区旱情与降雨量呈显著负相关关系(P<0.1),随着降雨量的增加TVDI减少,旱情越轻,主要分布在陕西省榆林市大部分地区,延安市中部部分地区,汉中市北部、西北部,安康市、渭南市北部、商洛市东部部分地区及宝鸡市西部、北部部分地区; 其余地区旱情变化并未受到降雨量显著影响。进一步分析表明,平均气温也不是影响陕西省旱情变化的主导因素。

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刘英
岳辉
侯恩科
关键词 干旱遥感MODIS双抛物线型NDVI-Ts特征空间陕西省    
Abstract

Based on MODIS normalized difference vegetation index (NDVI) and land surface temperature (Ts) data, the authors constructed a bi-parabolic NDVI-Ts space which was verified by the filed measured soil moisture, and monitored the spatial and temporal distribution characteristics of drought conditions in Shaanxi Province from 2000 to 2016 based on the TVDI obtained from bi-parabolic NDVI-Ts space. The results show that the NDVI-Ts space was bi-parabolic and there was a significant negative correlation (P<0.05) between TVDI and 10 cm depth filed measured soil moisture. Spatially, the drought in Shaanxi Province during 2000—2016 were mainly distributed in the northwest, north of Shaanxi and the northeastern regions of Guanzhong plain; the drought area of Shaanxi Province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. It is found that drought was significantly relieved in most northern part of Yulin City, the middle part of Yan’an City and the central part of Guanzhong Plain and some parts of southern Shaanxi, which accounted for 14.45 %. The drought conditions in 84.48 % of the province were changed, but the change failed to pass the significant test. 97.62% of the province had a small variation coefficient, which was between 0 and 0.8. It was mainly distributed in northern Shaanxi, south of Guanzhong Plain, and it showed that the drought conditions were stable in Shaanxi Province. There was a significant negative correlation between drought and annual precipitation, accounting for 23.74 % (P<0.1). With the increase of rainfall, TVDI decreased, and the drought was relieved. It was mainly distributed in most areas of Yulin City, central parts of Yan’an City, north and northwest of Hanzhong City, Ankang City, northern parts of Weinan City, eastern parts of Shangluo City and western and northern parts of Baoji City. It is found that the changes of drought in other areas were not significantly affected by precipitation. The annual temperature was not dominant factors that resulted in the change of drought in Shaanxi Province.

Key wordsdrought    remote sensing    MODIS    bi-parabolic NDVI-Ts space    Shaanxi Province
收稿日期: 2017-11-02      出版日期: 2019-05-23
:  TP79  
基金资助:国家自然科学基金项目“荒漠化矿区土壤湿度多分辨率时空演变机理研究”(41401496);中国博士后科学基金项目“荒漠化矿区湖泊水量平衡遥感估算”共同资助(2016M592815)
作者简介: 刘 英(1982-),女,副教授,博士,主要从事环境遥感研究。Email: liuying712100@163.com。
引用本文:   
刘英, 岳辉, 侯恩科. MODIS数据在陕西省干旱监测中的应用[J]. 国土资源遥感, 2019, 31(2): 172-179.
Ying LIU, Hui YUE, Enke HOU. Drought monitoring based on MODIS in Shaanxi. Remote Sensing for Land & Resources, 2019, 31(2): 172-179.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.24      或      https://www.gtzyyg.com/CN/Y2019/V31/I2/172
Fig.1  2000—2016年间NDVI-Ts特征空间散点图
时间 R2
10 cm深度 20 cm深度 50 cm深度
20130509 0.555* 0.405 0.211
20130524 0.325* 0.273 0.099
20130914 0.318* 0.236 0.267
20130930 0.445* 0.433 0.217
Tab.1  实测土壤湿度与TVDI可决系数R2
Fig.2  2000—2016年陕西省旱情时空分布
Fig.3  陕西省TVDI变化趋势及变异系数
Fig.4  TVDI与降雨量、平均温度和平均气温距平相关系数分级
[1] Liu L Y, Liao J S, Chen X Z , et al. The microwave temperature vegetation drought index (MTVDI) based on AMSR-E,brightness temperatures for long-term drought assessment across China(2003—2010)[J]. Remote Sensing of Environment, 2017,199(15):302-320.
doi: 10.1016/j.rse.2017.07.012
[2] 张强, 姚玉璧, 李耀辉 , 等. 中国西北地区干旱气象灾害监测预警与减灾技术研究进展及其展望[J]. 地球科学进展, 2015,30(2):196-213.
doi: 10.11867/j.issn.1001-8166.2015.02.0196
Zhang Q, Yao Y B, Li Y H , et al. Research progress and prospect on the monitoring and early warning and mitigation technology of meteorological drought disaster in Northwest China[J]. Advances in Earth Science, 2015,30(2):196-213.
[3] 王丽涛, 王世新, 周艺 , 等. 旱情遥感监测研究进展与应用案例分析[J]. 遥感学报, 2011,15(6):1322-1330.
doi: 10.11834/jrs.20110351
Wang L T, Wang S X, Zhou Y , et al. Advances and application analysis of drought monitoring using remote sensing[J]. Journal of Remote Sensing, 2011,15(6):1322-1330.
[4] 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.
doi: 10.1016/S0034-4257(01)00274-7
[5] Wan Z, Wang P, Li X P . Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains,USA[J]. International Journal of Remote Sensing, 2004,25(1):61-72.
doi: 10.1080/0143116031000115328
[6] Bajgiran P R, Darvishsefat A A, Khalili A , et al. Using AVHRR-based vegetation indices for drought monitoring in the northwest of Iran[J]. Journal of Arid Environments, 2008,72(6):1086-1096.
doi: 10.1016/j.jaridenv.2007.12.004
[7] Haroon M A, Zhang J H, Yao F M . Drought monitoring and performance evaluation of MODIS-based drought severity index (DSI) over Pakistan[J]. Natural Hazards, 2016,84(2):1349-1366.
doi: 10.1007/s11069-016-2490-y
[8] Du L T, Song N P, Liu K , et al. Comparison of two simulation methods of the temperature vegetation dryness index (TVDI) for drought monitoring in semi-arid regions of China[J]. Remote Sensing, 2017,9(2):177.
doi: 10.3390/rs9020177
[9] 闫娜, 李登科, 杜继稳 , 等. 基于MODIS产品LST/NDVI/EVI的陕西旱情监测[J]. 自然灾害学报, 2010,19(4):178-182.
Yan N, Li D K, Du J W , et al. Monitoring of drought situation in Shaanxi Province based on MODIS land product LST,NDVI and EVI[J]. Journal of Natural Disaster, 2010,19(4):178-182.
[10] 李菁, 王连喜, 沈澄 , 等. 几种干旱遥感监测模型在陕北地区的对比和应用[J]. 中国农业气象, 2014,35(1):97-102.
doi: 10.3969/j.issn.1000-6362.2014.01.015
Li J, Wang L X, Shen C , et al. Application and comparison of several drought monitoring models in Northern Shaanxi[J]. Chinese Journal of Agrometeorology, 2014,35(1):97-102.
[11] 白雪娇, 王鹏新, 解毅 , 等. 基于结构相似度的关中平原旱情空间分布特征[J]. 农业机械学报, 2015,46(11):345-351.
Bai X J, Wang P X, Xie Y , et al. Spatial distribution characteristics of droughts in Guanzhong Plain based on structural similarity[J]. Transactions of the Chinese Society for Agricultural, 2015,46(11):345-351.
[12] 权文婷, 周辉, 李红梅 , 等. FY-3C/MERSI与MODIS的多波段干旱指数反演及对比分析[J]. 干旱区地理, 2016,39(4):835-842.
Quan W T, Zhou H, Li H M , et al. Multiple band drought index(MBDI)retrieve and comparison between FY-3C/MERSI and MODIS[J]. Arid Land Geography, 2016,39(4):835-842.
[13] 刘英, 马保东, 吴立新 , 等. 基于NDVI-ST双抛物线特征空间的冬小麦旱情遥感监测[J]. 农业机械学报, 2012,43(5):55-63.
Liu Y, Ma B D, Wu L X , et al. Drought remote sensing for winter wheat based on double parabola NDVI-ST space[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012,43(5):55-63.
[14] Liu Y, Wu L X, Yue H . Biparabolic NDVI-Ts space and soil moisture remote sensing in an arid and semi-arid area[J]. Canadian Journal of Remote Sensing, 2015,41(3):159-169.
doi: 10.1080/07038992.2015.1065705
[15] 刘英, 侯恩科, 岳辉 . 基于MODIS的神东矿区植被动态监测与趋势分析[J]. 国土资源遥感, 2017,29(2):132-137.doi: 10.6046/gtzyyg.2017.02.19.
Liu Y, Hou E K, Yue H . Dynamic monitoring and trend analysis of vegetation change in Shendong mining area based on MODIS[J]. Remote Sensing for Land and Resources, 2017,29(2):132-137.doi: 10.6046/gtzyyg.2017.02.19.
[16] Carlson T N, Gillies R R, Perry E M . A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover[J]. Remote Sensing Review, 1994,9(1-2):161-173.
doi: 10.1080/02757259409532220
[17] Moran M S, Clarke T R, Inoue Y , et al. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index[J]. Remote Sensing of Environment, 1994,49(3):246-263.
doi: 10.1016/0034-4257(94)90020-5
[18] 何建村, 白云岗, 张严俊 . 基于MODIS数据新疆土壤干旱特征分析[J]. 干旱区地理, 2015,38(4):735-742.
He J C, Bai Y G, Zhang Y J . Soil drought characteristics in Xinjiang with remote sensing data[J]. Arid Land Geography, 2015,38(4):735-742.
[19] 刘英, 岳辉, 张锋 , 等. 基于LAI-Ts特征空间的河南省冬小麦返青—成熟期旱情监测[J]. 中国农业气象, 2018,39(2):129-139.
Liu Y, Yue H, Zhang F , et al. Drought monitoring of winter wheat in Henan Province based on LAI-Ts space[J]. Chinese Journal of Agrometeorology, 2018,39(2):129-139.
[20] Liu Y, Yue H . The temperature vegetation dryness index (TVDI) based on bi-parabolic NDVI-Ts space and gradient-based structural similarity (GSSIM) for long-term drought assessment across Shaanxi Province,China(2000—2016)[J]. Remote Sensing, 2018,10(6):959.
doi: 10.3390/rs10060959
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