Please wait a minute...
Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 172-179     DOI: 10.6046/gtzyyg.2019.02.24
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;
Download: PDF(3809 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

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.

Keywords drought      remote sensing      MODIS      bi-parabolic NDVI-Ts space      Shaanxi Province     
:  TP79  
Issue Date: 23 May 2019
E-mail this article
E-mail Alert
Articles by authors
Ying LIU
Enke HOU
Cite this article:   
Ying LIU,Hui YUE,Enke HOU. Drought monitoring based on MODIS in Shaanxi[J]. Remote Sensing for Land & Resources, 2019, 31(2): 172-179.
URL:     OR
Fig.1  Scatter plots in NDVI-Ts space from 2000 to 2016
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  Linear correlation R2 between TVDI and field-measured soil moisture
Fig.2  Drought distribution of Shaanxi Province during 2000—2016
Fig.3  Classification of TVDI slope and variation coefficient in Shaanxi Province
Fig.4  Correlation coefficient classification between TVDI and precipitation, annual temperature and temperature anomaly
[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 url:
[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
[2] 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
[3] 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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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.
[9] 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
[10] 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.
[11] 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.
[12] 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.
[13] 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 url:
[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.
[15] 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 url:
[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 url:
[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.
[18] 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.
[19] 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 url:
[1] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4] QIN Dahui, YANG Ling, CHEN Lunchao, DUAN Yunfei, JIA Hongliang, LI Zhenpei, MA Jianqin. A study on the characteristics and model of drought in Xinjiang based on multi-source data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 151-157.
[5] HU Yingying, DAI Shengpei, LUO Hongxia, LI Hailiang, LI Maofen, ZHENG Qian, YU Xuan, LI Ning. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1): 210-217.
[6] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[7] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[8] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[9] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[10] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[11] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[12] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[13] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[14] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech