Comparison and application of agricultural drought indexes based on MODIS data

doi:10.6046/gtzyyg.2017.02.31

Abstract
References
Related Articles
Metrics

Download: PDF(746 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract With northwest Liaoning Province as the study area, the authors analyzed the soil moisture content by using the method of apparent thermal inertia(ATI), anomalies of vegetation index(AVI) and vegetation supply water index(VSWI). The results show that the three indexes respectively in a certain extent can reflect the drought trend of the northwest area of Liaoning Province in 2009, but inversion results are not consistent, that the monitoring effect of ATI in high vegetation coverage rate is higher than expected, more in line with historical weather data, that AVI can effectively reflect the current crop growth season relative to the drought condition, and that VSWI exaggerates the influence of vegetation, which seems to be a serious lag.

Keywords hyperspectral remote sensing dynamic classifier selection spatial and spectral information multiple classifier system

Issue Date: 03 May 2017

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	SU Hongjun
	LIU Hao

Cite this article:

SU Hongjun,LIU Hao. Comparison and application of agricultural drought indexes based on MODIS data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 215-220.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.02.31 OR https://www.gtzyyg.com/EN/Y2017/V29/I2/215

[1] 王鹏新,孙威.基于植被指数和地表温度的干旱监测方法的对比分析[J].北京师范大学学报(自然科学版),2007,43(3):319-323.
Wang P X,Sun W.Comparison study on NDVI and LST based drought monitoring approaches[J].Journal of Beijing Normal University(Natural Science),2007,43(3):319-323.
[2] 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.
Zhang X Y,Li J P,Qin Q M,et al.Comparison and application of several drought monitoring models in Ningxia,China[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(8):18-23.
[3] Hatfield J L,Prueger J H.Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices[J].Remote Sensing,2010,2(2):562-578.
[4] 吴炳方,张峰,刘成林,等.农作物长势综合遥感监测方法[J].遥感学报,2004,8(6):498-514.
Wu B F,Zhang F,Liu C L,et al.An integrated method for crop condition monitoring[J].Journal of Remote Sensing,2004,8(6):498-514.
[5] 李兴华,李云鹏,杨丽萍.内蒙古干旱监测评估方法综合应用研究[J].干旱区资源与环境,2014,28(3):162-166.
Li X H,Li Y P,Yang L P.Application and evaluation of integrated drought monitoring method to Inner Mongolia[J].Journal of Arid Land Resources and Environment,2014,28(3):162-166.
[6] 孙灏,陈云浩,孙洪泉.典型农业干旱遥感监测指数的比较及分类体系[J].农业工程学报,2012,28(14):147-154.
Sun H,Chen Y H,Sun H Q.Comparisons and classification system of typical remote sensing indexes for agricultural drought[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(14):147-154.
[7] 路广平,郁凌峰,梁立章.2009年辽西北地区特大干旱的分析[J].东北水利水电,2011,29(10):50-51.
Lu G P,Yu L F,Liang L Z.Analysis of the severe drought in the northwest of Liaoning Province in 2009[J].Water Resources and Hydropower of Northeast China,2011,29(10):50-51.
[8] 郭虎,王瑛,王芳.旱灾灾情监测中的遥感应用综述[J].遥感技术与应用,2008,23(1):111-116.
Guo H,Wang Y,Wang F.Overview of remote sensing approaches to drought monitoring[J].Remote Sensing Technology and Application,2008,23(1):111-116.
[9] 夏虹,武建军,刘雅妮,等.中国用遥感方法进行干旱监测的研究进展[J].遥感信息,2005(1):55-58.
Xia H,Wu J J,Liu Y N,et al.Progress on drought monitoring by remote sensing in China[J].Remote Sensing Information,2005(1):55-58.
[10] 黄泽林,覃志豪.利用MODIS数据监测大面积土壤水分与农作物旱情研究[J].安徽农业科学,2008,36(11):4784-4787.
Huang Z L,Qin Z H.Study on monitoring soil moisture and crop drought in great area by using MODIS data[J].Journal of Anhui Agricultural Sciences,2008,36(11):4784-4787.
[11] Price J C.The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation[J].Water Resources Research,1980,16(4):787-795.
[12] Liang S L.Narrowband to broadband conversions of land surface albedo I:Algorithms[J].Remote Sensing of Environment,2001,76(2):213-238.
[13] Liang S L,Shuey C J,Russ A L,et al.Narrowband to broadband conversions of land surface albedo:II.Validation[J].Remote Sensing of Environment,2003,84(1):25-41.
[14] 吕蒙,钟悦之.NDVI在生态环境研究中的应用进展[J].农村经济与科技,2011,22(6):11-14.
Lyu M,Zhong Y Z.Application of NDVI in ecological and environmental studies[J].Rural Economy and Science-Technology,2011,22(6):11-14.
[15] 刘海岩,牛振国,陈晓玲.EOS-MODIS数据在我国农作物监测中的应用[J].遥感技术与应用,2005,20(5):531-536.
Liu H Y,Niu Z G,Chen X L.Applications of EOS-MODIS data on crop monitoring in China[J].Remote Sensing Technology and Application,2005,20(5):531-536.
[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 Reviews,1994,9(1/2):161-173.
[17] 莫伟华,王振会,孙涵,等.基于植被供水指数的农田干旱遥感监测研究[J].南京气象学院学报,2006,29(3):396-401.
Mo W H,Wang Z H,Sun H,et al.Remote sensing monitoring of farmland drought based on vegetation supply water index[J].Journal of Nanjing Institute of Meteorology,2006,29(3):396-401.
[18] 齐述华.干旱监测遥感模型和中国干旱时空分析[D].北京:中国科学院遥感应用研究所,2004.
Qi S H.Drought Monitoring Models with Remote Sensing and Spatio-Temporal Characteristics of Drought in China[D].Beijing:Institute of Remote Sensing Applications,Chinese Academy of Science,2004.
[19] 牟伶俐.农业旱情遥感监测指标的适应性与不确定性分析[D].北京:中国科学院遥感应用研究所,2006.
Mu L L.Suitability and Uncertainty Analysis of Agricultural Drought Indicator with Remote Sensing[D].Beijing:Institute of Remote Sensing Applications,Chinese Academy of Science,2006.
[20] 杨树聪,沈彦俊,郭英,等.基于表观热惯量的土壤水分监测[J].中国生态农业学报,2011,19(5):1157-1161.
Yang S C,Shen Y J,Guo Y,et al.Monitoring soil moisture by apparent thermal inertia method[J].Chinese Journal of Eco-Agriculture,2011,19(5):1157-1161.
[21] 齐述华,李贵才,王长耀,等.利用MODIS数据产品进行全国干旱监测的研究[J].水科学进展,2005,16(1):56-61.
Qi S H,Li G C,Wang C Y,et al.Study on monitoring drought in China with MODIS product[J].Advances in Water Science,2005,16(1):56-61.
[22] 郭其乐,陈怀亮,邹春辉,等.植被供水指数法在2008年河南省干旱灾害遥感监测中的应用研究[C]//2009农业环境科学峰会论文集.大连:中国农学会,2009.
Guo Q L,Chen H L,Zou C H,et al.The application of vegetation water supply index in the monitoring of drought disaster in Henan Province in 2008[C]//2009 Agricultural Environmental Science Summit.Dalian:Chinese Association of Agricultural Science Societies,2009.

[1]	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.
[2]	GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[3]	JIANG Yanan, ZHANG Xin, ZHANG Chunlei, ZHONG Chengcheng, ZHAO Junfang. Classification of remote sensing images based on multi-scale feature fusion using local binary patterns[J]. Remote Sensing for Natural Resources, 2021, 33(3): 36-44.
[4]	ZANG Chuankai, SHEN Fang, YANG Zhengdong. Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 45-53.
[5]	HU Xinyu, XU Zhanghua, CHEN Wenhui, CHEN Qiuxia, WANG Lin, LIU Hui, LIU Zhicai. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image[J]. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
[6]	WANG Ruijun, ZHANG Chunlei, SUN Yongbin, WANG Shen, DONG Shuangfa, WANG Yongjun, YAN Bokun. Application of hyperspectral spectroscopy to constructing polymetallic prospecting model in Hongshan, Gansu Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 222-231.
[7]	Donghui ZHANG, Yingjun ZHAO, Kai QIN. Design and construction of the typical ground target spectral information system[J]. Remote Sensing for Land & Resources, 2018, 30(4): 206-211.
[8]	Jing CUI, Xinfeng DONG, Rui DING, Shimin ZHANG, Conghe WANG, Hengxin LU, Yanyun SUN. Stratigraphic division of loess along loess profile based on hyperspectral remote sensing[J]. Remote Sensing for Land & Resources, 2018, 30(2): 202-207.
[9]	REN Guangli, YANG Min, LI Jianqiang, GAO Ting, LIANG Nan, YI Huan, YANG Junlu. Application of hyperspectral alteration information to gold prospecting: A case study of Fangshankou area,Beishan[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 182-190.
[10]	SU Hongjun, LIU Hao. A novel dynamic classifier selection algorithm using spatial-spectral information for hyperspectral classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 15-21.
[11]	LIN Na, YANG Wunian, WANG Bin. Pixel un-mixing for hyperspectral remote sensing image based on kernel method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 14-20.
[12]	CHAI Ying, RUAN Renzong, CHAI Guowu, FU Qiaoni. Species identification of wetland vegetation based on spectral characteristics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 86-90.
[13]	DAI Xiaoai, JIA Hujun, ZHANG Xiaoxue, WU Fenfang, GUO Shouheng, YANG Wunian, YANG Ye. Identification of hyperspectral features for subalpine typical vegetation in the upper reaches of the Minjiang River[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 174-180.
[14]	HE Hao, SHEN Yonglin, LIU Xiuguo, MA Li. Spatial-spectral constrained graph-based semi-supervised classification for hyperspectral image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 31-36.
[15]	CHEN Shulin, BI Yinli. Application of remote sensing technology to microbial reclamation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 16-23.

Viewed

Full text

Abstract

Cited

Shared

Discussed