FY-3C/MERSI与MODIS的多波段干旱指数反演及对比分析

Abstract

Abstract: Drought Monitoring using remote sensing technology is one of the most useful methods. Medium Resolution Spectral Imager(MERSI) on board the new Generation Polar-Orbiting Meteorological Satellite of China(FY-3) has 15 spectral channels with 1 km spatial resolution,which enhances the ability to observe fine surface features and provides a new data source for drought monitoring in large area. Satellite images from NOAA/AVHRR,Landsat/TM,TERRA/AQUA-MODIS have been the major data sources used in China's drought applications. Of all these satellite sensors,data from TERRA/AQUA-MODIS has moderate spatial resolution and high spectral and temporal resolution,which is especially favorable in validation of known drought indexes. In this paper,drought conditions of May,July and August in 2014 in Shaanxi Province,China were studied based on FY-3C/MERSI data and MODIS MOD09GA(daily reflectance products) and MODIS MOD11A1(daily land surface temperature products) of the same period,by building the Multiple Band Drought Index(MBDI) using the Normalized Difference Water Index(NDWI) and LST(Land Surface Temperature) indexes,comparing the correlation of MBDI results of MERSI and MODIS,taking correlation analysis of MBDI retrieved by MERSI and MODIS with 10 cm surface soil moisture data,comparing MBDI monitoring spatial distribution map between MERSI and MODIS,and analyzing the change trend of pixels under different drought degrees. The results show as follows:(1) it shows significant positive correlation between MERSI-MBDI and MODIS-MBDI,with R² of all three periods of drought process over 0.7,and the correlation coefficient got through 0.001 level test,indicating there is a good consistency between MERSI-MBDI and MODIS-MBDI;(2) it shows significant positive correlation between MODIS-MBDI and 10 cm surface soil moisture data,which all got through 0.05 level test;MERSI-MBDI has better correlation with 10 cm surface soil moisture data,which all got through 0.01 level test;(3) the retrieval of spatial and temporal characteristics of drought using MERSI-MBDI and MODIS-MBDI has a good consistency, which was also tally with the actual situation of the study area;pixel numbers under different drought degree was also basically identical with the trend. The results indicate that MERSI data can be greatly used in drought monitoring in Shaanxi Province,and FY-3C/MERSI could apply to continuous drought monitoring in large area,as a new generation of polar orbit meteorological satellite data in our country,according to the comparative analysis with MODIS-MBDI results. The conclusion has important significance for promoting satellite remote sensing application ability in China,reducing reliance on foreign satellite remote sensing data,and providing drought monitoring timely when drought occurred.

Key words: FY-3C/MERSI, MODIS, multiple band drought index

CLC Number:

S423

QUAN Wen-ting, ZHOU Hui, LI Hong-mei, HE Hui-juan, NENG Jia. Multiple Band Drought Index(MBDI) retrieve and comparison between FY-3C/MERSI and MODIS[J]., 2016, 39(4): 835-842.

References

[1] 白力改,燕琴,张丽,等. MODIS干旱指数对华北干旱的敏感性分析[J]. 干旱区地理,2012,35(5):708-716.[BAI Ligai, YAN Qin,ZHANG Li,et al. Sensitivity analysis of response of MODIS derived drought indices to drought in North China[J]. Arid Land Geography,2012,35(5):708-716.]

[2] 王鸣程,杨胜天,董国涛,等. 基于条件温度植被指数(VTCI)的中国北方地区土壤水分估算[J]. 干旱区地理,2012,35(3):446-455.[WANG Mingcheng,YANG Shengtian,DONG Guotao, et al. Estimation soil water in Northern China based on Vegetation Temperature Condition Index(VTCI)[J]. Arid Land Geography, 2012,35(3):446-455.]

[3] 陈斌,张学霞,华开,等. 温度植被干旱指数(TVDI)在草原干旱监测中的应用研究[J]. 干旱区地理,2013,36(5):930-937.[CHEN Bin,ZHANG Xuexia,HUA Kai,et al. Application study of temperature vegetation drought index(TVDI)in grassland drought monitoring[J]. Arid Land Geography,2013,36(5):930-937.]

[4] 王慧慧,周廷刚,杜嘉,等. 温度植被旱情指数在吉林省干旱监测中的应用[J]. 遥感技术与应用,2013,28(2):324-329.[WANG Huihui,ZHOU Tinggang,DU Jia,et al. Application of temperature-vegetation dryness index in monitoring drought in the Jilin Province[J]. Remote Sensing Technology and Application, 2013,28(2):324-329.]

[5] 张树誉,杜继稳,景毅刚. 基于MODIS资料的遥感干旱监测业务化方法研究[J]. 干旱地区农业研究,2006,24(3):1-6.[ZHANG Shuyu,DU Jiwen,JING Yigang. A study on routine operation remote sensing drought monitoring model using MODIS data[J]. Agricultural Research in the Arid Areas,2006,24(3):1-6.]

[6] 向大享,刘良明,韩涛. FY-3A MERSI数据干旱监测能力评价[J]. 武汉大学学报信息科学版,2010,35(3):334-338.[XIANG Daxiang,LIU Liangming,HAN Tao. Estimation of drought monitoring ability of FY-3A MERSI data[J]. Geomatics and Information Science ofWuhan University,2010,35(3):334-338.

[7] 朱琳,刘健,张晔萍,等. FY-3A/MERSI数据在中国北方干旱监测中的应用[J]. 遥感学报,2010(5):1004-1016.[ZHU Lin, LIU Jian,ZHANG Yeping,et al. Application of FY-3A/MERSI satellite data to drought monitoring in North China[J]. Journal of Remote Sensing,2010(5):1004-1016.]

[8] 蒋友严,韩涛,徐燕,等. 基于多源卫星数据的3种干旱遥感监测效果比较[J]. 干旱地区农业研究,2014(2):47-51.[JIANG Youyan,HAN Tao,Xu Yan,et al. Comparison of drought monitoring effects based on multi-source remote sensing data of satellite[J]. Agricultural Research in the Arid Areas,2014(2):47-51.]

[9] 冷松,武建军,周磊. 利用多波段遥感干旱监测方法研究[J]. 干旱区资源与环境,2013,27(2):102-107.[LENG Song,WU Jianjun,ZHOU Lei. The approach of drought monitoring by remote sensing with multiple bands[J]. Journal of Arid Land Resources and Environment,2013,27(2):102-107.]

[10] 李红梅,王宇亮,雷向杰,等. 基于GIS的陕西热量资源的变化特征分析[J]. 中国农业气象,2015,36(待刊).[LI Hongmei, WANG Yuliang,LEI Xiangjie,et al. Variation characteristics of thermal resources in Shaanxi Province based on GIS[J]. Chinese Journal of Agrometeorology,2015,36(forthcoming.)]

[11] 乔丽,杜继稳,江志红,等. 陕西省生态农业干旱区划研究[J]. 干旱区地理,2009,32(1):112-118.[QIAO Li,DU Jiwen,JIANG Zhihong,et al. Classification of ecological agricultural droughts in Shaanxi Province[J]. Arid Land Geography,2009, 32(1):112-118.

[12] 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 Pengxin,SUN Wei. Comparison study on NDVI and LST based drought monitoring approaches[J]. Journal of Beijing Normal University(Natural Science),2007,43(3):319-323.]

[13] 赵英时. 遥感应用分析原理与方法[M]. 北京:科学出版社, 2003:368.[ZHAO Yingshi. The principle and method of analysis of remote sensing application[M]. Beijing:Science Press, 2003:368.]

[14] 姚云军,秦其明,赵少华,等. 基于MODIS短波红外光谱特征的土壤含水量反演[J]. 红外与毫米波学报,2011,30(1):9-14.[YAO Yunjun,QIN Qiming,ZHAO Shaohua,et al. Retrieval of soil moisture based on MODIS shortwave infrared spectral feature[J]. Journal of Infrared and Millimeter Waves,2011,30(1):9-14.]

[15] 刘小磊,覃志豪. NDWI与NDVI指数在区域干旱监测中的比较分析——以2003年江西夏季干旱为例[J]. 遥感技术与应用, 2007,22(5):608-612.[LIU Xiaolei,QIN Zhihao. Comparative analysis between NDWI and NDVI indices in regional drought monitoring[J]. Remote Sensing Technology and Application, 2007,22(5):608-612.]

[16] 祝必琴,黄淑娥,陈兴鹃,等. 基于FY3B/MERSI水稻长势监测及其与AQUA/MODIS数据对比分析[J]. 江西农业大学学报, 2014,36(5):1009-1015.[ZHU Biqin,HUANG Shue,CHEN Xingjuan,et al. Monitoring of rice growth based on FY3B/MERSI with AQUA/MODIS data contrastive analysis[J]. Acta Agriculture Universitatis Jiangxiensis,2014,36(5):1009-1015.]

[17] 陈霞. 辅助纹理特征的ALOS影像土地利用/覆盖分类[D]. 杭州:浙江大学环境与资源学院,2010:1-69.[CHEN Xia. Land use/cover classification of ALOS imagery assisted with texture information[D]. Hangzhou:School of Resources and Environment of Zhejiang University,2010:1-69.]

[18] 杨永顺,董贵华,赵旭东,等. 基于卫星CCD与Landsat TM影像质量及生态监测应用比对研究-以青海湖区域为例[J]. 中国环境监测,2011,27(3):9-13.[YANG Yongshun,DONG Guihua, ZHAO Xudong,et al. The research of quality assessment and ecological monitoring for the HJ-CCD and Landsat TM image[J]. Environmental Monitoring in China,2011,27(3):9-13.]

[19] 姜琳,冯文兰,刘志红,等. FY-3A/MERSI与MODIS的温度植被干旱指数反演及对比分析[J]. 水土保持研究,2014,21(3):231-241.[JIANG Lin,FENG Wenlan,LIU Zhihong,et al. Tempeture vegetation drought index(TVDI)retrieve and comparison between FY-3A/MERSI and MODIS[J]. Research of Soil Water Conservation,2014,21(3):231-241.]

[20] 冯蜀青,殷青军,肖建设,等. 基于温度植被旱情指数的青海高寒区干旱遥感动态监测研究[J]. 干旱地区农业研究,2006,24(5):141-145.[FENG Shuqing,YIN Qingjun,XIAO Jianshe,et al. Monitoring drought dynamic variation based on temperature vegetation drought index in Qinghai high and cold area[J]. Agricultural Research in the Arid Areas,2006,24(5):141-145.]

[21] 张洁,武建军,周磊,等. 基于MODIS数据的农业干旱监测方法对比分析[J]. 遥感信息,2012(5):48-54.[ZHANG Jie,WU Jianjun,ZHOU Lei,et al. Comparative study on remotely sensed methods of monitoring agricultural drought based on MODIS data[J]. Remote Sensing Information,2012(5):48-54.]

[22] 孙丽,王飞,吴全. 干旱遥感监测模型在中国冬小麦区的应用[J]. 农业工程学报,2010(1):243-249.[SUN Li,WANG Fei, WU Quan. Drought monitoring by remote sensing in winterwheat-growing area of China[J]. Transactions of the Chinese Society of Agricultural Engineering,2010(1):243-249.]

Multiple Band Drought Index(MBDI) retrieve and comparison between FY-3C/MERSI and MODIS

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