应用MODIS遥感数据反演棉花水分信息
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摘要
明确适宜的水分信息表征参数类型以及植被指数类型是提高MODIS遥感数据作物水分含量监测精度的关键。本研究获取了棉花叶片尺度(EWTleaf和FMC)、冠层尺度(EWTcan和VWC)水分含量信息,以及观测点的6类MODIS植被指数(归一化植被指数NDVI、绿色植被指数VIgreen、差异红外指数6NDII6、差异红外指数7NDII7以及增强植被指数EVI),对水分含量表征参数与植被指数之间的相关关系进行了全面对比分析,筛选确定了最适宜的水分含量参数类型、植被指数类型以及各水分含量参数的估算模型。结果表明:①EWTcan与各植被指数之间的相关性普遍优于其他3个水分含量表征参数;②6类MODIS植被指数中,NDVI以及EVI与各类水分含量表征参数之间的相关性表现最佳;③EWTcan的最佳估算模型是由NDVI构建的一元线性模型(REP=45.38,r=0.681)。
Identifying the suitable water content parameter and the suitable MODIS vegetation index for crop water content estimation by MODIS data is crucial for improving the precision of water content remote sensing.In the study,cotton water content information at leaf(i.e.EWTleaf and FMC)and canopy(i.e.EWTcan and VWC)scales was obtained,respectively.The vegetation indices based on MODIS data were calculated,including 6types of indices,i.e.Normalized Difference Vegetation Index(NDVI),Green Vegetation Index(VIgreen),Normalized Difference Infrared Index 6(NDII6),Normalized Difference Infrared Index 7(NDII7),and Enhanced Vegetation Index(EVI).Based on a comprehensive analysis of correlation between water content parameters and vegetation indices,the suitable types of water content parameter and vegetation indices were determined,and furthermore,the estimation models for water content parameters were developed and the estimation precision were validated.The following conclusions were drawn:1)Comparing with thecorrelaitonship between other three water content parameters,i.e.EWTleaf,FMC,VWC,and vegetation indices,the EWTcan was significantly related to all vegetation indices;2) among six vegetation indices,NDVI and EVI have the strongest correlationship with water content parameters;3)the optimal estimation model for EWTcan was linear model based on NDVI,with REP=45.38,r=0.681.
Identifying the suitable water content parameter and the suitable MODIS vegetation index for crop water content estimation by MODIS data is crucial for improving the precision of water content remote sensing.In the study,cotton water content information at leaf(i.e.EWTleaf and FMC)and canopy(i.e.EWTcan and VWC)scales was obtained,respectively.The vegetation indices based on MODIS data were calculated,including 6types of indices,i.e.Normalized Difference Vegetation Index(NDVI),Green Vegetation Index(VIgreen),Normalized Difference Infrared Index 6(NDII6),Normalized Difference Infrared Index 7(NDII7),and Enhanced Vegetation Index(EVI).Based on a comprehensive analysis of correlation between water content parameters and vegetation indices,the suitable types of water content parameter and vegetation indices were determined,and furthermore,the estimation models for water content parameters were developed and the estimation precision were validated.The following conclusions were drawn:1)Comparing with thecorrelaitonship between other three water content parameters,i.e.EWTleaf,FMC,VWC,and vegetation indices,the EWTcan was significantly related to all vegetation indices;2) among six vegetation indices,NDVI and EVI have the strongest correlationship with water content parameters;3)the optimal estimation model for EWTcan was linear model based on NDVI,with REP=45.38,r=0.681.
引文
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[2]王洁,徐瑞松,马跃良,等.植被含水量的遥感反演方法及研究进展[J].遥感信息,2008,(1):100-105.
[3]PALTRIDGE G W,BARBER J.Monitoring grassland dryness and fire Potential in Australia with NOAA/AVHRR data[J].Remote Sensing of Environment,1998,25(3):381-394.
[4]CHUVIEEO E,RIAN D,AGUADO I,et al.Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper refleetance data:applications in fire danger assessment[J].International Journal of Remote sensing,2001,23(11):2145-2162.
[5]PETERSON S H,ROBERTS D A,Dennison P E.Mapping live fuel moisture with MODIS data:A multiple regression approach[J].Remote Sensing of Environment,2008,112(12),4272-4284.
[6]韩晓勇.基于MODIS和地物光谱的植被水分定量遥感研究[D].2009.
[7]CHEN D Y,HUANG J F,JACKSON T J.Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-infrared and short-wave infrared bands[J].Remote Sensing of Environment,2005,98(2):225-236.
[8]张友水,谢元礼.MODIS影像的NDVI和LSWI植被水分含量估算[J].地理科学,2008,28(1):72-76.
[9]MARTA Y,EMILIO C,DAVID R.Estimation of live fuel moisture content from MODIS images for fire risk assessment[J].Agricultural and Forest Meteorology,2008,148(4):523-536.
[10]DANSON F M,STEVEN M D,MALTHUS T J,et al.Highspectral resolution data for determining leaf water content[J].International Journal of Remote Sensing,1992,13(3):461-470.
[11]CECCATO P,GOBRON N,FLASSE S,et al.Designing a spectral index to estimate vegetation water content from remote sensing data:Part 1:Theoretical approach[J].Remote Sensing of Environment,2002,82(2):188-197.
[12]JACKSON T J,SCHMUGGE T J,WANG J R.Passive microwave remote sensing of soil moisture under vegetation canopies[J].Water Resources Research,1982,18(4):1137-1142.
[13]YILMAZ M T,HUNT JR E R,JACKSON T J.Remote sensing of vegetation water content from equivalent water thickness using satellite imagery[J].Remote Sensing of Environment,2008,112(5):2514-2522.