基于模型模拟的NDVI对叶片生化组分参数的敏感性及叶绿素估算
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摘要
归一化植被指数(NDVI)能够反映绿色植物的生长状况、监测作物长势,并与叶片尺度和冠层尺度的植被生化参数密切相关。研究方法主要是通过PROSAIL模型来分析NDVI的敏感性。在叶片尺度上,对NDVI变化的敏感性最高的参数是叶绿素含量(Cab),其次是叶肉结构参数(N);而干物质含量(Cm)和含水量(Cw)变化的敏感性较弱。在冠层尺度,NDVI对叶面积指数(LAI)的敏感性最高,其次是观测天顶角(VZA);叶倾角分布(LAD)和太阳高度角对NDVI的敏感性也较大。2017年5月16～18日野外实验采集数据及冬小麦叶片,通过实验室测得叶绿素浓度,结合2017年5月15日河北省石家庄市栾城区Landsat OLI影像,计算出采样点小麦的NDVI,建立叶绿素含量估算模型,生成栾城区冬小麦叶绿素含量分布图,得到研究区叶绿素浓度大部分在30～70μg·cm~(-2)。
Normalized Difference Vegetation Index(NDVI) can reflect the growth of green plants and be used to monitor crop growth, it is closely related to biochemical parameters of vegetation at leaf and canopy scales. The sensitivities of NDVI are analyzed basing on PROSAIL models. The results show that the sensitivity of NDVI to chlorophyll a+b content(Cab) is highest on the leaf scale, followed by leaf structure parameters(N), while the sensitivities of NDVI to dry matter content(Cm) and water content(Cw) are low. The sensitivity of NDVI to leaf area index(LAI) is highest on canopy scale, followed by the higher sensitivity of view zenith angle(VZA). Leaf angle distribution(LAD) and solar elevation angle can also cause the change of NDVI. Field data and winter wheat leaves samples are gathered on May 16～18, 2017 in Luancheng, Shijiazhuang city, the chlorophyll contents of wheat are measured in laboratory. The NDVI values are calculated using Landsat OLI image on May 15, 2017, and chlorophyll content estimation model is constructed. Chlorophyll contents in the study area are mainly 30～70 μg·cm~(-2).
Normalized Difference Vegetation Index(NDVI) can reflect the growth of green plants and be used to monitor crop growth, it is closely related to biochemical parameters of vegetation at leaf and canopy scales. The sensitivities of NDVI are analyzed basing on PROSAIL models. The results show that the sensitivity of NDVI to chlorophyll a+b content(Cab) is highest on the leaf scale, followed by leaf structure parameters(N), while the sensitivities of NDVI to dry matter content(Cm) and water content(Cw) are low. The sensitivity of NDVI to leaf area index(LAI) is highest on canopy scale, followed by the higher sensitivity of view zenith angle(VZA). Leaf angle distribution(LAD) and solar elevation angle can also cause the change of NDVI. Field data and winter wheat leaves samples are gathered on May 16～18, 2017 in Luancheng, Shijiazhuang city, the chlorophyll contents of wheat are measured in laboratory. The NDVI values are calculated using Landsat OLI image on May 15, 2017, and chlorophyll content estimation model is constructed. Chlorophyll contents in the study area are mainly 30～70 μg·cm~(-2).
引文
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[20] 吴朝阳,牛铮.植物光化学植被指数对叶片生化组分参数的敏感性[J].中国科学院大学学报,2008,25(3):346-354.
[21] Saberioon M M,Amin M S M,Anuar A R,et al.Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale[J].International Journal of Applied Earth Observations & Geoinformation,2014,32(10):35-45.
[22] 易秋香,黄敬峰,王秀珍,等.玉米叶绿素高光谱遥感估算模型研究[J].科技通报,2007,23(1):83-87.
[23] 贺振,贺俊平.基于NOAA-NDVI的河南省冬小麦遥感估产[J].干旱区资源与环境,2013,27(05):46-52.
[2] Datt B,Mcvicar T R,Van Niel T G,et al.Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes[J].Geoscience & Remote Sensing IEEE Transactions on,2003,41(6):1246-1259.
[3] 赵英时.遥感应用分析原理与方法[M].科学出版社,2013.
[4] Sims D A,Gamon J A.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages[J].Remote Sensing of Environment,2002,81(2):337-354.
[5] 蔡博峰,绍霞.基于PROSPECT+SAIL模型的遥感叶面积指数反演[J].国土资源遥感,2007(2):39-43.
[6] 吴朝阳,牛铮,汤泉.叶片光化学植被指数(PRI)的修正及其敏感性分析[J].光谱学与光谱分析,2008,28(9):2014-2018.
[7] 张雪红,田庆久,沈润平.冬小麦冠层光谱的方向性特征分析[J].光谱学与光谱分析,2010,30(6):1600-1605.
[8] 杨曦光,范文义,于颖.基于PROSPECT+SAIL模型的森林冠层叶绿素含量反演[J].光谱学与光谱分析,2010,30(11):3022-3026.
[9] 吴伶,刘湘南,周博天,等.利用PROSPECT+SAIL模型反演植物生化参数的植被指数优化模拟[J].应用生态学报,2012,23(12):3250-3256.
[10] 马晶晶,袁金国.基于模型模拟的植被NDVI与观测天顶角和LAI的关系[J].遥感技术与应用,2014,29(4):539-546.
[11] 赵虹,鲁蕾,颉耀文.基于PROSPECT+SAIL模型反演叶面积指数的较强适用性植被指数的筛选[J].兰州大学学报 (自科版),2014,50(1):89-94.
[12] Jacquemoud S,Baret F.PROSPECT:A model of leaf optical properties spectra [J].Remote Sensing of Environment,1990,34(2):75-91.
[13] 薛云,陈水森,夏丽华,等.几个典型的叶片/冠层模型[J].西部林业科学,2005,34(1):70-73.
[14] Verhoef W.Earth observation modeling based on layer scattering matrices[J].Remote Sensing of Environment,1985,17(2):165-178.
[15] 马晶晶,袁金国.基于模型模拟和OLI遥感影像的植被叶绿素含量估算研究[D].河北师范大学,2014.
[16] Liu J,Pattey E,Jégo G.Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons[J].Remote Sensing of Environment,2012,123(3):347-358.
[17] Tucker C J.Red and photographic infrared linear combinations for monitoring vegetation[J].Remote Sensing of Environment,1979,8(2):127-150.
[18] 袁金国,张莎,胡春梓.河北省植被多时相SPOT-VGT NDVI变化及与气候因子的关系[J].科技通报,2015(5):47-52.
[19] 苏伟,郭皓,赵冬玲,等.基于优化PROSAIL叶倾角分布函数的玉米LAI反演方法[J].农业机械学报,2016,47(3):234-241.
[20] 吴朝阳,牛铮.植物光化学植被指数对叶片生化组分参数的敏感性[J].中国科学院大学学报,2008,25(3):346-354.
[21] Saberioon M M,Amin M S M,Anuar A R,et al.Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale[J].International Journal of Applied Earth Observations & Geoinformation,2014,32(10):35-45.
[22] 易秋香,黄敬峰,王秀珍,等.玉米叶绿素高光谱遥感估算模型研究[J].科技通报,2007,23(1):83-87.
[23] 贺振,贺俊平.基于NOAA-NDVI的河南省冬小麦遥感估产[J].干旱区资源与环境,2013,27(05):46-52.