基于多角度高光谱遥感的植被红边波段角度效应分析——以大豆和玉米为例
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摘要
为了解成像几何关系对叶绿素含量与红边光谱响应之间关系的影响,利用无人机搭载高光谱成像仪对大豆和玉米进行多角度观测并构建二向反射分布函数(BRDF)模型,分析红边波段反射率与叶绿素相对含量(SPAD)的相关性在观测主平面中随观测角度变化的规律。研究发现:1)两种作物红边波段反射率与SPAD值均为负相关,在714 nm处具有最大相关系数(约-0.5);2)BRDF特性的差异导致观测角度对大豆的影响超过玉米,即:后向40°～45°观测可显著提升大豆SPAD值的相关性,而玉米在-30°～30°内不敏感;3)BRDF校正可显著提升无人机航测结果与冠层SPAD值的相关性。结果表明,遥感观测的角度效应严重影响红边波段对冠层叶绿素含量的光谱响应,且不同作物类型间差异巨大;研究结果可为高精度的农作物冠层叶绿素含量反演提供依据。
In order to understand the influence of imaging geometry on the relationship between chlorophyll content and "red edge" spectral response,the hyperspectral imaging sensor carried by unmanned aerial vehicle(UAV)was used to observe soybeans and maize from different angles.The correlation between reflectance of red edge bands in the main observing plane and relative chlorophyll content(SPAD) was analyzed by the bidirectional reflectance distribution function(BRDF) model.The results are summarized as follows.1) The reflectance of red-edge bands of soybeans and maize is negatively correlated with the SPAD value,and the maximum correlation coefficients of the two crops are about-0.5,both at 714 nm.2) Soybean and maize show different BRDF characteristics.In addition,the observation angle has more influence on soybean than maize.That is to say,for almost all the red-edge bands,backward 40°～45° observation can significantly improve the correlation of soybean′s SPAD values,while the results of maize are not sensitive to observation angles within-30° to 30°.3) The BRDF correction can significantly improve the correlation between UAV measurements and canopy SPAD values,which is raised from ±0.15 to around-0.3(soybean) and-0.5(maize),respectively.It should be pointed out that,in remote sensing,the angular effect of observation seriously affected the spectral response of red edge bands to the chlorophyll content of canopy,and there were great differences among different crop types.The results can provide guidance for high-accuracy inversion of the chlorophyll content of crop canopy.
In order to understand the influence of imaging geometry on the relationship between chlorophyll content and "red edge" spectral response,the hyperspectral imaging sensor carried by unmanned aerial vehicle(UAV)was used to observe soybeans and maize from different angles.The correlation between reflectance of red edge bands in the main observing plane and relative chlorophyll content(SPAD) was analyzed by the bidirectional reflectance distribution function(BRDF) model.The results are summarized as follows.1) The reflectance of red-edge bands of soybeans and maize is negatively correlated with the SPAD value,and the maximum correlation coefficients of the two crops are about-0.5,both at 714 nm.2) Soybean and maize show different BRDF characteristics.In addition,the observation angle has more influence on soybean than maize.That is to say,for almost all the red-edge bands,backward 40°～45° observation can significantly improve the correlation of soybean′s SPAD values,while the results of maize are not sensitive to observation angles within-30° to 30°.3) The BRDF correction can significantly improve the correlation between UAV measurements and canopy SPAD values,which is raised from ±0.15 to around-0.3(soybean) and-0.5(maize),respectively.It should be pointed out that,in remote sensing,the angular effect of observation seriously affected the spectral response of red edge bands to the chlorophyll content of canopy,and there were great differences among different crop types.The results can provide guidance for high-accuracy inversion of the chlorophyll content of crop canopy.
引文
[1] 方慧,宋海燕,曹芳,等.油菜叶片的光谱特征与叶绿素含量之间的关系研究[J].光谱学与光谱分析,2007,27(9):1731-1734.
[2] 蒙继华,吴炳方,李强子,等.农田农情参数遥感监测进展及应用展望[J].遥感信息,2010(3):122-128.
[3] 徐晋,蒙继华.农作物叶绿素含量遥感估算的研究进展与展望[J].遥感技术与应用,2016,31(1):74-85.
[4] 王娣,佃袁勇,乐源,等.基于高光谱植被指数的叶片净光合速率Pn反演[J].地理与地理信息科学,2016,32(4):42-48.
[5] 卢霞.矿区植被物化参数高光谱遥感估算研究[J].地理与地理信息科学,2010,26(5):37-40.
[6] OMRAN E E.Remote estimation of vegetation parameters using narrow band sensor for precision agriculture in arid environment[J].Egyptian Journal of Soil Science,2018,58(1):73-92.
[7] DENG L,MAO Z,LI X,et al.UAV-based multispectral remote sensing for precision agriculture:A comparison between different cameras[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,146:124-136.
[8] 刘佳,王利民,滕飞,等.RapidEye卫星红边波段对农作物面积提取精度的影响[J].农业工程学报,2016,32(13):140-148.
[9] CERNICHARO J,VERGER A,CAMACHO F.Empirical and physical estimation of canopy water content from CHRIS/PROBA Data[J].Remote Sensing,2013,5(10):5265-5284.
[10] VERRELST J,SCHAEPMAN M E,KOETZ B,et al.Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data[J].Remote Sensing of Environment,2008,112(5):2341-2353.
[11] 张东彦,COBURN C,赵晋陵,等.基于多角度成像数据的大豆冠层叶绿素密度反演[J].农业机械学报,2013,44(2):205-213.
[12] 李伟娜,韦玮,张怀清,等.基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J].遥感技术与应用,2017,32(5):809-817.
[13] 张雪红,赵峰,刘绍民,等.冬小麦红边参数各向异性特征分析[J].农业工程学报,2006,22(6):7-11.
[14] ROUJEAN J L,LEROY M,DESCHAMPS P Y.A bidirectional reflectance model of the earth′s surface for the correction of remote-sensing data[J].Journal of Geophysical Research-Atmospheres,1992,97(D18):20455-20468.
[15] NICODEMU.F E.Directional reflectance and emissivity of an opaque surface[J].Applied Optics,1965,4(7):767.
[16] MARKWELL J,OSTERMAN J C,MITCHELL J L.Calibration of the Minolta SPAD-502 leaf chlorophyll meter[J].Photosynthesis Research,1995,46(3):467-472.
[17] HOEL B O,SOLHAUG K A.Effect of irradiance on chlorophyll estimation with the Minolta SPAD-502 leaf chlorophyll meter[J].Annals of Botany,1998,82(3):389-392.
[18] 赵文强,林舟.SfM精细表面测量:分辨率和精度的评估及误差分析[J].地理与地理信息科学,2016,32(6):25-31.
[19] WANNER W,LI X,STRAHLER A H.On the derivation of kernels for kernel-driven models of bidirectional reflectance[J].Journal of Geophysical Research-Atmospheres,1995,100(D10):21077-21089.
[20] 赵云,谢东海,邓磊,等.利用多角度影像计算BRDF的方法与系统实现[J].遥感技术与应用,2018,33(4):741-749.
[21] KOUKAL T,ATZBERGER C,SCHNEIDER W.Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification[J].Remote Sensing of Environment,2014,151:27-43.
[2] 蒙继华,吴炳方,李强子,等.农田农情参数遥感监测进展及应用展望[J].遥感信息,2010(3):122-128.
[3] 徐晋,蒙继华.农作物叶绿素含量遥感估算的研究进展与展望[J].遥感技术与应用,2016,31(1):74-85.
[4] 王娣,佃袁勇,乐源,等.基于高光谱植被指数的叶片净光合速率Pn反演[J].地理与地理信息科学,2016,32(4):42-48.
[5] 卢霞.矿区植被物化参数高光谱遥感估算研究[J].地理与地理信息科学,2010,26(5):37-40.
[6] OMRAN E E.Remote estimation of vegetation parameters using narrow band sensor for precision agriculture in arid environment[J].Egyptian Journal of Soil Science,2018,58(1):73-92.
[7] DENG L,MAO Z,LI X,et al.UAV-based multispectral remote sensing for precision agriculture:A comparison between different cameras[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,146:124-136.
[8] 刘佳,王利民,滕飞,等.RapidEye卫星红边波段对农作物面积提取精度的影响[J].农业工程学报,2016,32(13):140-148.
[9] CERNICHARO J,VERGER A,CAMACHO F.Empirical and physical estimation of canopy water content from CHRIS/PROBA Data[J].Remote Sensing,2013,5(10):5265-5284.
[10] VERRELST J,SCHAEPMAN M E,KOETZ B,et al.Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data[J].Remote Sensing of Environment,2008,112(5):2341-2353.
[11] 张东彦,COBURN C,赵晋陵,等.基于多角度成像数据的大豆冠层叶绿素密度反演[J].农业机械学报,2013,44(2):205-213.
[12] 李伟娜,韦玮,张怀清,等.基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J].遥感技术与应用,2017,32(5):809-817.
[13] 张雪红,赵峰,刘绍民,等.冬小麦红边参数各向异性特征分析[J].农业工程学报,2006,22(6):7-11.
[14] ROUJEAN J L,LEROY M,DESCHAMPS P Y.A bidirectional reflectance model of the earth′s surface for the correction of remote-sensing data[J].Journal of Geophysical Research-Atmospheres,1992,97(D18):20455-20468.
[15] NICODEMU.F E.Directional reflectance and emissivity of an opaque surface[J].Applied Optics,1965,4(7):767.
[16] MARKWELL J,OSTERMAN J C,MITCHELL J L.Calibration of the Minolta SPAD-502 leaf chlorophyll meter[J].Photosynthesis Research,1995,46(3):467-472.
[17] HOEL B O,SOLHAUG K A.Effect of irradiance on chlorophyll estimation with the Minolta SPAD-502 leaf chlorophyll meter[J].Annals of Botany,1998,82(3):389-392.
[18] 赵文强,林舟.SfM精细表面测量:分辨率和精度的评估及误差分析[J].地理与地理信息科学,2016,32(6):25-31.
[19] WANNER W,LI X,STRAHLER A H.On the derivation of kernels for kernel-driven models of bidirectional reflectance[J].Journal of Geophysical Research-Atmospheres,1995,100(D10):21077-21089.
[20] 赵云,谢东海,邓磊,等.利用多角度影像计算BRDF的方法与系统实现[J].遥感技术与应用,2018,33(4):741-749.
[21] KOUKAL T,ATZBERGER C,SCHNEIDER W.Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification[J].Remote Sensing of Environment,2014,151:27-43.
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