基于无人机高光谱影像玉米叶绿素含量估算
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摘要
以无人机为平台搭载高光谱相机获得玉米农田高光谱影像,从中提取光谱特征参数,构建玉米叶片叶绿素含量估算模型,并制作玉米叶片叶绿素含量分布图。结果表明,以红边面积(SDr)、红边一阶微分最大值(Dr)、差值植被指数(DVI)为自变量构建的回归模型建模精度较高,以此反演玉米叶片SPAD值分布图并对填图结果进行精度检验,得出SPAD-Dr模型填图预测效果最佳(R2=0.89,RMSE=1.28,RE=2.31),可以作为玉米叶片叶绿素含量无人机高光谱影像遥感反演估算的基本模型。
UAV remote sensing system can quickly acquire high-resolution remote sensing images on farmland scale,which is significant for crop growth monitoring and agricultural production management. In this research,the hyperspectral images of maize field were acquired with a UHD185 camera mounted on a drone. The spectral parameters were extracted from the hyperspectral images to construct models for estimating chlorophyll content in maize leaves. Chlorophyll distribution maps of maize leaf were inversely estimated using these models. The results showed that the simple regression model separately built with red edge area( SDr),maximum first derivative values within red edge( Dr),or difference vegetation index( DVI) had higher modeling accuracy. These inversion models were used to make SPAD value distribution map of maize leaves,then,they were validated against observed results for the accuracy of the map,it was found that SPAD-Dr model was the best one in estimating the chlorophyll of maize leaves( R2= 0.89,RMSE = 1.28,and RE = 2.31). Therefore,this new method is feasible to be used for estimating the chlorophyll content of maize leaves.
UAV remote sensing system can quickly acquire high-resolution remote sensing images on farmland scale,which is significant for crop growth monitoring and agricultural production management. In this research,the hyperspectral images of maize field were acquired with a UHD185 camera mounted on a drone. The spectral parameters were extracted from the hyperspectral images to construct models for estimating chlorophyll content in maize leaves. Chlorophyll distribution maps of maize leaf were inversely estimated using these models. The results showed that the simple regression model separately built with red edge area( SDr),maximum first derivative values within red edge( Dr),or difference vegetation index( DVI) had higher modeling accuracy. These inversion models were used to make SPAD value distribution map of maize leaves,then,they were validated against observed results for the accuracy of the map,it was found that SPAD-Dr model was the best one in estimating the chlorophyll of maize leaves( R2= 0.89,RMSE = 1.28,and RE = 2.31). Therefore,this new method is feasible to be used for estimating the chlorophyll content of maize leaves.
引文
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[17]吴长山,项月琴,郑兰芬,等.利用高光谱数据对作物群体叶绿素密度估算的研究[J].遥感学报,2000,4(03):228-232.
[18]黄文江,王纪华,刘良云,等.冬小麦红边参数变化规律及其营养诊断[J].遥感技术与应用,2003,18(04):206-211.
[19]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,2007,21(01):83-87+105.
[20]王强,易秋香,包安明,等.基于高光谱反射率的棉花冠层叶绿素密度估算[J].农业工程学报,2012,28(15):125-132.
[21]宫兆宁,赵雅莉,赵文吉,等.基于光谱指数的植物叶片叶绿素含量的估算模型[J].生态学报,2014,34(20):5736-5745.
[22] Boochs F,Kupfer G,Dockter K,et al. Shape of the red edge asvitality indicator for plants[J]. International Journal of RemoteSensing,1990,11(10):1741-1753.
[23] Horler D H,Dockray M,Barber J. The red edge of plant leaf re-flectance[J]. International Journal of Remote Sensing,1983,4(2):273-288.
[24]王秀珍,黄敬峰,李云梅,等.水稻生物化学参数与高光谱遥感特征参数的相关分析[J].农业工程学报,2003,19(02):144-148.
[25] Filella I,Penuelas J. The red edge position and shape as indica-tors of plant chlorophyll content,biomass and hydric status[J].International Journal of Remote Sensing, 1994, 15(7):1459-1470.
[26] Jordan C F. Derivation of leaf‐area index from quality of lighton the forest floor[J]. Ecology,1969,50(4):663-666.
[27] Rouse J W,Haas R H,Schell J A,et al. Monitoring the vernaladvancements and retrogradation of natural vegetation[R].NASA/GSFC:Final Report,1974.
[28] Daughtry C T,Walthall C L,Kim M S,et al. Estimating cornleaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment,2000,74(2):229-239.
[29] Roujean J L,Breon F M. Estimating PAR absorbed by vegetationfrom bidirectional reflectance measurements[J]. Remote Sensingof Environment,1995,51(3):375-384.
[30] Rondeaux, Steven, Baret. Optimization of soil~adjustedvegetation indices[J]. Remote Sensing of Environment,1996,55(2):95-107.
[31]金震宇,田庆久,惠凤鸣,等.水稻叶绿素浓度与光谱反射率关系研究[J].遥感技术与应用,2003,18(03):134-137.
[2]赵英时,陈冬梅,杨立明,等.遥感应用分析原理与方法[M].北京:科学出版社,2013:81-86.
[3]童庆禧,张兵,张立福.中国高光谱遥感的前沿进展[J].遥感学报,2016,20(05):689-707.
[4]张兆明,何国金,江洪.基于EO—1Hyperion高光谱影像的福建森林叶面积指数反演[J].科学技术与工程,2013,13(31):9159-9162.
[5]王胜,潘洁,张衡,等.基于高光谱遥感影像的森林病虫害监测研究进展[J].林业资源管理,2014,(03):134-140.
[6]李军玲,郭其乐,任丽伟.基于近地高光谱和环境星高光谱数据的冬小麦越冬冻害遥感监测方法研究[J].自然灾害学报,2017,26(02):53-63.
[7]王冉,刘志刚,冯海宽,等.基于近地面高光谱影像的冬小麦日光诱导叶绿素荧光提取与分析[J].光谱学与光谱分析,2013,33(09):2451-2454.
[8]张东彦,刘镕源,宋晓宇,等.应用近地成像高光谱估算玉米叶绿素含量[J].光谱学与光谱分析,2011,31(03):771-775.
[9]乔振民,邢立新,李淼淼,等.Hyperion数据玉米叶绿素含量制图[J].遥感技术与应用,2012,27(02):275-281.
[10]罗红霞,阚应波,王玲玲,等.基于高光谱遥感技术的农作物病虫害应用研究现状[J].广东农业科学,2012,39(18):76-80.
[11]徐祎凡,施勇,李云梅.基于环境一号卫星高光谱数据的太湖富营养化遥感评价模型[J].长江流域资源与环境,2014,8(08):1111-1118.
[12]李德仁,李明.无人机遥感系统的研究进展与应用前景[J].武汉大学学报(信息科学版),2014,39(05):505-513.
[13]艾天成,李方敏,周治安,等.作物叶片叶绿素含量与SPAD值相关性研究[J].湖北农学院学报,2000,20(01):6-8.
[14]张金恒,王珂,王人潮.高光谱评价植被叶绿素含量的研究进展[J].上海交通大学学报(农业科学版),2003,21(01):74-80.
[15]杨峰,范亚民,李建龙,等.高光谱数据估测稻麦叶面积指数和叶绿素密度[J].农业工程学报,2010,26(02):237-243.
[16]孙雪梅,周启发,何秋霞.利用高光谱参数预测水稻叶片叶绿素和籽粒蛋白质含量[J].作物学报,2005,31(07):844-850.
[17]吴长山,项月琴,郑兰芬,等.利用高光谱数据对作物群体叶绿素密度估算的研究[J].遥感学报,2000,4(03):228-232.
[18]黄文江,王纪华,刘良云,等.冬小麦红边参数变化规律及其营养诊断[J].遥感技术与应用,2003,18(04):206-211.
[19]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,2007,21(01):83-87+105.
[20]王强,易秋香,包安明,等.基于高光谱反射率的棉花冠层叶绿素密度估算[J].农业工程学报,2012,28(15):125-132.
[21]宫兆宁,赵雅莉,赵文吉,等.基于光谱指数的植物叶片叶绿素含量的估算模型[J].生态学报,2014,34(20):5736-5745.
[22] Boochs F,Kupfer G,Dockter K,et al. Shape of the red edge asvitality indicator for plants[J]. International Journal of RemoteSensing,1990,11(10):1741-1753.
[23] Horler D H,Dockray M,Barber J. The red edge of plant leaf re-flectance[J]. International Journal of Remote Sensing,1983,4(2):273-288.
[24]王秀珍,黄敬峰,李云梅,等.水稻生物化学参数与高光谱遥感特征参数的相关分析[J].农业工程学报,2003,19(02):144-148.
[25] Filella I,Penuelas J. The red edge position and shape as indica-tors of plant chlorophyll content,biomass and hydric status[J].International Journal of Remote Sensing, 1994, 15(7):1459-1470.
[26] Jordan C F. Derivation of leaf‐area index from quality of lighton the forest floor[J]. Ecology,1969,50(4):663-666.
[27] Rouse J W,Haas R H,Schell J A,et al. Monitoring the vernaladvancements and retrogradation of natural vegetation[R].NASA/GSFC:Final Report,1974.
[28] Daughtry C T,Walthall C L,Kim M S,et al. Estimating cornleaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment,2000,74(2):229-239.
[29] Roujean J L,Breon F M. Estimating PAR absorbed by vegetationfrom bidirectional reflectance measurements[J]. Remote Sensingof Environment,1995,51(3):375-384.
[30] Rondeaux, Steven, Baret. Optimization of soil~adjustedvegetation indices[J]. Remote Sensing of Environment,1996,55(2):95-107.
[31]金震宇,田庆久,惠凤鸣,等.水稻叶绿素浓度与光谱反射率关系研究[J].遥感技术与应用,2003,18(03):134-137.