夏玉米叶片光合色素含量高光谱估算
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摘要
为了实现夏玉米叶片光合色素含量的快速、无损检测,以陕西省关中地区夏玉米"大丰26号"为研究对象,探究了不同总色素含量水平的玉米叶片反射光谱特征。分别提取与叶绿素a、叶绿素b、类胡萝卜素和总色素含量相关性较强的15个光谱参数,通过单变量回归、多元逐步回归和随机森林回归分析,建立光合色素含量估算模型并进行精度比较。结果表明:基于随机森林方法构建的光合色素估算模型精度最高,其中,叶绿素a、叶绿素b、类胡萝卜素的建模R~2为0.93,总色素的建模R~2为0.92;叶绿素a和类胡萝卜素的检验R~2为0.74,叶绿素b和总色素的检验R~2为0.71;各模型的均方根误差(RMSE)和相对误差(RE)相差不大;拟合精度由高到低依次为叶绿素a、类胡萝卜素、总色素和叶绿素b的RF模型。证实了随机森林方法在夏玉米叶片光合色素含量估算中的优越性,并构建了高精度的光合色素RF估算模型。
To realizing the rapid and non-destructive detection of photosynthetic pigment content of summer maize, the spectral reflectance characteristics of maize leaf with different total pigment contents were studied by taking "Dafeng 26" of summer maize in Guanzhong district of Shaanxi Province as the research object. First, 15 spectral parameters with good correlation of chlorophyll a, chlorophyll b, carotenoids and total pigments are extracted. Then, the estimation models of photosynthetic pigments were established by single variable regression, multiple stepwise regression and random forest regression analysis, respectively. The results showed that the estimation model of photosynthetic pigments based on random forest had the highest accuracy. Specifically, the R~2 of fitting model of chlorophyll a, chlorophyll b and carotenoids was 0.93, the R~2 of fitting model of total pigments was 0.92, the R~2 of validation model of chlorophyll a and carotenoids was 0.74, and the R~2 of validation model of chlorophyll b and total pigments was 0.71. The RMSE and RE of each model are similar. The fitting accuracy from high to low was the RF model of chlorophyll a, carotenoids, total pigments and chlorophyll b, respectively. The study constructed a high-precision RF estimation model of photosynthetic pigments, which has application and population value.
To realizing the rapid and non-destructive detection of photosynthetic pigment content of summer maize, the spectral reflectance characteristics of maize leaf with different total pigment contents were studied by taking "Dafeng 26" of summer maize in Guanzhong district of Shaanxi Province as the research object. First, 15 spectral parameters with good correlation of chlorophyll a, chlorophyll b, carotenoids and total pigments are extracted. Then, the estimation models of photosynthetic pigments were established by single variable regression, multiple stepwise regression and random forest regression analysis, respectively. The results showed that the estimation model of photosynthetic pigments based on random forest had the highest accuracy. Specifically, the R~2 of fitting model of chlorophyll a, chlorophyll b and carotenoids was 0.93, the R~2 of fitting model of total pigments was 0.92, the R~2 of validation model of chlorophyll a and carotenoids was 0.74, and the R~2 of validation model of chlorophyll b and total pigments was 0.71. The RMSE and RE of each model are similar. The fitting accuracy from high to low was the RF model of chlorophyll a, carotenoids, total pigments and chlorophyll b, respectively. The study constructed a high-precision RF estimation model of photosynthetic pigments, which has application and population value.
引文
[1] 唐延林,王纪华,黄敬峰,等.水稻成熟过程中高光谱与叶绿素、类胡萝卜素的变化规律研究[J].农业工程学报,2003,19(6):167-173.
[2] Vane G,Goetz A F H.Terrestrial imaging spectrometry:Current status,future trends [J].Remote Sensing of Environment,1993,44(2-3):117-126.
[3] 邵田田.玉米叶绿素和LAI的高光谱遥感模型研究[D].长春:东北地理与农业生态研究所,2012.
[4] 王弘,施润和,刘浦东,等.植物光学模型估算叶片类胡萝卜素含量的一种双归一化差值-比值植被指数[J].光谱学与光谱分析.2016,36(7):2189-2194.
[5] 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.
[6] 邹小波,张小磊,石吉勇,等.基于高光谱图像的黄瓜叶片叶绿素含量分布检测[J].农业工程学报,2014,30(13):169-175.
[7] 楚万林,齐雁冰,常庆瑞,等.棉花冠层叶片叶绿素含量与高光谱参数的相关性[J].西北农林科技大学学报(自然科学版),2016,44(9):65-73.
[8] 孔维平,毕银丽,李少朋,等.利用高光谱估测干旱胁迫下接菌根菌大豆叶绿素含量[J].农业工程学报,2014,30(12):123-131.
[9] 冯海宽,杨福芹,杨贵军,等.基于特征光谱参数的苹果叶片叶绿素含量估算[J].农业工程学报,2018,(6):182-188.
[10] 谢传奇,何勇,李晓丽,等.基于高光谱技术的灰霉病胁迫下番茄叶片SPAD值检测方法研究[J].光谱学与光谱分析,2012,32(12):3324-3328.
[11] 刘艳丽.随机森林综述[D].天津:南开大学,2008.
[12] 李粉玲,王力,刘京,等.基于高分一号卫星数据的冬小麦叶片SPAD值遥感估算[J].农业机械学报,2015,46(9):273-281.
[13] 王丽爱,马昌,周旭东,等.基于随机森林回归算法的小麦叶片SPAD值遥感估算[J].农业机械学报,2015,46(1):259-265.
[14] 岳继博,杨贵军,冯海宽.基于随机森林算法的冬小麦生物量遥感估算模型对比[J].农业工程学报,2016,32(18):175-182.
[15] 李旭青,刘湘南,刘美玲,等.水稻冠层氮素含量光谱反演的随机森林算法及区域应用[J].遥感学报,2014,18(4):923-945.
[16] 程立真,朱西存,高璐璐,等.基于随机森林模型的苹果叶片磷素含量高光谱估测[J].果树学报,2016,(10):1219-1229.
[17] 姜海玲,杨杭,陈小平,等.利用光谱指数反演植被叶绿素含量的精度及稳定性研究[J].光谱学与光谱分析,2015,35(4):975-981.
[18] Horler D N H,Dockray M,Barber J.The red edge of plant leaf reflectance[J].International Journal of Remote Sensing,1983,4(2):273-288.
[19] Collins W.Remote sensing of crop type and maturity[J].Photogrammetrc Engineering Remote Sensing,1978,44(1):43-55.
[20] Blackburn G A.Spectral indices for estimating photosynthetic pigment concentrations:A test using senescent tree leaves[J].International Journal of Remote Sensing,1998,19(4):657-675.
[21] Blackburn G A.Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales :An Evaluation of Some Hyperspectral Approaches[J].Remote Sensing of Environment,1998,66(3):273-285.
[22] Penuelas J,Baret F,Filella I.Semiempirical Indexes to Assess Carotenoids Chlorophyll-a Ratio from Leaf Spectral Reflectance[J].Photosynthetica,1995,31(2):221-230.
[23] Daughtry C S T,Walthall C L,Kim M S,et al.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensing of Environment,2000,74(2):229-239.
[24] Gupta R K,Vijayan D,Prasad T S.New hyperspectral vegetation characterization parameters[J].Advances in Space Research,2001,28(1):201-206.
[25] Horler D N H,Dockray M,Barber J,et al.Red edge measurements for remotely sensing plant chlorophyll content[J].Advances in Space Research,1983,3(2):273-277.
[26] 尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展[J].药物分析杂志,2008,(5):824-829.
[27] 秦占飞,申健,谢宝妮,等.引黄灌区水稻叶面积指数的高光谱估测模型[J].武汉大学学报(信息科学版),2017,42(8):1159-1166.
[28] 夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,(3):139-147.
[29] 梁亮,杨敏华,张连蓬,等.基于SVR算法的小麦冠层叶绿素含量高光谱反演[J].农业工程学报,2012,28(20):162-171.
[30] 唐延林,黄敬峰,王人潮.水稻不同发育时期高光谱与叶绿素和类胡萝卜素的变化规律[J].中国水稻科学,2004,18(1):59-66.
[31] 柳维扬,彭杰,窦中江,等.基于冠层尺度的枣树色素含量的高光谱估算模型[J].光谱学与光谱分析,2017,37(1):156-161.
[32] 李媛媛,常庆瑞,刘秀英,等.基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算[J].农业工程学报,2016,32(16):135-142.
[33] 刘雪峰,吕强,何绍兰,等.柑橘植株冠层氮素和光合色素含量近地遥感估测[J].遥感学报,2015,19(6):1007-1018.
[34] 姚雄,余坤勇,杨玉洁,等.基于随机森林模型的林地叶面积指数遥感估算[J].农业机械学报,2017,48(5):159-166.
[2] Vane G,Goetz A F H.Terrestrial imaging spectrometry:Current status,future trends [J].Remote Sensing of Environment,1993,44(2-3):117-126.
[3] 邵田田.玉米叶绿素和LAI的高光谱遥感模型研究[D].长春:东北地理与农业生态研究所,2012.
[4] 王弘,施润和,刘浦东,等.植物光学模型估算叶片类胡萝卜素含量的一种双归一化差值-比值植被指数[J].光谱学与光谱分析.2016,36(7):2189-2194.
[5] 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.
[6] 邹小波,张小磊,石吉勇,等.基于高光谱图像的黄瓜叶片叶绿素含量分布检测[J].农业工程学报,2014,30(13):169-175.
[7] 楚万林,齐雁冰,常庆瑞,等.棉花冠层叶片叶绿素含量与高光谱参数的相关性[J].西北农林科技大学学报(自然科学版),2016,44(9):65-73.
[8] 孔维平,毕银丽,李少朋,等.利用高光谱估测干旱胁迫下接菌根菌大豆叶绿素含量[J].农业工程学报,2014,30(12):123-131.
[9] 冯海宽,杨福芹,杨贵军,等.基于特征光谱参数的苹果叶片叶绿素含量估算[J].农业工程学报,2018,(6):182-188.
[10] 谢传奇,何勇,李晓丽,等.基于高光谱技术的灰霉病胁迫下番茄叶片SPAD值检测方法研究[J].光谱学与光谱分析,2012,32(12):3324-3328.
[11] 刘艳丽.随机森林综述[D].天津:南开大学,2008.
[12] 李粉玲,王力,刘京,等.基于高分一号卫星数据的冬小麦叶片SPAD值遥感估算[J].农业机械学报,2015,46(9):273-281.
[13] 王丽爱,马昌,周旭东,等.基于随机森林回归算法的小麦叶片SPAD值遥感估算[J].农业机械学报,2015,46(1):259-265.
[14] 岳继博,杨贵军,冯海宽.基于随机森林算法的冬小麦生物量遥感估算模型对比[J].农业工程学报,2016,32(18):175-182.
[15] 李旭青,刘湘南,刘美玲,等.水稻冠层氮素含量光谱反演的随机森林算法及区域应用[J].遥感学报,2014,18(4):923-945.
[16] 程立真,朱西存,高璐璐,等.基于随机森林模型的苹果叶片磷素含量高光谱估测[J].果树学报,2016,(10):1219-1229.
[17] 姜海玲,杨杭,陈小平,等.利用光谱指数反演植被叶绿素含量的精度及稳定性研究[J].光谱学与光谱分析,2015,35(4):975-981.
[18] Horler D N H,Dockray M,Barber J.The red edge of plant leaf reflectance[J].International Journal of Remote Sensing,1983,4(2):273-288.
[19] Collins W.Remote sensing of crop type and maturity[J].Photogrammetrc Engineering Remote Sensing,1978,44(1):43-55.
[20] Blackburn G A.Spectral indices for estimating photosynthetic pigment concentrations:A test using senescent tree leaves[J].International Journal of Remote Sensing,1998,19(4):657-675.
[21] Blackburn G A.Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales :An Evaluation of Some Hyperspectral Approaches[J].Remote Sensing of Environment,1998,66(3):273-285.
[22] Penuelas J,Baret F,Filella I.Semiempirical Indexes to Assess Carotenoids Chlorophyll-a Ratio from Leaf Spectral Reflectance[J].Photosynthetica,1995,31(2):221-230.
[23] Daughtry C S T,Walthall C L,Kim M S,et al.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J].Remote Sensing of Environment,2000,74(2):229-239.
[24] Gupta R K,Vijayan D,Prasad T S.New hyperspectral vegetation characterization parameters[J].Advances in Space Research,2001,28(1):201-206.
[25] Horler D N H,Dockray M,Barber J,et al.Red edge measurements for remotely sensing plant chlorophyll content[J].Advances in Space Research,1983,3(2):273-277.
[26] 尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展[J].药物分析杂志,2008,(5):824-829.
[27] 秦占飞,申健,谢宝妮,等.引黄灌区水稻叶面积指数的高光谱估测模型[J].武汉大学学报(信息科学版),2017,42(8):1159-1166.
[28] 夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,(3):139-147.
[29] 梁亮,杨敏华,张连蓬,等.基于SVR算法的小麦冠层叶绿素含量高光谱反演[J].农业工程学报,2012,28(20):162-171.
[30] 唐延林,黄敬峰,王人潮.水稻不同发育时期高光谱与叶绿素和类胡萝卜素的变化规律[J].中国水稻科学,2004,18(1):59-66.
[31] 柳维扬,彭杰,窦中江,等.基于冠层尺度的枣树色素含量的高光谱估算模型[J].光谱学与光谱分析,2017,37(1):156-161.
[32] 李媛媛,常庆瑞,刘秀英,等.基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算[J].农业工程学报,2016,32(16):135-142.
[33] 刘雪峰,吕强,何绍兰,等.柑橘植株冠层氮素和光合色素含量近地遥感估测[J].遥感学报,2015,19(6):1007-1018.
[34] 姚雄,余坤勇,杨玉洁,等.基于随机森林模型的林地叶面积指数遥感估算[J].农业机械学报,2017,48(5):159-166.
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