基于红边位置的马铃薯植株氮浓度估测方法研究
|
摘要
【目的】高光谱遥感技术可以用于植被生长状况的监测和研究光谱与植被理化性质间的关系。红边位置是与作物氮素营养关系较为密切的光谱参数,常用于作物叶绿素或氮素的含量监测,监测参数以及数据的计算都影响着该方法的准确性和实用性。为此,本研究优化了红边位置方法的参数,比较了六种方法对所得马铃薯氮浓度预测数据的翻译的准确性和精确度。【方法】于2014—2016年在内蒙古阴山北麓,进行了三个马铃薯品种、不同施氮量的田间试验。在马铃薯苗期、块茎形成期、块茎膨大期、淀粉积累期和收获期,使用红边位置获取了马铃薯冠层反射光谱,采用六种方法计算了该数据翻译的马铃薯地上部氮浓度,并分别与实测值进行了相关性分析。【结果】马铃薯生育后期一阶导数光谱中双峰现象较为明显。不同生育时期中苗期由于受到噪声光谱的影响氮浓度,与红边位置相关性较差,块茎形成期至淀粉积累期的氮浓度与红边位置相关性较高,其中块茎膨大期相关性最高。最大一阶导数法和拉格朗日内插法所得红边位置无连续性;线性外推法所得红边位置变幅与标准差最高,最大分别可达到44.6和9.3;多项式拟合法次之,变幅和标准差分别为15.1和2.6;倒高斯拟合法和线性四点内插法的变幅和标准差较小。在六种方法所得红边位置与马铃薯地上部氮浓度的预测模型中,线性外推法决定系数最高(R~2=0.55),预测值与观测值相关性最好(R~2=0.44,RMSE=3.96 g/kg,RE=11.46%);倒高斯拟合法与多项式拟合法模型决定系数相近,R~2均在0.40左右,倒高斯拟合法对氮浓度的预测能力更高一些(R~2=0.31,RMSE=4.33 g/kg, RE=12.03%)。【结论】红边位置能够对块茎形成期至淀粉积累期的植株氮浓度进行诊断,花期红边位置有轻微的饱和现象,但并不影响整体的预测,在花期和块茎膨大期采集光谱时需要注意传感器与植物冠层的距离,保证采集数据的准确性。线性外推法是最适合马铃薯冠层光谱的红边位置计算方法,所得红边位置变幅大,对马铃薯地上部氮浓度的变化较为敏感,回归模型决定系数和预测精度也最高,而且对于高氮浓度处的饱和现象有较好的缓解作用。
【Objectives】Hyper spectral remote sensing has been used for monitoring crop growth and estimating the relationship between crop growth and related physiological indexes. The red edge position is considered as a piece of spectra closely related to crop nitrogen nutrition, and often used to monitor the content of chlorophyll or nitrogen in crops.The monitoring parameters of the red edge position and the data interpret method determine the accuracy and availability of the monitoring. So the monitoring parameters in potato were optimized and the accuracy of the six often used algorithms were compared in this paper.【Methods】Field experiments were conducted in the northern Yinshan of Inner Mongolia of China from 2014 to 2016. Three potato cultivars were used as tested materials and 5 or 12 nitrogen application rates were set for the experiment. The canopy hyper spectral reflectance was collected at the seedling, tuber initiation, tuber bulking, starch accumulation and harvesting stages of potatoes. The nitrogen contents were measured and calculated using there flectants with six methods. The correlation between the measured and interpreted nitrogen contents was compared among the six algorithms.【Results】The double peak phenomenon in the first-order derivative spectrum of potato was more obvious at the late growth stages. The red edge position was poorly correlated to plant nitrogen concentration due to the influence of soil background and noise spectrum at the seedling stage.There was a stronger correlation between the plant nitrogen concentration and the red edge position from the tuber initiation to starch accumulation stage, and the tuber expansion stage had the highest correlation. There was no continuity between the red edge positions obtained by the largest first derivative method and Lagrange interpolation. The linear extrapolation method resulted in the highest amplitude and standard deviation of the red edge position, reaching 44.6 and 9.3 respectively, while polynomial fitting method was the second best with amplitude and standard deviation of 15.1 and 2.6. Inverted Gaussian fitting and linear four-point interpolation had small amplitude and standard deviations.The linear extrapolation method had the highest coefficient of determination(R~2 = 0.55) in predicting the aboveground plant nitrogen concentration among the six methods. The coefficients of determination(R~2), root mean square error(RMSE) and relative error(RE%) of the correlation between the predicted value and the observed value were 0.44, 3.96 g/kg and 11.46%, respectively. Inverse Gaussian fitting method and polynomial fitting method had similar coefficient of determination(R~2 = 0.30). Inverted Gaussian fitting method had better prediction ability in predicting plant nitrogen concentration(R~2 = 0.31, RMSE = 4.33 g/kg, RE = 12.03%).【Conclusions】The red edge position can detect plant nitrogen concentration of potato from tuberous formation to starch accumulation stage. In spite of a slight saturation in the estimation of plant N concentration, the influence on overall prediction is minimum. Linear extrapolation could result in a large amplitude of red edge position, thus is very sensitive to the change of plant nitrogen concentration and can minimize the influence of red edge bimodal phenomenon. Linear extrapolation method had the highest R~2, lowest RMSE and RE%, making it a very suitable method for calculating the position of the red edge.
【Objectives】Hyper spectral remote sensing has been used for monitoring crop growth and estimating the relationship between crop growth and related physiological indexes. The red edge position is considered as a piece of spectra closely related to crop nitrogen nutrition, and often used to monitor the content of chlorophyll or nitrogen in crops.The monitoring parameters of the red edge position and the data interpret method determine the accuracy and availability of the monitoring. So the monitoring parameters in potato were optimized and the accuracy of the six often used algorithms were compared in this paper.【Methods】Field experiments were conducted in the northern Yinshan of Inner Mongolia of China from 2014 to 2016. Three potato cultivars were used as tested materials and 5 or 12 nitrogen application rates were set for the experiment. The canopy hyper spectral reflectance was collected at the seedling, tuber initiation, tuber bulking, starch accumulation and harvesting stages of potatoes. The nitrogen contents were measured and calculated using there flectants with six methods. The correlation between the measured and interpreted nitrogen contents was compared among the six algorithms.【Results】The double peak phenomenon in the first-order derivative spectrum of potato was more obvious at the late growth stages. The red edge position was poorly correlated to plant nitrogen concentration due to the influence of soil background and noise spectrum at the seedling stage.There was a stronger correlation between the plant nitrogen concentration and the red edge position from the tuber initiation to starch accumulation stage, and the tuber expansion stage had the highest correlation. There was no continuity between the red edge positions obtained by the largest first derivative method and Lagrange interpolation. The linear extrapolation method resulted in the highest amplitude and standard deviation of the red edge position, reaching 44.6 and 9.3 respectively, while polynomial fitting method was the second best with amplitude and standard deviation of 15.1 and 2.6. Inverted Gaussian fitting and linear four-point interpolation had small amplitude and standard deviations.The linear extrapolation method had the highest coefficient of determination(R~2 = 0.55) in predicting the aboveground plant nitrogen concentration among the six methods. The coefficients of determination(R~2), root mean square error(RMSE) and relative error(RE%) of the correlation between the predicted value and the observed value were 0.44, 3.96 g/kg and 11.46%, respectively. Inverse Gaussian fitting method and polynomial fitting method had similar coefficient of determination(R~2 = 0.30). Inverted Gaussian fitting method had better prediction ability in predicting plant nitrogen concentration(R~2 = 0.31, RMSE = 4.33 g/kg, RE = 12.03%).【Conclusions】The red edge position can detect plant nitrogen concentration of potato from tuberous formation to starch accumulation stage. In spite of a slight saturation in the estimation of plant N concentration, the influence on overall prediction is minimum. Linear extrapolation could result in a large amplitude of red edge position, thus is very sensitive to the change of plant nitrogen concentration and can minimize the influence of red edge bimodal phenomenon. Linear extrapolation method had the highest R~2, lowest RMSE and RE%, making it a very suitable method for calculating the position of the red edge.
引文
[1]屈冬玉,谢开云,金黎平,等.中国马铃薯产业发展与食物安全[J].中国农业科学,2005, 38(2):358-362.Qu D Y, Xie K Y, Jing L P, et al. Development of potato industry and food security in China[J]. Scientia Agricultura Sinica, 2005,38(2):358-362.
[2] Food and Agriculture Organization of the United Nations.FAOSTAT[EB/OL]. http://www.fao.org/faostat/zh/#data/QC
[3]钟旭华,黄农荣,郑海波,等.水稻抽穗期叶色诊断指标与叶面积指数及结实期光强的关系[J].中国农学通报,2006, 22(10):147-153.Zhong X H, Huang N R, Zheng H B,et al. The relationship between leaf color threshold and leaf area index at heading and light intensity during grain-filling[J]. Chinese Agricultural Science Bulletin, 2006,22(10):147-153.
[4] Li F,Mistele B,Hu Y,et al. Optimising three-band spectral indices to assess aerial N concentration, N uptake and aboveground biomass of winter wheat remotely in China and Germany[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 92(2):112-123.
[5] Curran P J, Dungan J L, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine[J].Tree Physiology, 1990, 7(1-4):33-48.
[6] Main R, Cho M A, Mathieu R, et al. An investigation into robust spectral indices for leaf chlorophyll estimation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6):751-761.
[7] Yan Z, Xia Y, Yong C T, et al. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice[J]. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(1):1-10.
[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.Liu J, Wang L M, Teng F, et al. Impact of red-edge waveband of Rapid Eye satellite on estimation a crop planting area[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13):140-148.
[9]贺佳,刘冰峰,郭燕,等.冬小麦生物量高光谱遥感监测模型研究[J].植物营养与肥料学报,2017, 23(2):313-323.He J, Liu B F, Guo Y, et al. Biomass estimation model of winter wheat(Triticumaestivum L.)using hyperspectral reflectances[J].Journal of Plant Nutrition and Fertilizer, 2017, 23(2):313-323.
[10] Herrmann I, Pimstein A, Karnieli A, et al. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands[J]. Remote Sensing of Environment, 2011, 115(8):2141-2151.
[11]王磊,白由路,卢艳丽,等.基于光谱分析的玉米氮素营养诊断[J].植物营养与肥料学报,2011,17(2):333-340.Wang L, Bai Y L, Lu Y L, et al. Nitrogen nutrition diagnosis for corn based on spectral analysis[J]. Plant Nutrition and Fertilizer Science,2011,17(2):333-340.
[12] Dawson T P, Curran P J. Technical note a new technique for interpolating the reflectance red edge position[J]. International Journal of Remote Sensing,1998, 19(11):2133-2139.
[13] Baret F, Jacquemoud S, Guyot G, et al. Modeled analysis of the bio physical nature of spectral shift and comparison with information content of broad bands[J]. Remote Sensing of Environment,1992,41(2-3):133-142.
[14] Miller J R, Hare E W, J. Quantitative characterization of the vegetation red edge reflectance 1.An inverted-Gaussian reflectance model[J]. International Journal of Remote Sensing, 1990, 11(10):1755-1773.
[15] Cho M A, Skidmore A K. A new technique for extracting the red edge position from hyperspectral data:The linear extrapolation method[J]. Remote Sensing of Environment, 2006, 101(2):181-193.
[16] Guyot G, Leprieur C. Modeled analysis of the biophysical nature of spectral shift and comparison with information content of broad bands[J]. Remote Sensing of Environment, 1992, 41(2-3):133-142.
[17] Dawson T P, Curran P J. A new technique for interpolating the leaf area index[J]. IEEE Transaction on Geoscience and Remote Sensing,2003, 41(4):916-921.
[18]姚霞,田永超,刘小军.不同算法红边位置监测小麦冠层氮素营养指标的比较[J].中国农业科学,2010, 43(13):2661-2667.Yao X, Tian Y C, Liu X J. Comparative study on monitoring canopy leaf nitrogen status on red edge position with different algorithms in wheat[J]. Scientia Agricultura Sinica, 2010, 43(13):2661-2667.
[19]唐延林,黄敬峰,王秀珍,等.水稻、玉米、棉花的高光谱及其红边特征比较[J].中国农业科学,2004, 37(1):29-35.Tang Y L, Huang J F, Wang X Z, et al. Comparison of the characteristics of hyperspectra and the red edge in rice, corn and cotton[J]. Scientia Agricultura Sinica,2004, 37(1):29-35.
[20]胡昊,白由路,杨俐苹,等.不同氮营养冬小麦冠层光谱红边特征分析[J].植物营养与肥料学报,2009, 15(6):1317-1323.Hu H, Bai Y L, Yang L P, et al. Red edge parameters of winter wheat canopy under different nitrogen levels[J]. Plant Nutrition and Fertilizer Science,2009, 15(6):1317-1323.
[21]代辉,胡春胜,程一松.冬小麦冠层光谱红边特征分析[J].中国生态农业学报,2007, 15(5):80-83.Dai H, Hu C S, Cheng Y S. Red-edge characteristics of winter wheat canopy spectra[J]. Chinese Journal of Eco-Agriculture, 2007, 15(5):80-83.
[22]陈国庆,齐文增,李振,等.不同氮素水平下超高产夏玉米冠层的高光谱特征[J].生态学报,2010, 30(22):6035-6043.Chen G Q, Qi W Z, Li Z, et al. Analysis of hyperspectral on super high-yielding maize under different nitrogen levels[J]. Acta Ecologica Sinica,2010, 30(22):6035-6043.
[23] Pu R, Gong P, Biging G S, et al. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J]. Geoscience&Remote Sensing IEEE Transactions on, 2003,41(4):916-921.
[24]朱怀卫,娄运生,石一凡,等.UV-B增强下施硅对水稻冠层反射光谱特征的影响[J].中国农业气象,2017, 38(3):172-180.Zhu H W, Lou Y S, Shi Y F, et al. Effects of silicon supply on reflectance spectroscopy characteristics of rice canopy under elevated UV-B radiation[J]. Chinese Journal of Agrometeorology, 2017,38(3):172-180.
[25]秦占飞,常庆瑞,申健,等.引黄灌区水稻红边特征及SPAD高光谱预测模型[J].武汉大学学报(信息科学版),2016, 41(9):1168-1175.Qin Z F, Chang Q R, Shen J, et al. Red edge characteristics and SPAD estimation model using hyperspectral data for rice in Ningxia irrigation zone[J]. Geomatics and Information Science of Wuhan University,2016, 41(9):1168-1175.
[26]武晋雯,孙龙或,李书君,等.低温胁迫下玉米高光谱特征及产量构成的相关分析[J].中国农学通报,2015, 31(15):33-37.Wu J W, Sun L Y, Li S J, et al. Correlation analysis between hyperspectral characteristics and yield components of maize under low temperature stress[J]. Chinese Agricultural Science Bulletin,2015, 31(15):33-37.
[27]陈国庆,齐文增,李振,等.不同氮素水平下超高产夏玉米冠层的高光谱特征[J].生态学报,2010, 30(22):6035-6043.Chen G Q, Qi W Z, Li Z, et al. Analysis of hyperspectral on super high-yielding maize under different nitrogen levels[J]. Acta Ecologica Sinica, 2010, 30(22):6035-6043.
[2] Food and Agriculture Organization of the United Nations.FAOSTAT[EB/OL]. http://www.fao.org/faostat/zh/#data/QC
[3]钟旭华,黄农荣,郑海波,等.水稻抽穗期叶色诊断指标与叶面积指数及结实期光强的关系[J].中国农学通报,2006, 22(10):147-153.Zhong X H, Huang N R, Zheng H B,et al. The relationship between leaf color threshold and leaf area index at heading and light intensity during grain-filling[J]. Chinese Agricultural Science Bulletin, 2006,22(10):147-153.
[4] Li F,Mistele B,Hu Y,et al. Optimising three-band spectral indices to assess aerial N concentration, N uptake and aboveground biomass of winter wheat remotely in China and Germany[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 92(2):112-123.
[5] Curran P J, Dungan J L, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine[J].Tree Physiology, 1990, 7(1-4):33-48.
[6] Main R, Cho M A, Mathieu R, et al. An investigation into robust spectral indices for leaf chlorophyll estimation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6):751-761.
[7] Yan Z, Xia Y, Yong C T, et al. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice[J]. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(1):1-10.
[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.Liu J, Wang L M, Teng F, et al. Impact of red-edge waveband of Rapid Eye satellite on estimation a crop planting area[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13):140-148.
[9]贺佳,刘冰峰,郭燕,等.冬小麦生物量高光谱遥感监测模型研究[J].植物营养与肥料学报,2017, 23(2):313-323.He J, Liu B F, Guo Y, et al. Biomass estimation model of winter wheat(Triticumaestivum L.)using hyperspectral reflectances[J].Journal of Plant Nutrition and Fertilizer, 2017, 23(2):313-323.
[10] Herrmann I, Pimstein A, Karnieli A, et al. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands[J]. Remote Sensing of Environment, 2011, 115(8):2141-2151.
[11]王磊,白由路,卢艳丽,等.基于光谱分析的玉米氮素营养诊断[J].植物营养与肥料学报,2011,17(2):333-340.Wang L, Bai Y L, Lu Y L, et al. Nitrogen nutrition diagnosis for corn based on spectral analysis[J]. Plant Nutrition and Fertilizer Science,2011,17(2):333-340.
[12] Dawson T P, Curran P J. Technical note a new technique for interpolating the reflectance red edge position[J]. International Journal of Remote Sensing,1998, 19(11):2133-2139.
[13] Baret F, Jacquemoud S, Guyot G, et al. Modeled analysis of the bio physical nature of spectral shift and comparison with information content of broad bands[J]. Remote Sensing of Environment,1992,41(2-3):133-142.
[14] Miller J R, Hare E W, J. Quantitative characterization of the vegetation red edge reflectance 1.An inverted-Gaussian reflectance model[J]. International Journal of Remote Sensing, 1990, 11(10):1755-1773.
[15] Cho M A, Skidmore A K. A new technique for extracting the red edge position from hyperspectral data:The linear extrapolation method[J]. Remote Sensing of Environment, 2006, 101(2):181-193.
[16] Guyot G, Leprieur C. Modeled analysis of the biophysical nature of spectral shift and comparison with information content of broad bands[J]. Remote Sensing of Environment, 1992, 41(2-3):133-142.
[17] Dawson T P, Curran P J. A new technique for interpolating the leaf area index[J]. IEEE Transaction on Geoscience and Remote Sensing,2003, 41(4):916-921.
[18]姚霞,田永超,刘小军.不同算法红边位置监测小麦冠层氮素营养指标的比较[J].中国农业科学,2010, 43(13):2661-2667.Yao X, Tian Y C, Liu X J. Comparative study on monitoring canopy leaf nitrogen status on red edge position with different algorithms in wheat[J]. Scientia Agricultura Sinica, 2010, 43(13):2661-2667.
[19]唐延林,黄敬峰,王秀珍,等.水稻、玉米、棉花的高光谱及其红边特征比较[J].中国农业科学,2004, 37(1):29-35.Tang Y L, Huang J F, Wang X Z, et al. Comparison of the characteristics of hyperspectra and the red edge in rice, corn and cotton[J]. Scientia Agricultura Sinica,2004, 37(1):29-35.
[20]胡昊,白由路,杨俐苹,等.不同氮营养冬小麦冠层光谱红边特征分析[J].植物营养与肥料学报,2009, 15(6):1317-1323.Hu H, Bai Y L, Yang L P, et al. Red edge parameters of winter wheat canopy under different nitrogen levels[J]. Plant Nutrition and Fertilizer Science,2009, 15(6):1317-1323.
[21]代辉,胡春胜,程一松.冬小麦冠层光谱红边特征分析[J].中国生态农业学报,2007, 15(5):80-83.Dai H, Hu C S, Cheng Y S. Red-edge characteristics of winter wheat canopy spectra[J]. Chinese Journal of Eco-Agriculture, 2007, 15(5):80-83.
[22]陈国庆,齐文增,李振,等.不同氮素水平下超高产夏玉米冠层的高光谱特征[J].生态学报,2010, 30(22):6035-6043.Chen G Q, Qi W Z, Li Z, et al. Analysis of hyperspectral on super high-yielding maize under different nitrogen levels[J]. Acta Ecologica Sinica,2010, 30(22):6035-6043.
[23] Pu R, Gong P, Biging G S, et al. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J]. Geoscience&Remote Sensing IEEE Transactions on, 2003,41(4):916-921.
[24]朱怀卫,娄运生,石一凡,等.UV-B增强下施硅对水稻冠层反射光谱特征的影响[J].中国农业气象,2017, 38(3):172-180.Zhu H W, Lou Y S, Shi Y F, et al. Effects of silicon supply on reflectance spectroscopy characteristics of rice canopy under elevated UV-B radiation[J]. Chinese Journal of Agrometeorology, 2017,38(3):172-180.
[25]秦占飞,常庆瑞,申健,等.引黄灌区水稻红边特征及SPAD高光谱预测模型[J].武汉大学学报(信息科学版),2016, 41(9):1168-1175.Qin Z F, Chang Q R, Shen J, et al. Red edge characteristics and SPAD estimation model using hyperspectral data for rice in Ningxia irrigation zone[J]. Geomatics and Information Science of Wuhan University,2016, 41(9):1168-1175.
[26]武晋雯,孙龙或,李书君,等.低温胁迫下玉米高光谱特征及产量构成的相关分析[J].中国农学通报,2015, 31(15):33-37.Wu J W, Sun L Y, Li S J, et al. Correlation analysis between hyperspectral characteristics and yield components of maize under low temperature stress[J]. Chinese Agricultural Science Bulletin,2015, 31(15):33-37.
[27]陈国庆,齐文增,李振,等.不同氮素水平下超高产夏玉米冠层的高光谱特征[J].生态学报,2010, 30(22):6035-6043.Chen G Q, Qi W Z, Li Z, et al. Analysis of hyperspectral on super high-yielding maize under different nitrogen levels[J]. Acta Ecologica Sinica, 2010, 30(22):6035-6043.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |