基于高光谱的油菜叶面积指数估计
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摘要
以冬油菜为研究对象,2014-2015年度设计了不同施氮水平直播油菜小区试验,在不同生育时期测量冠层光谱、土壤背景光谱以及叶面积指数(leaf area index,LAI),通过相关分析选取了12个光谱特征参数和11个植被指数,建立6叶期至角果期LAI的5种线性和非线性定量反演模型。结果表明:二次多项式反演模型比较适合估算油菜LAI苗期时以红边参数为代表的光谱特征参数,可准确估算出LAI;6叶期时红边幅值预测模型R~2为0.81,RMSEP为0.39,RPD为1.62;8叶期时红蓝边面积比归一化预测模型R~2为0.79,RMSEP为0.60,RPD为2.30;10叶期时红边幅值预测模型R~2为0.92,RMSEP为0.47,RPD为2.36;盛花期时蓝边面积预测模型R~2为0.87,RMSEP为0.34,RPD为2.57;角果期时以RDVI为代表的植被指数也可准确估算出LAI,预测模型R~2为0.74,RMSEP为0.57,RPD为1.36。油菜全生育期采用相同光谱特征参数、植被指数建模估计LAI精度明显降低,预测R~2远小于0.75,RMSEP大于0.65,RPD值均小于1.40,表明难以采用统一参数建模准确估计油菜全生育期LAI,不同生长时期需选择合适的光谱参数、植被指数分段建模估计LAI。
Plot experiments of the winter rapeseed(Brassica napus L.)with different nitrogenous levels under direct seeding treatment were conducted in 2014-2015.The canopy spectral reflectance,soil background,LAI of each plot were measured at different stages.Correlation analysis between the canopy spectral reflectance and LAI was used to calculate eleven vegetation indices and twelve spectral parameters based on spectral position and area for optimizing five kinds of linear and nonlinear(logarithm,parabola,power and exponential)quantitative remote sensing inversion models to estimate LAI at the different and whole growth stages.The results showed that the quadratic polynomial inversion models perfectly estimated LAI of winter rapeseed using hyperspectral techniques.The spectral red edge parameters estimated accurately LAI at seedling stage.The predicted models based on Dr,NBR,Drproduced better estimation for LAI at six-leaf stage,eight-leaf stage and ten-leaf stage,respectively.R~2 was 0.81,0.79and0.92(P<0.01),respectively.RMSEP(root mean square error of predicted models)was 0.39,0.60and0.47,respectively.RPD(residual predictive deviation)was 1.62,2.30 and 2.36,respectively.The predicted models based on S_(Db)and RDVI produced better estimation for LAI at full-bloom stage and pod stage with R~2 of 0.87 and 0.74(P<0.01),RMSEP of 0.34 and 0.57,and RPD of 2.57 and 1.36.The unified validation of models(R~2?0.75,RMSEP>0.65,RPD<1.4)showed that there was low prediction precision with the unified spectral variables or vegetation indices monitoring LAI at the whole stages of growth.The prediction accuracy of monitoring model based on the appropriate spectral variables and vegetation indices to estimate LAI at different stages of the winter rapeseed growth was high.
Plot experiments of the winter rapeseed(Brassica napus L.)with different nitrogenous levels under direct seeding treatment were conducted in 2014-2015.The canopy spectral reflectance,soil background,LAI of each plot were measured at different stages.Correlation analysis between the canopy spectral reflectance and LAI was used to calculate eleven vegetation indices and twelve spectral parameters based on spectral position and area for optimizing five kinds of linear and nonlinear(logarithm,parabola,power and exponential)quantitative remote sensing inversion models to estimate LAI at the different and whole growth stages.The results showed that the quadratic polynomial inversion models perfectly estimated LAI of winter rapeseed using hyperspectral techniques.The spectral red edge parameters estimated accurately LAI at seedling stage.The predicted models based on Dr,NBR,Drproduced better estimation for LAI at six-leaf stage,eight-leaf stage and ten-leaf stage,respectively.R~2 was 0.81,0.79and0.92(P<0.01),respectively.RMSEP(root mean square error of predicted models)was 0.39,0.60and0.47,respectively.RPD(residual predictive deviation)was 1.62,2.30 and 2.36,respectively.The predicted models based on S_(Db)and RDVI produced better estimation for LAI at full-bloom stage and pod stage with R~2 of 0.87 and 0.74(P<0.01),RMSEP of 0.34 and 0.57,and RPD of 2.57 and 1.36.The unified validation of models(R~2?0.75,RMSEP>0.65,RPD<1.4)showed that there was low prediction precision with the unified spectral variables or vegetation indices monitoring LAI at the whole stages of growth.The prediction accuracy of monitoring model based on the appropriate spectral variables and vegetation indices to estimate LAI at different stages of the winter rapeseed growth was high.
引文
[1]谢巧云,黄文江,梁栋,等.最小二乘支持向量机方法对冬小麦叶面积指数反演的普适性研究[J].光谱学与光谱分析,2014,34(2):489-493.
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[11]董恒,何枋键,张城芳.基于辐射传输模型的FPARgreen与几种植被指数的关系研究[J].华中农业大学学报,2016,35(4):70-75.
[12]张钶,陈家赢,黄魏.基于OGC WPS的TVDI算法实现与共享[J].华中农业大学学报,2015,34(5):63-69.
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[16]刘占宇,黄敬峰,王福民,等.估算水稻叶面积指数的调节型归一化植被指数[J].中国农业科学,2008,41(10):3350-3356.
[17]谭昌伟,黄义德,黄文江,等.夏玉米叶面积指数的高光谱遥感植被指数法研究[J].安徽农业大学学报,2004,31(4):392-397.
[18]齐波,赵晋铭,盖钧镒,等.利用高光谱技术估测大豆育种材料的叶面积指数[J].作物学报,2015,41(7):1073-1085.
[19]黄敬峰,王渊,王福民,等.油菜红边特征及其叶面积指数的高光谱估算模型[J].农业工程学报,2006,8(8):22-26.
[20]张治礼,郑学勤.油菜叶片自然衰老过程中部分生理指标的变化规律[J].中国油料作物学报,2004,26(2):48-51.
[21]左青松,蒯婕,周广生,等.不同氮肥和密度对直播油菜冠层结构及群体特征的影响[J].作物学报,2015,41(5):758-765.
[22]程迪,刘咏梅,李京忠,等.青海祁连瑞香狼毒的光谱差异特征提取[J].应用生态学报,2015,26(8):2307-2313.
[23]胡珍珠,潘存德,肖冰,等.基于光谱特征参量的核桃叶片氮素含量估测模型[J].农业工程学报,2015,31(9):180-186.
[24]鞠昌华,田永超,曹卫星,等.油菜光合器官面积与导数光谱特征的相关关系[J].植物生态学报,2008,32(3):664-672.
[25]张晓艳,刘锋,王丽丽,等.花生叶面积指数与特征导数光谱的相关性[J].遥感技术与应用,2010,25(5):668-674.
[26]张晓东,毛罕平.油菜氮素光谱定量分析中水分胁迫与光照影响及修正[J].农业机械学报,2009,40(2):164-169.
[27]汤亮,朱艳,曹卫星.油菜绿色面积指数动态模拟模型[J].植物生态学报,2007,31(5):897-902.
[28]曹金华,朱家成,张书芬,等.覆盖对土壤温度及甘蓝型油菜丰油10号抗寒性和产量的影响[J].中国油料作物学报,2014,36(2):213-218.
[2]贺佳,刘冰锋,李军,等.不同生育时期冬小麦叶面积指数高光谱遥感监测模型[J].农业工程学报,2014,30(24):141-150.
[3]吴伟斌,杜俊毅,洪添胜,等.基于精确喷雾的水平叶面积指数检测[J].华中农业大学学报,2015,34(2):125-130.
[4]VINA A,GITELSONA A,NGUY-ROBERTSON A L,et al.Comparison of different vegetation indices for the remote assessment of green leaf area index of crops[J].Remote sensing of environment,2011,115,3468-3478.
[5]ABOU-ISMAIL O,HUANG J F,WANG R C.Rice yield estimation by integrating remote sensing with rice growth simulation model[J].Pedosphere,2004,14(4):519-526.
[6]石剑飞,殷璀艳,冷锁虎,等.采用数码图像处理法测定油菜叶面积的方法[J].中国油料作物学报,2010,32(3):379-382.
[7]杨劲峰,陈清,韩晓日,等.数字图像处理技术在蔬菜叶面积测量中的应用[J].农业工程学报,2002,18(4):155-158.
[8]TANGY L,WANG R C,HUANG J F.Relations between red edge characteristics and agronomic parameter of crops[J].Pedosphere,2004,14(4):467-474.
[9]TANG H,MATTHEW B,ZHAO F,et al.Deriving and validating leaf area index(LAI)at multiple spatialscales through lidar remote sensing:a case s tudy in Sierra National Forest[J].Remote sensing of environment,2014,143(5):131-141.
[10]姚付启,蔡焕杰,王海江,等.基于平稳小波变换的冬小麦覆盖度高光谱监测[J].农业机械学报,2012,43(3):173-180.
[11]董恒,何枋键,张城芳.基于辐射传输模型的FPARgreen与几种植被指数的关系研究[J].华中农业大学学报,2016,35(4):70-75.
[12]张钶,陈家赢,黄魏.基于OGC WPS的TVDI算法实现与共享[J].华中农业大学学报,2015,34(5):63-69.
[13]LI F,MIAOY X,FENG G H,et al.Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices[J].Field crops research,2014,157,111-123.
[14]HATFIELD J L,GITELSON A A,SCHEPERS J S,et al.Application of spectral remote sensing for agronomic decisions[J].Agronomy journal,2008,100:117-131.
[15]谢巧云,黄文江,蔡淑红,等.冬小麦叶面积指数遥感反演方法比较研究[J].光谱学与光谱分析,2014,34(5):1352-1356.
[16]刘占宇,黄敬峰,王福民,等.估算水稻叶面积指数的调节型归一化植被指数[J].中国农业科学,2008,41(10):3350-3356.
[17]谭昌伟,黄义德,黄文江,等.夏玉米叶面积指数的高光谱遥感植被指数法研究[J].安徽农业大学学报,2004,31(4):392-397.
[18]齐波,赵晋铭,盖钧镒,等.利用高光谱技术估测大豆育种材料的叶面积指数[J].作物学报,2015,41(7):1073-1085.
[19]黄敬峰,王渊,王福民,等.油菜红边特征及其叶面积指数的高光谱估算模型[J].农业工程学报,2006,8(8):22-26.
[20]张治礼,郑学勤.油菜叶片自然衰老过程中部分生理指标的变化规律[J].中国油料作物学报,2004,26(2):48-51.
[21]左青松,蒯婕,周广生,等.不同氮肥和密度对直播油菜冠层结构及群体特征的影响[J].作物学报,2015,41(5):758-765.
[22]程迪,刘咏梅,李京忠,等.青海祁连瑞香狼毒的光谱差异特征提取[J].应用生态学报,2015,26(8):2307-2313.
[23]胡珍珠,潘存德,肖冰,等.基于光谱特征参量的核桃叶片氮素含量估测模型[J].农业工程学报,2015,31(9):180-186.
[24]鞠昌华,田永超,曹卫星,等.油菜光合器官面积与导数光谱特征的相关关系[J].植物生态学报,2008,32(3):664-672.
[25]张晓艳,刘锋,王丽丽,等.花生叶面积指数与特征导数光谱的相关性[J].遥感技术与应用,2010,25(5):668-674.
[26]张晓东,毛罕平.油菜氮素光谱定量分析中水分胁迫与光照影响及修正[J].农业机械学报,2009,40(2):164-169.
[27]汤亮,朱艳,曹卫星.油菜绿色面积指数动态模拟模型[J].植物生态学报,2007,31(5):897-902.
[28]曹金华,朱家成,张书芬,等.覆盖对土壤温度及甘蓝型油菜丰油10号抗寒性和产量的影响[J].中国油料作物学报,2014,36(2):213-218.