基于优化光谱指数的新疆春小麦冠层叶绿素含量估算
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摘要
为筛选可用于干旱半干旱区春小麦冠层叶绿素含量估算的高光谱植被指数,2017年通过测定春小麦关键生育时期冠层的田间高光谱与叶绿素含量,利用光谱指数波段优化算法分别计算400～1 300 nm光谱波段中不同波段两两组合的比值光谱指数(ration spectral index,RSI)、归一化光谱指数(normalized difference spectral index,NDSI)、叶绿素指数(chlorophyll index,CI)、简化光谱指数(CI/NDSI,NPDI),并将这些参数及其他17个不同高光谱植被指数分别与实测冠层叶绿素含量进行Pearson相关分析,通过变量重要性准则筛选最优光谱参数,使用偏最小二乘回归法建立冠层叶绿素含量的预测模型。结果表明:(1)RSIs、NDSIs、CIs和NPDIs与冠层叶绿素含量的相关性都优于前人研究中定义的17种高光谱植被指数,并且冠层叶绿素含量与NDSI(R_(849),R_(850))、RSI(R_(849),R_(850)),CI(R_(849),R_(850))和NPDI(R_(849),R_(850))表现出强相关性。(2)用此4个优化光谱指数分别建模时,以CI(R_(849),R_(850))、 CI(R_(539),R_(553))、 CI(R_(540),R_(553))、 CI(R_(536),R_(553))为自变量的X-3模型预测精度最高(r~2=0.74,RMSE=0.272 mg·g~(-1))。(3)结合4个优化光谱指数构建的组合模型预测精度,其r~2=0.83,RMSE=0.187 mg·g~(-1)。
In order to screen the hyperspectral vegetation index for estimating the chlorophyll content of spring wheat in arid and semi-arid regions,the field hyperspectral and chlorophyll contents of the canopy were determined during the key growth period of spring wheat in 2017.Initially,various two-band combinations in the 400-1 300 nm band were used to optimize ration spectral index(RSI),normalized difference spectral index(NDSI),chlorophyll index(CI),and spectral index(CI/NDSI,NPDI).Then,the relationship between the indices(RSIs,NDSIs,CIs,NPDIs and 17 different hyperspectral vegetation indices from the literature) and measured canopy chlorophyll content were examined,and the optimal spectral parameters were demonstrated by variable importance in projection(VIP).Ultimately,the partial least squares regression(PLSR) method was utilized to develop a predictive model of canopy chlorophyll content.The results revealed that the newly identified NDSIs,RSIs,CIs and NPDIs always performed better than the hyperspectral vegetation indices from the former studies,and canopy chlorophyll content exhibited strong correlation,with NDSI(R_(849),R_(850)),RSI(R_(849),R_(850)),CI(R_(849),R_(850)) and NPDI(R_(849),R_(850)).According to the prediction models established respectively by the four optimized spectral indices,the X-3 model revealed that the highest r_(Pre)~2(0.74) and lowest RMSE_(Pre)(0.272 mg·g~(-1)) were identified with four optimized chlorophyll indices(CI(R_(849),R_(850)),CI(R_(539),R_(553)),CI(R_(540),R_(553)),and CI(R_(536),R_(553)).The combination model with four newly identified spectral indices had the best ability for estimating canopy chlorophyll content in spring wheat,and the accuracy of model was r~2=0.83 and RMSE=0.187 mg·g~(-1).
In order to screen the hyperspectral vegetation index for estimating the chlorophyll content of spring wheat in arid and semi-arid regions,the field hyperspectral and chlorophyll contents of the canopy were determined during the key growth period of spring wheat in 2017.Initially,various two-band combinations in the 400-1 300 nm band were used to optimize ration spectral index(RSI),normalized difference spectral index(NDSI),chlorophyll index(CI),and spectral index(CI/NDSI,NPDI).Then,the relationship between the indices(RSIs,NDSIs,CIs,NPDIs and 17 different hyperspectral vegetation indices from the literature) and measured canopy chlorophyll content were examined,and the optimal spectral parameters were demonstrated by variable importance in projection(VIP).Ultimately,the partial least squares regression(PLSR) method was utilized to develop a predictive model of canopy chlorophyll content.The results revealed that the newly identified NDSIs,RSIs,CIs and NPDIs always performed better than the hyperspectral vegetation indices from the former studies,and canopy chlorophyll content exhibited strong correlation,with NDSI(R_(849),R_(850)),RSI(R_(849),R_(850)),CI(R_(849),R_(850)) and NPDI(R_(849),R_(850)).According to the prediction models established respectively by the four optimized spectral indices,the X-3 model revealed that the highest r_(Pre)~2(0.74) and lowest RMSE_(Pre)(0.272 mg·g~(-1)) were identified with four optimized chlorophyll indices(CI(R_(849),R_(850)),CI(R_(539),R_(553)),CI(R_(540),R_(553)),and CI(R_(536),R_(553)).The combination model with four newly identified spectral indices had the best ability for estimating canopy chlorophyll content in spring wheat,and the accuracy of model was r~2=0.83 and RMSE=0.187 mg·g~(-1).
引文
[1]尼加提·卡斯木,师庆东,王敬哲,等.基于高光谱特征和偏最小二乘法的春小麦叶绿素含量估算[J].农业工程学报,2017,33(22):208. NIJAT K,SHI Q D,WANG J Z,et al.Estimation of spring wheat chlorophyll content based on hyperspectral features and PLSR model [J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(22):208.
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[7]潘蓓,赵庚星,朱西存,等.利用高光谱植被指数估测苹果树冠层叶绿素含量[J].光谱学与光谱分析,2013,33(8):2203. FAN B,ZHAO G X,ZHU X C,et al.Estimation of chlorophyll content in apple tree canopy based on hyperspectral parameters [J].Spectroscopy and Spectral Analysis,2013,33(8):2203.
[8]靳彦华,熊黑钢,张芳,等.不同地类春小麦拔节期冠层光谱与叶绿素差异研究[J].光谱学与光谱分析,2013,33(4):1043. JIN Y H,XIONG H G,ZHANG F,et al.Study on the difference in canopy spectral reflectance and chlorophyll content of spring wheat at jointing stage in different land [J].Spectroscopy and Spectral Analysis,2013,33(4):1043.
[9]SID’KO A F,BOTVICH I Y,PISMAN T I,et al.Estimation of chlorophyll content and yield of wheat crops from reflectance spectra obtained by ground-based remote measurements[J].Field Crops Research,2017,207:24.
[10]LI F,MISTELE B,HU Y C,et al.Comparing hyperspectral index optimization algorithms to estimate aerial N uptake using multi-temporal winter wheat datasets from contrasting climatic and geographic zones in China and Germany [J].Agricultural and Forest Meteorology,2013,180(8):44.
[11]LI F,MISTELE B,HU Y C,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.
[12]安海波,李斐,赵萌莉,等.基于优化光谱指数的牧草生物量估算[J].光谱学与光谱分析,2015,35(11):3155. AN H B,LI F,ZHAO M L,et al.Optimized spectral indices based estimation of forage grass biomass [J].Spectroscopy and Spectral Analysis,2015,35(11):3155.
[13]李丹,李斐,胡云才,等.基于光谱指数波段优化算法的小麦玉米冠层含氮量估测[J].光谱学与光谱分析,2016,36(4):1150. LI D,LI F,HU Y C,et al.Study on the estimation of nitrogen content in wheat and maize canopy based on band optimization of spectral parameters [J].Spectroscopy and Spectral Analysis,2016,36(4):1150.
[14]罗丹,常庆瑞,齐雁冰,等.基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J].麦类作物学报,2016,36(9):1225. LUO D,CHANG Q R,QI Y B,et al.Estimation model for chlorophyll content in winter wheat canopy based on spectral indices [J].Journal of Triticeae Crops,2016,36(9):1225.
[15]LIAO Q H.Chlorophyll content mapping of crops in Heihe River Basin based on hyperspectral vegetation indices [J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(S2):159.
[16]张永贺,陈文惠,郭乔影,等.桉树叶片光合色素含量高光谱估算模型[J].生态学报,2013,33(3):876. ZHANG Y H,CHEN W H,GUO Q Y,et al.Hyperspectral estimation models for photosynthetic pigment contents in leaves of Eucalyptus [J].Acta Ecologica Sinica,2013,33(3):876-887.
[17]章文龙,曾从盛,高灯州,等.闽江河口湿地秋茄叶绿素含量高光谱遥感估算[J].生态学报,2014,34(21):6190. ZHANG W L,ZENG C S,GAO D Z,et al.Estimating the chlorophyll content of Kandelia candel based on hyper-spectral remote sensing in the Min River Estuarine wetland [J].Acta Ecologica Sinica,2014,34(21):6190.
[18]WOLD S,SJOSTROM M,ERIKSSON L.PLS-regression:a basic tool of chemometrics [J].Chemometrics and Intelligent Laboratory Systems,2001,58(2):109.
[19]SHIBAYAMA M,AKIYAMA T.A spectroradiometer for field use.VI.Radiometric estimation for chlorophyll index for rice canopy [J].Japanese Journal of Crop Science,1986,55(4):433.
[2]李粉玲,常庆瑞.基于连续统去除法的冬小麦叶片全氮含量估算[J].农业机械学报,2017,48(7):174. LI F L,CHANG Q R.Estimation of winter wheat leaf nitrogen content based on continuum removed spectra [J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):174.
[3]HINZMAN L D,BAUER M E,CST D.Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat [J].Remote Sensing of Environment,1986,19(1):47.
[4]WU C Y,NIU Z,TANG Q,et al.Estimating chlorophyll content from hyperspectral vegetation indices:Modeling and validation [J].Agricultural and Forest Meteorology,2008,148(8):1230.
[5]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.
[6]孟庆野,董恒,秦其明,等.基于高光谱遥感监测植被叶绿素含量的一种植被指数MTCARI[J].光谱学与光谱分析,2012,32(8):2218. MENG Q Y,DONG H,QIN Q M,et al.MTCARI:A kind of vegetation index monitoring vegetation leaf chlorophyll content based on hyperspectral remote sensing [J].Spectroscopy and Spectral Analysis,2012,32(8):2218.
[7]潘蓓,赵庚星,朱西存,等.利用高光谱植被指数估测苹果树冠层叶绿素含量[J].光谱学与光谱分析,2013,33(8):2203. FAN B,ZHAO G X,ZHU X C,et al.Estimation of chlorophyll content in apple tree canopy based on hyperspectral parameters [J].Spectroscopy and Spectral Analysis,2013,33(8):2203.
[8]靳彦华,熊黑钢,张芳,等.不同地类春小麦拔节期冠层光谱与叶绿素差异研究[J].光谱学与光谱分析,2013,33(4):1043. JIN Y H,XIONG H G,ZHANG F,et al.Study on the difference in canopy spectral reflectance and chlorophyll content of spring wheat at jointing stage in different land [J].Spectroscopy and Spectral Analysis,2013,33(4):1043.
[9]SID’KO A F,BOTVICH I Y,PISMAN T I,et al.Estimation of chlorophyll content and yield of wheat crops from reflectance spectra obtained by ground-based remote measurements[J].Field Crops Research,2017,207:24.
[10]LI F,MISTELE B,HU Y C,et al.Comparing hyperspectral index optimization algorithms to estimate aerial N uptake using multi-temporal winter wheat datasets from contrasting climatic and geographic zones in China and Germany [J].Agricultural and Forest Meteorology,2013,180(8):44.
[11]LI F,MISTELE B,HU Y C,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.
[12]安海波,李斐,赵萌莉,等.基于优化光谱指数的牧草生物量估算[J].光谱学与光谱分析,2015,35(11):3155. AN H B,LI F,ZHAO M L,et al.Optimized spectral indices based estimation of forage grass biomass [J].Spectroscopy and Spectral Analysis,2015,35(11):3155.
[13]李丹,李斐,胡云才,等.基于光谱指数波段优化算法的小麦玉米冠层含氮量估测[J].光谱学与光谱分析,2016,36(4):1150. LI D,LI F,HU Y C,et al.Study on the estimation of nitrogen content in wheat and maize canopy based on band optimization of spectral parameters [J].Spectroscopy and Spectral Analysis,2016,36(4):1150.
[14]罗丹,常庆瑞,齐雁冰,等.基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J].麦类作物学报,2016,36(9):1225. LUO D,CHANG Q R,QI Y B,et al.Estimation model for chlorophyll content in winter wheat canopy based on spectral indices [J].Journal of Triticeae Crops,2016,36(9):1225.
[15]LIAO Q H.Chlorophyll content mapping of crops in Heihe River Basin based on hyperspectral vegetation indices [J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(S2):159.
[16]张永贺,陈文惠,郭乔影,等.桉树叶片光合色素含量高光谱估算模型[J].生态学报,2013,33(3):876. ZHANG Y H,CHEN W H,GUO Q Y,et al.Hyperspectral estimation models for photosynthetic pigment contents in leaves of Eucalyptus [J].Acta Ecologica Sinica,2013,33(3):876-887.
[17]章文龙,曾从盛,高灯州,等.闽江河口湿地秋茄叶绿素含量高光谱遥感估算[J].生态学报,2014,34(21):6190. ZHANG W L,ZENG C S,GAO D Z,et al.Estimating the chlorophyll content of Kandelia candel based on hyper-spectral remote sensing in the Min River Estuarine wetland [J].Acta Ecologica Sinica,2014,34(21):6190.
[18]WOLD S,SJOSTROM M,ERIKSSON L.PLS-regression:a basic tool of chemometrics [J].Chemometrics and Intelligent Laboratory Systems,2001,58(2):109.
[19]SHIBAYAMA M,AKIYAMA T.A spectroradiometer for field use.VI.Radiometric estimation for chlorophyll index for rice canopy [J].Japanese Journal of Crop Science,1986,55(4):433.
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