基于PLS和组合预测方法的冬小麦收获指数高光谱估测
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摘要
【目的】通过遥感反演测量收获指数(HI),可节省时间和人力,但需要提高精度。通过权重最优组合算法改善收获指数估算精度,为基于多时相多光谱信息的HI遥感估算提供新方法参考。【方法】利用测定的冬小麦多个关键生育期的冠层光谱数据,对筛选的44种常用植被指数与实测收获指数进行相关性分析,挑选出每个育期中5种最优的典型植被指数;应用偏最小二乘(PLS)的方法建模,分别得到基于单个生育期光谱信息的HI遥感估测模型;借鉴组合预测原理,应用组合预测方法对全部单生育期的各HI光谱模型赋予最优权重,最终构建基于多生育期数据的HI光谱组合预测模型。【结果】(1)利用PLS后,单一生育期的建模结果较单一植被指数有所改进,但仍有待提高;(2)应用组合预测原理的HI组合预测模型,显著改善了HI的估测精度,R2达到0.55,较单生育期的建模预测,提升了13%。【结论】基于多生育期信息的组合预测方法,对各单一生育期HI预测模型赋予最优权重进行优化组合,实质间接利用了各生育期对作物HI形成的贡献,显著提高冬小麦收获指数的估测精度,是一种新颖的作物HI遥感估测方法。
[Purpose]Harvest index can effectively reflect the ability of crop population photohyalates to be transformed into grain accumulation. Also it is a key index to evaluate the yield level of crop varieties. Harvest index can be measured in practice. Remote sensing inversion can save time and manpower,but the accuracy needs to be improved. [Method]The canopy spectral data of winter wheat in several key growth stages were used to analyze the correlation between the selected 44 common vegetation indexes and the measured harvest indexes. Five optimal typical vegetation indexes are selected in each growth stage. Then,the partial least squares(PLS) modeling was applied to obtain the HI remote sensing estimation models based on the spectral information of single growth period. Finally,the combination prediction theory was used to apply the combination prediction method to assign the optimal weight to each HI spectral model for each single growth periods,and finally a HI spectral combination prediction model based on the data of multiple growth periods was constructed. [Result](1)After using the PLS,the modeling results of single growth period have improved,compared with that of single vegetation index. However it still needs to be improved.(2)The HI combination prediction model based on the combination prediction principle significantly improved the estimation accuracy of HI,with R2 up to 0.55,which was 13% higher than the modeling prediction of single growth period. [Conclusion]Based on the combination prediction method of information of multiple growth stages,the optimal weight was given to the HI prediction model of each single growth stage to optimize the combination. In essence,the contribution of each growth stage to the HI formation of crops was indirectly utilized to significantly improve the estimation accuracy of winter wheat harvest index. It is a novel remote sensing estimation method of crop HI.
[Purpose]Harvest index can effectively reflect the ability of crop population photohyalates to be transformed into grain accumulation. Also it is a key index to evaluate the yield level of crop varieties. Harvest index can be measured in practice. Remote sensing inversion can save time and manpower,but the accuracy needs to be improved. [Method]The canopy spectral data of winter wheat in several key growth stages were used to analyze the correlation between the selected 44 common vegetation indexes and the measured harvest indexes. Five optimal typical vegetation indexes are selected in each growth stage. Then,the partial least squares(PLS) modeling was applied to obtain the HI remote sensing estimation models based on the spectral information of single growth period. Finally,the combination prediction theory was used to apply the combination prediction method to assign the optimal weight to each HI spectral model for each single growth periods,and finally a HI spectral combination prediction model based on the data of multiple growth periods was constructed. [Result](1)After using the PLS,the modeling results of single growth period have improved,compared with that of single vegetation index. However it still needs to be improved.(2)The HI combination prediction model based on the combination prediction principle significantly improved the estimation accuracy of HI,with R2 up to 0.55,which was 13% higher than the modeling prediction of single growth period. [Conclusion]Based on the combination prediction method of information of multiple growth stages,the optimal weight was given to the HI prediction model of each single growth stage to optimize the combination. In essence,the contribution of each growth stage to the HI formation of crops was indirectly utilized to significantly improve the estimation accuracy of winter wheat harvest index. It is a novel remote sensing estimation method of crop HI.
引文
[1]张福春,朱志辉.中国作物的收获指数.中国农业科学,1990,23(2):83~87.
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[4]Moriondo M,Maselli F,Bindi M.Asimple model of regional wheat yield based on NDVI data.European Journal of Agronomy,2007,26(3):266~274.
[5]任建强,陈仲新.基于时序归一化植被指数的冬小麦收获指数空间信息提取.农业工程学报,2010,26(8):160~167.
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[8]Birth G S,Mcvey G.Measuring the color of growing turf with a reflectance spectrophotometer.Agronomy Journal,1968,60:640~643.
[9]Jordan C F.Derivation of leaf area index from quality of light on the forest floor.Ecology,1969,50:663~666.
[10]Penuelas J,Pinol J,Ogaya R.Estimation of plant water concentration by the reflectance water index WI(R900/R970).International Journal of Remote Sensing,1997,18(13):2869~2875.
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[12]金秀良,徐新刚,李振海,等.基于新型植被指数对冬小麦蛋白质含量的估算研究.光谱学与光谱分析,2013,33(9):2541~2545.
[13]Dash J,Curran P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI).Advances in Space Research,2007,39(1):100~104.
[14]Chen P F,Haboudane D,Tremblay N,et al.New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat.Remote sensing of Enviroment,2010,114:1987.
[15]Giteison.Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation.Journal of Plant Physiology,2004,161:165~173.
[16]Zhang Yanjiang.Interpretation of the modified soil-adjusted vegetation index isolines in red-NIR reflectance space.Journal of Applied Remote Sensing,2007,1:1~12.
[17]G Metternicht.Vegetation indices derived from high-resolution airborne videography for precision crop management.International Journal of Remote Sensing,2003,24:2855.
[18]Penuelas J.Assessment of photosynthetic radiation-use efficiency with spectral reflectance.New Phytologist,1995,131:291.
[19]Broge N H,Leblanc E.Comparing prediction power and stability of broad band and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density.Remote Sensing of Environment,2001,76(2):156~172.
[20]Huete A R.A soil adjusted vegetation index SAVI.Remote Sensing of Environment,1988,25(3):295~309.
[21]RhewI C,Vander S A,Kearney A,et al.Validation of the normalized difference vegetation index as a measure of neighborhood greenness.Annals of Epidemiology,2011,21(12):946~952.
[22]CleversJ G.Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture.Remote Sensing of Environment,1989,29(1):25~37.
[23]Fitzgerald G,Rodriguez D.Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-the canopy chlorophyll content index(CCCI).Field Crops Research,2010,116(3):318~324.
[24]束美艳,顾晓鹤.基于新型植被指数的冬小麦LAI高光谱反演.中国农业科学,2018,51(18):3486~3496.
[25]Penuelas J.Refelctance indices associated with physiological changes in nitrogen and water-limited sunflower leaves.Remote Sensing Environment,1994,48(2):135~146.
[26]Gamon J A,Penuelas J.A narrow~waveband spectral index that tracks diurnal changes in photosynthetic efficiency.Remote Sensing of Environment,1992,41(1):35~44.
[27]Chen J M.Evaluation of vegetation indices and modified simple ratio for boreal application.Canadian Journal of Remote Sensing,1996,22(3):229~242.
[28]Fei Li,Martin L.Estimating N status of winter wheat using a handheld spectrometer in the North China Plain.Field Crops Research,2008(106):77~85.
[29]Wang Fumin.New vegetation index and its application in estimating leaf area index of rice.Rice Science,2007,14(3):195~203.
[30]Jean-Louis Roujean,Francois-Marie Breon.Estimating PAR absorbed by begetation from birdirectional reflectance measurements.Remote Sensing of Environment,1995,51(3):375~384.
[31]傅银贞.遥感植被指数分析及应用研究.福州:福州大学,2010.
[32]Haboudane D,Miller J R,et al.Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:modeling and validation in the context of precision agriculture.Remote Sensing of Environment,2004,90(3):337~352.
[33]Zhongsheng Cao,Xia Yao,et al.Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat.Agricultural and Forest Meteorology,2019,25:121~136.
[34]管晓梅.优化因子的偏最小二乘法及其在高光谱检测果糖含量中的应用.济南:山东师范大学,2018.
[35]张海云,刘桂兰.组合预测方法及其在粮食产量预测中的应用.农机化研究,2002,11(4):166~173.
[2]潘晓华,邓强辉.作物收获指数的研究进展.江西农业大学学报,2007,29(1):1~5.
[3]杜鑫,吴炳芳,蒙继华,等.基于遥感技术监测作物收获指数(HI)的可行性分析.中国农业气象,2010,31(3):453~457.
[4]Moriondo M,Maselli F,Bindi M.Asimple model of regional wheat yield based on NDVI data.European Journal of Agronomy,2007,26(3):266~274.
[5]任建强,陈仲新.基于时序归一化植被指数的冬小麦收获指数空间信息提取.农业工程学报,2010,26(8):160~167.
[6]潘晓华,邓强辉.作物收获指数的研究进展.江西农业大学学报,2007,29(1):1~5.
[7]Rouse J W,Hass R H,Deering D W,et al.Monitoring the vernal advancement and retrogradation(Green Wave Effect)of natural vegetation.NASA/GSFC Type III Final Report,Greenbelt,MD.US:NASA,1974:371.
[8]Birth G S,Mcvey G.Measuring the color of growing turf with a reflectance spectrophotometer.Agronomy Journal,1968,60:640~643.
[9]Jordan C F.Derivation of leaf area index from quality of light on the forest floor.Ecology,1969,50:663~666.
[10]Penuelas J,Pinol J,Ogaya R.Estimation of plant water concentration by the reflectance water index WI(R900/R970).International Journal of Remote Sensing,1997,18(13):2869~2875.
[11]A R Huete,H Q Liu,K Batchily.A comparison of vegetation indices over a global set of TM images for EOS-MODIS.Remote Sensing of Enviroment,1997,59:440~451.
[12]金秀良,徐新刚,李振海,等.基于新型植被指数对冬小麦蛋白质含量的估算研究.光谱学与光谱分析,2013,33(9):2541~2545.
[13]Dash J,Curran P J.Evaluation of the MERIS terrestrial chlorophyll index(MTCI).Advances in Space Research,2007,39(1):100~104.
[14]Chen P F,Haboudane D,Tremblay N,et al.New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat.Remote sensing of Enviroment,2010,114:1987.
[15]Giteison.Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation.Journal of Plant Physiology,2004,161:165~173.
[16]Zhang Yanjiang.Interpretation of the modified soil-adjusted vegetation index isolines in red-NIR reflectance space.Journal of Applied Remote Sensing,2007,1:1~12.
[17]G Metternicht.Vegetation indices derived from high-resolution airborne videography for precision crop management.International Journal of Remote Sensing,2003,24:2855.
[18]Penuelas J.Assessment of photosynthetic radiation-use efficiency with spectral reflectance.New Phytologist,1995,131:291.
[19]Broge N H,Leblanc E.Comparing prediction power and stability of broad band and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density.Remote Sensing of Environment,2001,76(2):156~172.
[20]Huete A R.A soil adjusted vegetation index SAVI.Remote Sensing of Environment,1988,25(3):295~309.
[21]RhewI C,Vander S A,Kearney A,et al.Validation of the normalized difference vegetation index as a measure of neighborhood greenness.Annals of Epidemiology,2011,21(12):946~952.
[22]CleversJ G.Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture.Remote Sensing of Environment,1989,29(1):25~37.
[23]Fitzgerald G,Rodriguez D.Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-the canopy chlorophyll content index(CCCI).Field Crops Research,2010,116(3):318~324.
[24]束美艳,顾晓鹤.基于新型植被指数的冬小麦LAI高光谱反演.中国农业科学,2018,51(18):3486~3496.
[25]Penuelas J.Refelctance indices associated with physiological changes in nitrogen and water-limited sunflower leaves.Remote Sensing Environment,1994,48(2):135~146.
[26]Gamon J A,Penuelas J.A narrow~waveband spectral index that tracks diurnal changes in photosynthetic efficiency.Remote Sensing of Environment,1992,41(1):35~44.
[27]Chen J M.Evaluation of vegetation indices and modified simple ratio for boreal application.Canadian Journal of Remote Sensing,1996,22(3):229~242.
[28]Fei Li,Martin L.Estimating N status of winter wheat using a handheld spectrometer in the North China Plain.Field Crops Research,2008(106):77~85.
[29]Wang Fumin.New vegetation index and its application in estimating leaf area index of rice.Rice Science,2007,14(3):195~203.
[30]Jean-Louis Roujean,Francois-Marie Breon.Estimating PAR absorbed by begetation from birdirectional reflectance measurements.Remote Sensing of Environment,1995,51(3):375~384.
[31]傅银贞.遥感植被指数分析及应用研究.福州:福州大学,2010.
[32]Haboudane D,Miller J R,et al.Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:modeling and validation in the context of precision agriculture.Remote Sensing of Environment,2004,90(3):337~352.
[33]Zhongsheng Cao,Xia Yao,et al.Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat.Agricultural and Forest Meteorology,2019,25:121~136.
[34]管晓梅.优化因子的偏最小二乘法及其在高光谱检测果糖含量中的应用.济南:山东师范大学,2018.
[35]张海云,刘桂兰.组合预测方法及其在粮食产量预测中的应用.农机化研究,2002,11(4):166~173.
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