植被指数与作物叶面积指数的相关关系研究
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摘要
作物长势参数是精细农业遥感监测的重要对象,叶面积指数(LAI)是作物长势最重要的参数之一,利用遥感手段快速获取作物的LAI具有重要的意义。为此,考虑到波段组合方式对LAI的反演效果的不可忽略性,采用4种不同的波段组合,结合PROSPECT和SAIL的模拟数据、地面实测数据和高光谱影像数据,从植被指数的饱和性和模型拟合精度两个角度对6个植被指数展开了评价。结果表明:TVI、MSAVI和MCARI23个植被指数在以上3个方面均表现较优,750～680 nm波段组合更加适合于LAI的反演。
Crop growth parameters play an important role in precision agricultural remote sensing monitoring. Leaf area index( LAI) is one of the most important parameters of crop structure characteristics. It is of great significance to rapidly obtain LAI of crops with remote sensing. Considering the non-negligible effect of the band combination on LAI inversion,four different band combinations are adopted to evaluate six vegetation indices from the saturation and anti-interference of the vegetation index and model fitting accuracy,combined with PROSPECT and SAIL simulation data,ground measurements and hyperspectral image data,performed evaluations on six vegetation indices. The results show that TVI,MSAVI and MCARI2 are superior in the above three aspects,and 750-680 nm band combination is more suitable in LAI inversion.
Crop growth parameters play an important role in precision agricultural remote sensing monitoring. Leaf area index( LAI) is one of the most important parameters of crop structure characteristics. It is of great significance to rapidly obtain LAI of crops with remote sensing. Considering the non-negligible effect of the band combination on LAI inversion,four different band combinations are adopted to evaluate six vegetation indices from the saturation and anti-interference of the vegetation index and model fitting accuracy,combined with PROSPECT and SAIL simulation data,ground measurements and hyperspectral image data,performed evaluations on six vegetation indices. The results show that TVI,MSAVI and MCARI2 are superior in the above three aspects,and 750-680 nm band combination is more suitable in LAI inversion.
引文
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[16]李开丽,蒋建军,茅荣正,等.植被叶面积指数遥感监测模型[J].生态学报,2005(6):1491-1496.
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[21] Chen R M,Cihlar J. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images[J]. Remote Sens Environ,1996,55:153-162.
[22] Gitelson A A,Kaufman Y J,Merzlyak M N. Use of a Green Channel in Remote Sensing of Global Vegetation from Eosmodis[J]. Remote Sens Environ,1996,58(3):289-298.
[23] Wu C,Niu Z,Tang Q,et al. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices[J]. Agricultural and Forest Meteorology,2009,149(6-7):1015-1021.
[24]董恒,孟庆野,王金梁,等.一种改进的叶绿素提取植被指数[J].红外与毫米波学报,2012(4):336-341.
[2]罗锡文,臧英,周志艳.精细农业中农情信息采集技术的研究进展[J].农业工程学报,2006,22(1):167-173.
[3]杨邦杰,裴志远.农作物长势的定义与遥感监测[J].农业工程学报,1999(3):214-218.
[4]方秀琴,张万昌.叶面积指数(LAI)的遥感定量方法综述[J].国土资源遥感,2003(3):58-62.
[5]吴炳方,曾源,黄进良.遥感提取植物生理参数LAI/FPAR的研究进展与应用[J].地球科学进展,2004,19(4):585-590.
[6] Verhoef W. Light scattering by leaf layers with applications to canopy reflectance modeling:the SAIL model[J]. Remote Sensing of Environment,1984,16:125-141.
[7] Gastellu-Etchegorry J P,Demarez V,Pinel V,et al.Modeling Radiative Transfer in Heterogeneous 3-D Vegetation Canopies[J]. Remote Sens Environ,1996,58(2):131-156.
[8] H F,S L. A hybrid inversion method for mapping leaf area index from MODIS data:experiments and application to broad leaf and needle leaf canopies[J]. Remote Sensing of Environment,2005,94(3):405-424.
[9] Fang H,Liang S. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model[J]. Remote Sensing of Environment,2003,85(3):257-270.
[10]陈雪洋,蒙继华,吴炳方,等.基于HJ-1 CCD的夏玉米FPAR遥感监测模型[J].农业工程学报,2010,26(S1):241-245.
[11]刘爱军,王保林,黄平平,等.基于反射率及导数的草原植被冠层光合有效吸收分量高光谱反演[J].草地学报,2012(6):1004-1010.
[12] Qi J,Kerr Y H,Moran M S,et al. Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region[J]. Remote Sensing of Environment,2000,73(1):18-30.
[13] Haboudane D. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:Modeling and validation in the context of precision agriculture[J]. Remote Sensing of Environment,2004,90(3):337-352.
[14] Berterretche M,Hudak A T,Cohen W B,et al. Comparison of regression and geostatistical methods for mapping Leaf Area Index(LAI)with Landsat ETM+data over a boreal forest[J]. Remote Sensing of Environment,2005,96(1):49-61.
[15]吴朝阳,牛铮.基于辐射传输模型的高光谱植被指数与叶绿素浓度及叶面积指数的线性关系改进[J].植物学通报,2008(6):714-721.
[16]李开丽,蒋建军,茅荣正,等.植被叶面积指数遥感监测模型[J].生态学报,2005(6):1491-1496.
[17]王纪华,赵春江,郭晓维,等.用光谱反射率诊断小麦叶片水分状况的研究[J].中国农业科学,2001(1):104-107.
[18]谭炳香,李增元,陈尔学,等. EO-1 Hyperion高光谱数据的预处理[J].遥感信息,2005(6):36-41.
[19] Huete A,Didan K,Miura T,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment,2002,83(1-2):195-213.
[20] Jordan C. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor[J]. Ecology,1969,50(4):663-666.
[21] Chen R M,Cihlar J. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images[J]. Remote Sens Environ,1996,55:153-162.
[22] Gitelson A A,Kaufman Y J,Merzlyak M N. Use of a Green Channel in Remote Sensing of Global Vegetation from Eosmodis[J]. Remote Sens Environ,1996,58(3):289-298.
[23] Wu C,Niu Z,Tang Q,et al. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices[J]. Agricultural and Forest Meteorology,2009,149(6-7):1015-1021.
[24]董恒,孟庆野,王金梁,等.一种改进的叶绿素提取植被指数[J].红外与毫米波学报,2012(4):336-341.