基于高光谱遥感的不同品种花生冠层叶面积指数的通用估算模型

doi:10.3969/j.issn.1000-6362.2016.06.012

摘要/Abstract

摘要： 为了快速、无损地监测花生冠层LAI，获取其长势信息，于2014年通过大田试验选用5个品种花生作为供试品种，使用ASD FieldSpec HandHeld便携式野外光谱仪采集花生不同生育时期的冠层高光谱数据，同时使用SUNSCAN冠层分析系统实测花生冠层叶面积指数（LAI），并应用光谱微分技术和统计分析技术，分别分析4种光谱形式和6种植被指数与LAI的相关关系，建立估算模型。结果表明：高光谱反射率及其光谱变换形式中最优波段与LAI的相关性均极显著，其中一阶微分光谱ρ'在793nm波段处构建的估测方程对花生冠层LAI的估算效果最好（r=-0.5391，P＜0.01，RE=0.2497），其模拟值与实测值的拟合度达极显著水平（R=0.4435，P＜0.01）；一阶微分光谱ρ'在734nm波段处LAI的实测值与模拟值的拟合效果最好（R=0.5485，P＜0.01）。6种植被指数所选的最优组合波段与LAI均通过了0.01水平的显著性检验（r≥0.5731，P＜0.01），其中归一化植被指数NDVI[760，976]对花生冠层LAI的估算效果最好（r=-0.6421，P＜0.01，RE=0.2167），模拟值与实测值的拟合度达极显著水平（R=0.6731，P＜0.01），且优于ρ'对LAI的估算效果；LAI实测值与模拟值拟合效果最好的为DVI[760，976] （R=0.6893，P＜0.01）。研究结果表明一阶导数光谱和植被指数对花生冠层LAI的估算精度均较高，植被指数的估算精度尤高，研究同时进一步证实了导数光谱和植被指数能较好地消除土壤、大气等环境背景信息的影响。

关键词: 花生, 高光谱遥感, 叶面积指数, 估算模型

Abstract: In order to monitor peanut canopy effectively and nondestructively and to get their growth information, hyperspectral data of peanut canopy was measured with ASD FieldSpec and canopy leaf area index was measured with SUNSCAN during different growing stages in the field experiment, including five cultivars with different ecological types. The correlations between four spectral forms, six vegetation index and LAI were analyzed, and the estimation models were established, by using spectral derivative technique and statistical analysis technique. The results showed that the correlation between the optimal bands of hyperspectral reflectance, its transformation forms and LAI were 1% significant, LAI could be estimated better by the first derivative spectra ρ' at 793 nm （r=-0.5391, P＜0.01, RE=0.2497）, the correlation coefficient between simulating data and testing data was 1% significant （R=0.4435, P＜0.01）; simulating LAI and testing LAI could be fitted mostly by the first derivative sPectra ρ ' at 734nm （R=0.5485, P＜0.01）. The correlations between the optimal bands of six vegetation index and LAI were 1% significant （r≥0.5731, P＜0.01）, LAI could be estimated mostly by NDVI [760, 976] （r=-0.6421，P＜0.01, RE=0.2167）, the correlation coefficient between simulating data and testing data was 1% significant （R=0.6731, P＜0.01）; simulating LAI and testing LAI could be fitted mostly by DVI [760, 976] （R=0.6893, P＜0.01）. The results indicated that the estimation accuracy of peanut canopy was higher than that of with ρ' and vegetation index, especially vegetation index. It confirmed further that ρ' and vegetation index played a great role on eliminating the effect of environment background including soil and atmosphere.

Key words: Peanut, Hyperspectral remote sensing, LAI, Estimation models

吕晓，殷红，蒋春姬，张兵兵，战莘晔，辛明月，张美玲. 基于高光谱遥感的不同品种花生冠层叶面积指数的通用估算模型[J]. 中国农业气象, 2016, 37(06): 720-727.

LV Xiao，YIN Hong，JIANG Chun-ji，ZHANG Bing-bing, ZHAN Shen-ye，XIN Ming-yue，ZHANG Mei-ling. General Estimation Model of Peanut Canopy LAI Based on Hyperspectral Remote Sensing[J]. Chinese Journal of Agrometeorology, 2016, 37(06): 720-727.

参考文献

[1]李卫国,赵春江,王纪华,等.基于卫星遥感的冬小麦拔节期长势监测[J].麦类作物学报,2007,27(3): 523-527. Li W G,Zhao C J,Wang J H,et al.Monitoring the growth condition of winter wheat in jointing stage based on land sat TM image[J].Journal of Triticeae Crops,2007,27(3): 523-527. (in Chinese) [2]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. [3]Inoue Y.Synergy of remote sensing and modeling for estimating ecophysiological processes in plant production[J]. Plant Production Science,2003,6(1): 3-16. [4]薄瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000:185-228,124-144. Bo R L,Gong P.Hyperspectral remote sensing and application[M].Beijing:Higher Education Press,2000: 185-228, 124-144.(in Chinese) [5]薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻产量预测模型[J].遥感学报,2005,9(1):100- 105. Xue L H,Cao W X,Luo W H,et al.Rice yield forecasting model with canopy reflectance spectra[J].Journal of Remote Sensing, 2005,9(1):100-105.(in Chinese) [6]王伟,彭彦昆,马伟.冬小麦叶绿素含量高光谱检测技术[J].农业机械学报,2010,41(5):172-177. Wang W,Peng Y K,Ma W.Chlorophyll content of winter wheat using leaf-level hyperspectral data[J]. Transactions of the Chinese Society for Agricultural Machinery,2010, 41(5): 172-177.(in Chinese) [7]Wiegand C L,Gausman H W,CuellarJ A,et al. Vegetation density as deduced from ERTS-1 MSS response[C].Third ERTS Symposia,NASA-SP-351, SectionA,1974:93-116. [8]刘伟东,郑兰芬,童庆禧,等.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析[J].遥感学报,2000,4(4):279-283. Liu W D,Zheng L F,Tong Q X,et al.Relationships between rice LAI,CH.D and hyperspectra data[J]. Journal of Remote Sensing,2000,4(4):279-283.(in Chinese) [9]赵娟,黄文江,张耀鸿,等.冬小麦不同生育时期叶面积指数反演方法[J].光谱学与光谱分析,2013, 33(9):2546-2552. Zhao J,Huang W J,Zhang Y H,et al.Inversion of LAI during different growth stages in winter wheat [J].Spectroscopy and Spectral Analysis,2013,33(9): 2546-2552.(in Chinese) [10]宋开山,张柏,王宗明,等.基于人工神经网络的大豆叶面积高光谱反演研究[J].中国农业科学,2006,39(6): 11382- 11451. Song K S,Zhang B,Wang Z M,et al.Soybean LAI estimation with in-situ collected hyperspectral data based on BP-Neural networks[J].Scientia Agricultura Sinica,2006,39(6): 11382- 11451.(in Chinese) [11]林卉,梁亮,张连蓬,等.基于支持向量机回归算法的小麦叶面积指数高光谱遥感反演[J].农业工程学报,2013,29(11): 139-146. Lin H,Liang L,Zhang L P,et al.Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm[J]. Transactions of the CSAE,2013,29(11):139-146.(in Chinese) [12]Patel N K,Patnaik C,Dutta S,et al.Study of crop growth parameters using airborne imaging spectrometer data[J]. International Journal of Remote Sensing,2001,22(12): 2401- 2411. [13]李鑫川,徐新刚,黄文江,等.基于分段方式选择敏感植被指数的冬小麦叶面积指数遥感反演[J].中国农业科学,2012,45(17):3486-3496. Li X C,Xu X G,Huang W J,et al.Retrieving LAI of winter wheat based on sensitive vegetation index by the segmentation method[J].Scientia Agricultura Sinica,2012, 45(17):3486-3496.(in Chinese) [14]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. Xie Q Y,Huang W J,Cai S H,et al.Comparative study on remote sensing invertion methods for estimating winter wheat LAI[J].Spectroscopy and Spectral Analysis,2014, 34(5):1352-1356.(in Chinese) [15]夏天,吴文斌,周清波,等.冬小麦叶面积指数高光谱遥感反演方法对比[J].农业工程学报,2013,29(3):139-147. Xia T,Wu W B,Zhou Q B,et al.Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing[J]. Transactions of the CSAE,2013, 29(3):139-147.(in Chinese) [16]辛明月,殷红,陈龙,等.不同生育期水稻叶面积指数的高光谱遥感估测模型[J].中国农业气象,2015,36(6):762-768. Xin M Y,Yin H,Chen L,et al.Estimation of rice canopy LAI with different growth stages based on hyperspectral remote sensing data[J].Chinese Journal of Agrometeorology,2015, 36(6):762-768.(in Chinese) [17]康树立,金焱.辽宁省花生产业发展浅析[J].农业科技通讯,2008,(10):23-25. Kang S L,Jin Y.Analysis on the development of peanut industry in Liaoning Province[J].Bullenin of Agricultural Science and Technology,2008,(10): 23-25.(in Chinese) [18] 李子扬,钱永刚,申庆丰,等.基于高光谱数据的叶面积指数遥感反演[J].红外与激光工程,2014, 43(3):944-949. Li Z Y,Qian Y G,Shen Q F,et al.Leaf area index retrieval from remotely sensed hyperspectral data[J].Infrared and Laser Engineering,2014,43(3): 944-949.(in Chinese)