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基于偏角光谱检索算法的油菜和水稻LAI反演研究

刘怡晨 马驿 仝春艳 段博 蒋琦

刘怡晨, 马驿, 仝春艳, 段博, 蒋琦. 基于偏角光谱检索算法的油菜和水稻LAI反演研究[J]. 中国生态农业学报(中英文), 2018, 26(7): 999-1010. doi: 10.13930/j.cnki.cjea.170846
引用本文: 刘怡晨, 马驿, 仝春艳, 段博, 蒋琦. 基于偏角光谱检索算法的油菜和水稻LAI反演研究[J]. 中国生态农业学报(中英文), 2018, 26(7): 999-1010. doi: 10.13930/j.cnki.cjea.170846
LIU Yichen, MA Yi, TONG Chunyan, DUAN Bo, JIANG Qi. Estimation of leaf area index of rapeseed and rice based on deflection angle and spectral retrieval algorithm[J]. Chinese Journal of Eco-Agriculture, 2018, 26(7): 999-1010. doi: 10.13930/j.cnki.cjea.170846
Citation: LIU Yichen, MA Yi, TONG Chunyan, DUAN Bo, JIANG Qi. Estimation of leaf area index of rapeseed and rice based on deflection angle and spectral retrieval algorithm[J]. Chinese Journal of Eco-Agriculture, 2018, 26(7): 999-1010. doi: 10.13930/j.cnki.cjea.170846

基于偏角光谱检索算法的油菜和水稻LAI反演研究

doi: 10.13930/j.cnki.cjea.170846
基金项目: 

国家高技术研究发展计划(863计划)项目 2013AA102401

详细信息
    作者简介:

    刘怡晨, 主要研究方向为高光谱农业遥感。E-mail:grace_liu@whu.edu.cn

    通讯作者:

    马驿, 主要研究方向为植被高光谱遥感。E-mail:mayi@whu.edu.cn

  • 中图分类号: S127

Estimation of leaf area index of rapeseed and rice based on deflection angle and spectral retrieval algorithm

Funds: 

the National High-tech R&D Program of China (863 Program) 2013AA102401

More Information
  • 摘要: 叶面积指数(LAI)是评价植被长势及产量预测的重要指标,对其进行精准快速估测有助于植被的生长状态诊断和管理。本研究以不同施氮水平、不同栽种方式下的油菜和不同品种水稻为试验对象,基于冠层高光谱曲线形态,引入偏角光谱检索算法(DABSR)提取光谱偏角,同时采用植被指数法和主成分分析法进行对比分析,探索适用于水稻、油菜LAI估算的统一模型构建方法。研究结果表明,估算油菜LAI时,DABSR反演精度较高,预测R2、RMSEP分别为0.74、0.47,偏移量MNB为0.16;主成分分析法反演精度次之,预测R2、RMSEP、MNB分别为0.73、0.48、-0.04;而植被指数法受不同生育期油菜株型、覆盖度影响反演精度普遍较低,精度较高模型的预测R2、RMSEP、MNB分别为0.61、0.57、0.17。在估算水稻LAI时,DABSR反演精度最优,预测R2、RMSEP、MNB可达0.70、0.80、0.05。综合考虑模型的验证精度、特征选择的合理性以及模型计算效率,DABSR偏角光谱检索法估算油菜和水稻LAI具有较高精度,且受施肥水平、栽种方式、生长期等因素影响较小,为构建精确的植被LAI统一估算模型提供了新思路。
  • 图  1  偏角光谱检索法(DABSR)基本流程

    点1、2、3、4为4个特征波长, θ表示角度阈值, α1α2为光谱偏角。

    Figure  1.  Basic processes of deflection angle based spectral retrieval algorithm (DABSR)

    Points 1, 2, 3, 4 represent 4 sensitive bands, θ denotes the threshold set for angles, α1 and α2 refer to the angle calculated.

    图  2  基于特征波长的光谱偏角变量构成

    Figure  2.  Spectral angles formed on the basis of sensitive bands

    图  3  不同生育期作物冠层光谱反射率(A:水稻; B:油菜)及与LAI值的相关性(C:水稻; D:油菜)

    Figure  3.  Canopy reflectance (A: rice; B: rapeseed) and correlation coefficients between LAI and reflectance (C: rice; D: rapeseed) at different growth periods

    图  4  DABSR在不同采样间隔及不同波长数量设定下筛选出的特征波长(以水稻为例, Nλ表示特征波长的个数)

    Figure  4.  Sensitive bands selected by DABSR with different sample resolutions and various amounts of bands (rice, Nλ denotes the number of sensitive bands)

    表  1  植被指数计算方法及参考文献

    Table  1.   Algorithm and references of vegetation indices

    植被指数Vegetation index 计算公式或定义Formulation 文献Reference
    归一化植被指数Normalized difference vegetation index (NDVI) (ρNIR-ρred)/(ρNIR+ρred) [26]
    比值植被指数Ratio vegetation index (RVI) ρNIR/ρred [27]
    差值植被指数Difference vegetation index (DVI) ρNIR-ρred [28]
    非线性植被指数Nonlinear vegetation index (NLI) (ρNIR2-ρred)/(ρNIR2+ρred) [29]
    土壤调节植被指数Soil-adjusted vegetation index (SAVI) (1+L)×(ρNIR-ρred)/(L+ρNIR+ρred) (L=0.5) [30]
    重归一化植被指数Renormalized difference vegetation index (RDVI) $ \sqrt {{\rm{NDVI}} \times ({\rho _{{\rm{NIR}}}} - {\rho _{{\rm{red}}}})} $ [31]
    改进的简单比值指数Modified simple ratio (MSR) $\left[ {({\rho _{{\rm{NIR}}}} - {\rho _{{\rm{red}}}}) - {\rm{1}}} \right]/(\sqrt {{\rho _{{\rm{NIR}}}} + {\rho _{{\rm{red}}}}} + {\rm{1}}) $ [32]
    红边指数Red edge chlorophyll index (CIred edge) (ρNIR/ρred)-1 [6]
    绿边指数Green chlorophyll index (CIgreen) (ρNIR/ρgreen)-1 [6]
    ρNIBρredρgreen分别表示近红波段、红波段和绿波段处的冠层光谱反射率。ρNIR, ρred, ρgreen are canopy reflectance of NIR band, red band and green band, respectively.
    下载: 导出CSV

    表  2  油菜与水稻LAI样本数据方差分析

    Table  2.   ANOVA of rapeseed and rice LAI data

    作物
    Crop
    因变量
    Dependent variable
    因子
    Factor
    平方和
    Sum of squares
    自由度
    Degree of freedom
    均方
    Mean square
    F
    油菜
    Rapeseed
    LAI 栽种方式Planting pattern 0.878 1 0.878 8.922**
    生育期Growth period 10.851 4 2.713 27.580**
    氮水平Nitrogen level 98.304 7 14.043 142.781**
    栽种方式×生育期Planting pattern × growth period 11.027 3 3.676 37.371**
    栽种方式×氮水平Planting pattern × nitrogen level 6.572 7 0.939 9.546**
    氮水平×生育期Nitrogen level × growth period 16.541 28 0.591 6.006**
    栽种方式×生育期×氮水平
    Planting pattern × growth period × nitrogen level
    3.490 21 0.166 1.689**
    水稻
    Rice
    LAI 品种Variety 157.696 46 3.428 8.724**
    生育期Growth period 102.661 2 51.330 130.624**
    **表示在5%水平下显著。** represents the significance at 5% level.
    下载: 导出CSV

    表  3  油菜与水稻冠层高光谱数据PCA筛选后的最优波段排序及其贡献率

    Table  3.   Sorted wavebands selected by contribution rate after PCA filter of canopy hyperspectral data of rapeseed and rice

    作物
    Crop
    权重系数排序
    Eigenvector sequence number
    主成分序号Numbers of PCA
    1 2 3 4
    油菜
    Rapeseed
    1 1 015 505 420 1 300
    2 1 010 510 425 1 200
    3 1 020 515 415 1 205
    4 1 005 520 430 1 195
    5 1 000 580 410 1 210
    6 1 025 575 405 1 190
    7 995 525 450 1 215
    8 990 585 445 1 185
    9 1 030 590 435 1 220
    10 985 570 400 1 295
    方差贡献率Variance contribution rate (%) 59.93 23.03 12.23 3.12
    累计贡献率Accumulative contribution rate (%) 59.93 82.96 95.19 98.31
    水稻
    Rice
    1 790 735 1 300 350
    2 785 730 1 200 355
    3 795 725 1 195 360
    4 815 720 1 205 365
    5 800 740 1 190 370
    6 780 365 1 210 375
    7 805 380 1 295 380
    8 820 360 1 185 385
    9 775 395 1 215 390
    10 810 385 1 180 395
    方差贡献率Variance contribution rate (%) 91.86 5.97 1.63 0.21
    累计贡献率Accumulative contribution rate (%) 91.86 97.84 99.47 99.67
    下载: 导出CSV

    表  4  油菜叶面积指数(Y)估计模型及检验

    Table  4.   Calibration and validation of prediction models of rapeseed LAI (Y)

    方法
    Method
    序号
    Serial number
    变量
    Variable (X)
    模型
    Model
    建模集Calibration (n=144) 预测集Validation (n=72)
    Rcal2 RMSEC Rval2 RMSEP MNB
    植被指数法
    Vegetation index method
    1 NDVI Y=4.64X-1.39 0.37 0.70 0.44 0.69 0.26
    2 RVI Y=0.22X+0.37 0.47 0.64 0.51 0.65 0.33
    3 DVI Y=6.07X-0.42 0.48 0.64 0.61 0.57 0.17
    4 NLI Y=2.34X+0.86 0.46 0.65 0.58 0.60 0.12
    5 SAVI Y=5.25X-0.98 0.47 0.64 0.60 0.59 0.15
    6 RDVI Y=5.60X-1.00 0.47 0.64 0.59 0.59 0.16
    7 MSR Y=0.94X+0.02 0.45 0.65 0.50 0.66 0.29
    8 CIred edge Y=0.22X+0.59 0.47 0.64 0.51 0.65 0.33
    9 CIgreen Y=0.41X+0.51 0.31 0.73 0.23 0.81 0.46
    PCA-STEPWISE 10 (5) 0.70 0.48 0.73 0.48 -0.04
    DABSR-STEPWISE 11 (6) 0.78 0.41 0.74 0.47 0.16
    PAC-STEPWISE:主成分分析法与逐步回归分析法结合; DABSR-STEPWISE: DABSR偏角光谱检索与逐步回归分析结合。
    PAC-STEPWISE: combination of principle component analysis and stepwise regression analysis; DABSR-STEPWISE: combination of deflection angle based spectral retrieval and stepwise regression analysis.
    下载: 导出CSV

    表  5  水稻叶面积指数(Y)估计模型及检验

    Table  5.   Calibration and validation of prediction models of rice LAI (Y)

    方法
    Method
    序号
    Serial number
    变量(X)
    Variable
    模型
    Model
    建模集Calibration (n=90) 预测集Validation (n=45)
    Rcal2 RMSEC Rval2 RMSEP MNB
    植被指数法
    Vegetation index method
    12 NDVI Y=14.61X-7.53 0.60 0.97 0.54 0.98 0.06
    13 RVI Y=0.10X+3.09 0.56 1.02 0.49 1.04 0.03
    14 DVI Y=15.76X-1.63 0.56 1.02 0.54 0.99 0.03
    15 NLI Y=6.64X+0.34 0.62 0.96 0.59 0.94 0.06
    16 SAVI Y=14.36X-4.11 0.61 0.96 0.61 0.92 0.04
    17 RDVI Y=15.26X-4.14 0.61 0.96 0.60 0.92 0.04
    18 MSR Y=0.91X+1.65 0.63 0.94 0.52 1.00 0.04
    19 CIred edge Y=2.38X+1.51 0.71 0.82 0.57 0.95 0.04
    20 CIgreen Y=0.38X+1.98 0.59 0.99 0.43 1.10 0.04
    PCA-STEPWISE 21 (7) 0.76 0.76 0.63 0.88 0.04
    DABSR-STEPWISE 22 (8) 0.77 0.74 0.70 0.80 0.05
    PAC-STEPWISE:主成分分析法与逐步回归分析法结合; DABSR-STEPWISE: DABSR偏角光谱检索与逐步回归分析结合。
    PAC-STEPWISE: combination of principle component analysis and stepwise regression analysis; DABSR-STEPWISE: combination of deflection angle based spectral retrieval and stepwise regression analysis.
    下载: 导出CSV
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  • 收稿日期:  2017-09-15
  • 录用日期:  2018-01-15
  • 刊出日期:  2018-07-01

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