马驿,,
仝春艳,
段博,
蒋琦
武汉大学遥感信息工程学院 武汉 430079
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计量
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被引次数:0
出版历程
收稿日期:2017-09-15
录用日期:2018-01-15
刊出日期:2018-07-01
Estimation of leaf area index of rapeseed and rice based on deflection angle and spectral retrieval algorithm
LIU Yichen,MA Yi,,
TONG Chunyan,
DUAN Bo,
JIANG Qi
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Funds: the National High-tech R&D Program of China (863 Program)2013AA102401
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Corresponding author:MA Yi, E-mail:mayi@whu.edu.cn
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摘要
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。
关键词:油菜/
水稻/
叶面积指数/
高光谱/
偏角光谱检索
Abstract:Leaf area index (LAI) provides insight into productivity, physiological and phenological status of vegetation. The quick and accurate estimation of LAI contributes to growth status diagnosis and yield prediction. A variety of methods have been used for the estimation of LAI, however, the specific spectral bands applied differ widely among the methods and data used. Based on the general shape of the canopy reflectance curve, the spectral angles are found to be of great importance for the LAI estimation. The general objectives of this study were (i) to find informative spectral angles extracted by deflection angle based spectral retrieval (DABSR) and spectral bands retained in the other two common methods, vegetation indices (Ⅵ) and principle component analysis (PCA), for estimating LAI in rapeseed and rice; (ii) to compare the accuracy of the three methods as well as determine whether a robust algorithm for LAI estimation of two various crops can be devised. As the two main crops in China, rapeseed and rice, with different leaf structures as well as canopy architecture, were taken as the experimental subjects. Different nitrogen application rates (0, 45, 90, 135, 180, 225, 270, 360 kg×hm-2) and planting treatments (directed sowing and transplanting) were set for rapeseed, while 45 varieties of rice under the same growing environment were employed in the experiment. It was revealed that, for LAI estimation of rapeseed, the model built with DABSR performed the best as the coefficient of determination (R2), root mean square error (RMSEP) and mean normalized bias (MNB) of the predictive model were 0.74, 0.47 and 0.16 respectively; the model built with PCA was of medium accuracy with 0.73, 0.48 and -0.04 for R2, RMSEP and MNB, respectively. The selected Ⅵ models were of significantly poorer accuracy with 0.61, 0.57 and 0.17 for R2, RMSEP and MNB respectively, as a result of the effect induced by flowers and pods on canopy reflectance spectrum. From the perspective of rice, the relationship model based on DABSR-STEPWISE was of the best accuracy, as the R2, RMSEP and MNB could reach up to 0.70, 0.80 and 0.05. The models built with VIs performed the worst among three methods (R2 ≤ 0.61, RMSEP ≤ 0.92 and MNB ≤ 0.04), while the PCA model performed in between with 0.63, 0.88 and 0.04 for R2, RMSEP and MNB individually. The red edge and the NIR bands were selected in most models and considered the most informative. Among the three methods, DABSR-STEPWISE, proposed on the basis of spectral angle, was the most suitable for estimating LAI of two kinds of crops under different growing environments. The analysis allowed development of universal algorithms for LAI estimation in various crops. Being of high accuracy and high computational efficiency, these findings have significant implications on the development of uniform and robust algorithms, which is crucial for LAI estimation of specie-specific crops.
Key words:Rapeseed/
Rice/
Leaf area index/
Hyperspectral remote sensing/
Deflection angle based spectral retrieval
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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, α1、α2为光谱偏角。
Figure1.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.
下载: 全尺寸图片幻灯片
图2基于特征波长的光谱偏角变量构成
Figure2.Spectral angles formed on the basis of sensitive bands
下载: 全尺寸图片幻灯片
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)
Figure3.Canopy reflectance (A: rice; B: rapeseed) and correlation coefficients between LAI and reflectance (C: rice; D: rapeseed) at different growth periods
下载: 全尺寸图片幻灯片
图4DABSR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)
Figure4.Sensitive bands selected by DABSR with different sample resolutions and various amounts of bands (rice, Nλ denotes the number of sensitive bands)
下载: 全尺寸图片幻灯片表1植被指数计算方法及参考文献
Table1.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] |
| ρNIB、ρred、ρgreen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
Table2.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
Table3.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
Table4.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
Table5.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参考文献
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