Crops ›› 2021, Vol. 37 ›› Issue (2): 183-190.doi: 10.16035/j.issn.1001-7283.2021.02.027

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Maize Canopy FAPAR Remote Sensing Estimation Combining Vegetation Indexes and Texture Characteristics

Wang Siyu1,2(), Nie Chenwei2, Yu Xun2,3, Shao Mingchao2,4, Wang Zixu2,5, Nuremanguli· Tuohuti2,6, Liu Yadong2, Cheng Minghan2,7, Guan Yunlan1(), Jin Xiuliang2()   

  1. 1Faculty of Geomatics, East China University of Technology, Nanchang 330013, Jiangxi, China
    2Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    3Henan Polytechnic University, Jiaozuo 454003, Henan, China
    4School of Earth Science and Engineering, Hebei University of Engineering, Handan 056006, Hebei, China
    5School of Earth Sciences and Resources, Chang'an University, Xi’an 710061, Shaanxi, China
    6School of Earth Sciences, China University of Geosciences, Wuhan 430074, Hubei, China
    7College of Agricultural Engineering, Hohai University, Nanjing 210098, Jiangsu, China
  • Received:2020-11-15 Revised:2021-01-09 Online:2021-04-15 Published:2021-04-16
  • Contact: Guan Yunlan,Jin Xiuliang E-mail:wsy_427@163.com;275332432@qq.com;jinxiuliang@caas.cn

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Abstract:

The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the most important parameters reflecting the crop yields. The unmanned aerial vehicle (UAV) remote sensing, which can obtain high-resolution vegetation canopy spectral information quickly and nondestructively, has become an important method of inverting the physicochemical parameters of crops. Taking maize with different sowing dates as the research object, this research extracted vegetation indexes (VIs) and texture features based on the UAV multispectral images. After that, a partial least squares regression (PLSR) method was used to invert maize FAPAR combining the two indexes, and compared with the traditional methods using VIs or texture features alone. The results showed that the accuracy of FAPAR inversion by using VIs alone (the validated RMSE is as low as 0.0733, and the validated rRMSE is as low as 8.66%) was higher than that by using texture feature alone (the validated RMSE is as low as 0.0950, and the validated rRMSE is as low as 11.23%). Moreover, the PLSR method was more accurate than the traditional methods in estimating FAPAR by VIs or texture features alone (the validated RMSE of VIs and texture features is as low as 0.0677 and 0.0524, and the validated rRMSE is as low as 8.01% and 6.19%). The accuracy of combining VIs and texture features using the PLSR method (the validated RMSE is as low as 0.0472, and the rRMSE is as low as 5.57%) was higher than that using VIs or texture features alone. The research indicated that it is feasible to invert FAPAR of maize canopy by combining VIs and texture features using the PLSR method, which provides a new idea for crop FAPAR remote sensing inversion.

Key words: FAPAR, Multispectral images, Vegetation index, Texture features, PLSR

Table 1

Statistics of in situ FAPAR"

日期
Date		 品种
Variety		 样本数
Sample number		 取值范围(×10-2)
Value range
2020-07-23 丰垦139 21 51.62~92.49
京农科728 21 50.07~93.82
郑单958 21 61.20~94.15
2020-08-02 丰垦139 24 38.25~94.19
京农科728 24 44.10~97.42
郑单958 24 46.05~97.43

Table 2

Fourteen vegetation indices and their formulas"

Table 3

Features of the eight textures"

Table 4

The best regression test results of maize FAPAR estimating using each vegetation indice"

植被指数
Vegetation indice		 回归模型
Regression model		 建模Modeling 验证Validating
R2(×10-2) RMSE(×10-2) rRMSE(%) R2(×10-2) RMSE(×10-2) rRMSE(%)
DVI y=-8.8039x2+6.3709x-0.2476 66.13 7.98 9.42 66.35 9.10 10.75
GNDVI y=-1.5792x2+2.4437x-0.0391 78.52 6.35 7.492 78.00 7.33 8.66
MTCI y=-0.1751x2+0.4515x+0.6248 61.13 8.54 10.08 70.63 7.90 9.34
TVI y=0.5125ln(x)-0.6934 56.48 9.04 10.67 57.39 10.05 11.88
MTVI2 y=-1.2913x2+2.3547x-0.1493 71.74 7.28 8.59 66.93 9.17 10.84
OSAVI y=1.169x1.0069 72.18 7.24 8.54 72.18 7.80 9.22
NDVI y=0.9303x1.2449 73.74 7.03 8.30 74.97 7.59 8.97
RVI1 y=0.0497ln(x)+0.636 33.95 11.14 13.14 37.53 11.14 13.16
RVI2 y=-0.0024x2+0.062x+0.5651 44.55 10.20 12.04 52.06 9.80 11.58
MNLI y=-5.4349x2+2.9606x+0.5071 77.83 6.45 7.61 75.59 8.59 10.15
SAVI y=1.3688x0.8208 66.58 7.93 9.36 67.87 8.21 9.70
MSR y=0.0908ln(x)+0.672 40.98 10.53 12.42 46.45 10.31 12.18
NLI y=0.3779x+0.5515 74.54 6.91 8.15 74.59 7.42 8.77
RDVI y=1.487x0.9052 68.16 7.74 9.13 69.25 8.08 9.55

Fig.1

Regression relationship of the FAPAR to GNDVI in maize"

Table 5

The best regression test results of maize FAPAR estimating using each texture"

纹理特征
Texture		 回归模型
Regression model		 建模Modeling 验证Validating
R2(×10-2) RMSE(×10-2) rRMSE(%) R2(×10-2) RMSE(×10-2) rRMSE(%)
均值Mean y=-0.0031x2+0.0493x+0.7169 58.73 8.80 10.38 55.53 9.50 11.23
方差Variance y=-0.0003x2+0.0192x+0.644 18.44 12.38 14.61 25.00 12.28 14.51
协同性Homogeneity y=-3.1473x2+1.8839x+0.6007 5.12 13.35 15.75 19.27 13.06 15.43
对比度Contrast y=-0.0005x2+0.0235x+0.6542 16.42 12.53 14.78 22.56 12.50 14.77
相异性Dissimilarity y=-0.0199x2+0.1895x+0.5184 11.85 12.87 15.19 19.65 12.79 15.11
信息熵Entropy y=-2.1224x2+14.464x-23.756 12.53 12.82 15.13 36.23 11.45 13.53
二阶矩Second moment y=-399x2+38.016x-0.0288 6.75 13.23 15.61 39.64 12.26 14.49
相关性Correlation y=-14.067x2+19.182x-5.6254 21.68 12.13 14.31 29.39 11.94 14.11

Fig.2

Regression relationship of the FAPAR to mean of texture feature in maize"

Table 6

Validation results of FAPAR estimation using PLSR method"

指标
Indice		 建模Modeling 验证Validating
R2
(×10-2)		 RMSE
(×10-2)		 rRMSE
(%)		 R2
(×10-2)		 RMSE
(×10-2)		 rRMSE
(%)
植被指数
Vegetation indice		 82.15 8.19 9.66 81.40 6.77 8.01
纹理Texture 89.12 6.39 7.54 87.05 5.24 6.19
结合Combination 94.39 4.59 5.41 90.81 4.72 5.57

Fig.3

Validation results of FAPAR estimation using PLSR method combined with vegetation index and texture features"

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