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作物杂志,2021, 第2期: 183–190 doi: 10.16035/j.issn.1001-7283.2021.02.027

所属专题： 玉米专题

• 农业信息技术 • 上一篇    下一篇

结合植被指数与纹理特征的玉米冠层FAPAR遥感估算研究

王思宇1,2(), 聂臣巍2, 余汛2,3, 邵明超2,4, 王梓旭2,5, 努热曼古丽·托乎提2,6, 刘亚东2, 程明瀚2,7, 官云兰1(), 金秀良2()   

  1. 1东华理工大学测绘工程学院,330013,江西南昌
    2中国农业科学院作物科学研究所,100081,北京
    3河南理工大学,454003,河南焦作
    4河北工程大学地球科学与工程学院,056006,河北邯郸
    5长安大学地球科学与资源学院,710061,陕西西安
    6中国地质大学（武汉）地球科学学院,430074,湖北武汉
    7河海大学农业工程学院,210098,江苏南京
  • 收稿日期:2020-11-15 修回日期:2021-01-09 出版日期:2021-04-15 发布日期:2021-04-16
  • 通讯作者: 官云兰,金秀良
  • 作者简介:王思宇,主要从事地理信息系统和农业遥感研究,E-mail： wsy_427@163.com
  • 基金资助:
    国家自然科学基金-面上项目(42071426);中国农业科学院基本科研业务费专项院级统筹项目(Y2020YJ07);中国农业科学院科技创新工程和基本科研业务费专项项目(ICS2020YJ01BX)

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

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摘要：

光合有效辐射吸收比率（fraction of absorbed photosynthetically active radiation,FAPAR）是反映作物产量的重要参数之一。无人机遥感能够快速无损地获取高分辨率植被冠层光谱信息,已成为进行物理化参数反演的重要手段。以不同播期玉米为研究对象,基于无人机搭载多光谱传感器,提取植被指数与植被纹理特征,使用偏最小二乘（partial least squares regression,PLSR）方法将二者结合反演玉米FAPAR,并与传统单独使用植被指数或植被纹理特征反演植被FAPAR的方法进行比较。结果表明：使用传统方法单独利用植被指数反演FAPAR（验证RMSE最低为7.33×10-2,rRMSE最低为8.66%）的效果比单独利用纹理特征反演FAPAR（验证RMSE最低为9.50×10-2,rRMSE最低为11.23%）的精度更高;使用PLSR方法单独利用植被指数或纹理特征估算FAPAR的效果比传统方法精度更高（植被指数与纹理特征的验证RMSE最低分别为6.77×10-2和5.24×10-2,rRMSE最低分别为8.01%和6.19%）;使用PLSR方法将植被指数与纹理特征相结合估算FAPAR（验证RMSE最低为4.72×10-2,rRMSE最低为5.57%）的效果比单独使用植被指数或纹理特征估算FAPAR的精度更高。综上,使用PLSR方法将植被指数和植被纹理特征相结合来反演玉米冠层FAPAR可行,为作物FAPAR遥感反演研究提供了新的思路。

关键词: FAPAR, 多光谱影像, 植被指数, 纹理特征, PLSR

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

表1

地面测量FAPAR数据统计信息

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

表2

选取的14种植被指数及其计算公式

表3

选取的8种纹理特征

表4

各植被指数估算玉米FAPAR的最佳回归检验结果

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

图1

玉米FAPAR与GNDVI的回归关系

表5

各纹理信息估算玉米FAPAR的最佳回归检验结果

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

图2

玉米FAPAR与纹理特征Mean的回归关系

表6

利用PLSR方法估算玉米FAPAR的验证结果

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

图3

使用PLSR方法结合植被指数与纹理特征估算的FAPAR验证结果

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