智能手机农作物叶面积指数测量算法改进
Improving the performance of smartphone-derived crop leaf area index
- 2023年27卷第2期 页码：441-455
收稿：2020-10-21，
纸质出版：2023-02-07
DOI： 10.11834/jrs.20210439
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摘要
叶面积指数LAI（Leaf Area Index）是表征植被冠层结构特征的一个重要参数，已经成为多个对地观测系统的陆表参数标准产品，也是定量遥感模型的重要输入参数。快速、准确地获取植被LAI对于开展遥感产品验证、促进遥感模型的发展具有极为重要的意义。随着传感器性能与应用软件功能扩展，智能手机已经成为植被LAI测量的新选择。然而，由于手机成像传感器窄视场角的限制，现有算法依赖于叶倾角分布函数为球型分布的假设，即G函数（单位叶面积在垂直于观测天顶角的平面上的投影）恒等于0.5。因而，传统算法无法解决植被叶倾角分布未知的情况。本文提出了一种基于形状匹配的G函数估算方法，基于有限长度方法和多幅影像间隙率，计算样方内的植被冠层聚集指数，利用泊松分布模型分别得到了植被冠层有效叶面积指数（LAI
eff
）和真实叶面积指数（LAI
tru
），并用黑龙江海伦农场两种农作物类型（玉米和大豆）的破坏性测量得到的时间序列真实LAI数据（LAI
des
）对算法进行了验证。结果表明，算法改进之前的均方根误差（RMSE）分别是0.84（垂直拍摄）和1.33（倾斜57°拍摄），改进后LAI
eff
（有效LAI）和LAI
tru
（真实LAI）的RMSE为分别为0.58（垂直拍摄）和0.56（垂直拍摄）。新算法得到的LAI值在时间序列变化趋势上与实测值更为一致。本文算法扩展了农作物LAI测量方法，为从智能手机影像中快速、准确提取植被LAI提供了可能。后续研究将会从分析外部光照环境变化对测量结果的影响和增加不同植被类型的验证数据两个方向进一步开展工作。
Abstract
As an important parameter of vegetation canopy structure
the Leaf Area Index (LAI) has become a standard land surface parameter product for many earth observation systems and an important input parameter for several quantitative remote sensing models. Rapid and accurate acquisition of vegetation LAI is of great significance for the verification of remote sensing products and promotion of the development of remote sensing models. With the improvement of smartphone sensor performance and the functions of application software
smartphones have become a new alternative to vegetation LAI measurement instruments. However
due to the limitation of the narrow Field Of View (FOV) angle of the smartphone camera sensor
the existing algorithm relies on the assumption that the leaf inclination belongs to the spherical distribution
which is that the
G
function (the projection of a unit leaf area on a plane perpendicular to the observed zenith angle) is equal to 0.5. Therefore
the traditional algorithm cannot solve the problem of unknown leaf inclination distribution. In this paper
a G function estimation method based on shape matching was proposed. Based on the finite length method and the gap fraction of multiple images
the vegetation canopy clumping index in the quadrat was calculated
and the effective LAI (LAI
eff
) and the real LAI (LAI
tru
) were obtained by using the Poisson distribution model. The algorithm was validated by data obtained from destructive measurements (LAI
des
) of two crop types (maize and soybean) at Hailun Farm in Heilongjiang Province
China. The measured time covers the main growth stages of the crop. The results showed that the Root Mean Square Error (RMSE) of the estimated LAI using the algorithm before improvement was 0.84 (vertical shooting) and 1.33 (tilted 57° shooting)
and the RMSE of LAI
eff
and LAI
tru
after the improvement was 0.58 and 0.56
respectively. The LAI values retrieved by the new algorithm are more consistent with the growing trend of LAI in the time series. The algorithm in this paper extends the measurement method of crop LAI
which provides the possibility to quickly and accurately extract vegetation LAI from smartphone-captured images. Further research will be considered in two directions: analyzing the influence of external light environment changes on the measurement results and adding validation data of different vegetation types.
关键词
Keywords
references
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谢蕊 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
焦子锑 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
董亚冬 北京师范大学 遥感科学国家重点实验室;中国科学院空天信息创新研究院
崔磊 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
尹思阳 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
张小宁 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
常雅轩 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
郭静 北京师范大学 地理科学学部遥感科学与工程研究院;北京师范大学 遥感科学国家重点实验室
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北京师范大学 遥感科学国家重点实验室
中国科学院空天信息创新研究院
中国科学院空天信息创新研究院 遥感科学国家重点实验室
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