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叶面积指数(leaf are index, LAI)是表征植被冠层结构特征点的一个重要参数，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叶面积指数测量仪器的新选择。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(LAIeff)和真实叶面积指数(LAItru)。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(LAIdes)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LAIeff和LAItru的RMSE为分别为0.58和0.56。新算法得到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叶面积指数测量方法，为从智能手机影像中快速、准确提取植被叶面积指数提供了可能。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。
As an important parameter of vegetation canopy structure, Leaf Are Index (LAI) has become one of standard land surface parameter products for many earth observation systems and as 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, smartphone has become a new alternative of 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, 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 quadrat was calculated, and the effective LAI (LAIeff) and the real LAI(LAItru) were obtained respectively by using the Poisson distribution model. The algorithm was validated by data obtained from destructive measurements (LAIdes ) 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 were 0.84 (vertical shooting) and 1.33 (tilted 57° shooting), and the RMSE of LAIeff and LAItru 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 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. The further research will be considerate in two directions: analyzing the influence of external light environment changes on the measurement results and adding validation data of different vegetation types.