基于多角度成像数据的大豆冠层叶绿素密度反演

Chlorophyll Density Inversion of Soybean Canopy Based on Multi-angle Imaging Hyperspectral Data

摘要:

利用多角度观测系统采集田间不同生育期大豆冠层的图谱数据，通过提取影像中土壤背景、大豆植株、光照叶片等不同目标地物的反射光谱，对比分析不同观测角度下成像光谱数据反演大豆冠层叶绿素密度的效果，探讨土壤、阴影叶片及角度变化对群体叶绿素密度反演的影响。结果表明：（0°, 20°, 40°, 60°）的天顶角组合有最高的预测模型决定系数（R2为0.834）和最小的均方根误差（RMSE为613）；（20°, 40°, 60°）天顶角组合的决定系数值高于（0°, 20°, 40°）的组合，且在混合植被、纯植被、光照植被3类数据中有一致的趋势。40°天顶角是反演叶绿素密度的最优角度。0°方位角（太阳主平面的后向观测）是反演叶绿素密度的最优角度。天顶角变化是影响大豆冠层叶绿素密度反演的主要因素。

Abstract:

Hyperspectral images of soybean at different growth stages were collected by using multi-angular observation system in the field. Reflectance spectra of different objectives including soil, soybean plants, and illuminated leaves were extracted and they were used to retrieve the chlorophyll density of soybean canopy by using reflectance spectra at different observation angles, and furthermore explored detailed model inversion effects of soybean group for ground soil, shadow leaves, and angle changes. Studies have been showed that when the angles combination was 0°, 20°, 40° and 60°, there were the highest determination coefficient of 0.834, and the lowest root mean square error of 6.13 for prediction model of chlorophyll density. Meanwhile, the zenith angle and azimuth angle was 40° and 0°, respectively, which had the best inversion model. The changes of zenith angles were the most key factor to affect assessment accuracy of chlorophyll density for soybean canopy.