基于PROSAIL模型的山地草原叶面积指数高光谱反演

【目的】 研究基于PROSAIL模型监测天然草地的动态变化,掌握草地的质量与数量。【方法】 研究使用地物光谱仪连续3年在天山北坡中段的2个山地草原样区采集光谱数据和配套数据,基于PROSAIL模型进行冠层LAI的高光谱反演,重点研究应用不同代价函数、植被种类变化对反演精度的影响。【结果】 多数代价函数反演LAI的决定系数(R²)在0.54~0.55,均方根误差(RMSE)在0.23~0.25,归一化均方根误差(NRMSE)在17~19。在9个来自不同统计类型的代价函数中,常用的RMSE代价函数的反演精度相对不高。将获取的427个样方数据依据种类数分成组,然后用PROSAIL进行LAI反演。种类数越多,RMSE在增大,R²在减少,反演精度越差。但精度的下降幅度不是均匀的,种类数≤2的组和种类数≤3的组之间精度差异最大。【结论】 在利用物理模型反演天然草地的叶面积指数时,不同代价函数获得的反演精度差别比较大;随着植被种类数量的增多,反演的精度是下降的。

Hyperspectral Inversion of Leaf Area Index in Mountain Steppe Ecosystems Based on the PROSAIL Model

【Objective】 Therefore, the monitoring of dynamic changes in natural grasslands is important for both ecology and agriculture. 【Method】 This study focused on the impact of changes in plant species on the inversion accuracy.Spectral data for two mountain steppe plots were collected using a spectrometer over three years and concurrently, supporting data were obtained. 【Result】 The results showed that when the physical model PROSAIL was used to invert the mountain steppe LAI, the inversion error was very large for a single solution.Adding random noise and averaging multiple solutions when solving significantly improved the LAI inversion accuracy.The coefficient of determination (R²) for the LAI inversion for most cost functions was between 0.54 and 0.55, root mean square error (RMSE) was between 0.23 and 0.25, and normalized root mean square error (NRMSE) was between 17 and 19.In nine cost functions from different statistical types, the inversion accuracy of the commonly used RMSE cost function was relatively low.The 427 samples obtained were divided into four groups according to the number of species.The inversion results indicated that a greater number of species corresponded to an increasing RMSE, decreasing R², and poorer LAI inversion accuracy, although the precision decrease was not uniform.Groups with up to two species and groups with up to three species had the greatest difference in inversion accuracy. 【Conclusion】 When using physical model to invert LAI of natural grassland, the precision of inversion obtained by different cost functions is different.As the number of vegetation species increases, the precision of inversion decreases.