ABSTRACT

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R ² = 0.26, RMSE = 3.9 µg/cm²), CCC (R ² = 0.65, RMSE = 0.33 g/m²), and LAI (R ² = 0.47, RMSE = 0.73 m²/m²), comparable to the estimations based on the machine learning method Random forest regression of LCC (R ² = 0.34, RMSE = 4.06 µg/cm²), CCC (R ² = 0.65, RMSE = 0.34 g/m²), and LAI (R ² = 0.47, RMSE = 0.75 m²/m²). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

摘要

在为生物多样性监测和气候变化研究制定指标时，对植被生化和生物物理变量的评估非常有用。在这里，我们比较了优点函数的辐射传递模型（RTM）反转和在RTM模拟数据集上训练的五种机器学习算法，这些算法预测了三种植物性状的叶绿素含量（LCC），冠层叶绿素含量（CCC）和叶面积指数（ LAI），在温带混交林中。2017年7月13日在德国巴伐利亚森林国家公园采集的Sentinel-2光谱数据预测了这三种植物性状的检索方法的准确性，同时采用了现场采集的评估方法。使用优值函数的RTM反演导致LCC（R ² = 0.26，RMSE = 3.9 µg / cm ²），CCC（R ²  = 0.65，RMSE = 0.33 g / m ²）和LAI（R ²  = 0.47，RMSE = 0.73 m ² / m ²），与估计值相当基于机器学习方法的LCC（R ²  = 0.34，RMSE = 4.06 µg / cm ²），CCC（R ²  = 0.65，RMSE = 0.34 g / m ²）和LAI（R ²  = 0.47，均方根误差= 0.75 m ² / m ²）。几种机器学习算法还产生了与使用优值函数的RTM反演相似的准确性和鲁棒性。在合成数据集上训练的回归方法的性能显示了从遥感数据跨不同植物功能类型快速，准确地绘制植物性状的希望。