While single-tree transpiration is challenging to compare with earth observation, canopy scale data are suitable for this purpose. To test the potentialities of the second approach, we equipped the trees at two measurement sites with sap flow sensors in spruce forests. The sites have contrasting topography. The measurement period covered the months between June 2020 and January 2021. To link plot scale transpiration with earth observations, we utilized Sentinel-2 and local meteorological data. Within a machine learning framework, we have tested the suitability of earth observations for modelling canopy transpiration. The R2 of the cross-validated trained models at the measurement sites was between 0.57 and 0.80. These results demonstrate the relevance of Sentinel-2 data for the data-driven upscaling of ecosystem fluxes from plot scale sap flow data. If applied to a broader network of sites and climatic conditions, such an approach could offer unprecedented possibilities for investigating our forests' resilience and resistance capacity to an intensified hydrological cycle in the contest of a changing climate.
翻译:虽然单树翻转与地球观测相比具有挑战性,但冠状数据适合这一目的。为测试第二种方法的潜力,我们为两个测量点的树木配备了采样森林中的树苗流感应器。这些地点的地形对比不同。测量期涵盖2020年6月至2021年1月的几个月。为了将地块浮转与地球观测联系起来,我们使用了哨兵2和当地气象数据。在一个机器学习框架内,我们测试了地球观测是否适合模拟采样采样。测量点经过交叉验证的经过培训的模型的R2在0.57至0.80之间。这些结果表明,Sentinel-2数据对于由地块缩放流数据驱动的生态系统通量的数据升级具有相关性。如果应用到更广泛的地点网络和气候条件,这种方法可以提供前所未有的可能性,用以调查我们的森林的复原力和抗力,以在变化气候中加剧的水文循环的抗力。