Linking Sap Flow Measurements with Earth Observations

TL;DR

This study demonstrates that Sentinel-2 satellite data, combined with machine learning, can effectively upscale sap flow measurements to estimate canopy transpiration across forests, aiding climate resilience assessments.

Contribution

The paper introduces a machine learning approach linking earth observation data with sap flow measurements for estimating forest transpiration at the canopy scale.

Findings

Sentinel-2 data achieved R2 between 0.57 and 0.80 in models.

Canopy transpiration can be reliably modeled using satellite and meteorological data.

Approach has potential for large-scale forest water flux monitoring.

Abstract

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.