**Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach**

**Florian Ellsäßer 1,\*, Alexander Röll <sup>1</sup> , Joyson Ahongshangbam <sup>1</sup> , Pierre-André Waite <sup>2</sup> , Hendrayanto <sup>3</sup> , Bernhard Schuldt <sup>4</sup> and Dirk Hölscher 1,5**


Received: 25 October 2020; Accepted: 9 December 2020; Published: 12 December 2020

**Abstract:** Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r<sup>2</sup> = 0.87 for trees and r<sup>2</sup> = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration.

**Keywords:** transpiration; method comparison; UAV; oil palm; multiple linear regression; support vector machine; random forest; artificial neural network
