Partitioning of Eddy Covariance Footprint Evapotranspiration Using Field Data, UAS Observations and GeoAI in the U.S. Chihuahuan Desert

This study proposes a new method for computing transpiration across an eddy covariance footprint using field observations of plant sap flow, phytomorphology sampling, uncrewed aerial system (UAS), deep learning-based digital image processing, and eddy covariance micrometeorological measurements. The method is applied to the Jornada Experimental Range, New Mexico, where we address three key questions: (1) What are the daily summer transpiration rates of Mesquite (Prosopis glandulosa) and Creosote (Larrea tridentata) individuals, and how do these species contribute to footprint-scale evapotranspiration? (2) How can the plant-level measurements be integrated for terrain-wide transpiration estimates? (3) What is the contribution of transpiration to total evapotranspiration within the eddy covariance footprint? Data collected from June to October 2022, during the North American Monsoon season, include hourly evapotranspiration and precipitation rates from the Ameriflux eddy covariance system (US Jo-1 Bajada site) and sap flux rates from heat-balance sensors. We used plant biometric measurements and supervised classification of multispectral imagery to upscale from the patch to footprint-scale estimations. A proportional relationship between the plant's horizontal projected area and the estimated number of water flow conduits was extended to the eddy covariance footprint via UAS data. Our results show that Mesquite's average daily summer transpiration is 2.84 mm/d, while Creosote's is 1.78 mm/d (a ratio of 1.6:1). The summer footprint integrated transpiration to evapotranspiration ratio (T/ET) was 0.50, decreasing to 0.44 during dry spells and increasing to 0.63 following significant precipitation. Further testing of this method is needed in different regions to validate its applicability. With appropriate adjustments, it could be relevant for other areas with similar ecological conditions.