Quantifying Evapotranspiration and Gross Primary Productivity Across Europe Using Radiative Transfer Process-Guided Machine Learning

Accurately quantifying water and carbon fluxes between terrestrial ecosystems and the atmosphere is highly valuable for understanding ecosystem biogeochemical processes for climate change mitigation and ecosystem management. Remote sensing can provide high spatial and temporal resolution reflectance data of terrestrial ecosystems to support quantifying evapotranspiration (ET) and gross primary productivity (GPP).  Conventional remote sensing-based ET and GPP algorithms are either based on empirical data-driven approaches or process-based models. Empirical data-driven approaches often have high accuracy for cases within the source data domain, but lack the links to a mechanistic understanding of ecosystem processes. Meanwhile, process-based models have high generalizability with incorporating physically based soil-vegetation radiative transfer processes, but usually have lower accuracy. To integrate the strengths of data-driven and process-based approaches, this study developed a radiative transfer process-guided machine learning approach (PGML) to quantify ET and GPP across Europe. Specifically, we used the Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE, van der Tol et al. 2009) radiative transfer model to generate synthetic datasets and developed a pre-trained neural network model to quantify ET and GPP. Furthermore, we utilized field measurements from 63 eddy covariance tower sites from 2016 to 2020 across Europe to fine-tune the neural networks with incorporating physical laws into the cost function. Results show that PGML can significantly improve the SCOPE simulations of net radiation (R2 from 0.91 to 0.96), sensible heat fluxes (R2 from 0.43 to 0.77), ET (R2 from 0.61 to 0.78), and GPP (R2 from 0.72 to 0.78) compared to eddy covariance observations. This study highlights the potential of PGML to integrate machine learning and radiative transfer models to improve the accuracy of land surface flux estimates for terrestrial ecosystems.