Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes Over Europe

Abstract

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m−2). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m−2. Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m−2 higher) for the PT‐JPL ET estimates. Substantial overestimation (>80 W m−2) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing

“…Higher KGE values indicate better model performance, with 1 indicating a perfect model. The agreement between the ET estimates and flux measurements becomes more consistent as the KGE approaches 1 (Hu et al, 2023). The performance of ECOET30_PTJPL was inter-compared with the performance of DNN postprocessed 30-m ECOSTRESS ET estimates (hereafter ECOET30m_PTJPL_NN) as well as the existing 70-m ECOSTRESS Level 3 PT-JPL ET (ECO3ET70m_PTJPL).…”

Soil and Atmospheric Drought Explain the Biophysical Conductance Responses in Diagnostic and Prognostic Evaporation Models Over Two Contrasting European Forest Sites

“…The divergence between prognostic and diagnostic evaporation model results prompts further inquiry into the structural complexity of biophysical conductance (Trebs et al., 2021) that needs resolution for large‐scale application of these models in understanding vegetation drought response characteristics. Increased attention is necessary to provide optimal conditions for SEB models to understand evaporation responses in complex environments (Hu et al., 2023; Renner et al., 2019).…”

Integrating machine learning with analytical surface energy balance model improved terrestrial evaporation through biophysical regulation

“…Utilizing an iterative approach that integrates four biophysical theories to solve for 𝑔 A and 𝑔 Surf , e.g., Surface Energy Balance theory 5 , the first-order PM equation 2 , the advection-aridity hypothesis 9 , and the Bowen ratio equation 10 , STIC consistently demonstrated stable and superior performance compared to other ET models that use Monin-Obukhov Similarity Theory and associated empirical functions to model [11][12][13][14] . However, the overestimation of ET in arid and semi-arid regions underscores the need for further enhancements in calculating the critical surface moisture factor (M) in the STIC model for better biophysical regulation in water-scarce conditions 11,12 .…”