Assessing the Performance of Multiple Satellite-Based Evapotranspiration Models over Tropical Forests

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration (ET) being a key ecohydrological process. However, monitoring ET over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of ET remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based ET models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on ET patterns. Our results showed a good agreement between modeled and observed ET using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day⁻¹ for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on ET, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor ET in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions.