STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil-water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB- 1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, lambda ET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

Automated summary

Improvement in ET estimates, particularly in Seasonally Dry Tropical Forests (SDTF), has been achieved through the use of remote sensing (RS) products based on multispectral and thermal sensors. The development of a RS-based SEB model called STEEP has addressed the limitations of existing models by considering the specificities of SDTF, such as contrasting phenological phases and soil-water availability. The STEEP model demonstrated enhanced accuracy in ET estimates during the dry season, outperforming traditional SEB models and comparable to global ET products.