In the realm of agritech and environmental monitoring, a groundbreaking development has emerged from the University of Campania “Luigi Vanvitelli” in Italy. Led by Jerzy Piotr Kabala, researchers have introduced geeSSEBI, a novel implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, designed to estimate evapotranspiration (ET) using Google Earth Engine and Landsat data. This innovation holds significant promise for the energy sector, particularly in optimizing water resource management and enhancing agricultural practices.
Evapotranspiration, the sum of water evaporation from soil and transpiration from plants, is a critical component of the water cycle and surface energy balance. Accurately mapping ET is essential for understanding climate change impacts and managing water resources efficiently. Traditional methods for measuring ET, such as eddy covariance and lysimeters, are costly and limited to small areas. Remote sensing offers a more scalable solution, but existing models often struggle with practical constraints and data scarcity.
Enter geeSSEBI, a tool that leverages the power of Google Earth Engine to process Landsat imagery and ERA5-land radiation data. This implementation not only simplifies the application of the S-SEBI model but also makes it accessible to a broader audience, including those in data-scarce regions. “The main strength of this model is that it requires only radiation data, besides the image acquired in the shortwave and infrared wavelengths,” explains Kabala. “This makes it suitable for areas where there is large uncertainty in the meteorological conditions.”
The model’s accuracy was rigorously tested against multiyear data from four eddy covariance towers belonging to the ICOS network, representing both forest and agricultural landscapes. The results were promising, with an RMSE of approximately 1 mm/day and a significant correlation with observed values. “The model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales,” Kabala notes. This performance is crucial for applications in the energy sector, where understanding water dynamics can optimize hydropower generation and irrigation systems.
One of the key innovations of geeSSEBI is its ability to upscale daily ET estimates to monthly timescales, making it a valuable tool for long-term water resource monitoring and ecosystem studies. This feature is particularly important for energy companies looking to integrate water management strategies with renewable energy production.
The development of geeSSEBI aligns with a broader trend of implementing surface energy balance models within the Google Earth Engine platform. This trend has significantly increased the accessibility and scalability of such models, enabling large-scale evaluations under diverse conditions. The open-source nature of geeSSEBI, published in Remote Sensing, allows for further refinement and improvement by the scientific community. As Kabala points out, “The widespread availability of the model code may empower future users to refine and improve the models further.”
The implications of this research extend beyond immediate applications. As the energy sector continues to evolve, the need for precise and scalable tools to monitor and manage water resources becomes increasingly critical. geeSSEBI represents a significant step forward in this direction, offering a user-friendly and powerful tool for evapotranspiration estimation. Its success could pave the way for similar implementations, enhancing our understanding of water dynamics and their impact on energy production.