When scientists look at the Earth’s available water for ecosystem services, they don’t just look at precipitation. They must also account for water moving from the ground to the atmosphere, a process known as evapotranspiration (ET). ET includes evaporation from soil and open water pools such as lakes, rivers, and ponds, as well as transpiration from plant leaves. The difference between precipitation and ET indicates the water balance available for societal needs, including agricultural and industrial production. However, measuring ET is challenging. A new study from the University of Illinois Urbana-Champaign presents a computer model that uses artificial intelligence (AI) for ET prediction based on remote sensing estimates.
The researchers created the “Dynamic Land Cover Evapotranspiration Model Algorithm” (DyLEMa) based on decision-tree machine learning models. This algorithm is intended to predict missing spatial and temporal ET data using trained seasonal machine learning models. DyLEMa was evaluated to the scale of Illinois on a daily 30 x 30-meter grid for 20 years using data from NASA, the U.S. Geological Survey, and the National Oceanic and Atmospheric Administration.
“DyLEMa is much more detailed and complex than other models. It distinguishes between different land uses, including forest, urban, and agriculture, and different crops, such as corn and soybean. The model includes precipitation, temperature, humidity, solar radiation, vegetation stage, and soil properties. This allows us to capture the surface dynamics accurately and predict ET based on multiple variables. This is especially important for agricultural landscapes where crops change rapidly,” said co-author Jorge Guzman, research assistant professor in ABE.
