Irrigation

Drone-based remote sensing offers the ability to rapidly generate ET maps within a season that can be used to make in-season decisions.

Random Forest (RF) and recurrent neural network models such as LSTM predict field-scale ET more accurately than process-based models. Vapor pressure and crop coefficients are key predictors for irrigated crops. ET forecasting for non-irrigated crop requires enhanced vegetation index. Short-term (3-day) forecasts have lower uncertainty, higher accuracy using RF.

A new framework based on a machine learning data-driven network to estimate and forecast evapotranspiration in agricultural areas was proposed.

The data comparison between potatoes and pine evapotranspiration (ET) shows that when there is a big rainfall or irrigation event, the differences between already available ET measurement based on remote sensing models and actual ET measurements are small. However, when soil is dry, ET measurement from Eddy covariance flux tower is more accurate.