Evaluation of Machine Learning Methods for Estimation of Leaf Chlorophyll Content (LCC) Across 15 Soybean Cultivars During Early Reproductive Stage

We explored the use of remote sensing for improving water use monitoring in agricultural areas by estimating evapotranspiration and validating it against smart field weighing lysimeter data. We found that Surface Energy Balance Algorithm for Land (SEBAL) was the most accurate approach for estimating actual evapotranspiration to help farmers understand crop water needs. Our results support smart tools in irrigation water management and monitoring with the aim to save the scarce water resources.