Machine learning revolutionizes methods to quantify the terrestrial biosphere

Researchers from the University establish a new methodology to improve, from space and through machine learning, the observation and analysis of the terrestrial biosphere. This statistical approach will represent a significant advance in monitoring crops and carbon sinks, as well as in predicting floods and droughts. The work has been published in the journal Science Advances. The new machine learning methodology makes it possible to improve the precision in the prediction of key parameters, such as the leaf area index, the gross primary productivity and the fluorescence of the chlorophyll induced by the sun, among others. The field of applications is huge and will be of great use to improve the monitoring of crops and carbon sinks, detect changes and anomalies, droughts and floods.