This publication describes an accurate plant evapotranspiration model based off remote sensing and meteorological data (Crop RS-Met).

Helman David, Bonfil David J and Lensky Itamar M. (2019). Crop RS-Met: A biophysical evapotranspiration and root-zone soil water content model for crops based on proximal sensing and meteorological data. Agricultural Water Management, 211, 210–219.

This publication looks into the use of a satellite derived statistic, NDVI (normalized difference vegetation index) and how it can be applied to fire risk mapping using machine learning to increase accuracy.

Michael Yaron, Helman David, Glickman Oren , Gabay David , Brenner Steve, Lensky Itamar M (2021). Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Science of the Total Environment.

Helman David, Bahat Idan, Netzer Yishai, Ben-Gal Alon, Alchanatis Victor, Peeters Aviva and Cohen Yafit (2018). Using Time Series of High-Resolution Planet Satellites Images to Monitor Grapevine Stem Water Potential in Commercial Vineyards. Remote Sensing, 10(10), 1615, doi: https://doi.org/10.3390/rs10101615.

Dr. David Helman's (my mentor) Lab website: http://davidhelman.weebly.com/