Articles | Volume 26, issue 20
Hydrol. Earth Syst. Sci., 26, 5373–5390, 2022
https://doi.org/10.5194/hess-26-5373-2022

Special issue: Experiments in Hydrology and Hydraulics

Hydrol. Earth Syst. Sci., 26, 5373–5390, 2022
https://doi.org/10.5194/hess-26-5373-2022
Research article
27 Oct 2022
Research article | 27 Oct 2022

FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms

Sara Sadri et al.

Data sets

MOD16A3GF MODIS/Terra Net Evapotranspiration Gap-Filled Yearly L4 Global 500 m SIN Grid V00 S. Running, Q. Mu, M. Zhao, and A. Moreno https://doi.org/10.5067/MODIS/MOD16A3GF.006

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Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.