Articles | Volume 26, issue 20
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, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood

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

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.