A machine learning framework for California vineyard water status monitoring using sUAS Imagery and short-term meteorological data - Rui Gao; Maria Mar Alsina; Alfonso F. Torres-Rua; Lawrence Hipps; William P. Kustas; Martha Anderson; Héctor Nieto; Andrew J. McElrone; Kyle Knipper; Nicolas Bambach Ortiz; Sebastian J. Castro; John H. Prueger; et al. (Irrigation Science, 2026)
Efficient irrigation management is essential for sustainable crop production under increasing temperatures and tightening water supplies. In vineyards, water status significantly influences vine growth, yield, and fruit quality, and deficit irrigation is often used to impose controlled stress while avoiding damaging levels of water limitation. This creates a practical need for routine, field-scale monitoring of vine water status. In this study, we developed an operational machine-learning framework to estimate grapevine leaf water potential (Ψ leaf ) by integrating daytime sUAS thermal imagery with short-term local meteorological information. When all candidate predictors were included, the trained eXtreme Gradient Boosting (XGB) model achieved R 2 = 0.71, RMSE = 0.14 MPa , and bias = − 0.06 MPa on the independent test dataset. A simplified XGB model using a compact predictor set–maximum air temperature in the 24 h prior to flight, air temperature at flight time, their difference, and canopy temperature – achieved R 2 = 0.63, RMSE = 0.16 MPa , and bias = − 0.06 MPa , with performance not significantly different from the full model at α = 0.05. This reduced-feature formulation supports vineyard-scale Ψ leaf estimation and mapping while maintaining strong predictive skill and low computational burden. Our research highlights the potential for broader applicability, particularly for monitoring rapidly developing hot and dry conditions and supporting adaptive water resource management.

Correction to: Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model - Karem Meza; Alfonso F. Torres-Rua; Lawrence Hipps; William P. Kustas; Rui Gao; Laura Christiansen; Kelly Kopp; Hector Nieto; Vicente Burchard-Levine; M. Pilar Martín; Calvin Coopmans; Ian Gowing (Irrigation Science, 2025)

Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model - Karem Meza; Alfonso F. Torres-Rua; Lawrence Hipps; William P. Kustas; Rui Gao; Laura Christiansen; Kelly Kopp; Hector Nieto; Vicente Burchard-Levine; M. Pilar Martín; Calvin Coopmans; Ian Gowing (Irrigation Science, 2025)

Downscaling UAV land surface temperature using a coupled wavelet-machine learning-optimization algorithm and its impact on evapotranspiration - Mahyar Aboutalebi; Alfonso F. Torres-Rua; Mac McKee; William P. Kustas; Hector Nieto; Maria Mar Alsina; Alex White; John H. Prueger; Lynn McKee; Joseph Alfieri; Lawrence Hipps; Calvin Coopmans; et al. (Irrigation Science, 2022)

LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning - Rui Gao; Alfonso F. Torres-Rua; Mahyar Aboutalebi; William A. White; Martha Anderson; William P. Kustas; Nurit Agam; Maria Mar Alsina; Joseph Alfieri; Lawrence Hipps; Nick Dokoozlian; Hector Nieto; et al. (Irrigation Science, 2022)

Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration - Mahyar Aboutalebi; Alfonso F. Torres-Rua; William P. Kustas; Héctor Nieto; Calvin Coopmans; Mac McKee (Irrigation Science, 2019)