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.

Influence of almond’s canopy-induced shadows on actual evapotranspiration estimation by the TSEB model using sUAS multispectral and thermal imagery - M. R. Duran-Gomez; A. Torres-Rua; L. Hipps; W. Kustas; N. E. Bambach; K. Knipper; I. Wright; A. J. McElrone; J. H. Prueger; J. G. Alfieri; C. Coopmans; K. Meza; et al. (Irrigation Science, 2026)
Over the past decade, the estimation of water requirements in almond orchards has improved through the application of remote sensing models like the Two-Source Energy Balance (TSEB) model using various remote sensing platforms. However, there is limited understanding of how canopy-induced shadows influence surface reflectance and thermal infrared (TIR) signals particularly from small Unmanned Aircraft System (sUAS) imagery in energy balance models, and the effect on Latent Heat Flux (LE) estimations. This study evaluates LE estimates from the Priestley-Taylor TSEB model (TSEB-PT) with and without shadow filtering using sUAS-based multispectral and TIR imagery. It establishes a baseline for the impact of shadow filtering on model inputs and performance. Datasets were collected in 2021 and 2022, as part of the USDA led Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) in almonds orchards across California. LAI-2200 C Plant Canopy Analyzer measurements facilitated the calibration of an empirical Leaf Area Index (LAI) model based on canopy fractional cover (FC) and NDVI (R 2 = 0.68). Shadow filtering caused land surface temperature (LST) differences up to 5 °C in young to semi-mature orchards (FC 0.40–0.80). In contrast, mature orchards (FC > 0.80) showed minimal influence due to the limited shadow occurrence on the imagery. Shadows appeared to reduce surface albedo (α alb ), mainly in interrow areas, thereby affecting the absorption of radiation and the partitioning of energy balance components. Their presence in sUAS imagery also hindered canopy delineation, impacting the accuracy of key TSEB inputs derived from canopy physical characteristics. Thus, the influence of shadow on TSEB estimated LE was more significant in lower fractional tree covers. While LE estimated by TSEB-PT without shadow filtering showed better agreement with observations, combining instantaneous TIR imagery with solar-noon shortwave data is recommended for accurate ETa assessment using sUAS datasets. These baseline results can be improved with more advanced formulations, supporting continued research on E/T partitioning and water stress in almond orchards under varying environmental conditions, particularly when there is advection of hot dry air.

A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales - Martha C. Anderson; William P. Kustas; John M. Norman; George T. Diak; Christopher R. Hain; Feng Gao; Yun Yang; Kyle R. Knipper; Jie Xue; Yang Yang; Wade T. Crow; Thomas R.H. Holmes; et al. (Agricultural and Forest Meteorology, 2024)

Performance of Sentinel-2 SAFER ET model for daily and seasonal estimation of grapevine water consumption - Anderson L. S. Safre; Ayman Nassar; Alfonso Torres-Rua; Mayhar Aboutalebi; João C. C. Saad; Rodrigo L. Manzione; Antonio Heriberto de Castro Teixeira; John H. Prueger; Lynn G. McKee; Joseph G. Alfieri; Lawrence E. Hipps; Hector Nieto; 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)