Thermal Sensing for Crop Water-Status Assessment and Precision Irrigation: Methods, Scale Effects, and Decision Pathways

Efficient irrigation management requires reliable identification of when and where crop water limitations occur, followed by the conversion of diagnostic information into practical management actions. Thermal sensing provides a non-contact means of observing changes in canopy energy exchange associated with stomatal regulation and reduced transpiration. This review evaluates the use of thermal information across the complete decision pathway from crop water-status assessment to precision irrigation. It first examines the physiological and energy-balance mechanisms underlying canopy temperature responses and discusses how crop type, phenological stage, canopy structure, meteorological conditions, and concurrent stresses influence thermal signals. It then compares ground-based sensors, mobile systems, unmanned aerial vehicles, and satellite observations in terms of temporal continuity, spatial coverage, resolution, and operational constraints. Particular attention is given to radiometric calibration, canopy extraction, mixed pixels, observation timing, reference-temperature construction, and field validation. The performance and limitations of canopy-air temperature differences, the crop water stress index, energy-balance approaches, and data-driven models are subsequently assessed. Methodologically, this review synthesizes literature systematically retrieved from the Web of Science Core Collection, selecting studies that explicitly integrate thermal sensing, crop water status diagnostics, and precision irrigation applications. Evidence across the reviewed studies shows that thermal measurements can support spatially explicit water-stress diagnosis, but fixed thresholds and models developed under individual experimental conditions often exhibit limited transferability across crops, seasons, and environments. More reliable irrigation decisions therefore require the integration of thermal observations with soil moisture, weather data, crop models, and phenological information. Future research should prioritize traceable calibration procedures, dynamic irrigation thresholds, uncertain-ty-aware multimodal models, and multi-season field validation. The agronomic value of thermal sensing should ultimately be evaluated through its effects on irrigation water use, crop yield, water productivity, operational cost, and system robustness.