Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water management. We reviewed over 200 research articles and discussed the tools, techniques, and challenges associated with spatial and temporal phenotyping for assessing crop water stress, highlighting recent advances and emerging technologies. Emerging technologies such as artificial intelligence (AI) and Internet of Things systems are transforming crop water‐stress phenotyping by enabling real‐time monitoring and robust predictive models. Despite these advancements, challenges persist, including data gaps, platform limitations, and the need for scalable integration frameworks. The review examines key physiological and spectral indicators of crop water stress across multiple spatial and temporal scales using ground‐based sensors, unmanned aerial vehicles, and satellites. It further discusses multiscale phenotyping approaches and data fusion techniques to improve spatial resolution and prediction accuracy. Challenges in harmonizing spatial and temporal data are discussed, along with the need for interdisciplinary collaboration among the phenotyping, modeling, and agronomy domains. The review concludes by identifying future directions, including edge computing, high‐resolution imaging, and robust spatiotemporal phenotyping frameworks to enhance crop water‐stress assessment. By leveraging remote sensing, modeling, and AI, future phenotyping systems can improve water‐stress assessment, advance precision agriculture, and ensure resilience in water‐limited agroecosystems.
Cudjoe et al. (Mon,) studied this question.