Remote sensing technologies and methodological trends for precision irrigation and crop water status assessment: A systematic review

Irrigated agriculture is increasingly challenged by climate change, water scarcity, and the need to improve irrigation efficiency, particularly in arid and semi-arid regions. In this context, remote sensing technologies have become widely adopted within precision agriculture frameworks to support crop water status assessment and irrigation management through optical, thermal, and hyperspectral observations acquired from satellite, unmanned aerial vehicle/remotely piloted aircraft system (UAV/RPAS), and proximal sensing platforms. In this review, precision agriculture is understood as the use of spatially and temporally explicit information to support site-specific crop and resource management. Within this framework, precision irrigation refers more specifically to the use of such information to optimise irrigation timing, amount, spatial distribution, or deficit-irrigation strategies according to crop water requirements and within-field variability. The rapid expansion of sensing technologies and analytical approaches, including machine learning and data-driven modelling, has generated a diverse and methodologically heterogeneous body of research focused on operational precision irrigation applications. This systematic review aims to synthesise technological and methodological patterns in the use of remote sensing for precision irrigation and crop water status assessment. This review integrates evidence across platforms, sensor types, analytical approaches, vegetation-index functional groups, irrigation systems, crop types, and climatic contexts within a precision irrigation-oriented framework. Studies published between 2015 and 2024 were retrieved from the Scopus and Web of Science databases using a structured search strategy focused on operational and technology-oriented agricultural applications. A total of 108 articles were analysed and categorised according to climate region, crop type, irrigation system, sensing platform, sensor type, vegetation index groups, and analytical methods. Multiple Correspondence Analysis (MCA) was used to explore category proximities and co-occurrence patterns within the reviewed corpus. The results identify recurrent combinations of sensing platforms, sensor types, analytical methods, and agronomic contexts. UAV/RPAS platforms (63% of studies) and multispectral sensors (69%) predominated, while regression-based approaches (51%) were more frequently reported than machine learning methods (34%). Exploratory analyses identified co-occurrence patterns involving UAV/RPAS platforms, multispectral sensors, and Structure/Cover indices, as well as satellite platforms and Water/Moisture indices. Thermal and hyperspectral sensors were less frequently represented than multispectral sensors, with thermal observations providing complementary information relevant to crop water status assessment. Overall, the findings highlight the methodological diversity of remote sensing applications within precision irrigated agriculture, while also identifying important limitations related to uncertainty assessment, methodological transferability, and operational integration into irrigation decision-support systems. The review provides a structured framework to support future research and the development of more scalable and operational precision irrigation strategies under increasing climate and water constraints.