Reliable field-level irrigation data is crucial for sustainable water resource management, improved agricultural monitoring and modelling, as well as supporting informed decision-making and climate resilience strategies. Despite advancements in remotely sensed irrigation mapping, field-level irrigation mapping in temperate regions remains challenging. Previous studies primarily focused on arid and semi-arid regions, while mapping in temperate regions faces challenges, such as frequent cloud cover and limited availability of up-to-date irrigation data for reference. In this study, we assessed the applicability of different time series for irrigation mapping, utilizing Sentinel-2, Sentinel-1 time series and Landsat-based Land Surface Temperature (LST) data over northern Germany. This area is characterized by heterogeneous field sizes, crop patterns, irrigation systems and management. An extensive amount of field-scale irrigation data was obtained directly from farmers through individual data-sharing agreements and consultations and used as a reference for model training and validation. The derived Vegetation Indices (VIs), Tasselled Cap components, and LST were aggregated over the growing season and specific key phenological stages. Subsequently, two machine learning algorithms, Random Forest (RF) and gradient boosting (XGBoost), were tested to classify irrigated areas. Overall accuracy achieved satisfactory levels (approximately 80% in most of the tested scenarios). The performance varied across different regions and showed the significance of availability of observations during the growing season, with the most important variables listed as LST, optical based VIs as well as Sentinel-1 based metrics for specific crops such as maize. The synergistic use of optical, radar and LST data significantly enhanced the classification accuracy, demonstrating the potential of integrating these data sources for improved irrigation mapping in temperate regions. In addition, the findings demonstrate substantial potential for applications in sustainable water resource planning and the use of remotely sensed data for climate adaptation strategies.