Refining European Crop Mapping Classification Through the Integration of Permanent Crops: A Case Study in Rapidly Transitioning Irrigated Landscapes Induced by Dam Construction

Monitoring agricultural land in regions undergoing rapid change is essential for supporting management, policy development, and biodiversity conservation. Dam construction and associated irrigation systems drive land use change transitions from annual to permanent crops and intensify cultivation systems. Mapping crop types at the parcel level, particularly permanent crops, is therefore critical. The EU Crop Map 2018, the first attempt to map annual crops across the European Union using remote sensing and machine learning, aggregates permanent crops into the generic class “shrublands and woodlands”. This study refines the EU Crop Map classification by distinguishing permanent crop types using an automated machine learning model integrating Sentinel S1 and S2 imagery. The study area surrounds the Alqueva reservoir in southern Portugal, one of the Europe’s largest artificial lakes, where recent irrigation system expansion has driven rapid permanent crop adoption. The model achieved 91% overall accuracy, demonstrating strong performance in distinguishing permanent crops, forests, and other occupations. It effectively identified almond groves (F1 score = 0.90), and distinguished three major olive grove cultivation systems (F1-score ≥ 0.78), though performance was lower for vineyards (0.71) and other permanent crops (0.48). Comparison with the Portuguese official land use product COS 2018 showed strong overall spatial alignment, despite several inconsistencies, and lower F1 scores (0.60) in the direct comparison the new mapping produced. This study used a large reference dataset, enabling the assessment of the effect of training set size on classification accuracy. While overall accuracy remained above 83%, even with only 5% of the training data, underrepresented classes experienced significant performance degradation, highlighting the critical need to address class imbalance in agricultural land cover mapping.