Reliable crop mapping is essential in land and water management studies to understand the spatial distribution and dynamics of agricultural practices, to model resource use and production, and to propose sustainable scenarios for agricultural and water management. This work presents high-resolution crop mapping for the Mediterranean area, which is particularly interesting due to the limited data availability and the high level of land use heterogeneity. The main European land use dataset, Corine Land Cover (CLC), lacks the specificity required for accurate agricultural classification, especially for crop differentiation, and does not provide frequent or timely updates, which are crucial for many applications. Other more recent EU-wide crop mapping efforts (d’Andrimont et al, 2021) still lack regional accuracy due to widely scattered training data. To overcome these limitations, a large-scale crop mapping initiative was implemented in Sardinia to test and validate an artificial intelligence–based approach for Mediterranean environments. In this context, irrigated agriculture is a key sector for the sustainable management of limited water resources. The method uses Sentinel‑2 time series and survey data from the LPIS (Land Parcel Identification System). The study relies on IOTA2, a land‑use map production chain first developed and tested at the French level, producing maps with 24 land‑use classes. The originality of the approach lies in the use of open‑source satellite images and an automated processing workflow based on supervised classifiers, making crop mapping faster and easily reproducible across years.Learning samples are derived from 2018 LPIS data, supplemented by CLC and CLCplus Backbone datasets for natural areas and the Urban Atlas for urban areas. Two nomenclatures are tested: a detailed versus a simplified one, with 32 and 26 thematic classes, respectively, both focusing on Mediterranean-relevant crop typologies. The two nomenclatures are evaluated with sampling rates of 10%, 50%, and 100% of training pixels. Results show that the simplified nomenclature achieves higher accuracy, with an Overall Accuracy (OA) of 0.77 compared to 0.61 for the detailed nomenclature, using 100% training pixels. Increasing the training sample rate improves classification quality in both nomenclatures: in the short nomenclature, OA values are 0.596, 0.613, and 0.774 for 10%, 50%, and 100% sampling rates. In the detailed nomenclature, the improvement is weaker, with OA values of 0.596, 0.601, and 0.610, indicating that increasing sample size does not resolve class confusion. Among agricultural classes, rice, citrus, vegetables, and grapevine achieve the highest classification scores, which are among the crops with the largest irrigation requirements. Nuts, cereals, and fruit trees perform poorly, mainly due to insufficient training samples. Overall, the proposed nomenclature significantly improves the crop classes available in the CLC by increasing crop specificity and differentiation. This study presents a framework for fully automatic crop‑map production in Mediterranean environments, ensuring fast reproducibility over the years thanks to the use of openly accessible satellite imagery and an automated processing chain. This can improve the accuracy and reliability of water accounting for the agricultural sector and help promote sustainable use of limited water resources in the Mediterranean areas.

Mountainous regions are characterised by unique biodiversity and provide essential ecosystem services. However, they are also particularly vulnerable to global change. In the Mediterranean mountains, climate change has a significant impact on plant productivity and phenology, through an increase in extreme drought events and changes in snowfall and temperature patterns. Additionally, the progressive abandonment of pastoralism is leading to profound changes in open and semi-open habitats (subalpine grasslands, heathlands and forest ecotones) through the densification and recolonisation of woody species (ericaceous shrubs and conifers in particular). There is an urgent need to develop a more profound understanding of these complex vegetation dynamics in order to inform the development of coherent guidelines for stakeholders in mountainous regions, including livestock farmers and nature reserve managers, to help them adapt their practices to preserve both foraging resources and rich biodiversity. In order to monitor past and current vegetation dynamics in open and semi-open habitats, we propose applying a method of classifying ecological trajectories into nine highly interpretable categories. This method involves adjusting a second-degree polynomial function and provides insight into the direction and acceleration of the studied trajectories (Rigal et al., 2020). To describe changes in vegetation functioning and composition, we calculated the Dynamic Habitat Index (DHI) using Landsat and Sentinel-2 time series data. Finally, we aimed to identify the impact of potential climate-related drivers in different contexts of agro-pastoral management. Our approach was applied to the alpine and subalpine landscapes of the Mediterranean Pyrenees in southern France, with the results highlighting the existence of differential trajectories depending on the habitat considered, and potentially the pastoral management practices in Catalan nature reserves with high biodiversity values. Our results encourage us to move beyond traditional remote sensing approaches based on calculating greening/browning trends to monitor vegetation dynamics patterns, providing a more detailed framework for analysing the complex changes affecting mountain ecosystems.

Reliable crop mapping is essential for understanding agricultural practices, optimizing resource use, and analyzing rural dynamics, while also supporting modelling and sustainable agriculture planning. However, obtaining 10 m crop distribution maps remains challenging in Mediterranean regions, where data availability is often limited and landscapes are fragmented. The main European land use dataset, Corine Land Cover (CLC), lacks both the crop specificity required for accurate crop differentiation and the temporal frequency needed for timely monitoring. This study addresses these limitations by implementing the IOTA² automated chain in Sardinia (Italy), to create a large-scale crop map specifically targeting Mediterranean crops. The methodology leverages open-source satellite imagery with supervised machine learning, using the 2018 Land Parcel Identification System (LPIS), CLC, and Urban Atlas dataset for training. We compared two nomenclatures, detailed (32 classes) versus simplified (25 classes), testing each across three training sample sizes (10%, 50%, and 100%). Results indicate that the simplified nomenclature (N25) provided more robust performances, achieving an overall accuracy (OA) of 0.77 with full sampling, compared to 0.61 for the detailed version. These OA values refer to the subset of reference polygons held out from the reference data for independent pixel-level validation. Moreover, a final map was produced using the entire reference dataset for training and evaluated through zonal area agreement. Mapping showed high performance for specific crops like rice, citrus, and grapevine, while classes such as cereals and fruit trees presented classification challenges due to high fragmentation of the landscape and irregular crop-distribution patterns. Despite these challenges, this work delivers a 10 m spatial resolution reproducible framework that enhances thematic details of current European datasets. By running as a single automated processing chain rather than a sequence of manually executed steps, it offers a scalable solution for rapid, annual crop monitoring in complex, data-scarce Mediterranean environments.