### Remote Sensing Perspective  
RS techniques are widely used in agriculture and agronomy [27; 28; 29; 30]. The monitoring and mapping of agricultural systems benefit particularly from RS due to specific challenges not common in other sectors [27; 31]. Agricultural production follows strong seasonal patterns related to the phenology of crops. The production depends on heterogenous environmental factors such as soil type and properties, climatic-driven variables, and agricultural management practices, all of which are highly fluctuative in time and space. RS can contribute considerably to providing a timely and accurate perspective of the agricultural sector, as it gathers information over large areas with high revisit frequency [27; 29; 32]. Various techniques for gathering this information, such as imaging spectroscopy, fluorescence spectroscopy, and thermal and microwave RS, provide different insights into agricultural systems [28; 33; 34]. RS offers physical measurements of crop areas, capturing their temporal and spatial development. This approach indirectly integrates key factors influencing crop productivity [35], such as sowing dates [36], pest infestations [37], irrigation practices [19], and levels of intensification [38], that are challenging to access or too costly to measure directly [33]. For example, satellite data can be particularly useful for assessing the extent of irrigation and identifying irrigated cropland over time. This information is crucial for mitigating the impacts of extreme weather and climate variability [19; 39]. Optical data, which depend on weather conditions, are particularly affected by cloud cover and therefore, may be less effective, especially in southern West Africa. Very high-resolution commercial satellites, such as IKONOS, QuickBird, WorldView, Planet, etc., can capture small fields with greater detail, but their high costs restrict their use in small-scale studies and limit their application for large assessments [40; 41; 42]. Instead, high-to-moderate-resolution satellite imagery such as from the Landsat and Sentinel missions, are generally more suitable for larger-scale studies. Landsat provides a long-term, multi-decadal record of moderate-resolution images (30 m), making it useful for tracking changes in arable land over time. In contrast, Sentinel-2, launched in 2015, provides higher spatial resolution (10-20 m compared to 30 m), higher return frequency (5 days versus Landsat's 16 days), and additional narrow bands in the red-edge and near-infrared regions. These features help to detect finer changes in crop status and improve the accuracy of mapping agricultural systems [29; 32]. Sentinel-1 Synthetic-aperture Radar (SAR) data, available since 2014, complements optical data by providing consistent measurements that are almost unaffected by cloud cover. This is particularly valuable for monitoring small fields in cloud-covered areas of West Africa. For example, Sentinel-1's SAR sensor offers detailed information on crop canopies and moisture status, which, when combined with Sentinel-2, enhances the ability to distinguish between different crop and land cover types [29; 32]. Open data archives such as those of Landsat, MODIS (Moderate-resolution Imaging Spectroradiometer), or the Sentinel fleet enable research on all geographical scales from local to global at high temporal and spatial resolution [43; 44]. MODIS sensors offer data more continuously due to their high temporal resolution and their globally uniform acquisition scheme, but the spatial resolution of 250 m or less is not satisfactory to delimit small-scale agricultural areas [43; 45]. However, the growing trend towards sensor constellations is overcoming this limitation, a trend also evident in the development of high-resolution commercial satellite constellations [43]. Additionally, integrating various data sources can overcome individual limitations and improve mapping accuracy, especially in regions with variable weather conditions.