The objective of this study is to develop a method for estimating the density of olive trees in delimited plots using low-resolution images from the Sentinel-2 satellite. This approach is particularly relevant in certain regions where high-resolution orthophotos, which are often costly and not always available, cannot be accessed. This study focuses on the Extremadura region in Spain, where 48,530 olive plots were analysed. Data from Sentinel-2’s multispectral bands were obtained for each plot, and a Random Forest Regression (RFR) model was used to correlate these values with the number of olive trees, previously counted from orthophotos using machine learning object detection techniques. The results show that the proposed method can predict olive tree density within an acceptable error margin, which is especially useful for distinguishing plots with a density greater than 300 olive trees per hectare—a key criterion for allocating agricultural subsidies in the region. Although the accuracy of the model is not optimal, an average error of ±15.04 olive trees per hectare makes it a viable tool for practical applications where extreme precision is not required. The developed method may also be extrapolated to other cases and crop types, such as fruit trees or forest masses, offering an efficient solution for annual density estimates without relying on costly aerial images. Future research could enhance the accuracy of the model by grouping plots according to additional characteristics, such as tree size or plantation type.

This study evaluates two approaches for detecting solar photovoltaic (PV) installations across agricultural areas, emphasizing their role in supporting sustainable energy monitoring, land management, and planning. Accurate PV mapping is essential for tracking renewable energy deployment, guiding infrastructure development, assessing land-use impacts, and informing policy decisions aimed at reducing carbon emissions and fostering climate resilience. The first approach applies deep learning-based semantic segmentation to high-resolution RGB orthophotos, using the pretrained “Solar PV Segmentation” model, which achieves an F1-score of 95.27% and an IoU of 91.04%, providing highly reliable PV identification. The second approach employs multitemporal pixel-wise spectral classification using Sentinel-2 imagery, where the best-performing neural network achieved a precision of 99.22%, a recall of 96.69%, and an overall accuracy of 98.22%. Both approaches coincided in detecting 86.67% of the identified parcels, with an average surface difference of less than 6.5 hectares per parcel. The Sentinel-2 method leverages its multispectral bands and frequent revisit rate, enabling timely detection of new or evolving installations. The proposed methodology supports the sustainable management of land resources by enabling automated, scalable, and cost-effective monitoring of solar infrastructures using open-access satellite data. This contributes directly to the goals of climate action and sustainable land-use planning and provides a replicable framework for assessing human-induced changes in land cover at regional and national scales.