This study evaluated multispectral and hyperspectral vegetation indices to estimate wildfire severity in the 2022 Sierra de la Culebra fire. Field Composite Burn Index data were correlated with satellite-derived indices. Results showed hyperspectral imagery provided more accurate severity estimates, particularly using Cellulose Absorption Index and Red Edge indices.

This study mapped land use changes in the Grombalia Region of Tunisia using Sentinel-2 imagery and machine learning. Three classifiers—Random Forest, SVM, and CNN—were compared. Random Forest achieved the highest accuracy. Results highlight agricultural changes over time and demonstrate the effectiveness of remote sensing for environmental monitoring.

This study explored whether satellite remote sensing can estimate black truffle mycelium biomass. Optical vegetation indices showed limited results, while Sentinel-1 radar backscatter had significant correlations, especially in spring. Findings suggest radar data capture soil moisture dynamics linked to fungal activity, offering a promising tool for sustainable truffle orchard management.