Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

Satellite Earth observations provide timely and spatially explicit information on crop phenology that can support decision making and sustainable agricultural land management. Accurate classification and mapping of croplands is primary information for agricultural assessments. This study presents a digital agriculture approach that integrates Earth Observation big data analytics based on machine learning technologies to classify and map main crop types. Two supervised machine learning models were calibrated using the Random Forest algorithm from phenological metrics, estimated from time series of NDVI and LAI vegetation indices calculated using Sentinel-2 MSI satellite acquisitions. Models were calibrated for the Toscana region in Italy. The results show a satisfactory overall accuracy (~78%) in cropland classification, and the model calibrated using LAI time series performed slightly better than the model calibrated using NDVI time series. The proposed approach offers the potential to accurately map crop types in a way that is useful to support agricultural land management and monitoring systems for large areas over time.