A rst-based cloud mask for fire-related applications

Satellite-based algorithms for fire detection and monitoring are generally applied after a preliminary phase of cloud-affected pixel identification in order to process only clear sky pixels. Performances of cloud masks usually available for satellite data are generally not suitable in fire-related applications because such products have been formerly developed for meteorological and/or climatological purposes. A not suitable cloud mask may be so responsible for omission errors, excluding cloudy contaminated pixels from further analysis, not only in case of opaque clouds, but also in the presence of semi-transparent clouds which, indeed, could permit a signal affected by fires to reach a satellite sensor. Conversely, if a cloud mask let reflective clouds out, false positives may be detected by a fire detection algorithm, due to their effect in the medium infrared (MIR) band. Since the “2nd Workshop on Geostationary Fire Monitoring and Applications”, the importance of a cloud mask tailored to fire-related applications has been clearly highlighted and our experience gained during several real time validation campaigns of the RST-FIRES algorithm (Robust Satellite Technique for Fire detection) confirmed that. In particular, in the first implementation of RST-FIRES on MSGSEVIRI data, the algorithm was applied only to pixels not declared as “cloudy” by the EUMETSAT CLM product. Unfortunately, CLM product showed to be not suitable for fire applications mainly because slipped off reflective clouds. In order to increase the reliability of the cloud detection phase, CLM product was combined with the RST-based OCA (One-channel Cloud-detection Approach) algorithm, only applied to two channels (one in the visible and the other one in the thermal infrared) so that it was indicated as OCA VIS-TIR. The higher reliability of this combined cloud detection scheme, as compared with the exclusive use of CLM product, showed to minimize false positives, while increasing omission errors because additional smoky pixels were flagged as “cloudy” and events under transparent clouds were undetected. This led us to develop a multispectral RST-based cloud detection scheme specifically tailored for fire-related applications. It was developed for discriminating spectral characteristics of different types of clouds, smoke, and clear-sky pixels following the heritage of the RST-based OCA VIS-TIR algorithm. The new cloud mask, named OCA MULTI-SPECTRAL, was preliminarily tested in the case of fire-affected pixels which, despite a strong MIR signal, were not detected because declared “cloudy” by the present scheme of cloud detection within the RST-FIRES system, based, as before mentioned, on the combination of EUMETSAT CLM product and OCA VISTIR. Performances of OCA MULTI-SPECTRAL have been also evaluated in comparison with the ones of the present cloud detection scheme. Some examples will be shown and discussed in this paper.