[1] Measurements of aerosol optical properties over the atmospheric column (aerosol optical thickness, spectral angular sky radiance (sky brightness), and downwelling hemispheric flux) have been used to derive climate-relevant aerosol parameters such as the phase function, the broadband single-scattering albedo, and the refractive index. These parameters are needed to estimate the direct short-wave radiative forcing by aerosols and to validate aerosol models in the satellite retrieval algorithms. Values of the broadband single-scattering albedo obtained in this study range between w 0 = 0.98 (marine aerosols) and 0.90 (continental pollution aerosols). The columnar ambient broadband refractive index is found to be m = 1.39 ± 0.044 À i (<0.003) for marine conditions and m = 1.48 ± 0.058 À i (0.01 ± 0.003) for polluted continental aerosols. Nonsphericity is shown to be important in the case of marine aerosols. Moreover, aerosol nonsphericity gives an additional contribution to the negative short-wave radiative forcing of marine aerosols under clear-sky conditions, which can be estimated as being 30 up to 50% of the radiative forcing estimated for spherical marine aerosols. In the case of continental polluted aerosols the optical properties can be represented by spherical particles, and no additional shape effect has to be considered. However, the aerosol absorption leads to an increase of about 40% of the radiative forcing estimated for nonabsorbing aerosol of the same size distribution.

Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby improving pest management results and

Forest fires occur in Portugal every year during late spring, summer and fall. However, the beginning and end of the most severe season of forest fires are very variable, as is their intensity, the area and the number of occurrences. It is obvious, that vegetation stress and droughts are strongly linked to the occurrence of forest fires and burned area, showing a strong response to the drought. The vegetation health index (VHI), retrieved from the NOAA/NESDIS, shows good results in the detection of droughts, monitoring vegetation conditions in different countries. VHI is computed combining two terms: vegetation condition index (VCI), and temperature condition index (TCI) reflecting moisture and thermal vegetation conditions. The main objective of this study was to investigate the potential of VHI-method to monitor environmental conditions, favourable to forest fires in Portugal. Results of the study show that 88% of forest fires with burned area higher than 1,000 ha in a week, are well related with vegetation stress or drought conditions, detected with VHI-method. The results also show that the monitoring of the evolution of the VHI indexes is important for prevention burnt areas, especially in the spring, since it can indicate conditions for vegetation growth, which increases the fuel availability and the fire risk in the summer.

No abstract

Forest fires, though part of a natural forest renewal process, when frequent and on a large -scale, have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air, and water quality, infrastructures, and the economy. Portugal endures extreme forest fires, with a record extent of burned areas in 2017. These complexes of extreme wildfire events (CEWEs) concentrated in a few days but with highly burned areas are, among other factors, linked to severe fire weather conditions. In this study, a comparison between several fire danger indices (named ‘multi-indices diagnosis’) is performed for the control period 2001–2021, 2007 and 2017 (May–October) for the Fire Weather Index (FWI), Burning Index (BI), Forest Fire Danger Index (FFDI), Continuous Haines Index (CHI), and the Keetch–Byram Drought Index (KBDI). Daily analysis for the so-called Pedrógão Grande wildfire (17 June) and the October major fires (15 October) included the Spread Component (SC), Ignition Component (IC), Initial Spread Index (ISI), Buildup Index (BUI), and the Energy Release Component (ERC). Results revealed statistically significant high above-average values for most of the indices for 2017 in comparison with 2001–2021, particularly for October. The spatial distribution of BI, IC, ERC, and SC had the best performance in capturing the locations of the two CEWEs that were driven by atmospheric instability along with a dry environment aloft. These results were confirmed by the hotspot analysis that showed statistically significant intense spatial clustering between these indices and the burned areas. The spatial patterns for SC and ISI showed high values associated with high velocities in the spread of these fires. The outcomes allowed us to conclude that since fire danger depends on several factors, a multi-indices diagnosis can be highly relevant. The implementation of a Multi-index Prediction Methodology should be able to further enhance the ability to track and forecast unique CEWEs since the shortcomings of some indices are compensated by the information retrieved by others, as shown in this study. Overall, a new forecast method can help ensure the development of appropriate spatial preparedness plans, proactive responses by civil protection regarding firefighter management, and suppression efforts to minimize the detrimental impacts of wildfires in Portugal.