Sofia L. Ermida

Calibration of the Fire Weather Index over Mediterranean Europe based on fire activity retrieved from MSG satellite imagery

Here we present a procedure that allows the operational generation of daily maps of fire danger over Mediterranean Europe. These are based on integrated use of vegetation cover maps, weather data and fire activity as detected by remote sensing from space. The study covers the period of July–August 2007 to 2009. It is demonstrated that statistical models based on two-parameter generalised Pareto (GP) distributions adequately fit the observed samples of fire duration and that these models are significantly improved when the Fire Weather Index (FWI), which rates fire danger, is integrated as a covariate of scale parameters of GP distributions. Probabilities of fire duration exceeding specified thresholds are then used to calibrate FWI leading to the definition of five classes of fire danger. Fire duration is estimated on the basis of 15-min data provided by Meteosat Second Generation (MSG) satellites and corresponds to the total number of hours in which fire activity is detected in a single MSG pixel during one day. Considering all observed fire events with duration above 1h, the relative number of events steeply increases with classes of increasing fire danger and no fire activity was recorded in the class of low danger. Defined classes of fire danger provide useful information for wildfire management and are based on the Fire Risk Mapping product that is being disseminated on a daily basis by the EUMETSAT Satellite Application Facility on Land Surface Analysis.

A User-Oriented Simplification of the (

Remote sensing from spaceborne sensors combining near- and middle-infrared information has proved to be an efficient means to monitor the effects of vegetation fires. Burn-sensitive spectral indices, such as the (V, W) index system, have been developed and successfully applied for burned area discrimination. The (V, W) index system provides useful capability to discriminate burned pixels, but the elaborate numerical computations involved are a major drawback in operational applications. This letter presents a simplified algorithm to compute the approximate values of indices V and W. The methodology developed is tested in a region located in the Brazilian Cerrado using remote-sensed data from the MODIS instrument. The simplification allows performing burned area discrimination with the same quality as the original algorithm. The methodology may be extended to other sensors and different combinations of bands and opens new perspectives to the generation of synergic longterm databases of burned area.

V,W

Inversions of the Earth Observation Satellite (EOS) Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to derive the land surface temperature (Ts) are presented based on building a global transfer function by neural networks trained with AMSR-E Tbs and retrieved microwave Ts*. The only required inputs are the Tbs and monthly climatological emissivities, minimizing the dependence on ancillary data. The inversions are accompanied by a coarse estimation of retrieval uncertainty, an estimate of the quality of the retrieval, and a series of flags to signal difficult inversion situations. For ∼75% of the land surface the root-mean-square difference (RMSD) between the training target Ts* and the neural network retrieved Ts is below 2.8 K. The RMSD when comparing with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky Ts is below 3.9 K for the same conditions. Over 10 ground stations, AMSR-E and MODIS Ts were compared with the in situ data. Overall, MODIS agrees better with the station Ts than AMSR-E (all-station mean RMSD of 2.4 K for MODIS and 4.0 for AMSR-E), but AMSR-E provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analyzed stations. At many stations the RMSD of the AMSR-E clear and cloudy sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric conditions. Closest agreement with the in situ Ts happens for stations with dense vegetation, where AMSR-E emissivity is less varying.

) Burn-Sensitive Vegetation Index System

The correction of directional effects on satellite-retrieved land surface temperature (LST) is of high relevance for a proper interpretation of spatial and temporal features contained in LST fields. This study presents a methodology to correct such directional effects in an operational setting. This methodology relies on parametric models, which are computationally efficient and require few input information, making them particularly appropriate for operational use. The models are calibrated with LST data collocated in time and space from MODIS (Aqua and Terra) and SEVIRI (Meteosat), for an area covering the entire SEVIRI disk and encompassing the full year of 2011. Past studies showed that such models are prone to overfitting, especially when there are discrepancies between the LSTs that are not related to the viewing geometry (e.g., emissivity, atmospheric correction). To reduce such effects, pixels with similar characteristics are first grouped by means of a cluster analysis. The models’ calibration is then performed on each one of the selected clusters. The derived coefficients reflect the expected impact of vegetation and topography on the anisotropy of LST. Furthermore, when tested with independent data, the calibrated models are shown to maintain the capability of representing the angular dependency of the differences between LST derived from polar-orbiter (MODIS) and geostationary (Meteosat, GOES and Himawari) satellites. The methodology presented here is currently being used to estimate the deviation of LST products with respect to what would be obtained for a reference view angle (e.g., nadir), therefore contributing to the harmonization of LST products.