Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land-atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors. This article provides an overview of the Land-SAF, with brief descriptions of algorithms and validation results. The set of parameters currently estimated and disseminated by the Land-SAF consists of three main groups: (i) the surface radiation budget, including albedo, land surface temperature, and downward short-and longwave fluxes; (ii) the surface water budget (snow cover and evapotranspiration); and (iii) vegetation and wild-fire parameters.

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T he aim of this work consi.sts of monitoring the recovcry process a&r jiw by mean.s of satellite imagery. The objective,s are to a.s.sess the regrowth pathways followed by diflerent species populations after a disturbance, to analyze the speed of recovery in the years JXlowing fire, and, finally, to estimate rate.s of regrowth. The test area is located in the north of the province of Alicante, on the Mediterranean coast (If Spain. This area, e%specially prone to forest j&s, show.s a remarkable land-use histoq and human pressure. The test areas belong to dicfcrent microclimatic xnes, shozc; diverse tiegetation communities, and have diflerent degrees of .stoniness; so we attempted to discover their post&-e behaviors according to their biogeographical conditions. To accomplish these objectives, we used nine Landsat 5 thematic mapper images from 1984 to IYY4 to which geometric and radiometric corrections were applied. Once the comparability betu>ecn images was guaranteed, we generated a normalized diflercncc vegetation index (NDVL) for each date. First, ux rlelrlonstr~~tclcl that the t1iflerence.s between ND\? itt1agcJ.s were snitablc for mappin, 0 hwned areas. Second, we nndertook a nonlinear regression analysis between NDVI values and the time elapsed .since the jire to a.s.se,s.s the recocety processes. The exponential adjustment between NDVI and time was in accord with the asyn@otic bellacior observed ~chen the rNmX?t7J process is complete. The paramcter,s supplied by the proposed tncthod arc' helpjid in yuanti$jng the ejt2cct.s of jire on dijj%rerrt eco- .