EXTENSION OF CLIMATE PARAMETERS OVER THE LAND-SURFACE BY THE USE OF NOAA-AVHRR AND ANCILLARY DATA

Climatic estimates relative to the land surface between ground station s ore of utmost importance to many agricultural and forestry activitie s. In the present paper, two methods are tested for extending the most important parameters for a climatic classification (mean annual tempe rature and length of the arid and cold seasons) over a complex region in central Italy (Tuscany). The first method is based on conventional multivariate regressions applied to the environmental factors most inf luential on climate variability (elevation, distance from the sea, and latitude). The second relies on the extraction of the climatic inform ation from an integrated NOAA-AVHRR data set composed of NDVI profiles and thermal infrared images of two growing seasons. In this latter ca se, a more flexible approach based on a fuzzy classification was adopt ed. The two methods yielded similar results, with some differences exp lainable by environmental considerations. A statistical procedure is a pplied for the optimal merging of the climatic estimates from the two methods. It is finally concluded that the information derived from sui tably processed NOAA-AVHRR data can supplement that from more conventi onal sources for the extension of fundamental climatic parameters over large land surfaces.