Predictive models for remotely-sensed data acquisition in Indonesia

Publisher Website
Google Scholar
Abstract
Indonesian spatio-temporal cloud cover distribution was quantified with the aid of Geostationary Meteorological Satellite (GMS) and Landsat data. For all land areas iterative interactive factorial analyses grouped GMS-derived pixels with similar cloud cover profiles into 18 classes. Statistics of Landsat and SPOT images, grouped by class, were used to quantify temporal profiles of probability of acquiring remotely-sensed data with 10 per cent, 20 per cent and 30 per cent cloud cover for any Indonesian land area. Analysis of the patio-temporal characteristics of local climatic conditions permitted one to explain these profiles and to verify the validity of their seasonal variations for long periods. These profiles were fitted with a seventh-order polynomial for use in computer simulation of predictive models of remotely-sensed data acquisition.

This publication has 1 reference indexed in Scilit: