Improved estimation of environmental parameters through locally calibrated multivariate regression analysis

Linear uni- and multivariate regression analyses are commonly applied to relate land surface parameters to the relevant spectral responses. In practice, this is often the only means to extract operationally useful information from remotely sensed data. The use of regression techniques over relatively wide areas is however constrained by the spatial variability of the observed relationships, which can originate from several causes. To overcome this problem, a modified approach based on the local calibration of regression models is proposed. The method, derivable from the fuzzy set theory, was originally introduced to enhance the performance of conventional multivariate regressions applied to spatially distributed data. The statistical bases of locally calibrated regressions are first presented, together with an operational method to find the optimal model configuration for each application. Two case studies are then described to illustrate the performances of the locally calibrated multivariate regressions compared to those of traditional procedures. The first case study, in particular, exhaustively showed the potential and limitations of the new procedures to extract climate parameters from mean monthly NOAA-AVHRR NDVI data. The second case study dealt with the estimation of forest composition by the use of Landsat TM images. Both investigations indicated that locally calibrated procedures can produce more accurate predictive models than conventional regressions. Additionally, these procedures can provide spatial estimates of accuracy statistics which are useful for a better interpretation of the results and for subsequent data integration.