A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems
The least squares error (LSE) technique is frequently used to estimate abundance fractions in linear spectral mixture analysis (LSMA). The LSE is typically equally weighted for all wavebands, assuming equally important effects. This is, however, not always the case and therefore traditional LSMA often results in suboptimal fraction estimates. This study presents a weighted LSMA approach that prioritises wavebands with minor or no negative effects on fraction estimates. Synthetic mixed pixel spectra compiled from in situ measured spectra of bare soil, citrus tree and weed canopies were used for validation. The results show markedly improved fraction estimates obtained for the weighted approach, with a mean absolute gain of 0.24 in R 2 and a mean absolute reduction in fraction abundance error of 0.06.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Affiliations: Department of Biosystems, M3-BIORES, Katholieke Universiteit Leuven, BE-3001 Leuven, Belgium
Publication date: January 1, 2009