SCAVI: A Sunlit Canopy Adjusted Vegetation Index

Abstract. The new Sunlit Canopy Adjusted Vegetation Index (SCAVI) uses subpixel scale spectral mixture analysis (SMA) principles for improved biophysical parameter estimation. SCAVI and a new NDVI-modified extension (SCAVI+N) were formulated after soil-adjusted vegetation index (VI) equations and tested using NASA COVER airborne multispectral data for boreal forest black spruce stands in Superior National Forest, Minnesota, USA. Fifteen VIs and 3 SMA fractions were compared. SCAVI was the top-ranked VI for each of leaf area index (LAI: r2 = 0.72), net primary productivity (NPP: 0.72), and biomass (BIO: 0.63), with an overall r2 = 0.69 being >10% higher than the next-ranked VI. Shadow-adjusted vegetation indices (SHAVI, SHAVI+N) were also formulated but had low predictive capabilities. The best result of all variables was from SMA shadow fraction with overall r2 = 0.78 (LAI 0.79, NPP 0.80, BIO 0.74). The importance of endmember-based analysis was clear, because these occupied the top 6 of the 18 rankings. ...