The application of EO-1 Hyperion hyperspectral data to estimate the GPP of temperate forest in Changbai Mountain, Northeast China

Abstract

Flux tower is a link between ground measurements and large-scale remote sensing data. A large number of remote sensing model methods are used to estimate the regional scale Gross Primary Productivity (GPP) based on this principle. In this study, Vegetation Photosynthesis Model (VPM) and Vegetation Indexes (VIs) were used to estimate the GPP based on Earth Observing 1 (EO-1) Hyperion hyperspectral data in Changbai Mountain temperate forest. Result shows that the two different types of remote sensing input data of the VPM has similar result at the same level. For different time scope, 3-day flux data can better match remote sensing data. For different footprint, the effect of 500, 1000, 1500 m almost no difference in our area. Among the comparison of the four types of VIs, Bands Ratio (BR), Bands Subtraction (BS) and Bands Difference (BD) have a higher correlation significant than Single Band (SB). 457 nm is the optimum band for SB. The best bands combination of BR, BS, and BD mainly focus on near infrared region. Our research shows that for VPM, and other Light Use Efficiency (LUE) remote sensing model, the difference is not significant between multispectral data and hyperspectral data. At the comparison of VPM and VIs, although the estimation of former is more accurate, the latter is more convenient for that the establishment of VIs just need several bands of remote sensing data. Our findings will help to improve future research on GPP estimation based on hyperspectral observations, which is being more important with increasing availability of hyperspectral satellite data products.