Improvement of terrestrial GPP estimation algorithms using satellite and flux data

In our research approach, Gross Primary Production (GPP) is directly estimated from canopy reflected light. Photosynthesis is done only exposed area by solar light. We consider that reflected radiance has information of the exposed area, since photosynthesis can be directly estimated from reflected light(1)). Photosynthesis process in chlorophyll consists of 2 processes. The one is light reactions that can detect by vegetation index. The other is carbon reduction is controlled by stomata opening and closing which effected by weather conditions. We study the relationship between these variables and photosynthesis conditions. This research objective is to improve accuracy of terrestrial GPP estimation algorithm by using Vegetation Index (VI) and combine with Fluxes data that can reveal empirical photosynthesis rate in each site around the world. The first part of GPP estimation algorithm is to find maximum GPP (Pmax_best) of plant under most favourable conditions (No stresses) from light response curve. Next step, we will analyze with weather conditions to find Pmax for GPP estimation. Present research's results show Pmax_best highest in deciduous needle leaf forest, grassland and evergreen needle leaf forest, respectively. Our results indicated that Pmax_best and VI have a tendency. Linear relationship was found between Pmax_best and NDVI in grassland (r(2) = 0.92), deciduous needle leaf forest (r(2) = 0.71) and paddy filed (r(2) = 0.87). These relationships can be one of representative for improving global GPP estimation algorithms in GCOM-C/SGLI project.