21 November 2012 Estimation of gross primary production capacity from global satellite observations
Author Affiliations +
To estimate gross primary production (GPP), the process of photosynthesis was considered as two separate phases: capacity and reduction. The reduction phase is influenced by environmental conditions such as soil moisture and weather conditions such as vapor pressure differences. For a particular leaf, photosynthetic capacity mainly depends on the amount of chlorophyll and the RuBisCO enzyme. The chlorophyll content can be estimated by the color of the leaf, and leaf color can be detected by optical sensors. We used the chlorophyll content of leaves to estimate the level of GPP. A previously developed framework for GPP capacity estimation employs a chlorophyll index. The index is based on the linear relationship between the chlorophyll content of a leaf and the maximum photosynthesis at PAR =2000 (μmolm -2s-1) on a light-response curve under low stress conditions. As a first step, this study examined the global distribution of the index and found that regions with high chlorophyll index values in winter corresponded to tropical rainforest areas. The seasonal changes in the chlorophyll index differed from those shown by the normalized difference vegetation index. Next, the capacity of GPP was estimated from the light-response curve using the index. Most regions exhibited a higher GPP capacity than that estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) observations, except in areas of tropical rainforest, where the GPP capacity and the MODIS GPP estimates were almost identical.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kanako Muramatsu, Kanako Muramatsu, Juthasinee Thanyapraneedkul, Juthasinee Thanyapraneedkul, Shinobu Furumi, Shinobu Furumi, Noriko Soyama, Noriko Soyama, Motomasa Daigo, Motomasa Daigo, } "Estimation of gross primary production capacity from global satellite observations", Proc. SPIE 8524, Land Surface Remote Sensing, 852421 (21 November 2012); doi: 10.1117/12.977336; https://doi.org/10.1117/12.977336

Back to Top