A Vegetation Index to Estimate Terrestrial Gross Primary Production Capacity for the Global Change Observation Mission-Climate (GCOM-C)/Second-Generation Global Imager (SGLI) Satellite Sensor

Remote Sensing

Juthasinee Thanyapraneedkul⁴  Kanako Muramatsu⁴  Motomasa Daigo⁹  Shinobu Furumi⁷  Noriko Soyama⁸  Kenlo Nishida Nasahara¹  Hiroyuki Muraoka²  Hibiki M. Noda¹  Shin Nagai⁵  Takahisa Maeda³  Masayoshi Mano⁶ 

[1] Faculty of Life and Environment Sciences, University of Tsukuba, Ibaraki 305-8577, Japan; E-Mails:;Institute for Basin Ecosystem Studies, Gifu University, Gifu 501-1193, Japan; E-Mail:;National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8561, Japan; E-Mail:;Kyousei Science Center for Life and Nature, Kita-uoya, Nishimachi, Nara 630-8506, Japan; E-Mail:;Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Kanagawa 237-0061, Japan; E-Mail:;National Institute for Agro-Environmental Sciences, Tsukuba 305-8604, Japan; E-Mail:;Graduate School of Department of Childcare and Education, Nara Saho College, Nara 630-8425, Japan; E-Mail:;Center for Research and Development of Liberal arts Education, Tenri University, Nara 632-0032, Japan; E-Mail:;Faculty of Economics, Doshisha University, Kyoto 602-8580, Japan; E-Mail:

关键词: GCOM-C/SGLI;    Vegetation index;    Gross primary production;    Photosynthesis;    Chlorophyll content;    Light-response curve;    FluxNet;   

DOI  :  10.3390/rs4123689

来源: mdpi

【 摘 要 】

To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called “GPP capacity”) is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, CI_green(ρ_NIR/ρ_green-1), had a strong linear correlation with chlorophyll content at the leaf scale (R² = 0.87, p < 0.001) and with GPP capacity at the canopy (R² = 0.78, p < 0.001) and satellite scales (R² = 0.72, p < 0.01). Therefore, CI_green is a robust and suitable vegetation index for estimating global GPP capacity.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.