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 - Tập 4 Số 12 - Trang 3689-3720
Juthasinee Thanyapraneedkul1, Kanako Muramatsu1, M. Daigo2, Shinobu Furumi3, Noriko Soyama4, Kenlo Nishida Nasahara5, Hiroyuki Muraoka6, Hibiki Noda5, Shin Nagai7, Tsuneaki Maeda8, Masayoshi Mano9, Yasuko Mizoguchi10
1Kyousei Science Center for Life and Nature, Kita-uoya, Nishimachi, Nara 630-8506, Japan
2Faculty of Economics, Doshisha University, Kyoto 602-8580, Japan
3Graduate School of Department of Childcare and Education, Nara Saho College, Nara 630-8425, Japan
4Center for Research and Development of Liberal arts Education, Tenri University, Nara 632-0032, Japan
5Faculty of Life and Environment Sciences, University of Tsukuba, Ibaraki 305-8577, Japan
6Institute for Basin Ecosystem Studies, Gifu University, Gifu 501-1193, Japan
7Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Kanagawa 237-0061, Japan
8National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8561, Japan
9National Institute for Agro-Environmental Sciences, Tsukuba 305-8604, Japan
10Hokkaido Research Center, Forestry and Forest Products Research Institute, Hokkaido 062-8516, Japan

Tóm tắt

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, CIgreen(ρNIR/ρgreen-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, CIgreen is a robust and suitable vegetation index for estimating global GPP capacity.

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