Use of light response curve parameters to estimate gross primary production capacity from chlorophyll indices of global observation satellite and flux data
The photosynthetic rate has a nonlinear relationship with PAR during the day. We previously developed an algorithm for estimating GPP capacity, which is defined GPP under low-stress condition, using light response curves (LRCs). In this study, we studied the characteristics of LRC parameters of the initial slope and the maximum gross photosynthesis rate (P max ), and formulas to calculate P max from the relationship between the chlorophyll index of the green and near-infrared (NIR) bands (CI green ) and the GPP capacity at PAR = 2000 μmol m −2 s −1 (GP2000) for nine vegetation types spanning tropical to subarctic climates on the Eurasian and North American continents using eddy covariance flux measurements and Moderate Resolution Imaging Spectrometer (MODIS) data. The slope of the relationship between CI green and GP2000 was highest for sites dominated by herbaceous plants such as open shrubland, savanna, and cropland (rice paddy); it was lower at sites dominated by woody plants. The yearly GPP/GPP capacity ratio was close to one in flux data. When the method was applied to satellite data, the daily GPP capacity exhibited a similar seasonal pattern to that of the Flux GPP and MODIS GPP products. Under high dryness conditions, Flux GPP showed the drop from the GPP capacity estimated from CI green and diurnal PAR data around noon, and they were nearly identical during the early morning and late afternoon. The instantaneous GPP capacity could be considered the baseline of the instantaneous GPP with stress-free conditions and important for quantifying midday depression at the sub-day scale. • The light-response-curve parameters were assessed for nine vegetation types. • GP2000 had a linear relationship with CI green . • The linear relationships were quantified for nine vegetation types. • Diurnal GPP capacity could be the baseline for the diurnal depression of GPP.
Authors
- Noriko Soyama
- Juthasinee Thanyapraneedkul (ORCID: https://orcid.org/0000-0002-7710-5340)
- Ana López‐Ballesteros (ORCID: https://orcid.org/0000-0003-0609-360X)
- Kanako Muramatsu
- Emi Yoneda
Institutions
- Thammasat University (TH)
- Centro de Investigación y Tecnología Agroalimentaria de Aragón (ES)
- Tenri University (JP)
- Nara Women's University (JP)
Publication Details
- Journal
- Science of Remote Sensing
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1016/j.srs.2024.100164
- Citations
- 4
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.84
Funders
- Japan Society for the Promotion of Science
- Japan Aerospace Exploration Agency