GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification
6 affiliations
- 1Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Ibaraki, Japan; Green-Bio Technology Research Center, Ibaraki University, Ibaraki, Japan; Corresponding author at: Green-Bio Technology Research Center, Ibaraki University, Ibaraki, Japan.
- 2Faculty of Agriculture, Kyushu University, Fukuoka, Japan
- 3Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Kanagawa, Japan
- 4Graduate School of Environmental Science, Hokkaido University, Hokkaido, Japan
- 5Faculty of Applied Biology, Kyoto Institute of Technology, Kyoto, Japan
- 6Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Ibaraki, Japan
Abstract
Satellite-derived terrestrial chlorophyll (Chl) content is generally estimated using model inversion or simple relationships with vegetation indices, which require parameterization for each species or plant functional type. Model inversion approaches require essential external inputs, such as leaf area index (LAI), carotenoid content, and land-cover classification. Although satellite-derived LAI products are generated frequently enough to capture both natural and artificial vegetation disturbances, land-cover classification products are produced less frequently, and carotenoid content products are rarely available. Thus, this study aimed to address these limitations by directly estimating Chl content from GCOM-C satellite observations, thereby eliminating the requirement of external inputs. For direct retrieval, reflectance must vary with Chl content up to a sufficiently high level; among the two green bands in GCOM-C/SGLI, Band 6 satisfies this criterion. Therefore, we developed an estimation method using this band to derive a proposed ground-area-based total Chl content (Chl ground ), the vertically integrated Chl content from the ground up through different canopy layers. This offers a computationally efficient, near-real-time detection method without requiring ancillary satellite-derived information. The results indicate that the general, broad-leaf, needle-leaf, and grass models achieved R 2 values of 0.86, 0.93, 0.91, and 0.94, respectively, even under varying soil moisture and the fraction of water-body area within a satellite-vegetated pixel. This independently derived Chl ground metric provides new information and insights into vegetation dynamics. Monthly global Chl ground distribution maps generated in this study reveal that Chl ground differs from LAI and other ecological parameters in both spatial distribution and peak timing, consistent with general ecological knowledge.
Related
- Exploiting Low-Rank Structure in Genomic Relationship Matrices for Computationally Efficient Optimum Contribution Selection
- The performance of directional area scattering factor proxies for correcting canopy structural and soil effects on leaf chlorophyll content estimation varies with canopy conditions in agricultural systems
- Riparian Tugai Forest Ecosystems of Azerbaijan: Vegetation Structure, Classification and Ecological Characteristics
- Vegetation cover change and classification-based zonation of Fanjing Mountain using google earth engine
- MetaMamba: Meta-learning with mamba for few-shot vegetation species classification using UAV-based hyperspectral imagery in desert rangeland
BibTeX
@article{akitsu2026gcomcsgli,
title = {{GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification}},
author = {Tomoko Kawaguchi Akitsu and Atsushi Kume and Roxanne Lai and Hideki Kobayashi and Tatsuro Nakaji and Yuko T. Hanba and Hiroshi Murakami},
journal = {Ecological Informatics},
year = {2026},
month = oct,
doi = {10.1016/j.ecoinf.2026.104097},
url = {https://doi.org/10.1016/j.ecoinf.2026.104097},
note = {CC BY},
}
RIS
TY - JOUR
TI - GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification
AU - Tomoko Kawaguchi Akitsu
AU - Atsushi Kume
AU - Roxanne Lai
AU - Hideki Kobayashi
AU - Tatsuro Nakaji
AU - Yuko T. Hanba
AU - Hiroshi Murakami
PY - 2026
DA - 2026/10/30
JO - Ecological Informatics
DO - 10.1016/j.ecoinf.2026.104097
UR - https://doi.org/10.1016/j.ecoinf.2026.104097
AB - Satellite-derived terrestrial chlorophyll (Chl) content is generally estimated using model inversion or simple relationships with vegetation indices, which require parameterization for each species or plant functional type. Model inversion approaches require essential external inputs, such as leaf area index (LAI), carotenoid content, and land-cover classification. Although satellite-derived LAI products are generated frequently enough to capture both natural and artificial vegetation disturbances, land-cover classification products are produced less frequently, and carotenoid content products are rarely available. Thus, this study aimed to address these limitations by directly estimating Chl content from GCOM-C satellite observations, thereby eliminating the requirement of external inputs. For direct retrieval, reflectance must vary with Chl content up to a sufficiently high level; among the two green bands in GCOM-C/SGLI, Band 6 satisfies this criterion. Therefore, we developed an estimation method using this band to derive a proposed ground-area-based total Chl content (Chl ground ), the vertically integrated Chl content from the ground up through different canopy layers. This offers a computationally efficient, near-real-time detection method without requiring ancillary satellite-derived information. The results indicate that the general, broad-leaf, needle-leaf, and grass models achieved R 2 values of 0.86, 0.93, 0.91, and 0.94, respectively, even under varying soil moisture and the fraction of water-body area within a satellite-vegetated pixel. This independently derived Chl ground metric provides new information and insights into vegetation dynamics. Monthly global Chl ground distribution maps generated in this study reveal that Chl ground differs from LAI and other ecological parameters in both spatial distribution and peak timing, consistent with general ecological knowledge.
SN - 1574-9541
SN - 1878-0512
ER -