A high-resolution global leaf chlorophyll content product using the Sentinel-2 data

Leaf chlorophyll content (LCC) serves as a critical indicator for quantifying photosynthetic carbon assimilation, providing fundamental data for terrestrial carbon cycle estimation. In the past five years, some global LCC remote sensing products have been generated, but their resolution ranges from 300 m to 500 m. This study employed an empirical relationship method based on the Chlorophyll Sensitive Index (CSI) to produce the Multi-source data Synergized Quantitative Global LCC product (MuSyQ Global LCC) with a resolution from 100 m to 10 m using the Google Earth Engine (GEE) platform. Validation results demonstrate that the 10m-resolution MuSyQ Global LCC product has an RMSE of 13.69 μg/cm<sup>2</sup>, R<sup>2</sup> of 0.37, and the RMSE is between 11.28 μg/cm<sup>2</sup> and 15.22 μg/cm<sup>2</sup> for different vegetation types. Its finer resolution reveals more spatial details compared with the existing global products. When upscaled to 500 m, it demonstrates high consistency with the MODIS LCC product, and MuSyQ Global LCC (RMSE = 14.16 μg/cm<sup>2</sup>) exhibits higher accuracy than MODIS LCC (RMSE = 14.74 μg/cm<sup>2</sup>).

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-12-24
DOI
https://doi.org/10.1038/s41597-025-05831-x
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A high-resolution global leaf chlorophyll content product using the Sentinel-2 data

Hu Zhang, Shangrong Lin, Qinhuo Liu Qinhuo Liu, Chenpeng Gu Chenpeng Gu et al.
Scientific Data
Remote Sensing in Agriculture
article

A high-resolution global leaf chlorophyll content product using the Sentinel-2 data

Hu Zhang, Shangrong Lin, Qinhuo Liu Qinhuo Liu, Chenpeng Gu Chenpeng Gu, Xiaohan Wang, Wentao Yu, Y. W. Dong, Jing Zhao, Faisal Mumtaz, Li Guan, Jing Li
article en

Abstract

Leaf chlorophyll content (LCC) serves as a critical indicator for quantifying photosynthetic carbon assimilation, providing fundamental data for terrestrial carbon cycle estimation. In the past five years, some global LCC remote sensing products have been generated, but their resolution ranges from 300 m to 500 m. This study employed an empirical relationship method based on the Chlorophyll Sensitive Index (CSI) to produce the Multi-source data Synergized Quantitative Global LCC product (MuSyQ Global LCC) with a resolution from 100 m to 10 m using the Google Earth Engine (GEE) platform. Validation results demonstrate that the 10m-resolution MuSyQ Global LCC product has an RMSE of 13.69 μg/cm<sup>2</sup>, R<sup>2</sup> of 0.37, and the RMSE is between 11.28 μg/cm<sup>2</sup> and 15.22 μg/cm<sup>2</sup> for different vegetation types. Its finer resolution reveals more spatial details compared with the existing global products. When upscaled to 500 m, it demonstrates high consistency with the MODIS LCC product, and MuSyQ Global LCC (RMSE = 14.16 μg/cm<sup>2</sup>) exhibits higher accuracy than MODIS LCC (RMSE = 14.74 μg/cm<sup>2</sup>).

Scientific DataVol. 12(1)
Sun Yat-sen University (CN), Institute of Remote Sensing and Digital Earth (CN), Aerospace Information Research Institute (CN), Shenzhen Institutes of Advanced Technology (CN), University of Chinese Academy of Sciences (CN), State Key Laboratory of Remote Sensing Science (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, National Key Research and Development Program of China
Openalex Percentile: Top 30%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.