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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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

Hu Zhang¹, Jing Li^2,3, Chenpeng Gu^4,5

¹State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.

Scientific Data

|December 24, 2025

Summary

This study introduces a new high-resolution Leaf Chlorophyll Content (LCC) product, MuSyQ Global LCC, using Google Earth Engine. The product offers finer spatial details for carbon cycle research.

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Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Published on: August 9, 2019

Area of Science:

Earth and Environmental Sciences
Remote Sensing
Plant Physiology

Background:

Leaf Chlorophyll Content (LCC) is vital for estimating photosynthetic carbon assimilation and understanding the terrestrial carbon cycle.
Existing global LCC remote sensing products have limited resolution (300m-500m), restricting detailed spatial analysis.

Purpose of the Study:

To develop a higher-resolution global Leaf Chlorophyll Content (LCC) product using multi-source satellite data.
To improve the spatial detail and accuracy of global LCC estimation for carbon cycle studies.

Main Methods:

Employed an empirical relationship method using the Chlorophyll Sensitive Index (CSI).
Utilized the Google Earth Engine (GEE) platform for data processing.
Generated the Multi-source data Synergized Quantitative Global LCC (MuSyQ Global LCC) product with resolutions from 10m to 100m.

Main Results:

The 10m-resolution MuSyQ Global LCC product achieved an RMSE of 13.69 μg/cm² and R² of 0.37.
The product revealed finer spatial details compared to existing lower-resolution global LCC datasets.
When upscaled to 500m, MuSyQ Global LCC showed high consistency with MODIS LCC and higher accuracy (RMSE=14.16 μg/cm²) than MODIS LCC (RMSE=14.74 μg/cm²).

Conclusions:

The MuSyQ Global LCC product provides a significant advancement in high-resolution LCC mapping.
This finer resolution LCC data can enhance the accuracy of terrestrial carbon cycle estimations.
The study demonstrates the capability of GEE in producing high-quality, high-resolution global environmental products.