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A novel light use efficiency model for estimating gross primary production across Europe using PROBA-V and Sentinel-3 FAPAR

Zhang, Mingyuan LU (2025) In Student thesis series INES NGEM01 20251
Dept of Physical Geography and Ecosystem Science
Abstract
Satellite remote sensing is a powerful tool for modelling gross primary production (GPP) at large scales. While model comparison is a prerequisite for understanding carbon dynamics comprehensively, existing GPP products rely on a limited number of satellite sensors. In this study, the contemporary fraction of absorbed photosynthetically active radiation product derived from PROBA-V and Sentinel-3 data was used to develop a novel light use efficiency (LUE) model to predict GPP across the European continent. The model development underwent two steps: first, a baseline model was established following a basic LUE structure; second, this baseline model was calibrated through biome-specific scalar integration, producing the final model (EU-GPP).... (More)
Satellite remote sensing is a powerful tool for modelling gross primary production (GPP) at large scales. While model comparison is a prerequisite for understanding carbon dynamics comprehensively, existing GPP products rely on a limited number of satellite sensors. In this study, the contemporary fraction of absorbed photosynthetically active radiation product derived from PROBA-V and Sentinel-3 data was used to develop a novel light use efficiency (LUE) model to predict GPP across the European continent. The model development underwent two steps: first, a baseline model was established following a basic LUE structure; second, this baseline model was calibrated through biome-specific scalar integration, producing the final model (EU-GPP). Bell-shaped and linear functions were applied to derive temperature and water scalars, respectively. Validation against the eddy covariance data from 61 ICOS stations showed improved model performance of EU-GPP over the baseline model for all biomes but croplands, reducing the average root mean squared error (RMSE) from 2.12 g C m-2 d-1 to 1.94 g C m-2 d-1 and increasing the average coefficient of determination (R2) from 0.63 to 0.70. Except for croplands, EU-GPP exhibited higher prediction accuracy during peak growing seasons and responded to the drought-induced stress uncaptured before calibration, demonstrating its enhanced viability in capturing interannual variability. Model comparison against other satellite-based GPP products demonstrated that EU-GPP outperformed the MODIS-based MOD17 product across all biomes and surpassed the PROBA-V/Sentinel-3 based Dry Matter Productivity product in all biomes but croplands and wetlands. This study presents a novel approach to LUE model development and provides an independent perspective for the monitoring of terrestrial carbon budgets across the European continent. (Less)
Popular Abstract
Gross primary production (GPP), which means the amount of carbon that plants can take in through photosynthesis, is a very important variable from a societal perspective because it is closely associated with agricultural food production, forestry, and fibre production. In addition, GPP balances out some carbon emissions from human activities, so it is also very important to understand under the changing global climate.

Good news is, we can roughly predict GPP over a large area with the help of satellite remote sensing. A widely used method to this end is called the light use efficiency (LUE) model, and it goes like: the amount of GPP is decided by how much the plants absorb the solar energy that they can use for photosynthesis, and how... (More)
Gross primary production (GPP), which means the amount of carbon that plants can take in through photosynthesis, is a very important variable from a societal perspective because it is closely associated with agricultural food production, forestry, and fibre production. In addition, GPP balances out some carbon emissions from human activities, so it is also very important to understand under the changing global climate.

Good news is, we can roughly predict GPP over a large area with the help of satellite remote sensing. A widely used method to this end is called the light use efficiency (LUE) model, and it goes like: the amount of GPP is decided by how much the plants absorb the solar energy that they can use for photosynthesis, and how efficiently this amount of absorbed energy is converted into productivity.

A big challenge is that most LUE GPP products we have today are only based on limited number of satellite sensors, and they have retired or are going to retire soon. It is not possible to understand GPP comprehensively if the base data behind the products are not independent. So, in this study, a new LUE GPP product is developed for the European continent, which can tell us what GPP looked like over the European continent for any day during 2014-2023. Newer and more independent data are used to build this product, including satellite data from PROBA-V and Sentinel-3 and climate data from ERA5-Land, which should allow us to continuously predict GPP over the coming years in the future. The product also has a good resolution, where every pixel corresponds to 300m in the real world.

Within the European context, this product got quite good agreement with the GPP estimates on the field level, which are typically used for validating the satellite-based estimates. What’s more, this product attained better prediction accuracy during summer seasons and also reflected the decline in GPP during extreme weather events, which show that it is quite good at capturing the interannual variability of plant productivity. Under the changing global climate, this gives us a good perspective to help monitor the terrestrial carbon balance across the European continent. (Less)
Please use this url to cite or link to this publication:
author
Zhang, Mingyuan LU
supervisor
organization
course
NGEM01 20251
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Physical Geography, Ecosystem Analysis, Gross primary production, Light use efficiency, Fraction of absorbed photosynthetically active radiation, PROBA-V, Sentinel-3, Europe
publication/series
Student thesis series INES
report number
725
language
English
id
9199209
date added to LUP
2025-06-16 09:47:44
date last changed
2025-06-16 09:47:44
@misc{9199209,
  abstract     = {{Satellite remote sensing is a powerful tool for modelling gross primary production (GPP) at large scales. While model comparison is a prerequisite for understanding carbon dynamics comprehensively, existing GPP products rely on a limited number of satellite sensors. In this study, the contemporary fraction of absorbed photosynthetically active radiation product derived from PROBA-V and Sentinel-3 data was used to develop a novel light use efficiency (LUE) model to predict GPP across the European continent. The model development underwent two steps: first, a baseline model was established following a basic LUE structure; second, this baseline model was calibrated through biome-specific scalar integration, producing the final model (EU-GPP). Bell-shaped and linear functions were applied to derive temperature and water scalars, respectively. Validation against the eddy covariance data from 61 ICOS stations showed improved model performance of EU-GPP over the baseline model for all biomes but croplands, reducing the average root mean squared error (RMSE) from 2.12 g C m-2 d-1 to 1.94 g C m-2 d-1 and increasing the average coefficient of determination (R2) from 0.63 to 0.70. Except for croplands, EU-GPP exhibited higher prediction accuracy during peak growing seasons and responded to the drought-induced stress uncaptured before calibration, demonstrating its enhanced viability in capturing interannual variability. Model comparison against other satellite-based GPP products demonstrated that EU-GPP outperformed the MODIS-based MOD17 product across all biomes and surpassed the PROBA-V/Sentinel-3 based Dry Matter Productivity product in all biomes but croplands and wetlands. This study presents a novel approach to LUE model development and provides an independent perspective for the monitoring of terrestrial carbon budgets across the European continent.}},
  author       = {{Zhang, Mingyuan}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{A novel light use efficiency model for estimating gross primary production across Europe using PROBA-V and Sentinel-3 FAPAR}},
  year         = {{2025}},
}