FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites
<p>Accurately estimating gross primary productivity (GPP) in terrestrial ecosystems is essential for understanding the global carbon cycle. Satellite-based light use efficiency (LUE) models are commonly employed for simulating GPP. However, the variables and algorithms related to environmental...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
|
| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/5115/2025/gmd-18-5115-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849229097680502784 |
|---|---|
| author | J. Lai J. Lai Y. Zhang A. Wang W. Fei Y. Diao R. Li J. Wu |
| author_facet | J. Lai J. Lai Y. Zhang A. Wang W. Fei Y. Diao R. Li J. Wu |
| author_sort | J. Lai |
| collection | DOAJ |
| description | <p>Accurately estimating gross primary productivity (GPP) in terrestrial ecosystems is essential for understanding the global carbon cycle. Satellite-based light use efficiency (LUE) models are commonly employed for simulating GPP. However, the variables and algorithms related to environmental limiting factors differ significantly across various LUE models, leading to high uncertainty in GPP estimation. In this work, we developed a series of FLAML-LUE models with different variable combinations. These models utilize the Fast Lightweight Automated Machine Learning (FLAML) framework, using variables of LUE models, to investigate the potential of estimating site-scale GPP. Incorporating meteorological data, eddy covariance measurements, and remote sensing indices, we employed FLAML-LUE models to assess the impact of various variable combinations on GPP across different temporal scales, including daily, 8 d, 16 d, and monthly intervals. Cross-validation analyses indicated that the FLAML-LUE model performs excellently in GPP prediction, accurately simulating both its temporal variations and magnitude, particularly in mixed forests and coniferous forests, with average <span class="inline-formula"><i>R</i><sup>2</sup></span> values for daily-scale simulations reaching 0.92 and 0.91, respectively. However, the model performed less effectively in alpine shrubland and typical grassland ecosystems, though it still outperformed both MODIS GPP and PML GPP in terms of performance. Furthermore, the model's adaptability under extreme climate conditions was evaluated, and the results showed that high temperatures and high vapor pressure deficit (VPD) lead to a slight decrease in model accuracy, though <span class="inline-formula"><i>R</i><sup>2</sup></span> remains around 0.8. Under drought conditions, the model's performance improved slightly in croplands and evergreen broadleaf forests, although it declined at some sites. This study offers an approach to estimate GPP fluxes and evaluate the impact of variables on GPP estimation. It has the potential to be applied in predicting GPP for different vegetation types at a regional scale.</p> |
| format | Article |
| id | doaj-art-6fe9a3b3421f4302bb8d886a5d3dec5c |
| institution | Kabale University |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-6fe9a3b3421f4302bb8d886a5d3dec5c2025-08-22T05:58:19ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-08-01185115514210.5194/gmd-18-5115-2025FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sitesJ. Lai0J. Lai1Y. Zhang2A. Wang3W. Fei4Y. Diao5R. Li6J. Wu7CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaUniversity of Chinese Academy of Sciences, Beijing 101408, ChinaCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), Wuxi University, Wuxi 214105, ChinaInstitute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, ChinaCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China<p>Accurately estimating gross primary productivity (GPP) in terrestrial ecosystems is essential for understanding the global carbon cycle. Satellite-based light use efficiency (LUE) models are commonly employed for simulating GPP. However, the variables and algorithms related to environmental limiting factors differ significantly across various LUE models, leading to high uncertainty in GPP estimation. In this work, we developed a series of FLAML-LUE models with different variable combinations. These models utilize the Fast Lightweight Automated Machine Learning (FLAML) framework, using variables of LUE models, to investigate the potential of estimating site-scale GPP. Incorporating meteorological data, eddy covariance measurements, and remote sensing indices, we employed FLAML-LUE models to assess the impact of various variable combinations on GPP across different temporal scales, including daily, 8 d, 16 d, and monthly intervals. Cross-validation analyses indicated that the FLAML-LUE model performs excellently in GPP prediction, accurately simulating both its temporal variations and magnitude, particularly in mixed forests and coniferous forests, with average <span class="inline-formula"><i>R</i><sup>2</sup></span> values for daily-scale simulations reaching 0.92 and 0.91, respectively. However, the model performed less effectively in alpine shrubland and typical grassland ecosystems, though it still outperformed both MODIS GPP and PML GPP in terms of performance. Furthermore, the model's adaptability under extreme climate conditions was evaluated, and the results showed that high temperatures and high vapor pressure deficit (VPD) lead to a slight decrease in model accuracy, though <span class="inline-formula"><i>R</i><sup>2</sup></span> remains around 0.8. Under drought conditions, the model's performance improved slightly in croplands and evergreen broadleaf forests, although it declined at some sites. This study offers an approach to estimate GPP fluxes and evaluate the impact of variables on GPP estimation. It has the potential to be applied in predicting GPP for different vegetation types at a regional scale.</p>https://gmd.copernicus.org/articles/18/5115/2025/gmd-18-5115-2025.pdf |
| spellingShingle | J. Lai J. Lai Y. Zhang A. Wang W. Fei Y. Diao R. Li J. Wu FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites Geoscientific Model Development |
| title | FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites |
| title_full | FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites |
| title_fullStr | FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites |
| title_full_unstemmed | FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites |
| title_short | FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites |
| title_sort | flaml version 2 3 3 model based assessment of gross primary productivity at forest grassland and cropland ecosystem sites |
| url | https://gmd.copernicus.org/articles/18/5115/2025/gmd-18-5115-2025.pdf |
| work_keys_str_mv | AT jlai flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT jlai flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT yzhang flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT awang flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT wfei flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT ydiao flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT rli flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites AT jwu flamlversion233modelbasedassessmentofgrossprimaryproductivityatforestgrasslandandcroplandecosystemsites |