Uncertainty Analysis of Gross Primary Production (GPP) Remote-Sensing Products and Its Influencing Factors in Southwest China.
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| Title: | Uncertainty Analysis of Gross Primary Production (GPP) Remote-Sensing Products and Its Influencing Factors in Southwest China. |
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| Authors: | Ge, Zhongxi1,2,3 (AUTHOR), Qu, Yanda1,2,3 (AUTHOR), Teng, Huiqin1,2,3 (AUTHOR), Tang, Bo-Hui1,2,3 (AUTHOR) tangbh@kust.edu.cn |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p764. 25p. |
| Subjects: | Machine learning, Error analysis in mathematics, Primary productivity (Biology), Carbon cycle, Normalized difference vegetation index, Remote sensing, Topography |
| Geographic Terms: | Southwest China, China |
| Abstract: | Highlights: What are the main findings? GPP products in Southwest China exhibit broadly consistent large-scale spatial patterns, but show substantial differences in magnitude, temporal variability, and uncertainty across model categories and temporal scales. Machine-learning-based GPP products demonstrate higher stability and lower relative uncertainty than other products, while vegetation indices, topography, and radiation emerge as the dominant drivers of GPP uncertainty. What are the implications of the main findings? Quantitative uncertainty assessment using three-cornered hat and explainable machine learning methods provides an effective framework for evaluating GPP products in regions with complex terrain and sparse flux towers. The results offer practical guidance for region-specific GPP product selection and highlight key pathways for reducing uncertainty in carbon sink assessment under heterogeneous environmental conditions. Gross primary production (GPP) is a key indicator to evaluating ecosystem carbon sinks. Southwest China is characterised by diverse ecosystems and abundant forest resources and represents one of the most important carbon reservoirs in China. Therefore, a quantitative assessment of the uncertainty of existing GPP products and their influencing factors is important. This study investigates GPP uncertainties and its influencing factors based on the three-cornered hat (TCH) and XGBoost and SHAP methods. Thirteen products were examined, including six products from the light use efficiency (LUE) model, two products from the process-based (Process) model, three products from the machine learning (ML) model and two products from satellite-based direct proxies (Proxies). The results reveal the following: (1) All products show similar spatial patterns, with Process products fluctuating notably in 2010, 2011, and 2014, while others remain stable. (2) Relative uncertainty is lowest annually, increasing monthly and daily; ML products exhibit greater stability. Among them, CEDAR has the least uncertainty and strongest agreement with flux observations (r = 0.82), whereas EC-LUE shows the highest uncertainty. (3) Vegetation index, elevation and radiation are more influential than other factors. These findings aid GPP product selection and uncertainty assessment in complex terrains with sparse ground data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? GPP products in Southwest China exhibit broadly consistent large-scale spatial patterns, but show substantial differences in magnitude, temporal variability, and uncertainty across model categories and temporal scales. Machine-learning-based GPP products demonstrate higher stability and lower relative uncertainty than other products, while vegetation indices, topography, and radiation emerge as the dominant drivers of GPP uncertainty. What are the implications of the main findings? Quantitative uncertainty assessment using three-cornered hat and explainable machine learning methods provides an effective framework for evaluating GPP products in regions with complex terrain and sparse flux towers. The results offer practical guidance for region-specific GPP product selection and highlight key pathways for reducing uncertainty in carbon sink assessment under heterogeneous environmental conditions. Gross primary production (GPP) is a key indicator to evaluating ecosystem carbon sinks. Southwest China is characterised by diverse ecosystems and abundant forest resources and represents one of the most important carbon reservoirs in China. Therefore, a quantitative assessment of the uncertainty of existing GPP products and their influencing factors is important. This study investigates GPP uncertainties and its influencing factors based on the three-cornered hat (TCH) and XGBoost and SHAP methods. Thirteen products were examined, including six products from the light use efficiency (LUE) model, two products from the process-based (Process) model, three products from the machine learning (ML) model and two products from satellite-based direct proxies (Proxies). The results reveal the following: (1) All products show similar spatial patterns, with Process products fluctuating notably in 2010, 2011, and 2014, while others remain stable. (2) Relative uncertainty is lowest annually, increasing monthly and daily; ML products exhibit greater stability. Among them, CEDAR has the least uncertainty and strongest agreement with flux observations (r = 0.82), whereas EC-LUE shows the highest uncertainty. (3) Vegetation index, elevation and radiation are more influential than other factors. These findings aid GPP product selection and uncertainty assessment in complex terrains with sparse ground data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050764 |
