XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
Abstract
:1. Introduction
- (1)
- Incorporating spatial information into the extreme random trees model, which integrates the advantages of machine learning methods and spatial features, enhancing the predictive performance of the model. This model can offer novel approaches for the high-resolution reconstruction of diverse geographical data.
- (2)
- Utilizing the constructed model to reconstruct a 1 km high-resolution XCO2 dataset for China from 2016 to 2020.
- (3)
- Based on the predicted results, identifying and analyzing the spatiotemporal patterns of XCO2 within the Chinese region. These long-term, high-resolution XCO2 data contribute to our understanding of the spatiotemporal distribution characteristics of and variations in XCO2, thus improving the formulation of policies and strategies for carbon emission reduction.
2. Materials and Methods
2.1. Study Area
2.2. XCO2 Data
2.3. Influencing Factors
2.4. Data Processing
2.5. Spatial Extreme Random Trees
3. Results
3.1. Model Comparison
3.2. Spatial Extreme Random Trees Validation
3.3. Spatiotemporal Variations in XCO2 in China
4. Conclusions
- (1)
- The SExtraTrees model proposed in this study is an effective technique for predicting national long-term time series XCO2 data. The SExtraTrees model outperforms Kriging interpolation and extreme random trees models in terms of model fitting and prediction while maintaining spatial consistency with the original XCO2 data and ensuring high predictive accuracy.
- (2)
- The 1 km-resolution XCO2 products reconstructed using the spatial extreme random trees model provide fundamental data and technical support for regional XCO2 monitoring. These predicted XCO2 datasets can also serve as a reference for calibrating other low-resolution XCO2 datasets. The obtained results highlight the importance of combining geographic spatial correlations with machine learning methods to achieve high precision and robustness. The spatial extreme random trees model can also be applied to the resolution reconstruction of other environmental factors.
- (3)
- Based on the resolution reconstruction results of the spatial extreme random trees model, XCO2 exhibits significant spatial and temporal heterogeneity. From 2016 to 2020, XCO2 in China shows an increasing trend each year. Nationally, the spatial distribution of XCO2 aligns with China’s economic development and urbanization level, with higher XCO2 concentrations in the eastern regions and lower concentrations in the western regions of China. The distribution of carbon concentrations varies among different regions, mainly due to differences in the geographical environment, economic development level, and industrial structure. Higher vegetation coverage can enhance the carbon sequestration capacity. Given the spatiotemporal changes and differences in XCO2, this study provides scientific references for reducing carbon emissions, which is essential for crafting efficient policies aimed at reducing carbon emissions and addressing climate change.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Spatial Resolution | Source | Website |
---|---|---|---|
DEM | 1 km × 1 km | Resource and Environmental Science Data Center | https://www.resdc.cn (accessed on 10 February 2024) |
GPP | 5 km × 5 km | Global Ecology Group | https://globalecology.unh.edu/ (accessed on 10 February 2024) |
NDVI | 1 km × 1 km | Resource and Environmental Science Data Center | https://www.resdc.cn (accessed on 10 February 2024) |
NTL | 1.5 km × 1.5 km | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences | https://www.zybuluo.com/novachen/note/1162587 (accessed on 10 February 2024) |
POP | 1 km × 1 km | Oak Ridge National Laboratory | https://landscan.ornl.gov/ (accessed on 10 February 2024) |
WND | 1 km × 1 km | Resource and Environmental Science Data Center | https://www.resdc.cn (accessed on 10 February 2024) |
PRE | 1 km × 1 km | National Earth System Science Data Center | http://loess.geodata.cn/ (accessed on 10 February 2024) |
TEM | 1 km × 1 km | National Earth System Science Data Center | http://loess.geodata.cn/ (accessed on 10 February 2024) |
RH | 1 km × 1 km | Resource and Environmental Science Data Center | https://www.resdc.cn (accessed on 10 February 2024) |
Model | R2 | MAE (ppm) | RMSE (ppm) |
---|---|---|---|
Kriging | 0.961 | 0.697 | 0.699 |
ExtraTrees | 0.939 | 0.726 | 0.866 |
SExtraTrees | 0.985 | 0.407 | 0.434 |
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Li, X.; Jiang, S.; Wang, X.; Wang, T.; Zhang, S.; Guo, J.; Jiao, D. XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees. Atmosphere 2024, 15, 440. https://doi.org/10.3390/atmos15040440
Li X, Jiang S, Wang X, Wang T, Zhang S, Guo J, Jiao D. XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees. Atmosphere. 2024; 15(4):440. https://doi.org/10.3390/atmos15040440
Chicago/Turabian StyleLi, Xuwen, Sheng Jiang, Xiangyuan Wang, Tiantian Wang, Su Zhang, Jinjin Guo, and Donglai Jiao. 2024. "XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees" Atmosphere 15, no. 4: 440. https://doi.org/10.3390/atmos15040440
APA StyleLi, X., Jiang, S., Wang, X., Wang, T., Zhang, S., Guo, J., & Jiao, D. (2024). XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees. Atmosphere, 15(4), 440. https://doi.org/10.3390/atmos15040440