Research on High Spatiotemporal Resolution of XCO2 in Sichuan Province Based on Stacking Ensemble Learning
Abstract
:1. Introduction
2. Data and Methods
2.1. Overview of the Study Area
2.2. Data and Processing
2.2.1. XCO2 Concentration Satellite Observation Data
- (1)
- OCO-2 XCO2 data
- (2)
- OCO-3 XCO2 data
- (3)
- GOSAT XCO2 data
- (4)
- TCCON ground station data
2.2.2. Normalized Vegetation Parameters
2.2.3. Meteorological Reanalysis Data
2.2.4. Other Relevant Factor Data
2.2.5. Data Preprocessing
3. Research Methods
3.1. Spearman’s Correlation Analysis
3.2. Stacking Ensemble Learning Model Construction
- (1)
- The data are split into the initial training set D and the initial test set V.
- (2)
- The 10-fold cross-validation method is employed to train each base learner. The original dataset D is divided into 10 mutually exclusive subsets, labeled D1 to D10. For each iteration, the union of 9 subsets is used as the training set, while the remaining subset serves as the test set. This process constructs the training and test sets for the primary learner, ensuring that each primary learner is trained and validated on 10 distinct sets of training and test data.
- (3)
- To construct a new training dataset for the Stacking ensemble model, the process involves leveraging the outputs of the four base learners. Each base learner is trained and tested using the same 10-fold cross-validation datasets. For the nth base learner, after completing the 10-fold cross-validation, 10 prediction results are generated. These results are vertically stacked by row to form the prediction set , representing the sample data predictions under the base learner. Simultaneously, the 10 prediction results are averaged to produce , which serves as the input dataset for the second-layer meta-learner. This approach ensures that the meta-learner receives a comprehensive and refined input, enhancing the overall predictive performance of the Stacking ensemble model.
- (4)
- The meta-learner LR is used for secondary training, the new training set and test set generated by the base learner in the previous stage are inputted into the second-layer meta-learner for secondary training, and the final XCO2 concentration prediction result is obtained.
3.3. Optuna-Stacking Integrated Model
3.4. Model Evaluation Methods
4. Results and Analysis
4.1. Hyperparameter Settings
4.2. Model Cross-Validation Results
4.3. Stacking Seasonal Cross-Validation
4.4. Stacking XCO2 Dataset Verification with TCCON Sites
4.5. Temporal and Spatial Changes in Regional Carbon Concentration
4.5.1. Temporal Characteristics of Atmospheric XCO2 Concentration in Sichuan Province
4.5.2. Spatial Distribution of Atmospheric XCO2 Concentration in Sichuan Province
5. Conclusions
- (1)
- In order to solve the problem of low spatial coverage and insufficient temporal resolution in the XCO2 observation data of monitoring satellites, a high-coverage XCO2 concentration inversion model integrating multi-source remote sensing data was proposed, and the accuracy of the Optuna-optimized Stacking ensemble learning model was comprehensively verified and evaluated, with the R2 reaching 0.983, an RMSE of 0.87 ppm, and an MAE of 0.19 ppm. The results show that the Stacking XCO2 dataset is significantly consistent with ground observation data, with the correlation coefficient between the model estimate and the observed value at the XH site being 0.96, and the correlation coefficient at the HF site being as high as 0.98. The model has shown a certain application potential in XCO2 concentration monitoring and effectively reflects the long-term dynamic change characteristics of the atmospheric carbon dioxide concentration in Sichuan Province.
- (2)
- The atmospheric CO2 concentration in Sichuan Province from 2015 to 2022 generally showed a fluctuating upward trend, from 398.9 ppm in 2015 to 416.6 ppm in 2022, but the growth rate has generally shown a downward trend in recent years. At the same time, the variation characteristics of the XCO2 concentration on the annual, seasonal, and monthly scales were analyzed. In each year, the XCO2 concentration showed the characteristics of being high in spring and winter and low in summer and autumn. Therefore, the atmospheric XCO2 concentration in Sichuan Province has significant volatility and periodic characteristics.
- (3)
- The atmospheric CO2 concentration in Sichuan Province shows obvious regional differences in spatial distribution, and the overall distribution pattern is “high in the east and low in the west”. Overall, the spatial distribution of the XCO2 concentration is uneven, and the difference in extreme XCO2 values between the different regions can reach 2.8 ppm, which is related to the local carbon source and carbon sink.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data | Data Source | Temporal Resolution | Spatial Resolution |
---|---|---|---|
XCO2 | OCO-2 | 16 d | 1.29 km × 2.25 km |
OCO-3 | 16 d | 1.29 km × 2.25 km | |
GOSAT | 3 d | 10 km × 10 km | |
NDVI | MOD13Q1/MYD13Q1 | Month | 250 m × 250 m |
Weather | ERA5 | Month | 0.25° × 0.25° |
Altitude | NASA | - | 30 m × 30 m |
Land use type | ESA CCI | Year | 30 m × 30 m |
Population density | Sichuan Statistical Yearbook | - | 30″ × 30″ |
Site | Latitude | Longitude | Start Date End Date |
---|---|---|---|
HF | 31.9° N | 117.17° E | 2 November 2015–31 December 2022 |
XH | 39.8° N | 116.96° E | 14 June 2018–31 December 2022 |
Model | Hyperparameters | Numeric |
---|---|---|
XGboost | max_depth | 10 |
learing_date | 0.5 | |
n_estimators | 116 | |
colsample_bytree | 0.3 | |
subsample | 0.5 | |
KNN | k | 3 |
RF | n_estimators | 100 |
max_depth | 12 | |
GBDT | n_estimators | 178 |
learning_rate | 0.2 | |
max_depth | 8 |
Model | R2 | RMSE | MAE |
---|---|---|---|
GBDT | 0.890 | 1.87 | 0.45 |
KNN | 0.922 | 1.64 | 0.30 |
RF | 0.943 | 1.48 | 0.28 |
XGboost | 0.964 | 1.35 | 0.22 |
Stacking | 0.983 | 0.87 | 0.19 |
Season | R2 | RMSE | MAE |
---|---|---|---|
Spring | 0.959 | 1.20 | 0.36 |
Summer | 0.967 | 1.35 | 0.41 |
Autumn | 0.980 | 0.95 | 0.22 |
Winter | 0.976 | 1.13 | 0.39 |
Season | Maximum/ppm | Minimum/ppm | Average Value/ppm |
---|---|---|---|
Spring | 425.235 | 397.646 | 410.190 |
Summer | 427.332 | 393.351 | 408.731 |
Autumn | 420.922 | 394.156 | 406.589 |
Winter | 423.085 | 397.122 | 409.853 |
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Li, Z.; Zhao, N.; Zhang, H.; Wei, Y.; Chen, Y.; Ma, R. Research on High Spatiotemporal Resolution of XCO2 in Sichuan Province Based on Stacking Ensemble Learning. Sustainability 2025, 17, 3433. https://doi.org/10.3390/su17083433
Li Z, Zhao N, Zhang H, Wei Y, Chen Y, Ma R. Research on High Spatiotemporal Resolution of XCO2 in Sichuan Province Based on Stacking Ensemble Learning. Sustainability. 2025; 17(8):3433. https://doi.org/10.3390/su17083433
Chicago/Turabian StyleLi, Zhaofei, Na Zhao, Han Zhang, Yang Wei, Yumin Chen, and Run Ma. 2025. "Research on High Spatiotemporal Resolution of XCO2 in Sichuan Province Based on Stacking Ensemble Learning" Sustainability 17, no. 8: 3433. https://doi.org/10.3390/su17083433
APA StyleLi, Z., Zhao, N., Zhang, H., Wei, Y., Chen, Y., & Ma, R. (2025). Research on High Spatiotemporal Resolution of XCO2 in Sichuan Province Based on Stacking Ensemble Learning. Sustainability, 17(8), 3433. https://doi.org/10.3390/su17083433