High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model
Abstract
Highlights
- The study develops a customized stacked ensemble model that generalizes predictions across multiple country, such as Germany, France, and Japan.
- It produces gap-filled high-resolution monthly, seasonal, and yearly maps, highlighting vegetation dynamics and seasonal cycles.
- The customized stacked ensemble model provides reliable cross-country predictions at 1 resolution, validated against TCCON and CAMS, supporting large-scale environmental monitoring.
- Seasonal and yearly analyses show vegetation dynamics and photosynthetic activity significantly influence , enhancing the model’s adaptability for agriculture, different climate assessments, and future global mapping.
Abstract
1. Introduction
- ⇒
- Amalgamate the ODIAC emissions, vegetation and environmental features to analyze the influence of emissions, environmental, and vegetative features in the prediction of ;
- ⇒
- Generate continuous high-resolution monthly, seasonal, and yearly maps using a customized stacked ensemble model;
- ⇒
- Leverage transfer learning to assess the model’s applicability;
- ⇒
- Conduct sensitive analysis of features in predictions of using the auxiliary features retrieved from OCO-2 and ERA-5 reanalysis data;
- ⇒
- Validate the predicted with TCCON and comparison with CAMS and NDVI data.
2. Related Work
3. Methodology
3.1. Data Preparation
3.2. Generalized Stacked Ensemble Model
4. Results
4.1. Prediction of Monthly Using the MSD Dataset
4.2. Prediction of Seasonal and Yearly Using the MSD Dataset
4.3. Prediction of Using MSD Dataset Without Spatial Attributes
4.4. Analyzing Feature Importance in the Prediction of
4.5. Comparing Different Regressors in Prediction of Using MSD
4.6. TCCON Validation
4.7. Comparison of Predicted with CAMS, and NDVI
4.8. Comparison with Existing Works
5. Sensitivity Comparison of the OCO-2 Retrievals and ERA-5 Reanalysis Data
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Spatial Resolution | Temporal Resolution | Training | Validation | |
---|---|---|---|---|---|
OCO-2 | concentration | 1.29 km × 2.29 km | 16 days | Target Variable | |
ODIAC | emissions | 1 | Monthly | ✓ | |
ERA-5 | Surface pressure | × | Hourly | ✓ | |
Temperature | |||||
10 m u-component of wind | |||||
10 m v-component of wind | |||||
MODIS | EVI | 1 | Monthly | ✓ | |
NIR | |||||
NDVI | ✓ | ||||
TCCON | Point | Daily | ✓ | ||
CAMS | × | Daily | ✓ |
Temporal Models | Data | Output |
---|---|---|
Monthly | OCO-2 data with monthly ERA-5, MODIS vegetation, and ODIAC features | Monthly map |
Seasonal | OCO-2 data, ERA-5, MODIS vegetation, and ODIAC features seasonally aggregated | Seasonal map |
Yearly | OCO-2 data, ERA-5, MODIS vegetation, and ODIAC features yearly aggregated | Yearly map |
Seasons | Error (in ppm) | ||
---|---|---|---|
MAE | RMSE | ||
Spring | 0.79 | 0.41 | 0.88 |
Summer | 0.86 | 1.47 | 0.87 |
Autumn | 0.84 | 1.35 | 0.91 |
Winter | 0.91 | 1.48 | 0.87 |
Year | 0.84 | 1.43 | 0.90 |
Study Regions | Finland | ||
---|---|---|---|
Dataset Size | Range (in ppm) | Dataset Size | Range (in ppm) |
16,969 | 394.53–423.16 | 846 | 371.57–418.98 |
30,105 | 392.82–418.56 | 16,578 | 399.57–413.24 |
Prediction Model | Error Metric | ||
---|---|---|---|
RMSE (in ppm) | MAE (in ppm) | ||
Stacked Ensemble | 1.42 | 0.84 | 0.90 |
RF | 1.77 | 0.91 | 0.88 |
ERT | 1.76 | 0.90 | 0.88 |
LightGBM | 2.53 | 1.37 | 0.76 |
XgBoost | 2.49 | 1.66 | 0.77 |
CatBoost | 2.30 | 1.23 | 0.80 |
TCCON Station | Bias (in ppm) | RMSE (in ppm) | MAE (in ppm) | |
---|---|---|---|---|
Bremen | 1.35 | 1.15 | 1.02 | 0.86 |
Garmisch | −0.94 | |||
Karlshruhe | −1.32 | |||
Orleans | −0.35 | |||
Paris | −0.74 | |||
Rikubestu | 0.003 | |||
Saga | −1.24 | |||
Tsukuba | −0.9 |
Studies | Resolution | Coverage | Model | Result | Validation |
---|---|---|---|---|---|
Li et al. [15] | , 8-day | Global | ERT | TCCON | |
He et al. [23] | , daily | China | LGB | Temporal-based = 0.89 RMSE = 1.30 ppm | TCCON, Flask data and comparison with CT |
He et al. [25] | , daily | China | RF, ERT, XGB, LGB, and CB | RF = 0.878 RMSE = 1.123 MAE = 0.867 | Comparison with CT and ground-based station |
Wang et al. [22] | , daily | China | RF | = 0.91 RMSE = 1.68 MAE = 0.88 | OCO-2 retrievals |
Zhang et al. [24] | , monthly | China | GWNN | Spatial-based = 0.936 RMSE = 1.360 MAPE = 0.242% | TCCON and CAMS |
Pais et al. [16] | 1 , monthly | Germany | MLR, RR, LR, RT, RF, and ERT | Minimum MAE = 0.707 RMSE = 1.187 | TCCON |
Hu et al. [65] | , Daily | Yangtze River Delta | TCN, CAM, and LSTM | = 0.92 MAE = 0.34 RMSE = 0.62 MAPE = 0.007 | TCCON |
Li et al. [66] | , Monthly | China | STEL model | = 0.8970 RMSE = 1.4213 MAPE = 0.2475 | CAMS |
Chen et al. [67] | , 1-h | Yangtze River Delta | RF | = 0.940 RMSE = 1.031 ppm | TCCON |
Pais et al. [17] | 1 , monthly | France | Multiple ML, DL, and hybrid kriging | Minimum MAE = 0.6010 RMSE = 1.032 | TCCON |
Studies | Resolution | Coverage | Model | Result | Validation |
---|---|---|---|---|---|
Proposed Study | 1 ; monthly, seasonal, and yearly | Germany, France, and Japan | Generalized Stacked Ensemble Model | Monthly RMSE: 1.42 MAE: 0.84 : 0.90 | TCCON, CAMS , and NDVI |
Seasonal RMSE: 1.18 MAE: 0.85 : 0.88 | |||||
Yearly RMSE: 1.43 MAE: 0.84 : 0.90 |
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Pais, S.M.; Bhattacharjee, S.; Madasamy, A.K.; Balamurugan, V.; Chen, J. High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model. Remote Sens. 2025, 17, 3415. https://doi.org/10.3390/rs17203415
Pais SM, Bhattacharjee S, Madasamy AK, Balamurugan V, Chen J. High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model. Remote Sensing. 2025; 17(20):3415. https://doi.org/10.3390/rs17203415
Chicago/Turabian StylePais, Spurthy Maria, Shrutilipi Bhattacharjee, Anand Kumar Madasamy, Vigneshkumar Balamurugan, and Jia Chen. 2025. "High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model" Remote Sensing 17, no. 20: 3415. https://doi.org/10.3390/rs17203415
APA StylePais, S. M., Bhattacharjee, S., Madasamy, A. K., Balamurugan, V., & Chen, J. (2025). High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model. Remote Sensing, 17(20), 3415. https://doi.org/10.3390/rs17203415