Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data
Abstract
:1. Introduction
2. Data and Method
2.1. Data
- (1)
- OCO-2 XCO2
- (2)
- Carbon Tracker XCO2
- (3)
- CAMS XCO2 and XCH4
- (4)
- TCCON XCO2
- (5)
- Vegetation data
- (6)
- Meteorological data
2.2. Method
3. Results and Discussion
3.1. DNN Testing and Accuracy Verification
3.2. Performance of Model in Unknown Time
3.3. Comparison of DNN and Other Datasets
3.4. Correlation and Importance of Features
3.5. Spatial–Temporal Characteristics of China and Its Surrounding XCO2
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Time | 2019 | 2020 | |
---|---|---|---|
FH | 1 January–30 June | 170 × 105 | 161 × 105 |
SH | 1 July–31 December | 169 × 105 | 164 × 105 |
Use Model | Train Time | Unknown Time |
---|---|---|
19FH | 1 January 2019–30 June 2019 | 1 July 2019–10 July 2019 |
19SH | 1 July 2019–31 December 2019 | 20 June 2019–30 June 2019 1 January 2020–10 January 2020 |
20FH | 1 January 2020–30 June 2020 | 21 December 2019–31 December 2019 1 July 2020–10 July 2020 |
20SH | 1 July 2020–31 December 2020 | 20 June 2020–30 June 2020 |
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Tian, W.; Zhang, L.; Yu, T.; Yao, D.; Zhang, W.; Wang, C. Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data. Atmosphere 2024, 15, 985. https://doi.org/10.3390/atmos15080985
Tian W, Zhang L, Yu T, Yao D, Zhang W, Wang C. Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data. Atmosphere. 2024; 15(8):985. https://doi.org/10.3390/atmos15080985
Chicago/Turabian StyleTian, Wenjie, Lili Zhang, Tao Yu, Dong Yao, Wenhao Zhang, and Chunmei Wang. 2024. "Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data" Atmosphere 15, no. 8: 985. https://doi.org/10.3390/atmos15080985