Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Field Data Acquisition
2.2. Data Acquisition and Processing
2.3. pCO2 Modeling, Model Calibration, and Validation
2.3.1. Band Ratio Modeling
2.3.2. Machine-Learning Modeling
2.3.3. Model Calibration and Validation
2.4. Data Analysis
3. Results
3.1. pCO2 Model Calibration and Validation
3.2. Spatiotemporal Analysis of pCO2 Based on the Inversion Results
3.3. CO2 Flux through Water–Atmosphere Interface of Chagan Lake
3.4. Correlation between pCO2 and Its Influencing Factors
4. Discussion
4.1. Temporal and Spatial Variations of pCO2 in Chagan Lake
4.2. Chagan Lake as Carbon Source or Sink
4.3. Uncertainty Analysis
- (1)
- Although the lake water samples we collected met the basic requirements (including water sample collection depth (0.1–0.5 m), water sample storage temperature (4 °C), etc.), due to the limitations of time and in situ sampling conditions, the number of samples acquired was only modest. This might have had an impact on the accuracy of the models we subsequently constructed using the in situ data. Thus, we will endeavor to collect more in situ samples to improve our models and the accuracy of their predictions.
- (2)
- Because the time span of our data was small, it needs to be increased. Also, the influence of wind speed on CO2 needs to be more accurately assessed in the analysis of climate impact factors.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Image | Date | Resolution | Cloud Cover |
---|---|---|---|
LC08_119029_20200722 | 22 July 2020 | 30 m | <5% |
LC08_119029_20201010 | 10 October 2020 | 30 m | <5% |
LC08_119029_20210420 | 20 April 2021 | 30 m | <5% |
LC08_119029_20210522 | 22 May 2021 | 30 m | <5% |
LC08_119029_20210607 | 7 June 2021 | 30 m | <5% |
LC08_119029_20210623 | 23 June 2021 | 30 m | <20% |
LC08_119029_20210709 | 9 July 2021 | 30 m | - |
LC08_120028_20210902 | 2 September 2021 | 30 m | - |
LC08_119029_20210911 | 11 September 2021 | 30 m | - |
LC08_119029_20211013 | 13 October 2021 | 30 m | <5% |
LC08_119029_20221016 | 16 October 2022 | 30 m | <5% |
LT05_119029_19890802 | 2 August 1989 | 30 m | <20% |
LT05_119029_19891021 | 21 October 1989 | 30 m | <5% |
LT05_119029_19900704 | 4 July 1990 | 30 m | <20% |
LT05_119029_19900906 | 6 September 1990 | 30 m | <5% |
LT05_119029_19901024 | 24 October 1990 | 30 m | <5% |
LT05_119029_19910418 | 18 April 1991 | 30 m | <5% |
LT05_119029_19910504 | 4 May 1991 | 30 m | <5% |
LT05_119029_19910605 | 5 June 1991 | 30 m | <5% |
LT05_119029_19910824 | 24 August 1991 | 30 m | <20% |
LT05_119029_19910909 | 9 September 1991 | 30 m | <5% |
LT05_119029_19911027 | 27 October 1991 | 30 m | <5% |
Factors | B1 | B2 | B3 | B4 | B5 | B6 | B7 |
p | −0.252 | −0.168 | 0.182 | −0.048 | −0.382 | 0.046 | 0.228 |
Factors | B3 − B2 | B5 − B3 | B5 − B4 | B4 − B3 | exp(B3) − exp(B5))/ (exp(B3) + exp(B5) | (B5 − B2)/ (B5 + B2) | |
p | 0.703 | −0.911 | −0.636 | −0.600 | −0.910 | −0.621 |
Regression Model | Formula | R2 |
---|---|---|
Band difference model | y = −4639.749 × (B5 − B3) − 353.845 | 0.83 |
y = 3824.755 × (B3 − B2) − 109.721 | 0.48 | |
y = −3976.502 × (B5 − B4) + 157.229 | 0.39 | |
Band ratio model | y = −93.365 × ((B5 − B2)/(B5 + B2)) + 22.669 | 0.37 |
y = −156.216 × ((B5-B2)/(B5 + B2)) + 1928.883 × ((B3 + B4)/2) − 214.638 | 0.67 | |
Exponential model | y = 93273.487 × (exp(B3) − exp(B5))2/(exp(B3) + exp(B5))2 − 127.066 | 0.82 |
Random Forest Parameters | Values | XGBoost Parameters | Values |
---|---|---|---|
n_estimators | 80 | n_estimators | 50 |
random_state | 110 | learning_rate | 0.11 |
max_features | sqrt | booster | gbtree |
max_depth | 5 | max_depth | 5 |
min_samples_leaf | 3 | gamma | 2 |
n_jobs | −1 | lambda | 7 |
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Zhao, R.; Yang, Q.; Wen, Z.; Fang, C.; Li, S.; Shang, Y.; Liu, G.; Tao, H.; Lyu, L.; Song, K. Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change. Remote Sens. 2023, 15, 5680. https://doi.org/10.3390/rs15245680
Zhao R, Yang Q, Wen Z, Fang C, Li S, Shang Y, Liu G, Tao H, Lyu L, Song K. Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change. Remote Sensing. 2023; 15(24):5680. https://doi.org/10.3390/rs15245680
Chicago/Turabian StyleZhao, Ruixue, Qian Yang, Zhidan Wen, Chong Fang, Sijia Li, Yingxin Shang, Ge Liu, Hui Tao, Lili Lyu, and Kaishan Song. 2023. "Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change" Remote Sensing 15, no. 24: 5680. https://doi.org/10.3390/rs15245680
APA StyleZhao, R., Yang, Q., Wen, Z., Fang, C., Li, S., Shang, Y., Liu, G., Tao, H., Lyu, L., & Song, K. (2023). Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change. Remote Sensing, 15(24), 5680. https://doi.org/10.3390/rs15245680