Analysis of Influencing Factors of Terrestrial Carbon Sinks in China Based on LightGBM Model and Bayesian Optimization Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area
2.2. Data Resource
2.3. Methods
2.3.1. CASA Model
2.3.2. kNDVI
2.3.3. Rh Empirical Model
2.3.4. NEP Calculation
2.3.5. Pearson Correlation
2.3.6. VIF Test
2.3.7. LightGBM Model
2.3.8. Bayesian Optimization
2.3.9. The 5-Fold Cross-Validation Method
2.3.10. SHAP
2.4. Methodological Framework
3. Results
3.1. NEP Simulation and Analysis
3.2. Preprocessing LightGBM Model Construction
3.2.1. Factor Choosing
3.2.2. Factor Pre-Test
3.2.3. Factor Process
3.3. LightGBM Modeling with Bayesian Optimization
3.3.1. Model Construction
3.3.2. Model Evaluation
3.4. Model Explanation Base on SHAP Value
3.4.1. NEP Drivers’ Contribution Ranking
3.4.2. Nonlinear Response Analysis of NEP to Driving Factors
- (1)
- EVI
- (2)
- SM
- (3)
- NO2
- (4)
- DEM
- (5)
- SO2
- (6)
- O3
- (7)
- GDP and POP
- (8)
- LUCC
3.4.3. Interactive Relationship Characterization Between Variables
- (1)
- EVI: interaction with SM
- (2)
- SM: interaction with NO2
- (3)
- NO2: interaction with the EVI
- (4)
- DEM: interaction with the EVI
- (5)
- SO2: interaction with the EVI
- (6)
- O3: interaction with the EVI
- (7)
- GDP and POP: interaction with SM
- (8)
- LUCC: interaction with the EVI
3.4.4. The Influencing Effect Analysis for NEP Drivers
4. Discussion
4.1. Limitations of Carbon Sink Modeling
4.2. Impact of LUCC on Carbon Sinks
4.3. Application of LightGBM in Carbon Sink Attribution Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CASA | Carnegie–Ames–Stanford approach |
DEM | digital elevation model |
EVI | enhanced vegetation index |
GDP | gross domestic product |
GPP | gross primary productivity |
kNDVI | kernel normalized difference vegetation index |
LUCC | land use and land cover change |
MAE | mean absolute error |
MRZ | middle tropical zone |
MSZ | middle subtropical zone |
MTZ | middle temperate zone |
NEP | net ecosystem productivity |
NPP | net primary productivity |
NRZ | north tropical zone |
NSZ | north subtropical zone |
NTZ | north temperate zone |
PCZ | plateau climate zone |
POP | population |
Rh | heterotrophic respiration |
RMSE | root mean square error |
SHAP | Shapley additive explanations |
SM | soil moisture |
SSZ | south subtropical zone |
STZ | south temperate zone |
Appendix A
Appendix A.1. Comparation Between MODIS NPP and Our Simulation
Appendix A.2. Tukey Test for NEP in Different Climate Zones
Multiple Comparison of Means—Tukey HSD, FWER = 0.05 | ||||||
---|---|---|---|---|---|---|
group1 | group2 | meandiff | p-adj | lower | upper | reject |
NTZ | MTZ | 529.6591 | 0 | 523.0064 | 536.3118 | TRUE |
NTZ | STZ | 190.2974 | 0 | 183.9476 | 196.6473 | TRUE |
NTZ | NSZ | 164.2991 | 0 | 157.937 | 170.6611 | TRUE |
NTZ | MSZ | 398.6029 | 0 | 392.1926 | 405.0132 | TRUE |
NTZ | SSZ | 504.3487 | 0 | 497.9808 | 510.7167 | TRUE |
NTZ | NRZ | 596.4504 | 0 | 590.0097 | 602.8911 | TRUE |
NTZ | MRZ | 813.1031 | 0 | 806.2442 | 819.962 | TRUE |
NTZ | PCZ | 119.1706 | 0 | 112.8199 | 125.5213 | TRUE |
MTZ | STZ | −339.362 | 0 | −341.454 | −337.27 | TRUE |
MTZ | NSZ | −365.36 | 0 | −367.489 | −363.231 | TRUE |
MTZ | MSZ | −131.056 | 0 | −133.325 | −128.787 | TRUE |
MTZ | SSZ | −25.3104 | 0 | −27.4566 | −23.1641 | TRUE |
MTZ | NRZ | 66.7913 | 0 | 64.4381 | 69.1445 | TRUE |
MTZ | MRZ | 283.444 | 0 | 280.1124 | 286.7756 | TRUE |
MTZ | PCZ | −410.489 | 0 | −412.583 | −408.394 | TRUE |
STZ | NSZ | −25.9984 | 0 | −26.7686 | −25.2282 | TRUE |
STZ | MSZ | 208.3055 | 0 | 207.2055 | 209.4054 | TRUE |
STZ | SSZ | 314.0513 | 0 | 313.2336 | 314.8689 | TRUE |
STZ | NRZ | 406.153 | 0 | 404.8881 | 407.4178 | TRUE |
STZ | MRZ | 622.8057 | 0 | 620.1295 | 625.4819 | TRUE |
STZ | PCZ | −71.1268 | 0 | −71.7969 | −70.4568 | TRUE |
NSZ | MSZ | 234.3038 | 0 | 233.1357 | 235.472 | TRUE |
NSZ | SSZ | 340.0497 | 0 | 339.1423 | 340.957 | TRUE |
NSZ | NRZ | 432.1513 | 0 | 430.8267 | 433.476 | TRUE |
NSZ | MRZ | 648.8041 | 0 | 646.0991 | 651.509 | TRUE |
NSZ | PCZ | −45.1284 | 0 | −45.9055 | −44.3514 | TRUE |
MSZ | SSZ | 105.7458 | 0 | 104.5459 | 106.9458 | TRUE |
MSZ | NRZ | 197.8475 | 0 | 196.3076 | 199.3874 | TRUE |
MSZ | MRZ | 414.5002 | 0 | 411.6836 | 417.3168 | TRUE |
MSZ | PCZ | −279.432 | 0 | −280.537 | −278.328 | TRUE |
SSZ | NRZ | 92.1017 | 0 | 90.7489 | 93.4544 | TRUE |
SSZ | MRZ | 308.7544 | 0 | 306.0356 | 311.4732 | TRUE |
SSZ | PCZ | −385.178 | 0 | −386.002 | −384.354 | TRUE |
NRZ | MRZ | 216.6527 | 0 | 213.7677 | 219.5378 | TRUE |
NRZ | PCZ | −477.28 | 0 | −478.549 | −476.011 | TRUE |
MRZ | PCZ | −693.933 | 0 | −696.611 | −691.254 | TRUE |
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Usage in Study | Data Type | Accuracy Indicators | Original Resolution | Dataset Resource | Download Platform |
---|---|---|---|---|---|
NEP calculation section | Precipitation | - | 0.008333°, monthly | Peng, et al. [45] | geodata, accessed on 30 August 2023, https://www.geodata.cn/ |
Temperature | - | 0.008333°, monthly | Peng, et al. [45] | geodata, accessed on 2 September 2023, https://www.geodata.cn/ | |
NDVI | - | 1 km × 1 km, monthly | MOD 13Q1, MODIS | geodata, accessed on 15 September 2023, https://www.geodata.cn/ | |
Landcover | - | 30 m, yearly | CLCD [46] | zenodo, accessed on 22 June 2024, https://zenodo.org/records/8176941 | |
Radiation | - | 10 km × 10 km, hourly | ERA5 LAND [47] | GEE, accessed on 2 July 2024, https://developers.google.com/earth-engine/ | |
Influencing factors analysis section | Population, POP | - | 1 km × 1 km, yearly | Xu [48] | RESDC, accessed on 3 March 2024, http://www.resdc.cn |
Gross Domestic Product, GDP | - | 1 km × 1 km, yearly | Xu [49] | RESDC, accessed on 2 March 2024, http://www.resdc.cn | |
Sulfur Dioxide, SO2 | CV-R2: 0.84, RMSE: 10.07 μg/m3, MAE: 4.68 μg/m3 | 1 km × 1 km, netCDF, yearly | CHAP [50] | geodata, accessed on 30 August 2024, https://www.geodata.cn/ | |
Nitrogen Dioxide, NO2 | CV-R2: 0.93, RMSE: 4.89 μg/m3, MAE: 3.48 μg/m3 | 1 km × 1 km, netCDF, yearly | CHAP [51] | geodata, accessed on 30 August 2024, https://www.geodata.cn/ | |
Ozone, O3 | CV-R2: 0.89; RMSE: 15.77 µg/m3; MAE: 10.48 mg/m3 | 1 km × 1 km, netCDF, yearly | Wei, et al. [52] | geodata, accessed on 30 August 2024, https://www.geodata.cn/ | |
Soil Moisture | ubRMSE: 0.045–0.051, 0.041–0.052; R2: 0.866–0.893, 0.883–0.919 | 1 km × 1 km, daily, netCDF type | SMCI1.0 [53] | The China’s National Tibetan Plateau Data Center, accessed on 31 August 2024. | |
Enhanced Vegetation Index, EVI | - | 1 km × 1 km, yearly | MOD13Q1, MODIS | RESDC, accessed on 7 November 2024, http://www.resdc.cn | |
Digital Elevation Model, DEM | resampled based on the latest SRTM V4.1 radar image to the spatial resolution of 1 km | 1 km × 1 km, yearly | original SRTM V4.1 | RESDC, accessed on 7 November 2024, http://www.resdc.cn |
Hyperparameter | Description | Explanation | Value Range |
---|---|---|---|
learning_rate | Learning rate | Controls the magnitude of weight updates for each iteration; a smaller learning rate typically leads to a more stable training process, but requires more iterations. | [0.01, 0.1] |
feature_fraction | Proportion of features used for each tree | Reduces model variance through random sampling to prevent overfitting. | [0.5, 1] |
bagging_fraction | Proportion of samples used for building trees | Reduces model variance through random sampling to prevent overfitting. | [0.8, 1] |
min_child_samples | Minimum number of samples in leaf nodes | Controls the model’s complexity; a higher value results in a simpler model. | [10, 100] |
num_leaves | Maximum number of leaf nodes | Closely related to the complexity of the tree; increasing the number of leaves enhances the model’s fitting ability, but may also lead to overfitting. | [10, 200] |
max_depth | Maximum depth of the tree | Limits the depth of the tree to control the model’s complexity and prevent overfitting. If set to −1, there are no limits. | [3, 8] |
n_estimators | Number of decision trees | Controls the total number of iterations of the model; more trees result in a more complex model. | [100, 1000] |
min_gain_to_split | Minimum gain to perform a split | Increasing this value can reduce the complexity of the model. | [0, 1] |
reg_alpha | L1 regularization parameter | Can lead to a sparse model, aiding in feature selection. | [3, 10] |
reg_lambda | L2 regularization parameter | Reduces overfitting by increasing the penalty term. | [2, 10] |
Influencing Aspects | Factor | Influencing Mechanism with NEP |
---|---|---|
Subsurface properties | SM | Soil moisture is one of the key factors affecting the growth of vegetation. At the same time, it can comprehensively feedback the influence of meteorological environmental conditions like water–heat balance. The vegetation will grow vigorously with suitable SM, while both the photosynthesis efficiency and the diversity of species in the ecosystem reach a high level, resulting a stable ecosystem structure. Based on that, plants can perform the function of carbon sinks better. On the contrary, vegetation growth will be limited due to exposure to water stress, then stomata will be closed, which in turn affects photosynthesis, respiration, and, consequently, the dysfunction of the carbon uptake. |
EVI | EVI is a commonly used index used to quantify and analyze the growth status and health of vegetation in the remote sensing field. It is calculated by combining the reflectance data of NIR, Red, and Blue bands. Due to the introduction of blue band, it can better resist the influence of atmospheric scattering and soil background and shows great sensitivity to detect the vegetation cover change. Furthermore, it can assess the health and stability of ecosystems. | |
DEM | DEM is a kind of ground elevation data commonly used in terrain analysis, which can reflect the spatial characteristics and pattern of vegetation distribution as a sign related to the plant growth condition. | |
Atmosphere components | SO2 | Sulfur dioxide is chemically active and easily interacts with other atmospheric components, causing acid rain and affecting plant growth. Sulfate aerosols are easily generated after meteorological oxidation and other processes, which may have impact on terrestrial carbon sink through the cooling effect and diffusion–radiation fertilization effect. |
NO2 | Nitrogen dioxide was chosen as a major indicator for nitrogen deposition. Within a certain amount of nitrogen deposition, it is conducive for plant photosynthesis and growth, while excessive nitrogen deposition may lead to an imbalance in the proportion of plant nutrient elements, thus changing the morphology and structure of the plant and increasing its sensitivity to natural stresses. Other issues like affected micro-organisms’ activity and soil acidification will be triggered under the same circumstances. | |
O3 | Except for the well-known properties of air pollutants, ozone is a type of greenhouse gas with interactive effects in the atmosphere, climate, and ecosystem. Ozone formation originates from the oxidation process of oxygen atoms decomposed by ultraviolet radiation in the stratosphere. It also shows warming effect on atmospheric temperatures in the troposphere. In addition, increased concentrations of carbon dioxide contribute to global warming, which in turn may affect the rate of chemical reactions in the atmosphere and the production of ozone. | |
Human activity | LUCC | LUCC directly represents structural changes of the terrestrial ecosystem from human activities. |
POP | POP embodies the pressure of human activities on the ecosystem from the acceptance level. | |
GDP | GDP, as the core economic indicator of the society system, indirectly reflects the intensity of anthropogenic life and production. Moreover, mutual compromises and trade-offs between financial benefits and environmental protection implements should be considered when conducting economic activities. |
Variable | VIF | |
---|---|---|
0 | const | 204.0833 |
1 | POP | 3.8633 |
2 | GDP | 3.1652 |
3 | SO2 | 1.6768 |
4 | NO2 | 2.7715 |
5 | O3 | 2.2426 |
6 | LUCC | 1.0023 |
7 | SM | 2.4383 |
8 | EVI | 2.6518 |
9 | DEM | 1.9366 |
Count | Mean | std | min | 0.25 | 0.5 | 0.75 | max | |
---|---|---|---|---|---|---|---|---|
NEP | 9,459,982 | 268.588 | 293.804 | −54.314 | −10.905 | 199.914 | 484.279 | 1828.840 |
POP | 9,459,982 | 149.391 | 529.074 | 0 | 2 | 20 | 147 | 54,388 |
GDP | 9,459,982 | 1064.058 | 8958.257 | 0 | 9 | 74 | 531 | 3,100,009 |
SO2 | 9,459,982 | 11.561 | 2.452 | 2.700 | 9.900 | 11.500 | 13.100 | 44.700 |
NO2 | 9,459,982 | 17.220 | 6.222 | 4.800 | 13.400 | 15.200 | 18.400 | 63.200 |
O3 | 9,459,982 | 98.150 | 10.165 | 59.800 | 91.500 | 97.700 | 104.800 | 134.200 |
LUCC | 9,459,982 | 0.036 | 0.433 | 0 | 0 | 0 | 0 | 9 |
SM | 9,459,982 | 287.039 | 105.634 | 35.115 | 216.836 | 301.395 | 365.227 | 599.260 |
EVI | 9,459,982 | 0.395 | 0.251 | −0.087 | 0.126 | 0.446 | 0.614 | 1.000 |
DEM | 9,459,982 | 1835.057 | 1740.314 | −268 | 431 | 1162 | 3191 | 8405 |
RMSE (gC/m2/yr) | MAE (gC/m2/yr) | R2 | |
---|---|---|---|
Fold1 | 0.2961 | 0.1761 | 0.9124 |
Fold2 | 0.2960 | 0.1758 | 0.9125 |
Fold3 | 0.2958 | 0.1757 | 0.9124 |
Fold4 | 0.2955 | 0.1757 | 0.9126 |
Fold5 | 0.2965 | 0.1762 | 0.9121 |
mean | 0.2960 | 0.1759 | 0.9124 |
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Zou, Y.; Wang, X. Analysis of Influencing Factors of Terrestrial Carbon Sinks in China Based on LightGBM Model and Bayesian Optimization Algorithm. Sustainability 2025, 17, 4836. https://doi.org/10.3390/su17114836
Zou Y, Wang X. Analysis of Influencing Factors of Terrestrial Carbon Sinks in China Based on LightGBM Model and Bayesian Optimization Algorithm. Sustainability. 2025; 17(11):4836. https://doi.org/10.3390/su17114836
Chicago/Turabian StyleZou, Yana, and Xiangrong Wang. 2025. "Analysis of Influencing Factors of Terrestrial Carbon Sinks in China Based on LightGBM Model and Bayesian Optimization Algorithm" Sustainability 17, no. 11: 4836. https://doi.org/10.3390/su17114836
APA StyleZou, Y., & Wang, X. (2025). Analysis of Influencing Factors of Terrestrial Carbon Sinks in China Based on LightGBM Model and Bayesian Optimization Algorithm. Sustainability, 17(11), 4836. https://doi.org/10.3390/su17114836