A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation
Abstract
:1. Introduction
2. Variables and Data Collection
2.1. Target Variable Dataset
- (1)
- PD: 0.89 < Rd
- (2)
- DDM: 0.53 ≤ Rd ≤ 0.89
- (3)
- PDM: 0.17 ≤ Rd < 0.53
- (1)
- Non-absorbing (NA): 0.95 < SSA
- (2)
- Weakly absorbing (WA): 0.90 < SSA ≤ 0.95
- (3)
- Moderately absorbing (MA): 0.85 ≤ SSA ≤ 0.90
- (4)
- Strongly absorbing (SA): SSA < 0.85
2.2. Satellite Input Variable Candidates
3. Methods
3.1. Machine Learning Approach and Training Process
3.2. Classification Model Assessments
3.2.1. Statistical Assessment
3.2.2. Assessment Using AERONET Aerosol Optical Properties
4. Determination of the Optimal Input Variables
- (1)
- All input variable candidates (N = 4906 for Level 1.5 and 1119 for Level 2.0);
- (2)
- TROPOMI input variable candidates (N = 8693 for Level 1.5 and 1804 for Level 2.0);
- (3)
- MODIS input variable candidates (N = 5714 for Level 1.5 and 1348 for Level 2.0).
5. Results
5.1. Statistical Assessment and Classification Sensitivity of the RF Model
5.2. Evaluation of the RF Model with Aerosol Optical Properties from AERONET Data
6. Evaluation of the Threshold-Based Aerosol Classification Methods
7. Discussion
8. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor (Mission) | Product (Level) | Variables | Notes |
---|---|---|---|
TROPOMI (Sentinel-5P) | AI (L2) | Aerosol index | A qualitative measure indicating the presence of absorbing aerosols |
Solar zenith angle | The angle between the zenith and the sun | ||
CO (L2) | CO column amount | The number of molecules of CO from the surface to top of atmosphere per unit area | |
NO2 (L2) | Tropospheric NO2 column density | The number of molecules of NO2 from the surface to top of the troposphere per unit area | |
MODIS (Aqua) | MYD04 (L2) | Aerosol optical depth | A measure of the extinction of the solar radiance by aerosols |
Ångström exponent | A power law relationship with AOD An indicator of particle size | ||
TOA reflectance (deep blue; 412, 470 and 660 nm) | A ratio of reflected radiance to the incident solar radiance | ||
MCD12C1 (L3) | Land cover type | Major land cover type among land classes (annual) | |
Percent of urban area | A ratio of urban area (annual) |
Initial Input Variable Sets | ||||||
---|---|---|---|---|---|---|
Dataset Name | Input Variables | AERONET Data Level | The Number of Data | OA (%) | ||
Total | Training (60%) | Test (40%) | ||||
All input variable candidates (11 variables) | TROPOMI
| Level 1.5 | 4906 | 2946 | 1960 | 59% |
Level 2.0 | 1119 | 674 | 445 | 58% | ||
TROPOMI input variable candidates (4 variables) |
| Level 1.5 | 8693 | 5218 | 3475 | 51% |
Level 2.0 | 1804 | 1086 | 718 | 53% | ||
MODIS input variable candidates (7 variables) |
| Level 1.5 | 5714 | 3432 | 2282 | 56% |
Level 2.0 | 1348 | 812 | 536 | 52% |
Optimal Input Variable Set | |||||
---|---|---|---|---|---|
Input Variables | AERONET Data Level | The Number of Data | OA (%) | ||
Total | Training (60%) | Test (40%) | |||
TROPOMI
| Level 1.5 | 4906 | 2946 | 1960 | 59% |
Seven Aerosol Classes (PD, DDM, PDM, SA, MA, WA, and NA (Sulfate)) | Four Aerosol Classes (PD, DDM, SA, and NA (Sulfate)) | |||
---|---|---|---|---|
Average | Standard Deviation | Average | Standard Deviation | |
SSA440 | 0.007 | 0.006 | 0.002 | 0.003 |
SSA675 | 0.008 | 0.009 | 0.004 | 0.005 |
SSA870 | 0.010 | 0.011 | 0.005 | 0.007 |
SSA1020 | 0.012 | 0.012 | 0.006 | 0.008 |
FMF | 0.027 | 0.020 | 0.005 | 0.006 |
Rd | 0.047 | 0.016 | 0.016 | 0.019 |
Overall accuracy | 59% | 73% |
Method | Variables | Classified Aerosol Types | AERONET-Based Aerosol Types |
---|---|---|---|
Lee et al. [8] | Aerosol index AE AOD | Smoke | SA and MA |
Dust | PD and DDM | ||
Sulfate, Seasalt+Sulfate | WA and NA (sulfate) | ||
Seasalt | They were not compared due to small number of classified cases (less than 10) | ||
Dust+Smoke | |||
Torres et al. [6] | Aerosol index CO | Carbonaceous | SA and MA |
Dust | PD and DDM | ||
Sulfate | WA and NA (sulfate) |
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Choi, W.; Lee, H.; Park, J. A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation. Remote Sens. 2021, 13, 609. https://doi.org/10.3390/rs13040609
Choi W, Lee H, Park J. A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation. Remote Sensing. 2021; 13(4):609. https://doi.org/10.3390/rs13040609
Chicago/Turabian StyleChoi, Wonei, Hanlim Lee, and Jeonghyeon Park. 2021. "A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation" Remote Sensing 13, no. 4: 609. https://doi.org/10.3390/rs13040609
APA StyleChoi, W., Lee, H., & Park, J. (2021). A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation. Remote Sensing, 13(4), 609. https://doi.org/10.3390/rs13040609