Quality Assessment and Application Scenario Analysis of AGRI Land Aerosol Product from the Geostationary Satellite Fengyun-4B in China
Abstract
:1. Introduction
2. Data and Methods
2.1. FY-4B LDA
2.2. Evaluation Datasets
2.2.1. Himawari-9/AHI
2.2.2. MODIS
2.2.3. AERONET
2.3. Methods
- (1)
- N: The number of samples, i.e., the number of point pairs matched between the FY-4B LDA AOD and the AERONET ground-based data;
- (2)
- Correlation coefficient (R): The level of data correlation between the results to be validated and the ground-based data;
- (3)
- Mean Error (ME, Bias): The average value of the difference between the data to be validated and the ground-based measurements. The mean error reflects the volatility of the data. A smaller ME indicates that the error is more evenly distributed across the entire dataset, indicating more reliable results;
- (4)
- Root Mean Square Error (RMSE): The degree of difference between the data to be validated and the measurements; the smaller the value of RMSE, the smaller the difference between the values to be validated and the measured values of the site;
- (5)
- Expected Error (EE15): EE15 is a widely used evaluation parameter to describe the overall performance of satellite aerosol versus ground-based measurements and stands for the degree of data correlation between the satellite datasets and the measurements [40]. Generally, the satellite retrieval results to be validated are considered to have a good match with the AERONET measurements when at least 67% of the satellite results fall within the range of (EE15) [41];
- (6)
- EA ± EP(EE): Expected Accuracy (EA) is the best linear fit of the ground-based measurements to the mean deviation, indicating the degree of fluctuation of the mean deviation of the values to be validated from the true values. Expected Precision (EP) indicates the best linear fit to the one-fold standard deviation of the deviation, indicating the degree of fluctuation of the deviation between the value to be validated and the true value. The Expected Error (EE), defined as EA ± EP, is a function of the AERONET measurements, expressed in the notation of Huang et al. [42]. It is considered to meet the accuracy requirement if at least 68% of the data to be validated falls within the EE envelope when the satellite retrieval results are validated against AERONET;
- (7)
- Global Climate Observing System Fraction (GCOSF): The Global Climate Observing System (GCOS) stipulates that the accuracy of satellite-retrieved AOD values should be better than 0.03 or 10% when compared to true values [43]. The GCOSF represents the overall percentage of AOD products that meet the requirements of GCOS [44]. The specific calculation principles are shown in the following formula.
3. Results and Analysis
3.1. Validation by AERONET
3.1.1. Overall Validation
3.1.2. Regional Validation
3.1.3. Temporal Validation
3.2. Dependence Analysis
3.2.1. Aerosol Type Dependence Analysis
3.2.2. Surface Type Dependence Analysis
3.3. Comparison with Other Sensors
4. Discussion and Application Analysis
5. Conclusions
- (1)
- Validation results based on AERONET ground-based data indicated that LDA AOD is moderately consistent with ground-based measurements (R = 0.55, Bias = 0.328, RMSE = 0.533, within EE15 = 34%). The LDA AOD in the southern coastal and western plateau regions of China is easy to overestimate.
- (2)
- Analysis results in inland and coastal areas indicate that LDA AOD exhibits significant overestimation in coastal regions, and the algorithm’s retrieval performance in coastal (R = 0.43, Bias = 0.334, RMSE = 0.597, GCOSF = 15%) regions is inferior to that in inland areas (R = 0.60, Bias = 0.163, RMSE = 0.509, GCOSF = 17%).
- (3)
- Error analysis revealed that the retrieval bias of LDA AOD was highly dependent on the satellite zenith angle and solar zenith angle, especially in low-angle regions where the bias changed significantly with increasing angle. Additionally, the retrieval bias of LDA AOD was greatly influenced by water vapor content.
- (4)
- Analysis of aerosol type dependence indicated that LDA AOD was significantly affected by the absorption and scattering characteristics of aerosol particles. Moderately absorbing and strongly absorbing aerosols had a greater impact on the retrieval bias of LDA AOD, while non-absorbing and slightly absorbing aerosols had a smaller effect on the bias of LDA AOD.
- (5)
- The LDA AOD algorithm performs more reliably in inland areas, regions with stable NDVI, or conditions with lower water vapor content. Analysis results of aerosol type dependence indicate that the LDA AOD algorithm is more accurate when the proportion of fine particulate aerosols is lower or the aerosol absorption capacity is weaker. Additionally, during the spring or morning hours, LDA AOD shows greater consistency with AERONET measurements when atmospheric conditions are more stable.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Wang, N.; Li, B.; Jin, Z.; Wang, W. Quality Assessment and Application Scenario Analysis of AGRI Land Aerosol Product from the Geostationary Satellite Fengyun-4B in China. Sensors 2024, 24, 5309. https://doi.org/10.3390/s24165309
Wang N, Li B, Jin Z, Wang W. Quality Assessment and Application Scenario Analysis of AGRI Land Aerosol Product from the Geostationary Satellite Fengyun-4B in China. Sensors. 2024; 24(16):5309. https://doi.org/10.3390/s24165309
Chicago/Turabian StyleWang, Nan, Bingqian Li, Zhili Jin, and Wei Wang. 2024. "Quality Assessment and Application Scenario Analysis of AGRI Land Aerosol Product from the Geostationary Satellite Fengyun-4B in China" Sensors 24, no. 16: 5309. https://doi.org/10.3390/s24165309