STATISTICAL STUDY OF MODIS ALGORITHMS IN ESTIMATING AEROSOL OPTICAL DEPTH OVER THE CZECH REPUBLIC
DOI:
https://doi.org/10.14311/CEJ.2019.04.0043Keywords:
AERONET, AOD, DB, DT, DTB, MODIS, Remote sensingAbstract
As a result of the rapid development of remote sensing techniques and accurate satellite observations, it has become customary to use these technologies in ecological and aerosols studies on a regional and global level. In this paper, we analyse the performance of three Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms in estimating Aerosol Optical Depth (AOD) in the Czech Republic to gain knowledge about their accuracy and uncertainty. The Dark Target (DT), the Deep Blue (DB), and the merged algorithm (DTB) of the MODIS latest collection 6.1 Level 2 aerosol products (MOD04_L2) were tested by comparing its results with the measurements of Aerosol Robotic Network (AERONET) Level 3 Version 2.0 ground station at Brno airport. The DT algorithm is compatible the best with AERONET observations with a correlation coefficient (R = 0.823), retrievals falling within the EE envelope (EE% = 82.67%), root mean square error (RMSE = 0.059), and mean absolute error (MAE = 0.044). The DTB algorithm provided close results of the DT algorithm but with less accuracy, on the other hand the DB algorithm has the lowest accuracy between all, but this algorithm was able to provide a bigger sample size than the other two algorithms.
Downloads
References
Zhang, L., M. Zhang, and Y.B. Yao., 2019. Multi-Time Scale Analysis of Regional Aerosol Optical Depth Changes in National-Level Urban Agglomerations in China Using Modis Collection 6.1 Datasets from 2001 to 2017. Remote Sensing, 11(2).
Kaufman, Y.J., et al., 1997. Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. Journal of Geophysical Research-Atmospheres, 102(D14): 16815-16830.
Cheng, A.Y.S., M.H. Chan, and X. Yang., 2006. Study of aerosol optical thickness in Hong Kong, validation, results, and dependence on meteorological parameters. Atmospheric Environment, 40(24): 4469-4477.
IPCC (2007), Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Poschl, U., 2005. Atmospheric aerosols: Composition, transformation, climate and health effects. Angewandte Chemie-International Edition, 44(46): 7520-7540.
Remer, L.A., et al., 2005. The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences, 62(4): 947-973.
Sun, L., et al., 2016. A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database. Journal of Geophysical Research-Atmospheres, 121(12): 7172-7196.
Tanre, D., M. Herman, and Y.J. Kaufman., 1996. Information on aerosol size distribution contained in solar reflected spectral radiances. Journal of Geophysical Research-Atmospheres, 101(D14): 19043-19060.
Tanre, D., et al., 1997. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. Journal of Geophysical Research-Atmospheres, 102(D14): 16971-16988.
Kaufman, Y.J., D. Tanre, and O. Boucher., 2002. A satellite view of aerosols in the climate system. Nature, 419(6903): 215-223.
Li, Z., et al., 2009. Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective. Annales Geophysicae, 27(7): 2755-2770.
Bilal, M., et al., 2013. A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces. Remote Sensing of Environment, 136: 135-145.
Wei, J., et al., 2019. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison. Atmospheric Environment, 201: 428-440.
Jeong, M.J., et al., 2011. Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra. Ieee Transactions on Geoscience and Remote Sensing, 49(12): 4877-4888.
Sayer, A.M., et al., 2014. MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and "merged" data sets, and usage recommendations. Journal of Geophysical Research-Atmospheres, 119(24): 13965-13989.
Levy, R.C., et al., 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420.
King, M.D., et al., 2013. Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites. Ieee Transactions on Geoscience and Remote Sensing, 51(7): 3826-3852.
Gupta, P., et al., 2016. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm. Atmospheric Measurement Techniques, 9(7): 3293-3308.
Levy, R.C., et al., 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(11): 2989-3034.
Hsu, N.C., et al., 2006. Deep blue retrievals of Asian aerosol properties during ACE-Asia. Ieee Transactions on Geoscience and Remote Sensing, 44(11): 3180-3195.
Hsu, N.C., et al., 2004. Aerosol properties over bright-reflecting source regions. Ieee Transactions on Geoscience and Remote Sensing, 42(3): 557-569.
Zawadzka, O. and Markowicz, K., 2014. Retrieval of aerosol optical depth from optimal interpolation approach applied to SEVIRI data. Remote Sensing, 6, 7182-7211; doi:10.3390/rs6087182
Chu, D.A., et al., 2002. Validation of MODIS aerosol optical depth retrieval over land. Geophysical Research Letters, 29(12).
Remer, L.A., et al., 2002. Validation of MODIS aerosol retrieval over ocean. Geophysical Research Letters, 29(12).
Holben, B.N., et al., 1998. AERONET - A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1): 1-16.
Holben, B.N., et al., 2001. An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET. Journal of Geophysical Research-Atmospheres, 106(D11): 12067-12097.
Goddard Space Flight Center. Aerosol Robotic Network 2019. Available online at https://aeronet.gsfc.nasa.gov/ (accessed on 10th of May, 2019).
Dubovik, O., et al., 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. Journal of Geophysical Research-Atmospheres, 105(D8): 9791-9806.
Liu, Y., et al., 2004. Validation of multiangle imaging spectroradiometer (MISR) aerosol optical thickness measurements using aerosol robotic network (AERONET) observations over the contiguous United States. Journal of Geophysical Research-Atmospheres, 109(D6).
Wei, J. and L. Sun, 2017. Comparison and Evaluation of Different MODIS Aerosol Optical Depth Products Over the Beijing-Tianjin-Hebei Region in China. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(3): 835-844.
Remer, L.A., et al., 2013. MODIS 3 km aerosol product: algorithm and global perspective. Atmospheric Measurement Techniques, 6(7): 1829-1844.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
¨
