Improved aerosol optical depth and ångstrom exponent retrieval over land From MODIS based on the non-lambertian forward model

Journal article


Leiku, Yang, Xue, Yong, Guang, Jie, Hassan, Kazemian, Zhang, Jiahua and Li, Chi 2014. Improved aerosol optical depth and ångstrom exponent retrieval over land From MODIS based on the non-lambertian forward model. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2303317
AuthorsLeiku, Yang, Xue, Yong, Guang, Jie, Hassan, Kazemian, Zhang, Jiahua and Li, Chi
Abstract

In this letter, an improved algorithm for aerosol retrieval is presented by employing the non-Lambertian forward model (forward model) (NL_FM) in the Moderate Resolution Imaging Spectroradiometer (MODIS) dark target (DT) algorithm to reduce the uncertainties induced when using the Lambertian FM (L_FM). This new algorithm was applied to MODIS measurements of the whole year of 2008 over Eastern China. By comparing the results with that of AERONET, we found that the accuracy of the aerosol optical depth (AOD) retrieval was improved with the regression plots concentrating around the 1 : 1 line and two-thirds falling within the expected error (EE) envelope EE = ±0.05±0.1τ (from 53.6% with L_FM to 68.7% with NL_FM at band 0.55 μm). Surprisingly, more accurate retrieval of the AOD demonstrated significantly improved the Ångstrom exponent (AE) retrieval, which is related to particle size parameters. The regression plots tended to concentrate around the 1 : 1 line, and many more fell within the EE = ±0.4 from 53.6% with L_FM to 80.9% with NL_FM. These results demonstrate that including the NL_FM in the MODIS DT algorithm has the potential to significantly improve both AOD and AE retrievals with respect to AERONET in comparison to the L_FM used in the current MODIS operational retrievals.

KeywordsRemote sensing; Aerosol optical depth; Atmospheric radiation; Particle size; Regression analysis
Year2014
JournalIEEE Geoscience and Remote Sensing Letters
PublisherIEEE
ISSN1545598X
15580571
Digital Object Identifier (DOI)https://doi.org/10.1109/LGRS.2014.2303317
Web address (URL)http://hdl.handle.net/10545/621926
http://creativecommons.org/licenses/by/4.0/
hdl:10545/621926
Publication dates28 Feb 2014
Publication process dates
Deposited30 Oct 2017, 10:11
Rights

Archived with thanks to IEEE Geoscience and Remote Sensing Letters

ContributorsBeijing Normal University, Institute of Remote Sensing and Digital Earth and London Metropolitan University
File
File Access Level
Open
File
File Access Level
Open
Permalink -

https://repository.derby.ac.uk/item/938yw/improved-aerosol-optical-depth-and-ngstrom-exponent-retrieval-over-land-from-modis-based-on-the-non-lambertian-forward-model

Download files

  • 33
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

An Improved Geographically and Temporally Weighted Regression for Surface Ozone Estimation from Satellite-Based Precursor Data
Wang, X., Xue, Y., Sun, Y., Jin, C. and Wu, S. 2023. An Improved Geographically and Temporally Weighted Regression for Surface Ozone Estimation from Satellite-Based Precursor Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. pp. 1-14. https://doi.org/10.1109/JSTARS.2023.3327881
Studying the Regional Transmission of Air Pollution Based on Spatiotemporal Multivariable Data
Lu, X., Xue, Y., He, B., Jiang, X., Wu, S. and Wang, X. 2023. Studying the Regional Transmission of Air Pollution Based on Spatiotemporal Multivariable Data. Atmosphere. 14 (9), pp. 1-17. https://doi.org/10.3390/atmos14091438
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization
Jin, Chunlin, Jiang, Xingxing, Sun, Yuxin, Wu, Shuhui and Xue, Yong 2021. Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization. Remote Sensing. 13 (22), p. 4689. https://doi.org/10.3390/rs13224689
Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data
Xue, Yong 2021. Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data. Remote Sensing. 13 (21), p. 4276. https://doi.org/10.3390/rs13214276
COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness
Varotsos, Costas A, Krapivin, Vladimir F, Xue, Yong, Soldatov, Vladimir and Voronova, Tatiana 2021. COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness. Safety Science. 142, p. 105370. https://doi.org/10.1016/j.ssci.2021.105370
Nowcasting of air pollution episodes in megacities: A case study for Athens, Greece
Varotsos, Costas A., Mazei, Yuri, Saldaev, Damir, Efstathiou, Maria, Voronova, Tatiana and Xue, Yong 2021. Nowcasting of air pollution episodes in megacities: A case study for Athens, Greece. Atmospheric Pollution Research. 12 (7), p. 101099. https://doi.org/10.1016/j.apr.2021.101099
Remote sensing evaluation of total suspended solids dynamic with markov model: a case study of inland reservoir across administrative boundary in south China
Zhao, Jing, Zhang, Fujie, Chen, Shuisen, Wang, Chongyang, Chen, Jinyue, Zhou, Hui and Xue, Yong 2020. Remote sensing evaluation of total suspended solids dynamic with markov model: a case study of inland reservoir across administrative boundary in south China. Sensors. 20 (23), p. 6911. https://doi.org/10.3390/s20236911
Diagnostic model for the society safety under COVID-19 pandemic conditions
Varotsos, Costas A., Krapivin, Vladimir F. and Xue, Yong 2021. Diagnostic model for the society safety under COVID-19 pandemic conditions. Safety Science. 136, p. 105164. https://doi.org/10.1016/j.ssci.2021.105164
Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field
Bi, Haixia, Xu, Lin, Cao, Xiangyong, Xue, Yong and Xu, Zongben 2020. Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2020.2992177
Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8
Xie, Yanqing, Xue, Yong, Guang, Jie, Mei, Linlu, She, Lu, Li, Ying, Che, Yahui and Fan, Cheng 2019. Deriving a global and hourly data set of aerosol optical depth over land using data from four geostationary satellites: goes-16, msg-1, msg-4, and himawari-8. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2019.2944949
An active deep learning approach for minimally supervised polsar image classification
Xue, Yong 2019. An active deep learning approach for minimally supervised polsar image classification. IEEE Transactions on Geoscience and Remote Sensing. 57 (11), pp. 9378-9395. https://doi.org/10.1109/TGRS.2019.2926434
A Heterogeneous and Interactive Big Earth Data Framework
Bi, H., Xue, Y., Merritt, P., Windmill, C. and Davis, B. 2019. A Heterogeneous and Interactive Big Earth Data Framework. 2019 International Conference on Big Data Engineering. IEEE. https://doi.org/10.1145/3341620.3341628
Big earth data: a comprehensive analysis of visualization analytics issues
Merritt, Patrick, Bi, Haixia, Davis, Bradley, Windmill, Christopher and Xue, Yong 2019. Big earth data: a comprehensive analysis of visualization analytics issues. Big Earth Data. 2 (4), pp. 321-350. https://doi.org/10.1080/20964471.2019.1576260
High Performance Time Series Quantitative Retrieval from Satellite Images on a GPU Cluster
Liu, J., Xue, Y., Ren, K., Song, J., Windmill, C. and Merritt, P. 2019. High Performance Time Series Quantitative Retrieval from Satellite Images on a GPU Cluster. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12 (8). https://doi.org/10.1109/JSTARS.2019.2920077
Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data.
She, Lu, Xue, Yong, Yang, Xihua, Leys, John, Guang, Jie, Che, Yahui, Fan, Cheng, Xie, Yanqing and Li, Ying 2018. Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2018.2867000
Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China.
Che, Yahui, Xue, Yong, Guang, Jie, She, Lu and Guo, Jianping 2018. Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2018.09.004
A physically based PM 2.5 estimation method using AERONET data in Beijing Area
Chen, Guili, Guang, Jie, Xue, Yong, Li, Ying, Che, Yahui and Gong, Shaoqi 2018. A physically based PM 2.5 estimation method using AERONET data in Beijing Area. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2018.2817243
Dust detection and intensity estimation using Himawari-8/AHI observation.
She, Lu, Xue, Yong, Yang, Xihua, Guang, Jie, Li, Ying, Che, Yahui, Fan, Cheng and Xie, Yanqing 2018. Dust detection and intensity estimation using Himawari-8/AHI observation. Remote Sensing. https://doi.org/10.3390/rs10040490
SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm.
She, Lu, Mei, Linlu, Xue, Yong, Che, Yahui and Guang, Jie 2017. SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm. Remote Sensing. https://doi.org/10.3390/rs9030253
Validation of aerosol products from AATSR and MERIS/AATSR synergy algorithms—Part 1: Global Evaluation.
Che, Yahui, Mei, Linlu, Xue, Yong, Guang, Jie, She, Lu and Li, Ying 2018. Validation of aerosol products from AATSR and MERIS/AATSR synergy algorithms—Part 1: Global Evaluation. Remote Sensing. https://doi.org/10.3390/rs10091414
Using SeaWiFS measurements to evaluate radiometric stability of pseudo-invariant calibration sites at top of atmosphere
Li, Chi, Xue, Yong, Liu, Quanhua, Ouazzane, Karim and Zhang, Jiahua 2014. Using SeaWiFS measurements to evaluate radiometric stability of pseudo-invariant calibration sites at top of atmosphere. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2329138
Ensemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertainties
Xie, Yanqing, Xue, Yong, Che, Yahui, Guang, Jie, Mei, Linlu, Voorhis, Dave, Fan, Cheng, She, Lu and Xu, Hui 2017. Ensemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertainties. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2017.2757910
Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe
Xue, Yong, He, Xingwei, de Leeuw, Gerrit, Mei, Linlu, Che, Yahui, Rippin, Wayne, Guang, Jie and Hu, Yincui 2017. Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.06.036
Multicore processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: Implementation, performance, and energy efficiency
Liu, Jia, Feld, Dustin, Xue, Yong, Garcke, Jochen and Soddemann, Thomas 2015. Multicore processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: Implementation, performance, and energy efficiency. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2015.2438893
High-throughput geocomputational workflows in a grid environment
Liu, Jia, Xue, Yong, Palmer-Brown, Dominic, Chen, Ziqiang and He, Xingwei 2015. High-throughput geocomputational workflows in a grid environment. Computer. https://doi.org/10.1109/MC.2015.331
An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data
Liu, Jia, Feld, Dustin, Xue, Yong, Garcke, Jochen, Soddemann, Thomas, Pan, Peiyuan and Fraunhofer Institute of Algorhithms and Scientific Computing 2016. An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2015.1130087
Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China
Che, Yahui, Xue, Yong, Mei, Linlu, Guang, Jie, She, Lu, Guo, Jianping, Hu, Yincui, Xu, Hui, He, Xingwei, Di, Aojie and Fan, Cheng 2016. Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China. Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-16-9655-2016
Dust aerosol optical depth retrieval and dust storm detection for Xinjiang Region using Indian National Satellite Observations
Di, Aojie, Xue, Yong, Yang, Xihua, Leys, John, Guang, Jie, Mei, Linlu, Wang, Jingli, She, Lu, Hu, Yincui, He, Xingwei, Che, Yahui and Fan, Cheng 2016. Dust aerosol optical depth retrieval and dust storm detection for Xinjiang Region using Indian National Satellite Observations. Remote Sensing. https://doi.org/10.3390/rs8090702
Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications
Liu, Jia, Liu, Longli, Xue, Yong, Dong, Jing, Hu, Yincui, Hill, Richard, Guang, Jie and Li, Chi 2016. Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2016.10.002