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Spatial Statistical Data Fusion for Remote Sensing Applications

Author

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  • Hai Nguyen
  • Noel Cressie
  • Amy Braverman

Abstract

Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models, and it is crucial to accurately estimate aerosol concentrations from remote sensing instruments so as to minimize errors “downstream” in climate models. Currently, space-based observations of aerosols are available from two remote sensing instruments on board NASA's Terra spacecraft: the Multiangle Imaging SpectroRadiometer (MISR), and the MODerate-resolution Imaging Spectrometer (MODIS). These two instruments have complementary coverage, spatial support, and retrieval characteristics, making it advantageous to combine information from both sources to make optimal inferences about global aerosol distributions. In this article, we predict the true aerosol process from two noisy and possibly biased datasets, and we also estimate the uncertainties of these estimates. Our data-fusion methodology scales linearly and bears some resemblance to Fixed Rank Kriging (FRK), a variant of kriging that is designed for spatial interpolation of a single, massive dataset. Our spatial statistical approach does not require assumptions of stationarity or isotropy and, crucially, allows for change of spatial support. We compare our methodology to FRK and Bayesian melding, and we show that ours has superior prediction standard errors compared to FRK and much faster computational speed compared to Bayesian melding.

Suggested Citation

  • Hai Nguyen & Noel Cressie & Amy Braverman, 2012. "Spatial Statistical Data Fusion for Remote Sensing Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1004-1018, September.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:1004-1018
    DOI: 10.1080/01621459.2012.694717
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    Citations

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    Cited by:

    1. Jun Wang & Yang Wang & Hui Zeng, 2016. "A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis," Journal of Geographical Systems, Springer, vol. 18(1), pages 45-66, January.
    2. Chen, Yenming J. & Chang, Kuo-Hao & Sheu, Jiuh-Biing & Liu, Chih-Hao & Chang, Chy-Chang & Chang, Chieh-Hsin & Wang, Guan-Xun, 2023. "Vulnerability-based regionalization for disaster management considering storms and earthquakes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    3. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    4. Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.
    5. Brian J. Reich & Howard H. Chang & Kristen M. Foley, 2014. "A spectral method for spatial downscaling," Biometrics, The International Biometric Society, vol. 70(4), pages 932-942, December.
    6. Andrew M. Raim & Scott H. Holan & Jonathan R. Bradley & Christopher K. Wikle, 2021. "Spatio-temporal change of support modeling with R," Computational Statistics, Springer, vol. 36(1), pages 749-780, March.
    7. Si Cheng & Bledar A. Konomi & Georgios Karagiannis & Emily L. Kang, 2024. "Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets," Environmetrics, John Wiley & Sons, Ltd., vol. 35(4), June.
    8. Esmail Yarali & Firoozeh Rivaz, 2020. "Incorporating covariate information in the covariance structure of misaligned spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    9. Margaret C Johnson & Brian J Reich & Josh M Gray, 2021. "Multisensor fusion of remotely sensed vegetation indices using space‐time dynamic linear models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 793-812, June.
    10. Jialing Tao & Kaibo Wang & Bo Li & Liang Liu & Qi Cai, 2016. "Hierarchical models for the spatial–temporal carbon nanotube height variations," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6613-6632, November.
    11. Jonathan Bradley & Noel Cressie & Tao Shi, 2015. "Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 1-28, March.
    12. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
    13. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    14. Hang Zhang & Yong Liu & Dongyang Yang & Guanpeng Dong, 2022. "PM 2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
    15. Landrum, Carla & Castrignanò, Annamaria & Mueller, Tom & Zourarakis, Demetrio & Zhu, Junfeng & De Benedetto, Daniela, 2015. "An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics," Agricultural Water Management, Elsevier, vol. 147(C), pages 144-153.

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