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Spatial Statistical Data Fusion for Remote Sensing Applications


  • Hai Nguyen
  • Noel Cressie
  • Amy Braverman


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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    References listed on IDEAS

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    Cited by:

    1. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 0. "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. 0, pages 1-29.
    2. repec:eee:csdana:v:117:y:2018:i:c:p:138-153 is not listed on IDEAS
    3. repec:spr:jagbes:v:22:y:2017:i:2:d:10.1007_s13253-017-0276-7 is not listed on IDEAS
    4. 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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