Next Article in Journal
The Highest Gradient Model: A New Method for Analytical Assessment of the Efficiency of LiDAR-Derived Visualization Techniques for Landform Detection and Mapping
Next Article in Special Issue
A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems
Previous Article in Journal
The Estimation of the North American Great Lakes Turbulent Fluxes Using Satellite Remote Sensing and MERRA Reanalysis Data
Previous Article in Special Issue
Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(2), 142;

Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW 2500, Australia
Author to whom correspondence should be addressed.
Academic Editors: Naser El-Sheimy, Zahra Lari, Adel Moussa, Josef Kellndorfer, Richard Gloaguen and Prasad S. Thenkabail
Received: 26 August 2016 / Accepted: 23 January 2017 / Published: 9 February 2017
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
Full-Text   |   PDF [3520 KB, uploaded 9 February 2017]   |  


Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual evolutions. Currently, two main remote sensing instruments for total-column CO2 are the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse gases Observing SATellite (GOSAT), both of which produce estimates of CO2 concentration, called profiles, at 20 different pressure levels. Operationally, each profile estimate is then convolved into a single estimate of column-averaged CO2 using a linear pressure weighting function. This total-column CO2 is then used for subsequent analyses such as Level 3 map generation and colocation for validation. In principle, total-column CO2 in these applications may be more efficiently estimated by making optimal estimates of the vector-valued CO2 profiles and applying the pressure weighting function afterwards. These estimates will be more efficient if there is multivariate dependence between CO2 values in the profile. In this article, we describe a methodology that uses a modified Spatial Random Effects model to account for the multivariate nature of the data fusion of OCO-2 and GOSAT. We show that multivariate fusion of the profiles has improved mean squared error relative to scalar fusion of the column-averaged CO2 values from OCO-2 and GOSAT. The computations scale linearly with the number of data points, making it suitable for the typically massive remote sensing datasets. Furthermore, the methodology properly accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. View Full-Text
Keywords: EM algorithm, Fixed Rank Kriging; multivariate geostatistics; Spatial Random Effects model EM algorithm, Fixed Rank Kriging; multivariate geostatistics; Spatial Random Effects model

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Nguyen, H.; Cressie, N.; Braverman, A. Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets. Remote Sens. 2017, 9, 142.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top