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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 10
Geosci. Model Dev., 8, 3311–3319, 2015
https://doi.org/10.5194/gmd-8-3311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 3311–3319, 2015
https://doi.org/10.5194/gmd-8-3311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 20 Oct 2015

Development and technical paper | 20 Oct 2015

Mapping of satellite Earth observations using moving window block kriging

J. M. Tadić, X. Qiu, V. Yadav, and A. M. Michalak J. M. Tadić et al.
  • Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA

Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty.

Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps, (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of column-integrated carbon dioxide (XCO2) from the GOSAT (Greenhouse Gases Observing Satellite) satellite, and solar induced fluorescence (SIF) from the GOME-2 (Global Ozone Monitoring Experiment-2) instrument.

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