A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems

T. C. Hill, T. Quaife, M. Williams

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    9 Citations (Scopus)
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    We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The "disaggregation" approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a "zero-order" model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set "truth." Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality.

    Original languageEnglish
    Article numberD08117
    Number of pages12
    JournalJournal of Geophysical Research
    Publication statusPublished - 29 Apr 2011


    • Leaf-area index
    • Kalman filter
    • Models
    • Water
    • Flux
    • Parameter
    • Systems
    • Fusion
    • Energy
    • Bias


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