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Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions

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Abstract

Context

The open and free access to Landsat and MODIS products have greatly promoted scientific investigations on spatiotemporal change in land mosaics and ecosystem functions at landscape to regional scales. Unfortunately, there is a major mismatch in spatial resolution between MODIS products at coarser resolution (≥ 250 m) and landscape structure based on classified Landsat scenes at finer resolution (30 m).

Objectives

Based on practical needs for downscaling popular MODIS products at 500 m resolution to match classified land cover at Landsat 30 m resolution, we proposed an innovative modelling approach so that landscape structure and ecosystem functions can be directly studied for their interconnections. As a proof-of-concept of our downscaling approach, we selected the watershed of the Kalamazoo River in southwestern Michigan, USA as the testbed.

Methods

MODIS products for three fundamental variables of ecosystem function are downscaled to ensure the approach can be extrapolated to multiple functional measurements. They are blue-sky albedo (0–1), evapotranspiration (ET, mm), and gross primary production (GPP, Mg C ha−1 year−1). An object-oriented classification of Landsat images in 2011 was processed to generate a land cover map for landscape structure. The downscaling model was tested for the five Level IV ecoregions within the watershed.

Results

We achieved satisfactory downscaling models for albedo, ET, and GPP for all five ecoregions. The adjusted R2 was > 0.995 for albedo, 0.915–0.997 for ET, and 0.902–0.962 for GPP. The estimated albedo, ET, and GPP values appear different in the region. The estimated albedo was the lowest for water (0.076–0.107) and the highest for cropland (0.166–0.172). Estimated ET was the highest for the built-up cover type (525.6–687.1 mm) and the lowest for forest (209.7–459.7 mm). The estimated GPP was the highest for the build-up cover type (8.65–9.85 Mg C ha−1 year−1) and the lowest for forest.

Conclusions

Estimated values for albedo, ET, and GPP appear reasonable for their ranges in the Kalamazoo River region and are consistent with values reported in the literature. Despite these promising results, the downscaling approach relies on strong assumptions and can carry substantial uncertainty. It is only valid at a spatial scale where similar climate, soil, and landforms exist (i.e., values in isolated patches of the same cover type are similar). Plausibly, the uncertainties associated with each estimation, as well as the model residuals, can be explored for other pattern-process relationships within the landscape.

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Acknowledgements

This study was supported, in part, by the NASA Carbon Cycle & Ecosystems program (NNX17AE16G), the Great Lakes Bioenergy Research Center funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Numbers DE-SC0018409 and DE-FC02-07ER64494; and the Long-term Ecological Research Program (DEB 1637653) at the Kellogg Biological Station, and the NASA Science of Terra and Aqua program (NNX14AJ32G). We thank the fruitful discussion at LEES Lab meetings where several members made constructive suggestions for model development. Isabel Arroca assisted in formatting the references. The reviews from two anonymous reviewers helped improving the quality of this manuscript.

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Authors and Affiliations

  1. Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, 48824, USA

    Jiquan Chen, Pietro Sciusco, Zutao Ouyang, Geoffrey M. Henebry & David. P. Roy

  2. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, 48823, USA

    Jiquan Chen, Pietro Sciusco, Zutao Ouyang, Rong Zhang, Geoffrey M. Henebry & David. P. Roy

  3. Department of Biology, University of South Dakota, Vermillion, SD, 57069, USA

    Ranjeet John

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  1. Jiquan Chen
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Chen, J., Sciusco, P., Ouyang, Z. et al. Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions. Landscape Ecol 34, 2917–2934 (2019). https://doi.org/10.1007/s10980-019-00928-2

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