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

Abstract

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). 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. 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. We achieved satisfactorymore » 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. 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. Finally, 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.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [1]
  1. Michigan State Univ., East Lansing, MI (United States)
  2. Univ. of South Dakota, Vermillion, SD (United States)
Publication Date:
Research Org.:
Univ. of Wisconsin, Madison, WI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Aeronautic and Space Administration (NASA)
OSTI Identifier:
1600743
Grant/Contract Number:  
SC0018409; FC02-07ER64494
Resource Type:
Accepted Manuscript
Journal Name:
Landscape Ecology
Additional Journal Information:
Journal Volume: 34; Journal Issue: 12; Journal ID: ISSN 0921-2973
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; downscaling; MODIS; landsat; GPP; ET; albedo; Kalamazoo River watershed

Citation Formats

Chen, Jiquan, Sciusco, Pietro, Ouyang, Zutao, Zhang, Rong, Henebry, Geoffrey M., John, Ranjeet, and Roy, David. P. Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions. United States: N. p., 2019. Web. doi:10.1007/s10980-019-00928-2.
Chen, Jiquan, Sciusco, Pietro, Ouyang, Zutao, Zhang, Rong, Henebry, Geoffrey M., John, Ranjeet, & Roy, David. P. Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions. United States. doi:https://doi.org/10.1007/s10980-019-00928-2
Chen, Jiquan, Sciusco, Pietro, Ouyang, Zutao, Zhang, Rong, Henebry, Geoffrey M., John, Ranjeet, and Roy, David. P. Wed . "Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions". United States. doi:https://doi.org/10.1007/s10980-019-00928-2. https://www.osti.gov/servlets/purl/1600743.
@article{osti_1600743,
title = {Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions},
author = {Chen, Jiquan and Sciusco, Pietro and Ouyang, Zutao and Zhang, Rong and Henebry, Geoffrey M. and John, Ranjeet and Roy, David. P.},
abstractNote = {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). 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. 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. 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. 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. Finally, 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.},
doi = {10.1007/s10980-019-00928-2},
journal = {Landscape Ecology},
number = 12,
volume = 34,
place = {United States},
year = {2019},
month = {11}
}

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