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• W4310378889 startingPage "6062" @default.
• W4310378889 abstract "Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks." @default.
• W4310378889 created "2022-12-10" @default.
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• W4310378889 date "2022-11-29" @default.
• W4310378889 modified "2023-10-14" @default.
• W4310378889 title "How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production" @default.
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• W4310378889 doi "https://doi.org/10.3390/rs14236062" @default.
• W4310378889 hasPublicationYear "2022" @default.
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