The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation |
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| Institution: | 1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;2. Plant Functional Biology and Climate Change Cluster (C3), University of Technology, Sydney, NSW, 2007, Australia;3. University of Chinese Academy of Sciences, Beijing, 100049, China;4. School of Geography and Environmental Science, Monash University, Melbourne, Victoria, 3800, Australia;5. Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina, Northern Territory, 0909, Australia;6. Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia;7. Australian Supersite Network, Terrestrial Ecosystem Research Network, University of Technology, Sydney, NSW, 2007, Australia;8. National Centre for Groundwater Research and Training, University of Technology, Sydney, NSW, 2007, Australia |
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| Abstract: | Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally. |
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| Keywords: | Enhanced vegetation index Flux Gross primary production Remote sensing Climate change Carbon cycle |
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