A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands |
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| Institution: | 1. Graduate Student, Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China, and University of Chinese Academy of Sciences, Beijing 100049, China;2. Associate Professor, Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;3. Research Assistant Professor, Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA;4. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;5. Postdoctoral Researcher, Laboratoire des Sciences du Climat et de l''Environnement, CEA-CNRS-UVSQ;6. Professor, Chinese Academy of Meteorological Sciences, Beijing 100081, China.;1. School of Natural Resources, University of Nebraska Lincoln, Lincoln, NE 68583, USA;2. Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada;3. Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada;4. Alberta Environment and Parks, Government of Alberta, Edmonton, AB T5J 5C6, Canada;5. Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA;1. Fenner School of Environment and Society, The Australian National University, ACT, Canberra, Australia;2. CSIRO Oceans and Atmosphere Flagship, ACT, Canberra, Australia;3. Bushfire & Natural Hazards Cooperative Research Centre, Melbourne, Australia;4. CSIRO Land and Water Flagship, ACT, Australia |
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| Abstract: | Gross primary productivity (GPP) is a key component of ecosystem carbon fluxes and the carbon balance between the biosphere and the atmosphere. Accurate estimation of GPP is essential for quantifying plant production and carbon balance for grasslands. Satellite-derived vegetation indices (VIs) are often used to approximate GPP. The widely used VIs include atmospherically resistant vegetation index, enhanced vegetation index (EVI), normalized difference greenness index, normalized difference vegetation index, reduced simple ratio, ratio vegetation index, and soil-adjusted vegetation index (SAVI). The evaluation of the performance of these VIs for approximating GPP, however, has been limited to one or two VIs and/or using GPP observations from one or two sites. In this study, we examined the relationships between the nine VIs derived from the moderate resolution imaging spectroradiometer (MODIS) and tower-based GPP at five eddy covariance flux sites over the grasslands of northern China. Our results showed that the nine VIs were generally good predictors of GPP for grasslands of northern China. Overall, EVI was the best predictor. The correlation between EVI and GPP also declined from the south to the north, indicating that EVI and GPP exhibited closer relationships in more southerly sites with higher vegetation cover. We also examined the seasonal influence on the correlation between VIs and GPP. SAVI exhibited the best correlation with GPP in spring when the grassland canopy was sparse, while EVI exhibited the best correlation with GPP in summer when the grassland cover was dense. Our results also showed that VIs could capture variations in observed GPP better in drought period than in nondrought period for an alpine meadow site because of the suppression of vegetation growth by drought. |
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