Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data
1
Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China
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Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China
3
Department of Biology Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC C3H 3P8, Canada
4
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5
Center for Ecological Forecasting and Global Change, College of Forestry, Northwest Agriculture and Forest University, Yangling 712100, China
6
School of Environmental Science and Engineering, Tianjin University, China-Australia Centre for Sustainable Urban Development, Tianjin 300072, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 708; https://doi.org/10.3390/rs10050708
Received: 19 March 2018
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Revised: 28 April 2018
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Accepted: 3 May 2018
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Published: 4 May 2018
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPPEC). Temperature can explain most of the seasonal variation in VIs and GPPEC fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPPVPM) and GPPEC indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPPEC fluxes in terms of patterns and magnitudes, explaining about 85% of GPPEC changes over the study years (p < 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink/source.
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Keywords:
coastal wetland; eddy covariance; gross primary production; MODIS; vegetation indices; VPM
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MDPI and ACS Style
Kang, X.; Yan, L.; Zhang, X.; Li, Y.; Tian, D.; Peng, C.; Wu, H.; Wang, J.; Zhong, L. Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data. Remote Sens. 2018, 10, 708. https://doi.org/10.3390/rs10050708
AMA Style
Kang X, Yan L, Zhang X, Li Y, Tian D, Peng C, Wu H, Wang J, Zhong L. Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data. Remote Sensing. 2018; 10(5):708. https://doi.org/10.3390/rs10050708
Chicago/Turabian StyleKang, Xiaoming, Liang Yan, Xiaodong Zhang, Yong Li, Dashuan Tian, Changhui Peng, Haidong Wu, Jinzhi Wang, and Lei Zhong. 2018. "Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data" Remote Sensing 10, no. 5: 708. https://doi.org/10.3390/rs10050708
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