Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data

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.

[1]  A. Arneth,et al.  Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa , 2013 .

[2]  Y A N E R Y A N *, B I N Z H A O,et al.  Closing the carbon budget of estuarine wetlands with tower-based measurements and MODIS time series , 2008 .

[3]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[4]  Yadvinder Malhi,et al.  The productivity, metabolism and carbon cycle of tropical forest vegetation , 2012 .

[5]  W. Oechel,et al.  Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation , 2002 .

[6]  Qichun Yang,et al.  Evaluating the ecological performance of wetland restoration in the Yellow River Delta, China. , 2009 .

[7]  Isidro A. Pérez,et al.  GPP and maximum light use efficiency estimates using different approaches over a rotating biodiesel crop. , 2015 .

[8]  K. Hibbard,et al.  A Global Terrestrial Monitoring Network Integrating Tower Fluxes, Flask Sampling, Ecosystem Modeling and EOS Satellite Data , 1999 .

[9]  S. Goetz,et al.  Satellite remote sensing of primary production : an improved production efficiency modeling approach , 1999 .

[10]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[11]  Gang Fu,et al.  Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau , 2016, Remote. Sens..

[12]  Xiangming Xiao,et al.  Modeling Carbon Fluxes Using Multi-Temporal MODIS Imagery and CO2 Eddy Flux Tower Data in Zoige Alpine Wetland, South-West China , 2014 .

[13]  Christopher Conrad,et al.  Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Wei Li,et al.  Variability and Changes in Climate, Phenology, and Gross Primary Production of an Alpine Wetland Ecosystem , 2016, Remote. Sens..

[15]  Zirui Liu,et al.  Evaluating Parameter Adjustment in the MODIS Gross Primary Production Algorithm Based on Eddy Covariance Tower Measurements , 2014, Remote. Sens..

[16]  R. Delaune,et al.  Carbon Sequestration and Soil Accretion in Coastal Wetland Communities of the Yellow River Delta and Liaohe Delta, China , 2015, Estuaries and Coasts.

[17]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[18]  Leif T. Jensen,et al.  A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments , 1997 .

[19]  Yu Wang,et al.  Agro-C: A biogeophysical model for simulating the carbon budget of agroecosystems , 2009 .

[20]  Rasmus Fensholt,et al.  Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone , 2017, Remote. Sens..

[21]  Ü. Rannik,et al.  Gap filling strategies for defensible annual sums of net ecosystem exchange , 2001 .

[22]  James G. Titus,et al.  Greenhouse effect and coastal wetland policy: How Americans could abandon an area the size of Massachusetts at minimum cost , 1991 .

[23]  X. Cui,et al.  The fluxes of CO2 from grazed and fenced temperate steppe during two drought years on the Inner Mongolia Plateau, China. , 2011, The Science of the total environment.

[24]  G. Jia,et al.  Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data , 2013 .

[25]  B. Cicin-Sain,et al.  Integrated Coastal and Ocean Management: Concepts And Practices , 1998 .

[26]  A. Gitelson,et al.  Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production , 2014 .

[27]  Renqing Wang,et al.  Soil organic carbon of degraded wetlands treated with freshwater in the Yellow River Delta, China. , 2011, Journal of environmental management.

[28]  Craig E. Tweedie,et al.  Spatial and Temporal Variation in Primary Productivity (NDVI) of Coastal Alaskan Tundra: Decreased Vegetation Growth Following Earlier Snowmelt , 2013 .

[29]  Rama Rao Nidamanuri,et al.  Seasonal variations in phenology and productivity of a tropical dry deciduous forest from MODIS and Hyperion. , 2015 .

[30]  T. Vesala,et al.  Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes , 2007 .

[31]  Junhong Bai,et al.  Spatial and Temporal Distributions of Soil Organic Carbon and Total Nitrogen in Two Marsh Wetlands with Different Flooding Frequencies of the Yellow River Delta, China , 2012 .

[32]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[33]  Xiangming Xiao,et al.  Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data , 2007 .

[34]  M. Kirschbaum,et al.  The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage , 1995 .

[35]  Xiaoyong Cui,et al.  Modeling impacts of climate change on carbon dynamics in a steppe ecosystem in Inner Mongolia, China , 2011 .

[36]  S. Frolking,et al.  Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in Northern Asia , 2003 .

[37]  Xiangming Xiao,et al.  Modeling gross primary production of maize cropland and degraded grassland in northeastern China , 2010 .

[38]  Hong Yang,et al.  Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China , 2011 .

[39]  B. Bolker,et al.  Incorporating spatial heterogeneity created by permafrost thaw into a landscape carbon estimate , 2012 .

[40]  S. Frolking,et al.  Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest , 2005 .

[41]  Wang Yu-zhen.  Study on the Wetland Resource and Biodiversity in the Yellow River Delta , 2007 .

[42]  Jinwei Dong,et al.  Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought , 2014 .

[43]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[44]  Xiangming Xiao,et al.  Modeling gross primary production of a temperate grassland ecosystem in Inner Mongolia, China, using MODIS imagery and climate data , 2008 .

[45]  Minseok Kang,et al.  Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images , 2017 .

[46]  Jiyuan Liu,et al.  Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .

[47]  W. Cohen,et al.  Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .

[48]  S. Wofsy,et al.  Physiological responses of a black spruce forest to weather , 1997 .

[49]  Gregory D. Steyer,et al.  A Landscape-Scale Assessment of Above- and Belowground Primary Production in Coastal Wetlands: Implications for Climate Change-Induced Community Shifts , 2017, Estuaries and Coasts.

[50]  Xiangming Xiao,et al.  Satellite-based estimation of evapotranspiration of an old-growth temperate mixed forest , 2009 .

[51]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[52]  D. Baldocchi.  Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future , 2003 .

[53]  W. Oechel,et al.  A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS , 2008 .

[54]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[55]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[56]  Xiangming Xiao,et al.  Modeling gross primary productivity for winter wheat―maize double cropping system using MODIS time series and CO2 eddy flux tower data , 2009 .

[57]  Asko Noormets,et al.  Phenology of ecosystem processes : applications in global change research , 2009 .

[58]  E. Rastetter,et al.  Potential Net Primary Productivity in South America: Application of a Global Model. , 1991, Ecological applications : a publication of the Ecological Society of America.

[59]  Jesse W. Campbell,et al.  Large historical growth in global terrestrial gross primary production , 2017, Nature.

[60]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[61]  W. Walker,et al.  Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support , 2003 .

[62]  Peter A. Vanrolleghem,et al.  Uncertainty in the environmental modelling process - A framework and guidance , 2007, Environ. Model. Softw..

[63]  S. Verma,et al.  Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data , 2011 .

[64]  Yonghoon Choi,et al.  Vegetation succession and carbon sequestration in a coastal wetland in northwest Florida: Evidence from carbon isotopes , 2001 .

[65]  Fulu Tao,et al.  Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties , 2005 .

[66]  D. Baldocchi,et al.  CO2 fluxes over plant canopies and solar radiation: a review , 1995 .

[67]  R. Fensholt,et al.  Evaluating temporal consistency of long-term global NDVI datasets for trend analysis , 2015 .

[68]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[69]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[70]  Lunche Wang,et al.  Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data , 2014, Remote. Sens..

[71]  D. Baldocchi,et al.  Using digital camera and Landsat imagery with eddy covariance data to model gross primary production in restored wetlands , 2017 .

[72]  Nicolas Barbier,et al.  Relationships between phenology, radiation and precipitation in the Amazon region , 2011 .

[73]  Serge Rambal,et al.  Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements , 2013 .

[74]  Shan Chen,et al.  Modeling gross primary production of two steppes in Northern China using MODIS time series and climate data , 2012 .

[75]  R. Leuning,et al.  Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates , 2005 .

[76]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[77]  Dan Liu,et al.  Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database , 2014 .

[78]  Yuesi Wang,et al.  Carbon exchange in a freshwater marsh in the Sanjiang Plain, northeastern China , 2011 .

[79]  D. Hollinger,et al.  MODELING GROSS PRIMARY PRODUCTION OF AN EVERGREEN NEEDLELEAF FOREST USING MODIS AND CLIMATE DATA , 2005 .