A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery

[1] Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground-based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower-based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m2/16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.

[1]  W. Oechel,et al.  On the use of MODIS EVI to assess gross primary productivity of North American ecosystems , 2006 .

[2]  Jing M. Chen,et al.  Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration , 2011 .

[3]  Ye Qi,et al.  Effects of climate variability on the carbon dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in the Sierra Nevada (CA) , 2000 .

[4]  S. Ganguly,et al.  Amazon forests did not green‐up during the 2005 drought , 2009 .

[5]  D. Hollinger,et al.  Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements , 2007 .

[6]  A. Huete,et al.  Amazon Forests Green-Up During 2005 Drought , 2007, Science.

[7]  José M. Paruelo,et al.  REGIONAL PATTERNS OF NORMALIZED DIFFERENCE VEGETATION INDEX IN NORTH AMERICAN SHRUBLANDS AND GRASSLANDS , 1995 .

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

[9]  W. Cropper,et al.  Carbon exchange of a mature, naturally regenerated pine forest in north Florida , 2008 .

[10]  Markus Reichstein,et al.  Cross-site evaluation of eddy covariance GPP and RE decomposition techniques , 2008 .

[11]  T. A. Black,et al.  The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements. , 2008, The Science of the total environment.

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

[13]  J. Monteith.  SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[14]  K. Hibbard,et al.  Postfire carbon pools and fluxes in semiarid ponderosa pine in Central Oregon , 2007 .

[15]  Ramakrishna R. Nemani,et al.  Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.

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

[17]  A. Viña,et al.  Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity , 2006 .

[18]  K. Davis,et al.  Comparing net ecosystem exchange of carbon dioxide between an old-growth and mature forest in the upper Midwest, USA , 2005 .

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

[20]  T. Vesala,et al.  On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .

[21]  M. Susan Moran,et al.  Carbon dioxide exchange in a semidesert grassland through drought‐induced vegetation change , 2010 .

[22]  W. Oechel,et al.  Mature semiarid chaparral ecosystems can be a significant sink for atmospheric carbon dioxide , 2007 .

[23]  Timothy Martin,et al.  Surface energy and carbon dioxide fluxes above different vegetation types within ABLE , 2006 .

[24]  Hans Peter Schmid,et al.  Biometric and eddy-covariance based estimates of annual carbon storage in five eastern North American deciduous forests , 2002 .

[25]  N. Phillips,et al.  Water use and carbon exchange of red oak- and eastern hemlock-dominated forests in the northeastern USA: implications for ecosystem-level effects of hemlock woolly adelgid. , 2008, Tree physiology.

[26]  Jing M. Chen,et al.  Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U.S. against flux measurements and a process model , 2012 .

[27]  Hideki Kobayashi,et al.  Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales , 2011 .

[28]  A. Gitelson,et al.  Remote estimation of crop gross primary production with Landsat data , 2012 .

[29]  D. Baldocchi,et al.  Inter-annual variability in carbon dioxide exchange of an oak/grass savanna and open grassland in California , 2007 .

[30]  T. Carlson.  An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

[31]  Ge Sun,et al.  Response of carbon fluxes to drought in a coastal plain loblolly pine forest , 2010 .

[32]  S. Running,et al.  Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data , 2008 .

[33]  R. Valentini,et al.  A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization , 2003 .

[34]  Patrick E. Van Laake,et al.  Mapping PAR using MODIS atmosphere products , 2005 .

[35]  Shunlin Liang,et al.  Mapping High-Resolution Incident Photosynthetically Active Radiation over Land from Polar-Orbiting and Geostationary Satellite Data , 2007 .

[36]  Andrew E. Suyker,et al.  Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems , 2005 .

[37]  Christopher M. Gough,et al.  Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery , 2012 .

[38]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[39]  D. Sims,et al.  Potential of MODIS EVI and surface temperature for directly estimating per‐pixel ecosystem C fluxes , 2005 .

[40]  H. Schmid,et al.  Uncertainty of annual net ecosystem productivity estimated using eddy covariance flux measurements , 2006 .

[41]  Todd N. Rosenstiel,et al.  Climatic influences on net ecosystem CO2 exchange during the transition from wintertime carbon source to springtime carbon sink in a high-elevation, subalpine forest , 2005, Oecologia.

[42]  R. Valentini,et al.  Cross validation of open-top chamber and eddy covariance measurements of ecosystem CO2 exchange in a Florida scrub-oak ecosystem , 2003 .

[43]  Carl J. Bernacchi,et al.  Carbon budget of mature no-till ecosystem in North Central Region of the United States , 2005 .

[44]  F. Woodward.  Climate and plant distribution , 1987 .

[45]  Eric A. Davidson,et al.  Spatial and temporal variability in forest–atmosphere CO2 exchange , 2004 .

[46]  J. Moreno,et al.  Remote sensing of sun‐induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP) , 2010 .

[47]  S. Wofsy,et al.  Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest , 2007 .

[48]  D. Kuang,et al.  Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest , 2010 .

[49]  Anatoly A. Gitelson,et al.  Remote estimation of gross primary productivity in crops using MODIS 250m data , 2013 .

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

[51]  Paul V. Bolstad,et al.  Using Light-Use and Production Efficiency Models to Predict Photosynthesis and Net Carbon Exchange During Forest Canopy Disturbance , 2008, Ecosystems.

[52]  Patrick E. Van Laake,et al.  Simplified atmospheric radiative transfer modelling for estimating incident PAR using MODIS atmosphere products , 2004 .

[53]  Mark Heuer,et al.  Influences of biomass heat and biochemical energy storages on the land surface fluxes and radiative temperature , 2007 .

[54]  Y. Malhi,et al.  Tropical forests and atmospheric carbon dioxide. , 2000, Trends in ecology & evolution.

[55]  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 .

[56]  Ge Sun,et al.  Drought during canopy development has lasting effect on annual carbon balance in a deciduous temperate forest. , 2008, The New phytologist.

[57]  Marcy E. Litvak,et al.  An eddy covariance mesonet to measure the effect of forest age on land–atmosphere exchange , 2006 .

[58]  Chaoyang Wu,et al.  Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize , 2010 .

[59]  W. Oechel,et al.  A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data , 2010, Remote Sensing of Environment.

[60]  P. Jarvis.  The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field , 1976 .