Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling

Remote Sensing of Environment

Volumn 114, Issue 10, 2010, Pages 2248-2258

  Wang, Hesong ^a,b   Jia, Gensuo ^a   Fu, Congbin ^a   Feng, Jinming ^a   Zhao, Tianbao ^a   Ma, Zhuguo ^a  

^a INSTITUTE OF ATMOSPHERIC PHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Flux site; Gross primary production (GPP); Maximal light use efficiency ( max); Modeling; Northern China; Satellite

Indexed keywords

BROWN SPOTS; COMPLEX PATTERN; COORDINATED OBSERVATIONS; COVER TYPES; DATA SETS; DRYLAND; DYNAMIC MODELING; EDDY COVARIANCE; ENHANCED VEGETATION INDEX; FLUX MEASUREMENTS; GROSS PRIMARY PRODUCTION; GROWING SEASON; LAND COVER; LAND-COVER TYPES; LIGHT USE EFFICIENCY; MIXED PIXEL; MODELING; NEW APPROACHES; NORTHERN CHINA; PLANT FUNCTIONAL TYPE; REGIONAL DISTRIBUTION; REGIONAL SCALE; REMOTE SENSING MODELS; SATELLITE DATA; SPATIAL AND TEMPORAL VARIATION; SPATIAL HETEROGENEITY; SPECIES COMPOSITION; VEGETATION FRACTIONS; VEGETATION TYPE;

COORDINATION REACTIONS; LANDFORMS; REMOTE SENSING; SATELLITES;

VEGETATION;

ALBEDO; ALPINE ENVIRONMENT; COMMUNITY COMPOSITION; DATA SET; ECOREGION; EDDY COVARIANCE; FLUX MEASUREMENT; FUNCTIONAL CHANGE; GROWING SEASON; HETEROGENEITY; HUMIDITY; LAND COVER; LIGHT USE EFFICIENCY; NUMERICAL MODEL; PRIMARY PRODUCTION; REMOTE SENSING; SATELLITE DATA; SPATIOTEMPORAL ANALYSIS; UNCERTAINTY ANALYSIS; VEGETATION TYPE;

CHINA;

PUNCTUM BLANDIANUM;

EID: 77955269043     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2010.05.001     Document Type: Article

References (41)

1. 2 exchange to the canopy: A generalized model of forest photosynthesis compared with measurements by eddy correlation. Oecologia 1996, 106:257-265.
2. Ahl D.E., Gower S.T., Mackay D.S., Burrows S.N., Norman J.M., Diak G.R. Heterogeneity of light use efficiency in a northern Wisconsin forest: implications for modeling net primary production with remote sensing. Remote Sensing of Environment 2004, 93:168-178.
3. Chasmer L., Barr A., Hopkinson C., McCaughey H., Treitz P., Black A., et al. Scaling and assessment of GPP from MODIS using a combination of airborne lidar and eddy covariance measurements over jack pine forests. Remote Sensing of Environment 2009, 113:82-93.
4. Cheng Y.B., Middleton E.M., Hilker T., Coops N.C., Black T.A., Krishnan P. Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest. Measurement Science and Technology 2009, 20(095107):15.
5. Coops N.C., Ferster C.J., Waring R.H., Nightingale J. Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States. Remote Sensing of Environment 2009, 113:680-690.
6. Drolet G.G., Huemmrich K.F., Hall F.G., Middleton E.M., Black T.A., Barr A.G., et al. A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest. Remote Sensing of Environment 2005, 98:212-224.
7. Drolet G.G., Middleton E.M., Huemmrich K.F., Hall F.G., Amiro B.D., Barr A.G., et al. Regional mapping of gross light-use efficiency using MODIS spectral indices. Remote Sensing of Environment 2008, 112:3064-3078.
8. Falge E., Baldocchi D., Olson R., Anthoni P., Aubinet M., Bernhofer C., et al. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology 2001, 107:43-69.
9. Falge E., Baldocchi D., Tenhunen J., Aubinet M., Bakwin P., Berbigier P., et al. Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agricultural and Forest Meteorology 2002, 113:53-74.
10. Falge E., Tenhunen J., Baldocchi D., Aubinet M., Bakwin P., Berbigier P., et al. Phase and amplitude of ecosystem carbon release and uptake potentials as derived from FLUXNET measurements. Agricultural and Forest Meteorology 2002, 113:75-95.
11. Garbulsky M.F., Penuelas J., Papale D., Filella I. Remote estimation of carbon dioxide uptake by a Mediterranean forest. Global Change Biology 2008, 14:2860-2867.
12. Heinsch F.A., Zhao M.S., Running S.W., Kimball J.S., Nemani R.R., Davis K.J., et al. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing 2006, 44:1908-1925.
13. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83:195-213.
14. Jenkins J.P., Richardson A.D., Braswell B.H., Ollinger S.V., Hollinger D.Y., Smith M.L. Refining light-use efficiency calculations over a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agricultural and Forest Meteorology 2007, 143:64-79.
15. Jung M., Verstraete M., Gobron N., Reichstein M., Papale D., Bondeau A., et al. Diagnostic assessment of European gross primary production. Global Change Biology 2008, 14:2349-2364.
16. Landsberg J.J., Waring R.H. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 1997, 95:209-228.
17. Li Z.Q., Yu G.R., Xiao X.M., Li Y.N., Zhao X.Q., Ren C.Y., et al. Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data. Remote Sensing of Environment 2007, 107:510-519.
18. Liang S.L. Narrowband to broadband conversions of land surface albedo I algorithms. Remote Sensing of Environment 2000, 76:213-238.
19. Liu H.Z., Tu G., Fu C.B., Shi L.Q. Three-year variations of water, energy and fuxes of cropland and degraded grassland surfaces in a semi-arid area of northeastern China. Advances in Atmospheric Sciences 2008, 25(6):1009-1020.
20. Maki M., Ishiahra M., Tamura M. Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment 2004, 90:441-450.
21. Maselli F., Papale D., Puletti N., Chirici G., Corona P. Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems. Remote Sensing of Environment 2009, 113:657-667.
22. 2 flux under varied sky conditions. Remote Sensing of Environment 2007, 109:274-284.
23. Okin G.S. Relative spectral mixture analysis-A multitemporal index of total vegetation cover. Remote Sensing of Environment 2007, 106:467-479.
24. Potter C.S., Randerson J.T., Field C.B., Matson P.A., Vitousek P.M., Mooney, et al. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles 1993, 7(4):811-841.
25. Prince S.D., Goward S.N. Global primary production: A remote sensing approach. Journal of Biogeography 1995, 22:815-835.
26. Running S.W., Nemani R., Glassy J.M., Thornton P.E. MODIS daily photosynthesis (PSN) and annual net primary production (NPP) product (MOD17), algorithm theoretical basis document, version 3.0 http://modis.gsfc.nasa.gov/.
27. Running S.W., Nemani R.R., Heinsch F.A., Zhao M.S., Reeves M., Hashimoto H. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 2004, 54:547-560.
28. Sellers P., Dickinson R., Randall D. Modeling the exchanges of energy, water and carbon between continents and the atmosphere. Science 1997, 275:502-509.
29. Sims D.A., Rahman A.F., Cordova V.D., El-Masri B.Z., Baldocchi D.D., Flanagan L.B., et al. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research 2006, 111:G04015. 10.1029/2006JG000162.
30. Sims D.A., Rahman A.F., Cordova V.D., El-Masri B.Z., Baldocchi D.D., Bolstad P.V., 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. Remote Sensing of Environment 2008, 112:1633-1646.
31. Tian Y., Dickinson R.E., Zhou L., Zeng X., Dai Y., Myneni R.B., et al. Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. Journal of Geophysical Research 2004, 109:D01103. 10.1029/2003JD003777.
32. Trotter G.M., Whitehead D., Pinkney E.J. The photochemical reflectance index as a measure of photosynthetic light use efficiency for plants with varying foliar nitrogen contents. International Journal of Remote Sensing 2002, 23:1207-1212.
33. Turner D.P., Ritts W.D., Cohen W.B., Gower S.T., Zhao M.S., Running S.W., et al. Scaling gross primary production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sensing of Environment 2003, 88:256-270.
34. Turner D.P., Ritts W.D., Cohen W.B., Maeirsperger T.K., Gower S.T., Kirschbaum A.A., et al. Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring. Global Change Biology 2005, 11:666-684.
35. Veroustraete F., Sabbe H., Eerens H. Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sensing of Environment 2002, 83:376-399.
36. Webb E.K., Pearman G.I., Leuning R. Correction of flux measurements for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society 1980, 106:85-100.
37. Wu J.B., Xiao X.M., Guan D.X., Shi T.T., Jin C.J., Han S.J. Estimation of the gross primary production of an old-growth temperate mixed forest using eddy covariance and remote sensing. International Journal of Remote Sensing 2009, 30:463-479.
38. Xiao X.M., Hollinger D., Aber J., Goltz M., Davidson E.A., Zhang Q.Y., et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment 2004, 89:519-534.
39. Xiao X.M., Zhang Q.Y., Braswell B., Urbanski S., Boles S., Wofsy S., et al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 2004, 91:256-270.
40. 2 eddy flux tower data. Agriculture Ecosystems and Environment 2009, 129:391-400.
41. Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003, 84:471-475.