Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize

Journal of Geophysical Research Atmospheres

Volumn 115, Issue 12, 2010, Pages

  Wu, Chaoyang ^a,b   Niu, Zheng ^a   Gao, Shuai ^a,b  

^a INSTITUTE OF REMOTE SENSING APPLICATIONS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; ESTIMATION; METEOROLOGY; RADIOMETERS; REMOTE SENSING; SATELLITE IMAGERY;

CARBON CYCLES; DETERMINATION COEFFICIENTS; EDDY COVARIANCE TECHNIQUE; ENHANCED VEGETATION INDEX; ESTIMATION MODELS; GROSS PRIMARY PRODUCTION; IN-SITU; LIGHT USE EFFICIENCY; METEOROLOGICAL DATA; METEOROLOGICAL MEASUREMENTS; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; NORMALIZED DIFFERENCE VEGETATION INDEX; PHOTOSYNTHETICALLY ACTIVE RADIATION; SURFACE REFLECTANCE; VEGETATION INDEX;

VEGETATION;

CARBON CYCLE; CLIMATE CHANGE; EDDY COVARIANCE; ESTIMATION METHOD; LIGHT USE EFFICIENCY; MAIZE; METEOROLOGY; MODIS; NDVI; PHOTOSYNTHETICALLY ACTIVE RADIATION; PRIMARY PRODUCTION; REMOTE SENSING; SURFACE REFLECTANCE;

ZEA MAYS;

EID: 77954482777     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009JD013023     Document Type: Article

References (52)

1. Bacour, C., F. Baret, D. Béal, M. Weiss, and K. Pavageau (2006), Neural network estimation of LAI, /ÄPAR, fCover and LAI x Cab, from top of canopy MERIS reflectance data: Principles and validation, Remote Sens. Environ., 105, 313-325.
2. Baldocchi, D. D., C. A. Vogel, and B. Hall (1997), Seasonal variation of carbon dioxide exchange rates above and below a borealjack pine forest, Agrie. For. Meteorol., 83, 147-170.
3. Behrenfeld, M. J., J. T. Rand erson, C. R. McClain, G. C. Feldman, S. O. Los, C. J. Tucker, P. G. Falkowski, C. B. Field, R. Frouin, W. E. Esaias, D. D. Kolber, and N. H. Pollack (2001), Biospheric primary production during an ENSO transition, Science, 291, 2594-2597.
4. Broge, N. H., and E. Leblanc (2001), Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density, Remote Sens. Environ., 76(2), 156-172.
5. Broge, N. H., and J. V. Mortensen (2002), Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sens. Environ., 81, 45-57.
6. Brooks, A., and G. D. Farquhar (1985), Effects of temperature on the CO2/O2 specificity of ribulose-1, 5-bisphosphate carboxylase/oxygenase and the rate of respiration in the light, Planta, 165, 397-406.
7. Chen, W. J., T. A. Black, P. C. Yang, A. G. Barr, H. H. Neumann, Z. Nesic, P. D. Blanken, M. D. Novak, J. Eley, R. J. Ketler, and R. Cuenca (1999), Effect of climatic variability on the annualcarbon sequestration by a bor-ealaspen forest, Global Change Biol., 5, 41-53.
8. Clevers, J. G. P. W. (1989), The application of a weighted infra-red vegetation index for estimating leaf area index by correcting for soil moisture, Remote Sens. Environ., 29, 25-37.
9. Coops, N. C., T. A. Black, R. S. Jassal, J. A. Trofymow, and K. Morgenstern (2007), Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand, Remote Sens. Environ., 107, 385-401.
10. Drolet, G. G., K. F. Huemmrich, and F. G. Hall (2005), A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest, Remote Sens. Environ., 98, 212-224.
11. Gamon, J. A., L. Serrano, and J. S. Surfus (1997), The photochemical reflectance index: An optical indicator ofphotosynthesis radiation use efficiency across species, functional types, and nutrient levels, Oeeologia, 112, 492-501.
12. Gamon, J. A., A. F. Rahman, J. L. Dungan, M. Schildhauer, and K. F. Huemmrich (2006), Spectral Network (SpecNet)-What is it and why do we need it? Remote Sens. Environ., 103, 227-235.
13. Gebremichael, M., and A. P. Barross (2006), Evaluation of MODIS gross primary productivity (GPP) in tropical monsoon regions, Remote Sens. Environ., 100, 150-166.
14. Gitelson, A. A., A. Viña, V. Ciganda, D. C. Rundquist, and T. J. Arkebauer (2005), Remote estimation of canopy chlorophyll content in crops, Geophys. Res. Lett., 32, L08403, doi:10.1029/2005GL022688. (Pubitemid 41043257)
15. Gitelson, A. A., A. Viña, S. B. Verma, D. C. Rundquist, T. J. Arkebauer, G. Keydan, B. Leavitt, V. Cigand a, G. G. Burba, and A. E. Suyker (2006), Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity, J. Geophys. Res., 111, D08S11, doi:10.1029/2005JD006017.
16. Gitelson, A.A., A.Viña, J.G.Masek, S.B.Verma, and A.E.Suyker (2008), Synoptic monitoring of gross primary productivity of maize using land sat data, IEEE Geosei. Remote Sens. Lett., doi:10.1109/LGRS.2008.915598.
17. Huete, A. R. (1988), A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295-309.
18. Huete, A. R., H. Q. Liu, K. Batchily, and W. J. D. van Leeuwen (1997), A comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sens. Environ., 59, 440-451.
19. Huete, A., K.Didan, T. Miura, E. Rodriguez, X. Gao, and L. Ferreira (2002), Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195-213.
20. Inoue, Y., J. Penuelas, A. Miyata, and M. Mano (2008), Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice, Remote Sens. Environ., 112, 156-172.
21. Kucharik, C. J., J. M. Norman, and S. T. Gower (1998), Measurements of branch area and adjusting leaf area index indirect measurements, Agric. For. Meteorol., 91, 69-88.
22. Law, B. E., D. D. Baldocchi, and P. M. Anthoni (1999), Below-canopy and soil CO2 fluxes in a ponderosa pine forest, Agric. For. Meteorol., 94, 171-188.
23. Li, X., X. Li, Z. Li, M. Ma, J. Wang, Q. Xiao, Q. Liu, T. Che, E. Chen, G. Yan, Z. Hu, L. Zhang, R. Chu, P. Su, Q. Liu, S. Liu, J. Wang, Z. Niu, Y. Chen, R. Jin, W. Wang, Y. Ran, X. Xin, and H. Ren (2009), Water-shed allied telemetry experimental research, J. Geophys. Res., 114, D22103, doi:10.1029/2008JD011590.
24. Li, Z. Q., G. R. Yu, X. M. Xiao, Y. N. Li, X. Q. Zhao, C. Y. Ren, L. M. Zhang, and Y. L. Fu (2007), Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data, Remote Sens. Environ., 107, 510-519.
25. Liang, S., T. Zheng, R. Liu, H. Fang, S. C Tsay, and S. Running (2006), Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data, J. Geophys. Res., 111, D15208, doi:10.1029/2005JD006730. (Pubitemid 46028996)
26. Liu, R., S. Liang, H. He, J. Liu, and T. Zheng (2008), Mapping incident photosynthetically active radiation from MODIS data over China, Remote Sens. Environ., 112, 998-1009.
27. Metz, B., O. Davidson, H. D. Coninck, M. Loos, and L. Meyer (2006), Special report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Clim. Change, World Meterorol. Organ., Geneva.
28. Monteith, J. L. (1972), Solar radiation and production in tropical ecosystems, J. Appl. Ecol., 9, 747-766.
29. Nakaji, T., H. Oguma, and Y. Fujinuma (2006), Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles, Int. J. Remote Sens., 27, 493-509.
30. Nakaji, T., R. Ide, H. Oguma, N. Saigusa, and Y. Fujinum (2007), Utility of spectral vegetation index for estimation of gross CO2 flux under varied sky conditions, Remote Sens. Environ., 109, 274-284.
31. Nichol, C. J., K. F. Huemmrich, and T. A. Black (2000), Remote sensing of photosynthetic-light-use efficiency of boreal forest, Agric. For. Meteorol., 101, 131-142.
32. Osmond, B., G. Ananyev, J. Berry, C. Langdon, Z. Kolber, G. H. Lin, R. Monson, C. Nichol, U. Rascher, U. Schurr, S. Smith, and D. Yakir (2004), Changing the way we think about global change research: Scaling up in experimental ecosystem science, Global Change Biol., 10, 393-407.
33. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey (2003), The MODIS cloud products: Algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459-473.
34. Rouse, J. W., R. H. Haas, J. A. Schell, D. W. Deering, and J. C. Harlan (1974), Monitoring the vernal advancements and retrogradation of natural vegetation, final report, pp. 1-137, NASA Goddard Space Flight Center, Greenbelt, Md.
35. Ruimy, A., L. Kergoat, and A. Bondeau (1999), Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency, Global Change Biol., 5, 56-64. (Pubitemid 29370532)
36. Running, S. W., P. E. Thornton, R. Nemani, and J. M. Glassy (2000), Global terrestrial gross and net primary productivity from the Earth Observing System, in Methods in Ecosystem Science, edited by O. E. Sala, R. B. Jackson, and H. A. Mooney, pp. 44-57, Springer, New York.
37. Running, S. W., R. R. Nemani, F. A. Heinsch, M. S. Zhao, M. Reeves, and H. Hashimoto (2004), A continuous satellite-derived measure of global terrestrial primary production, Bioscience, 54, 547-560.
38. Sims, D. A., A. F. Rahman, V. D. Cordova, D. D. Baldocchi, L. B. Flanagan, A. H. Goldstein, D. Y. Hollinger, L. Misson, R. K. Monson, H. P. Schmid, S. C. Wofsy, and L. K. Xu (2005), Midday values of gross CO2 flux and light use efficiency during satellite overpasses can be used to directly estimate eight-day mean flux, Agrie. For. Meteorol., 131, 1-12.
39. Sims, D. A., A. F. Rahman, V. D. Cordova, B. Z. El-Masri, D. D. Baldocchi, L.B.Flanagan, A.H. Goldstein, D.Y. Hollinger, L.Misson, R. K. Monson, W.C.Oechel, H.P.Schmid, S.C.Wofsy, and L.Xu (2006a), On the use of MODIS EVI to assess gross primary productivity of North American ecosystems, J. Geophys. Res., 111, G04015, doi:10.1029/2006JG000162. (Pubitemid 47491027)
40. Sims, D. A., H. Luo, S. Hastings, W. C. Oechel, A. F. Rahman, and J. A. Gamon (2006b), Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem, Remote Sens. Environ., 103, 289-303.
41. Sims, D. A., A. F. Rahman, V. D. Cordova, B. Z. El-Masri, D. D. Baldocchi, P. V. Bolstad, L. B. Flanagan, A. H. Goldstein, D. Y. Hollinger, L. Misson, R. K. Monson, W. C. Oechel, H. P. Schmid, S. C. Wofsy, and L. K. Xu (2008), 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 Sens. Environ., 112, 1633-1646.
42. Viña, A., and A. A. Gitelson (2005), New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops, Geophys. Res. Lett., 32, L17403, doi:10.1029/2005GL023647. (Pubitemid 41536209)
43. Wang, H., N. Saigusa, S. Yamamoto, H. Kondo, T. Hirano, A. Toriyama, and Y. Fujinuma (2004), Net ecosystem CO2 exchange over a larch forest in Hokkaido, Japan, Atmos. Environ., 38, 7021-7032.
44. Waring, R. H., N. C. Coops, W. Fan, and J. M. Nightingale (2006), MODIS enhanced vegetation index predicts tree species richness across forested ecor-egions in the contiguous U.S.A., Remote Sens. Environ., 103, 218-226.
45. Wilson, K. B., P. J. Hanson, P. J. Mulholland, D. D. Baldocchi, and S. D. Wullschleger (2001), A comparison of methods for determining forest evapotranspiration and its components: Sap-flow, soil water budget, eddy covariance and catchment water balance, Agrie. For. Meteorol., 106, 153-168.
46. Wu, C., Z. Niu, Q. Tang, W. Huang, B. Rivard, and J. Feng (2009), Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices, Agrie. For. Meteorol., 149, 1015-1021.
47. Xiao, X., B. Braswell, Q. Zhang, S. Boles, S. Frolking, and B. Moore (2003), Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in northern Asia, Remote Sens. Environ., 84, 385-392.
48. Xiao, X. M., D. Hollinger, J. Aber, M. Goltz, E. A. Davidson, Q. Y. Zhang, and B. Moore III (2004), Satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sens. Environ., 89, 519-534.
49. Yan, H., Y. Fu, X. Xiao, H. Q. Huang, H. He, and E. Laura (2009), Modeling gross primary productivity for winter wheat-maize double cropping system using MODIS time series and CO2 eddy flux tower data, Agrie. Eeosyst. Environ., 129, 391-400.
50. Yuan, W., S. Liu, G. S. Zhou, G. Y. Zhou, L. L. Tieszen, D. Baldocchi, C. Bernhofer, H. Gholz, A. H. Goldstein, M. L. Goulden, D. Y. Hollinger, Y. Hu, B. E. Law, P. C. Stoy, T. Vesala, and S. C. Wofsy (2007), Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes, Agrie. For. Meteorol., 143, 189-207.
51. Zhang, Q., E. M. Middleton, H. A. Margolis, G. G. Drolet, A. A. Barr, and T. A. Black (2009), Can a satellite-derived estimate of the fraction of PAR absorbed by chlorophyll (FAPARchl) improve predictions of light-use efficiency and ecosystem photosynthesis for a boreal aspen forest? Remote Sens. Environ., 113, 880-888.
52. Zhang, X. Y., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, and A. Huete (2003), Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471-475.