Advertisement

Chinese Geographical Science

, Volume 25, Issue 5, pp 537–548 | Cite as

Sensitivity of near real-time MODIS gross primary productivity in terrestrial forest based on eddy covariance measurements

  • Xuguang Tang
  • Hengpeng Li
  • Guihua Liu
  • Xinyan Li
  • Li Yao
  • Jing Xie
  • Shouzhi Chang
Article

Abstract

As an important product of Moderate Resolution Imaging Spectroradiometer (MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity (GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance (EC) technique, few have comprehensively validated the performance of MODIS estimates (Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), and mixed forest (MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.

Keywords

MOD17A2 FLUXNET community eddy covariance (EC) gross primary productivity (GPP) forest ecosystem evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Archibald S A, Kirton A, Van der Merwe M et al., 2009. Drivers of inter-annual variability in net ecosystem exchange in a semi-arid savanna ecosystem, South Africa. Biogeosciences, 6(2): 251–266. doi:  10.5194/bgd-5-3221-2008 CrossRefGoogle Scholar
  2. Aubinet M, Heinesch B, Longdoz B, 2002. Estimation of the carbon sequestration by a heterogeneous forest: night flux corrections, heterogeneity of the site and inter-annual variability. Global Change Biology, 8(11): 1053–1071. doi:  10.1046/j.1365-2486.2002.00529.x CrossRefGoogle Scholar
  3. Beer C, Reichstein M, Tomelleri E et al., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329(5993): 834–838. doi:  10.1126/science.1184984 CrossRefGoogle Scholar
  4. Berbigier P, Bonnefonda J M, Mellmann P, 2001. CO2 and water vapour fluxes for 2 years above Euroflux forest site. Agricultural and Forest Meteorology, 108(3): 183–197. doi:  10.1016/S0168-1923(01)00240-4 CrossRefGoogle Scholar
  5. Betts A K, Zhao M, Dirmeyer P A et al., 2006. Comparison of ERA40 and NCEP/DOE near-surface data sets with other ISLSCP-II data sets. Journal of Geophysical Research: Atmospheres (1984–2012), 111(D22). doi:  10.1029/2006JD007174
  6. Blonquist J, Montzka S A, Yakir D et al., 2010. The potential of carbonyl sulfide as a tracer for gross primary productivity at flux tower sites. American Geophysical Union Fall Meeting, B21G-07. doi:2010AGUFM.B21G.07BGoogle Scholar
  7. Carvalhais N, Reichstein M, Ciais P et al., 2010. Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints. Global Change Biology, 16(10): 2813–2829. doi:  10.1111/j.1365-2486.2010.02173 CrossRefGoogle Scholar
  8. Chasmer L, Barr A, Hopkinson C et al., 2009. 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, 113(1): 82–93. doi:  10.1016/j.rse.2008.08.009 CrossRefGoogle Scholar
  9. Cohen W B, Maiersperger T K, Yang Z et al., 2003. Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sensing of Environment, 88(3): 233–255. doi:  10.1016/j.rse.2003.06.006 CrossRefGoogle Scholar
  10. Coops N C, Black T A, Jassal R P S et al., 2007. Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand. Remote Sensing of Environment, 107(3): 385–401. doi:  10.1016/j.rse.2006.09.010 CrossRefGoogle Scholar
  11. Desai A R, 2010. Climatic and phenological controls on coherent regional interannual variability of carbon dioxide flux in a heterogeneous landscape. Journal of Geophysical Research, 115: G00J02. doi:  10.1029/2010JG001423
  12. Don A, Rebmann C, Kolle O et al., 2009. Impact of afforestation-associated management changes on the carbon balance of grassland. Global Change Biology, 15(8): 1990–2002. doi:  10.1111/j.1365-2486.2009.01873.x CrossRefGoogle Scholar
  13. Dragoni D, Schmid H P, Wayson C A et al., 2011. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Global Change Biology, 17(2): 886–897. doi:  10.1111/j.1365-2486.2010.02281.x CrossRefGoogle Scholar
  14. Garbulsky M F, Peñuelas J, Papale D et al., 2008. Remote estimation of carbon dioxide uptake of terrestrial ecosystems. Global Change Biology, 14(12): 2860–2867. doi:  10.1111/j.1365-2486.2008.01684.x CrossRefGoogle Scholar
  15. Granier A, Pilegaard K, Jensen N O, 2002. Similar net ecosystem exchange of beech stands located in France and Denmark. Agricultural and Forest Meteorology, 114(1): 75–82. doi:  10.1016/S0168-1923(02)00137-5 CrossRefGoogle Scholar
  16. Grant R F, Hutyra L R, de Oliveira R C et al., 2009. Modeling the carbon balance of Amazonian rain forests: resolving ecological controls on net ecosystem productivity. Ecological Monographs, 79(3): 445–463. doi:  10.1890/08-0074.1 CrossRefGoogle Scholar
  17. Grünwald T, Bernhofer C, 2007. A decade of carbon, water and energy flux measurements of an old spruce forest at the Anchor Station Tharandt. Tellus B, 59(3): 387–396. doi:  10.1111/j.1600-0889.2007.00259.x CrossRefGoogle Scholar
  18. Heinsch F A, Zhao M, Running S W et al., 2006. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1908–1925. doi:  10.1109/TGRS.2005.853936 CrossRefGoogle Scholar
  19. Houghton R A, Hackler J L, Lawrence K T, 1999. The US carbon budget: contributions from land-use change. Science, 285 (5427): 574–578. doi:  10.1126/science.285.5427.574 CrossRefGoogle Scholar
  20. Kanamitsu M, Ebisuzaki W, Woollen J et al., 2002. Ncep-Doe Amip-Ii Reanalysis (r-2). Bulletin of the American Meteorological Society, 83(11): 1631–1643. doi:  10.1175/BAMS-83-11-1631 CrossRefGoogle Scholar
  21. Kanniah K D, Beringer J, Hutley L B et al., 2009. Evaluation of collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia. Remote Sensing of Environment, 113(9): 1808–1822. doi:  10.1016/j.rse.2009.04.013 CrossRefGoogle Scholar
  22. Knohl A, Schulze E D, Kolle O et al., 2003. Large carbon uptake by an unmanaged 250-year-old deciduous forest in Central Germany. Agricultural and Forest Meteorology, 118(3): 151–167. doi:  10.1016/S0168-1923(03)00115-1 CrossRefGoogle Scholar
  23. Lagergren F, Eklundh L, Grelle A et al., 2005. Net primary production and light use efficiency in a mixed coniferous forest in Sweden. Plant, Cell & Environment, 28(3): 412–423. doi:  10.1111/j.1365-3040.2004.01280.x CrossRefGoogle Scholar
  24. Liang S, Wang K, Zhang X et al., 2010. Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 225–240. doi:  10.1109/JSTARS.2010.2048556 CrossRefGoogle Scholar
  25. Lloyd J, Taylor J, 1994. On the temperature dependence of soil respiration. Functional Ecology, 8(3): 315–323. doi:  10.2307/2389824 CrossRefGoogle Scholar
  26. Morales P, Sykes M T, Prentice I C et al., 2005. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Global Change Biology, 11(12): 2211–2233. doi:  10.1111/j.1365-2486.2005.01036.x CrossRefGoogle Scholar
  27. Myneni R B, Ramakrishna R, Nemani R et al., 1997. Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 35(6): 1380–1393. doi:  10.1109/36.649788 CrossRefGoogle Scholar
  28. Nightingale J M, Coops N C, Waring R H et al., 2007. Comparison of MODIS gross primary production estimates for forests across the USA with those generated by a simple process model, 3-PGS. Remote Sensing of Environment, 109(4): 500–509. doi:  10.1016/j.rse.2007.02.004 CrossRefGoogle Scholar
  29. Pan S, Tian H, Dangal S R et al., 2014. Modeling and monitoring terrestrial primary production in a changing global environment: toward a multiscale synthesis of observation and simulation. Advances in Meteorology, ID965936. doi:  10.1155/2014/965936 Google Scholar
  30. Papale D, Valentini R, 2003. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology, 9(4): 525–535. doi:  10.1046/j.1365-2486.2003.00609.x CrossRefGoogle Scholar
  31. Propastin P, Ibrom A, Knohl A et al., 2012. Effects of canopy photosynthesis saturation on the estimation of gross primary productivity from MODIS data in a tropical forest. Remote Sensing of Environment, 121(6): 252–260. doi:  10.1016/j.rse.2012.02.005 CrossRefGoogle Scholar
  32. Rahman A F, Sims D A, Cordova V D et al., 2005. Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes. Geophysical Research Letters, 32(19): L19404. doi:  10.1029/2005GL024127 CrossRefGoogle Scholar
  33. Reichstein M, Falge E, Baldocchi D et al., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11(9): 1424–1439. doi:  10.1111/j.1365-2486.2005.001002.x CrossRefGoogle Scholar
  34. Richardson A D, Anderson R S, Arain M A et al., 2012. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Global Change Biology, 18(2): 566–584. doi:  10.1111/j.1365-2486.2011.02562.x CrossRefGoogle Scholar
  35. Running S W, Nemani R R, Heinsch F A et al., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6): 547–560. doi:  10.1641/0006-3568 CrossRefGoogle Scholar
  36. Saigusa N, Yamamoto S, Hirata R et al., 2008. Temporal and spatial variations in the seasonal patterns of CO2 flux in boreal, temperate, and tropical forests in East Asia. Agricultural and Forest Meteorology, 148(5): 700–713. doi:  10.1016/j.agrformet.2007.12.006 CrossRefGoogle Scholar
  37. Seiler T J, Rasse D P, Li J H et al., 2009. Disturbance, rainfall and contrasting species responses mediated aboveground biomass response to 11 years of CO2 enrichment in a Florida scrub-oak ecosystem. Global Change Biology, 15(2): 356–367. doi:  10.1111/j.1365-2486.2008.01740.x CrossRefGoogle Scholar
  38. Sjöström M, Zhao M, Archibald S et al., 2013. Evaluation of MODIS gross primary productivity for Africa using eddy covariance data. Remote Sensing of Environment, 131(4): 275–286. doi:  10.1016/j.rse.2012.12.023 CrossRefGoogle Scholar
  39. Tan B, Woodcock C E, Hu J et al., 2006. The impact of gridding artifacts on the local spatial properties of MODIS data: implications for validation, compositing, and band-to-band registration across resolutions. Remote Sensing of Environment, 105(2): 98–114. doi:  10.1016/j.rse.2006.06.008 CrossRefGoogle Scholar
  40. Tang X G, Wang Z M, Liu D W et al., 2012. Estimating the net ecosystem exchange for the major forests in the northern United States by integrating MODIS and AmeriFlux data. Agricultural and Forest Meteorology, 156(4): 75–84. doi:  10.1016/j.agrformet.2012.01.003 CrossRefGoogle Scholar
  41. Turner D P, Urbanski S, Bremer D et al., 2003. A cross-biome comparison of daily light use efficiency for gross primary production. Global Change Biology, 9(3): 383–395. doi:  10.1046/j.1365-2486.2003.00573.x CrossRefGoogle Scholar
  42. Verma M, Friedl M A, Richardson A D et al., 2014. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set. Biogeosciences, 11(8): 2185–2200. doi:  10.5194/bgd-10-11627-2013 CrossRefGoogle Scholar
  43. Wang X, Ma M, Li X et al., 2013. Validation of MODIS-GPP product at 10 flux sites in northern China. International Journal of Remote Sensing, 34(2): 587–599. doi:  10.1080/01431161.2012.715774 CrossRefGoogle Scholar
  44. Wang Y, Woodcock C E, Buermann W et al., 2004. Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote Sensing of Environment, 91(1): 114–127. doi:  10.1016/j.rse.2004.02.007 CrossRefGoogle Scholar
  45. Williams C A, Hanan N P, Baker I et al., 2008. Interannual variability of photosynthesis across Africa and its attribution. Biogeosciences, 113: G04015. doi:  10.1029/2008JG000718 Google Scholar
  46. Wu C Y, Munger J W, Niu Z et al., 2010. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sensing of Environment, 114(12): 2925–2939. doi:  10.1016/j.rse.2010.07.012 CrossRefGoogle Scholar
  47. Wu C, Chen J M, Huang N, 2011. Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: evaluation and calibration. Remote Sensing of Environment, 115(12): 3424–3435. doi:  10.1016/j.rse.2011.08.006 CrossRefGoogle Scholar
  48. Xiao J, Zhuang Q, Law B E et al., 2010. A continuous measure of gross primary production for the conterminous U.S. derived from MODIS and AmeriFlux data. Remote Sensing of Environment, 114(3): 576–591. doi:  10.1016/j.rse.2009.10.013 CrossRefGoogle Scholar
  49. Yang F H, Ichii K, White M A et al., 2007. Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through support vector machine approach. Remote Sensing of Environment, 110(1): 109–122. doi:  10.1016/j.rse.2007.02.016 CrossRefGoogle Scholar
  50. Zhao M, Running S W, 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329(5994): 940–943. doi:  10.1126/science.1192666 CrossRefGoogle Scholar
  51. Zhao M, Running S W, Nemani R R, 2006. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. Biogeosciences, 111(G1). doi:  10.1029/2004JG000004 Google Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xuguang Tang
    • 1
    • 2
  • Hengpeng Li
    • 1
  • Guihua Liu
    • 2
  • Xinyan Li
    • 1
  • Li Yao
    • 1
  • Jing Xie
    • 3
  • Shouzhi Chang
    • 4
  1. 1.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina
  2. 2.Key Laboratory of Poyang Lake Wetland and Watershed Research (Jiangxi Normal University)Ministry of EducationNanchangChina
  3. 3.Department of GeographyUniversity of ZurichZurichSwitzerland
  4. 4.Jilin UniversityCollege of GeoExploration Science and TechnologyChangchunChina

Personalised recommendations