Advertisement

Climatic Change

, Volume 103, Issue 1–2, pp 117–136 | Cite as

Remotely sensed soil moisture integration in an ecosystem carbon flux model. The spatial implication

  • Willem W. Verstraeten
  • Frank Veroustraete
  • Wolfgang Wagner
  • Tom Van Roey
  • Walter Heyns
  • Sara Verbeiren
  • Jan Feyen
Article

Abstract

While remote sensing is able to provide spatially explicit datasets at regional to global scales, extensive application to date has been found only in the reporting and verification of ecosystem carbon fluxes under the Kyoto Protocol. One of the problems is that new remote sensing datasets can be used only with models or data assimilation schemes adapted to include a data input interface dedicated to the type and format of these remote sensing datasets. In this study, soil water index data (SWI), derived from the ERS scatterometer (10-daily time period with a spatial resolution of 50 km), are integrated into the ecosystem carbon balance model C-Fix to assess 10-daily Net Ecosystem Productivity (NEP) patterns of Europe from the remote sensing perspective on an approximate 1-by-1 km2 pixel scale using NDVI-AVHRR data. The modeling performance of NEP obtained with and without the assimilation of remotely sensed soil moisture data in the carbon flux model C-Fix is evaluated with EUROFLUX data. Results show a general decrease of the RRMSE of up to 11 with an average of 3.46. C-Fix is applied at the European scale to demonstrate the potential of this ecosystem carbon flux model, based on remote sensing inputs. More specifically, the strong impact of soil moisture on the European carbon balance in the context of the Kyoto Protocol (anthropogenic carbon emissions) is indicated at the country level. Results suggest that several European countries shift from being a carbon sink (i.e., NEP > 1) to being a carbon source (i.e., NEP < 0) whether or not short-term water availability (i.e., soil moisture) is considered in C-Fix NEP estimations.

Keywords

Normalize Difference Vegetation Index Soil Respiration Gross Primary Productivity Ecosystem Respiration Relative Root Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bartholomé E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from Earth observation data. Int J Remote Sens 26:1959–1977CrossRefGoogle Scholar
  2. Black K (2007) Scaling up from the stand to regional level: an analysis based on the major forest species in Ireland. Proceedings of the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories, International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria 27–28 September 2007, p. 9–20Google Scholar
  3. Ceballos A, Scipal K, Wagner W, Martínez-Fernández L (2005) Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero basin. Hydrol Process 19(8):1549–1566CrossRefGoogle Scholar
  4. Chhabra A, Dhadwall VK (2004) Estimating terrestrial net primary productivity over India using satellite data. Curr Sci 86(2):269–271Google Scholar
  5. Chow VT, Maidment DR, Mays LW (1993) Applied hydrology. McGraw-Hill, Singapore, pp 572Google Scholar
  6. Ciais P (2010) Atmospheric inversions for estimating CO2 fluxes: methods and perspectives. doi: 10.1007/s10584-010-9909-3
  7. Ciais P, Reichstein M, Viovy N et al (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437:529–533CrossRefGoogle Scholar
  8. Cramer W, Bondeau A, Woodward FI et al (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Glob Chang Biol 7(4):357–373CrossRefGoogle Scholar
  9. Dias AC, Louro M, Arroja L, Capela I (2007) Uncertainties in the estimates of carbon in harvested wood products for Portugal. Proceedings of the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories, International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria 27–28 September 2007, p. 41–48Google Scholar
  10. Davidson EA, Janssens IA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440:165–173CrossRefGoogle Scholar
  11. Dolman AJ, Moors EJ, Elbers JA, Snijders W (1998) Evaporation and surface conductance of three temperate forests in the Netherlands. Ann Sci For 55:255–270CrossRefGoogle Scholar
  12. Dolman AJ, Moors EJ et al (2003) Factors controlling forest atmosphere exchange of water, energy and carbon in European forests. In: Valentini R (ed) Fluxes of carbon, water and energy of European forests. Springer, HeidelbergGoogle Scholar
  13. Field CB (2007) Natural versus anthropogenic control of ecosystem carbon stocks. Proceedings of the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories, International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria 27–28 September 2007, p. 59–60Google Scholar
  14. Gobron N, Pinty B, Aussedat O et al (2006) Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: methodology and results using joint research center products derived from SeaWiFS against ground-based estimations. J Geophys Res 111:D13110. doi: 10.1029/2005JD006511 CrossRefGoogle Scholar
  15. Goward SN, Dye DG (1987) Evaluating North-American net primary production with satellite observations. Adv Space Res 7:165–174CrossRefGoogle Scholar
  16. Grace J, Rayment M (2000) Respiration in the balance. Nature 404:819–820CrossRefGoogle Scholar
  17. Gusti M, Jonas M (2010) Terrestrial full carbon account for Russia: revised uncertainty estimates and their role in a bottom-up/top-down accounting exercise. doi: 10.1007/s10584-010-9911-9
  18. Hawkins M, Black K, Gallagher G, Connolly J (2007) Resolution of stochastic issues in estimating forest biomass carbon stock changes using non-linear mixed models. Proceedings of the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories, International Institute for Applied System Analysis A-2361 Laxenburg, Austria 27–28 September 2007, p. 97–100Google Scholar
  19. IIASS (2007) 2nd international workshop on uncertainty in greenhouse gas inventories, 27–28 September 2007, International Institute of Applied Systems Analysis (IIASA). Laxenburg, Austria, p 233Google Scholar
  20. Janssens IA, Freibauer A, Ciais P et al (2003) Europe’s terrestrial biosphere absorbs 7 to 12% of European anthropogenic CO2 emissions. Science 300:1538–1542CrossRefGoogle Scholar
  21. Leip (2010) The uncertainty of the uncertainty... on the example of the quality assessment of the greenhouse gas inventory for agriculture in Europe. doi: 10.1007/s10584-010-9915-5
  22. Lu L, Li X, Veroustraete F (2005) Terrestrial productivity and its spatio-temporal variability in Western China. Acta Ecol Sinica 25:1–12Google Scholar
  23. Maisongrande P, Ruimy A, Dedieu G, Saugier B (1995) Monitoring seasonal and interannual variations of gross primary productivity, net primary productivity and ecosystem productivity using a diagnostic model and remotely sensed data. Tellus 47(B):178–190Google Scholar
  24. McCree KJ (1972) Test of current definitions of photosynthetically active radiation against leaf photosynthesis data. Agric For Meteorol 10:442–453Google Scholar
  25. Myneni RB, Williams DL (1994) On the relationship between fAPAR and NDVI. Remote Sens Environ 49:200–211CrossRefGoogle Scholar
  26. Nahorski, Horabik (2010) Compliance and emission trading rules for asymmetric emission uncertainty estimates. doi: 10.1007/s10584-010-9916-4
  27. Nemani R, White M, Thornton P, Nishida K, Reddy S, Jenkins J, Running S (2002) Recent trends in hydrologic balance have enhanced the terrestrial carbon sink in the Unite States. Geophys Res Lett 10:106-1–106-4Google Scholar
  28. Nilsson S, Jonas M, Obersteiner M, Victor DG (2001) Verification: the gorilla in the struggle to slow global warming. For Chron 77:475–478Google Scholar
  29. Pandey JS, Wat SR, Devotta S (2007) Development of emission factors for GHGs and Associated uncertainties. Proceedings of the 2nd International Workshop on Uncertainty in Greenhouse Gas Invetories, International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria 27–28 September 2007, p. 163–168Google Scholar
  30. Pellarin T, Calvet J-C, Wagner W (2006) Evaluation of ERS scatterometer soil moisture products over a half-degree region in southwestern France. Geophys Res Let 33(17):L17401CrossRefGoogle Scholar
  31. Penman J, Gytarsky M, Hiraishi T, Krug T, Kruger D, Pipatti R, Buendia L, Miwa K, Ngara T, Tanabe K, Wagner F (2003) Good practice guidance for land use, use change and forestry. IPCC National Greenhouse Gas Inventories Programme, Technical Support UnitGoogle Scholar
  32. Rosenqvist Å, Milne A, Lucas R, Imhoff M, Dobson C (2003) A review of remote sensing technology in support of the Kyoto Protocol. Environ Sci Policy 6:441–455CrossRefGoogle Scholar
  33. Theloke J, Pfeiffer H, Pregger T, Scholz Y, Köble R, Kummer U, Nicklass D, Thiruchittampalam B, Friedrich R (2007) Development of a methodology for temporal and spatial resolution of greenhouse gas emission inventories for validation. Proceedings of the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories, International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria 27–28 September 2007 p. 203–206Google Scholar
  34. Schils R, Kuikman P, Liski J et al (2008) Review of existing information on the interrelations between soil and climate change. ClimSoil report, Alterra. Wageningen, The NetherlandsGoogle Scholar
  35. Scipal K, Scheffler C, Wagner W (2005) Soil moisture–runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing. HESS 9:173–183Google Scholar
  36. Shvidenko A, Nilsson S (2010) Can the uncertainty of full carbon accounting of forest ecosystems be made acceptable to policymakers? doi: 10.1007/s10584-010-9918-2
  37. Soussana J-F et al (2004) Greenhouse gas emissions from European grasslands. In: Discussion paper originated from a workshop in Clermont-Ferrand, France, pp 1–93Google Scholar
  38. Steffen W, Noble I, Canadell J, Apps M et al (1998) The terrestrial carbon cycle: implications for the Kyoto protocol. Science 280:1393–1394CrossRefGoogle Scholar
  39. Szemesová J, Gera M (2010) Uncertainty analysis for estimation of landfill emissions and data sensitivity for the input variation. doi: 10.1007/s10584-010-9919-1
  40. UNFCC (2005) Report on national greenhouse gas inventory data for the period 1990–2003 and status of reporting. FCCC/SBI/2005/17, pp 1–23Google Scholar
  41. Valentini R, Matteucci G, Dolman AJ et al (2000) Respiration as the main determinant of carbon balance in European forests. Nature 404:861–865CrossRefGoogle Scholar
  42. van Oijen M, Thomson AM (2010) Toward Bayesian uncertainty quantification for forestry models used in the United Kingdom Greenhouse Gas Inventory for land use, land use change, and forestry. doi: 10.1007/s10584-010-9917-3
  43. Veroustraete F, Patyn J, Myneni RB (1994) Forcing of a simple ecosystem model with fAPAR and climate data to estimate regional scale photosynthetic assimilation. In: Veroustraete F (ed) Modelling and climate change effects. Academic, The HagueGoogle Scholar
  44. Veroustraete F, Sabbe H, Eerens H (2002) Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sens Environ 83:376–399CrossRefGoogle Scholar
  45. Veroustraete F, Sabbe H, Rasse DP, Bertels L (2004) Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors. Int J Remote Sens 25:769–792CrossRefGoogle Scholar
  46. Veroustraete F, Li Q, Verstraeten WW, Xi C, Bao A, Dong Q-H, Liu T, Willems P (2009) Soil moisture content retrieval based on apparent thermal inertia for the Xinjiang province in China. Int J Remote Sens (in press)Google Scholar
  47. Verstraeten WW, Muys B, Feyen J, Veroustraete F, Minnaert M, Meiresonne L, De Schrijver A (2005) Comparative analysis of the actual evapotranspiration of Flemish forest and cropland, using the soil water balance model WAVE. HESS 9(3):225–241Google Scholar
  48. Verstraeten WW, Veroustraete F, van der Sande CJ, Grootaers I, Feyen J (2006a) Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Rem Sens Environ 101(3):299–314CrossRefGoogle Scholar
  49. Verstraeten WW, Veroustraete F, Feyen J (2006b) On temperature and water limitation in the estimation net ecosystem productivity: Implementation in the PEM C-Fix. Ecol Model 199:4–22CrossRefGoogle Scholar
  50. Verstraeten WW, Veroustraete F, Feyen J (2008a) Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors 8:70–117CrossRefGoogle Scholar
  51. Verstraeten WW, Veroustraete F, Heyns W, Van Roey T, Feyen J (2008b) On uncertainties in carbon flux modelling and remotely sensed data assimilation: the Brasschaat pixel case. Adv Space Res 41:20–35CrossRefGoogle Scholar
  52. Wagner W, Lemoine G, Rott H (1999) A method for estimating soil moisture from ERS scatterometer and soil data. Rem Sens Environ 70:191–207CrossRefGoogle Scholar
  53. Wagner W, Scipal K, Pathe C (2003) Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J Geophys Res 108(D19):4611–4619CrossRefGoogle Scholar
  54. Wagner W, Jonas M, Hoffmann C, Gangkofner U, Hasenauer S, Hollaus M, Schiller C, Kressler F (2005) The role of earth observation in the good practice guidance for reporting land use, land use change and forestry activities as specified by the Kyoto Protocol. In: Abstracts of the ENVISAT & ERS symposium, Salzburg, Austria, 6–10 September 2004, ESA SP-572Google Scholar
  55. Wagner W, Blöschl G, Pampaloni P, Calvet J-C, Bizzarri B, Wigneron J-P, Kerr Y (2007) Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Nord Hydrol 38(1):1–20CrossRefGoogle Scholar
  56. Wilson KB, Baldocchi B, Aubinet M et al (2002a) Energy partitioning between latent and sensible heat flux during the warm season at FLUXNET sites. Water Resour Res 38(12):1294–1323CrossRefGoogle Scholar
  57. Wilson K, Goldstein A, Falge E et al (2002b) Energy balance closure at FLUXNET sites. Agric For Meteorol 113:223–243CrossRefGoogle Scholar
  58. Winiwarter W, Muik B (2010) Statistical dependence in input data of national greenhouse gas inventories: effects on the overall inventory uncertainty. doi: 10.1007/s10584-010-9921-7

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Willem W. Verstraeten
    • 1
  • Frank Veroustraete
    • 2
  • Wolfgang Wagner
    • 3
  • Tom Van Roey
    • 2
  • Walter Heyns
    • 2
  • Sara Verbeiren
    • 2
  • Jan Feyen
    • 4
  1. 1.Geomatics EngineeringKatholieke Universiteit Leuven (K.U. Leuven)HeverleeFlanders
  2. 2.Flemish Institute for Technological Research (VITO)MolFlanders
  3. 3.Institute of Photogrammetry and Remote SensingVienna University of Technology (T.U. Wien)ViennaAustria
  4. 4.Laboratory for Soil and Water ManagementKatholieke Universiteit Leuven (K.U. Leuven)HeverleeFlanders

Personalised recommendations