Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

Remote Sensing of Environment

Volumn 168, Issue , 2015, Pages 360-373

  Tramontana, Gianluca ^a   Ichii, Kazuito ^b   Camps Valls, Gustau ^c   Tomelleri, Enrico ^d   Papale, Dario ^a  

^a UNIVERSITY OF TUSCIA   (Italy)

^b JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY   (Japan)

^c UNIVERSITY OF VALENCIA   (Spain)

^d INSTITUTE FOR RENEWABLE ENERGY   (Italy)

Author keywords

GPP; MOD17; Random Forest; Uncertainty; Upscaling; Vegetation indices

Indexed keywords

ARTIFICIAL INTELLIGENCE; BALLOONS; CARBON; CLIMATE CHANGE; CORRELATION METHODS; DECISION TREES; ECOLOGY; ECOSYSTEMS; FORECASTING; FORESTRY; LEARNING SYSTEMS; MEAN SQUARE ERROR; REMOTE SENSING;

GPP; MOD17; RANDOM FORESTS; UNCERTAINTY; UPSCALING; VEGETATION INDEX;

UNCERTAINTY ANALYSIS;

ACCURACY ASSESSMENT; BIOMASS; CARBON FLUX; EDDY COVARIANCE; ERROR ANALYSIS; FOREST ECOSYSTEM; LIGHT USE EFFICIENCY; MACHINE LEARNING; OPTIMIZATION; PRIMARY PRODUCTION; REMOTE SENSING; SATELLITE DATA; UNCERTAINTY ANALYSIS;

CORRELATION; ECOSYSTEMS; FORESTS; PRODUCTION; REMOTE SENSING;

EUROPE;

EID: 84939552113     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.07.015     Document Type: Article

References (47)

1. Aubinet M., Vesala T., Papale D. Eddy covariance: A practical guide to measurement and data analysis 2012, Springer, Dordrecht Heidelberg London New York, (460 pp.).
2. Beer C., Ciais P., Reichstein M., Baldocchi D., Law B.E., Papale D., et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Global Biogeochemical Cycles 2009, 23:1-13. (GB2018). 10.1029/2008GB003233, 2009.
3. Beer C., Reichstein M., Ciais P., Farquhar G.D., Papale D. Mean annual GPP of Europe derived from its water balance. Geophysical Research Letters 2007, 34:1-4. (L05401). 10.1029/2006GL029006.
4. Beer C., Reichstein M., Tomelleri E., Ciais P., Jung M., Carvalhais N., et al. Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. Science 2010, 329:834-838. 10.1126/science.1184984.
5. Breiman L. Random forests. Machine Learning 2001, 45:5-32. 10.1023/A:1010933404324.
6. Cutler D.R., Edwards T.C., Beard K.H., Cutler A., Hess K.T., Gibson J., et al. Random forests for classification in ecology. Ecology 2007, 88(11):2783-2792. 10.1890/07-0539.1.
7. Desai A.R., Richardson A.D., Moffat A.M., Kattge J., Hollinger D.Y., Barr A., et al. Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agricultural and Forest Meteorology 2008, 148:821-838. 10.1016/j.agrformet.2007.11.012.
8. Evans J.S., Cushman S.A. Gradient modeling of conifer species using random forests. Landscape Ecology 2009, 24:673-683. 10.1007/s10980-009-9341-0.
9. Friedl M.A., Sulla-Menashe D., Tan B., Schneider A., Ramankutty N., Sibley A., et al. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 2010, 114:168-182. 10.1016/j.rse.2009.08.016.
10. Gao B.C. NDWI - A Normalized Difference Water Index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 1996, 58:257-266.
11. Genuer R., Poggi J.M., Tuleau-Malot C. Variable selection using random forests. Pattern Recognition Letters 2010, 31:2225-2236. 10.1016/j.patrec.2010.03.014.
12. Giri C., Zhu Z., Reed B. A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment 2005, 94:123-132. 10.1016/j.rse.2004.09.005.
13. Gislason P.O., Benediktsson J.A., Sveinsson J.R. Random Forests for land cover classification. Pattern Recognition Letters 2006, 27:294-300. 10.1016/j.patrec.2005.08.011.
14. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical learning 2001, 587-604. Springer Series in Statistics Springer New York Inc., New York, NY, USA.
15. Haxeltine A., Prentice I.C. BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability and competition among plant functional types. Global Biogeochemical Cycles 1996, 10:693-710. 10.1029/96GB02344.
16. Heinsch F.A., Zhao M., 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(7):1908-1925. 10.1109/TGRS.2005.853936.
17. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20(8):832-844. 10.1109/34.709601.
18. Jung M., Le Maire G., Zaehle S., Luyssaert S., Vetter M., Churkina G., et al. Assessing the ability of three land ecosystem models to simulate gross carbon uptake of forests from boreal to Mediterranean climate in Europe. Biogeosciences 2007, 4:647-656. 10.5194/bg-4-647-2007.
19. Jung M., Reichstein M., Bondeau A. Towards global empirical upscaling of FLUXNET Eddy Covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences 2009, 6:2001-2013. 10.5194/bg-6-2001-2009.
20. Jung M., Reichstein M., Margolis H.A., Cescatti A., Richardson A.D., Arain M.A., et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research 2011, 116:1-16. (G00J07). 10.1029/2010JG001566.
21. Jung M., Verstraete M., Gobronz N., Reichstein M., Papale D., Bondeau A., et al. Diagnostic assessment of European gross primary production. Global Change Biology 2008, 14:1-16. 10.1111/j.1365-2486.2008.01647.x.
22. Jung M., Vetter M., Herold M., et al. Uncertainties of modelling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models. Global Biogeochemical Cycles 2007, 21:GB4021. 10.1029/2006GB002915.
23. Jung M., Zscheischler J. A guided hybrid genetic algorithm for feature selection with expensive cost functions. Procedia Computer Science 2013, 18:2337-2346. 10.1016/j.procs.2013.05.405.
24. Krinner G., Viovy N., de Noblet-Ducoudre N., Ogee J., Polcher J., Friedlingstein P., et al. A dynamic global vegetation model for studies of the coupled atmosphere biosphere system. Global Biogeochemical Cycles 2005, 19:GB1015. 10.1029/2003GB002199.
25. Lasslop G., Reichstein M., Papale D., Richardson A.D., Arneth A., Barr A., et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global Change Biology 2010, 16:187-208. 10.1111/j.1365-2486.2009.02041.x.
26. Moffat A.M., Beckstein C., Churkina G., Mund M., Heimann M. Characterization of ecosystem responses to climatic controls using artificial neural networks. Global Change Biology 2010, 16:2737-2749. 10.1111/j.1365-2486.2010.02171.x.
27. Myneni R.B., Hoffman S., Knyazikhin Y., Privette J.L., Glassy J., Tian Y., et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment 2002, 83:214-231.
28. O'Hagan A. Probabilistic uncertainty specification: Overview, elaboration techniques and their application to a mechanistic model of carbon flux. Environmental Modelling and Software 2012, 36:35-48. 10.1016/j.envsoft.2011.03.003.
29. Oliveira S., Oehler F., San-Miguel-Ayanz J., Camia A., Pereira J.M.C. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management 2012, 275:117-129. 10.1016/j.foreco.2012.03.003.
30. Olofsson P., Lagergren F., Lindroth A., Lindström J., Klemedtsson L., Kutsch W., et al. Towards operational remote sensing of forest carbon balance across Northern Europe. Biogeosciences 2008, 5:817-832. 10.5194/bg-5-817-2008.
31. Papale D., Reichstein M., Aubinet M., Canfora E., Bernhofer C., Longdoz B., et al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: Algorithms and uncertainty estimation. Biogeosciences 2006, 3:571-583. 10.5194/bg-3-571-2006.
32. Papale D., Valentini R. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology 2003, 9:525-535. 10.1046/j.1365-2486.2003.00609.x.
33. Rahman A.F., Sims D.A., Cordova V.D., El-Masri B.Z. Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes. Geophysical Research Letters 2005, 32:l19404. 10.1029/2005GL024127.
34. Reichstein M., Falge E., Baldocchi D., Papale D., Aubinet M., Berbigier P., et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm. Global Change Biology 2005, 11:1424-1439. 10.1111/j.1365-2486.2005.001002.x.
35. Running S.W., Baldocchi D.D., Turner D.P., Gower S.T., Bakwin P.S., Hibbard K.A. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sensing of Environment 1999, 70:108-127.
36. Running S.W., Thornton P.E., Nemani R., Glassy J.M. Global terrestrial gross and net primary productivity from the Earth Observing System. Methods in Ecosystem Science 2000, 44-57. Springer-Verlag, New York. O.E. Sala, R.B. Jackson, H.A. Mooney, R.W. Howarth (Eds.).
37. Schimel S.D., Braswell B.H., Parton W.J. Equilibration of the terrestrial water, nitrogen, and carbon cycles. Proceedings of the National Academy of Sciences of the United States of America 1997, Aug 5, 94(16):8280-8283.
38. 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, 12:1633-1646. 10.1016/j.rse.2007.08.004.
39. Sitch S., Smith B., Prentice I.C., Arneth A., Bondeau A., Cramer W., et al. Evaluation of ecosystem dynamics plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 2003, 9:161-185. 10.1046/j.1365-2486.2003.00569.x.
40. Sjöström M., Zhao M., Archibald S., Arneth A., Cappelaere B., Falk U., et al. Evaluation of MODIS gross primary productivity for Africa using eddy covariance data. Remote Sensing of Environment 2011, 131:275-286. 10.1016/j.rse.2010.12.013.
41. Vermote E.F., Vermeulen A. MODIS algorithm technical background document - Atmospheric correction algorithm: Spectral reflectance (MOD09), version 4.0 http://modis.gsfc.nasa.gov/data/atbd/atbd_mod08.pdf.
42. Wan Z., Zhang Y., Zhang Q., Li Z.L. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment 2002, 83:163-180. 10.1016/S0034-4257(02)00093-7.
43. Xiao X., Zhang Q., 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. 10.1016/j.rse.2004.03.010.
44. Xiao J., Zhuang Q., Law B.E., Chen J., Baldocchi D.D., Cook D.R., et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sensing of Environment 2010, 114:576-591. 10.1016/j.rse.2009.10.013.
45. Yang F., Ichii K., White M.A., Hashimoto H., Michaelis A.R., Votava P., et al. Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach. Remote Sensing of Environment 2007, 110:109-122. 10.1016/j.rse.2007.02.016.
46. Zhao M., Heinsch F.A., Nemani R.R., Running S.W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 2005, 95:164-176. 10.1016/j.rse.2004.12.011.
47. Zhao M., Running S.W., Nemani R. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. Journal of Geophysical Research 2006, 111:1-13. (G01002). 10.1029/2004JG000004.