Improving data-oriented light use efficiency models of gross primary productivity with remotely sensed spectral indices [Elektronische Ressource] / Anna Görner. Gutachter: Christiane Schmullius ; Markus Reichstein

Anna Görner

Informations

• Publié par: friedrich-schiller-universitat_jena
• Publié le: 01 janvier 2011
• Nombre de lectures: 16
• Langue: English
• Poids de l'ouvrage: 7 Mo

Extrait

Improving data-oriented
light use efficiency models of
gross primary productivity with
remotely sensed spectral indices
Dissertation
zur Erlangung des akademischen Grades doctor rerum naturalium
(Dr. rer. nat.)
vorgelegt dem
Rat der Chemisch-Geowissenschaftlichen Fakultät der
Friedrich-Schiller-Universitaet Jena
von
Anna Goerner
Dipl.-Geooekol.
geboren am 11.03.1983 in Pirna
2011Gutachter:
1: Prof. Dr. Christiane Schmullius, Friedrich Schiller Universität Jena
2: Dr. Markus Reichstein, Max-Planck-Institut für Biogeochemie, Jena
Tag der öffentlichen Verteidigung: 01. Juli 2011Acknowledgments
I am grateful for discussions with many colleagues at the MPI for Biogeochemistry
about and beyond the subject of this thesis. I especially wish to thank Markus
Reichstein for his trustful supervision. He was always efficient in sorting out my
scientific confusions when help was needed and otherwise enabled and encour-
aged me to shape this thesis work according to my own interests. Likewise, I am
grateful to Christiane Schmullius for her guidance regarding many aspects of this
thesis and for integrating me in her Earth Observation group as much as possible.
I also appreciate the discussions on surface anisotropy with François-Marie Bréon.
Parts of this thesis have—in modified form—been published in peer reviewed jour-
nals (Goerner et al., 2009, 2011). I wish to thank my coauthors Markus Reichstein,
Serge Rambal, Enrico Tomelleri, Niall Hanan, Dario Papale, Danilo Dragoni, Chris-
tiane Schmullius as well as the anonymous referees, John Gamon and the editors
Georg Wohlfahrt and Marvin Bauer for their constructive comments on these two
manuscripts.
I gratefully acknowledge the financial and logistic support by the Max Planck Soci-
ety.Contents
1 Background and motivation 1
1.1 Carbon assimilation in terrestrial ecosystems . . . . . . . . . . . . . 2
1.2 What determines gross primary productivity? . . . . . . . . . . . . . 3
1.2.1 Mechanistic basis of carbon input into ecosystems . . . . . . 4
1.2.2 Focus: water limitation . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Measuring productivity . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Direct measurement . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Measurement with eddy covariance . . . . . . . . . . . . . . 7
1.4 Ecosystem light use efficiency — How is it constrained? . . . . . . . 9
1.4.1 How is light use (LUE) determined (on a local scale)? 9
1.4.2 Constraints of ecosystem light use efficiency . . . . . . . . . 10
1.5 Estimating primary productivity on regional and global scales . . . . 11
1.5.1 Prognostic modelling of gross primary productivity . . . . . . 11
1.5.2 Diagnostic of gross primary productivity—Overview 12
1.5.3 LUE models of primary productivity—focus on MOD17 . . . . 12
1.6 Estimating light use efficiency from space . . . . . . . . . . . . . . . 17
1.6.1 Estimating LUE with fluorescence measurements . . . . . . . 17
1.6.2 photochemical reflectance index (PRI) as proxy for LUE . . . 18
1.7 Aims of this study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 Data, data preparation and methodology 27
2.1 Flux data from eddy covariance measurements . . . . . . . . . . . . 27
2.1.1 Processing of flux according to FLUXNET
standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.1.2 A note on uncertainty . . . . . . . . . . . . . . . . . . . . . . 29
2.1.3 Study-specific preparation of eddy covariance data and as-
sociated measurements . . . . . . . . . . . . . . . . . . . . . 30
2.2 Remotely sensed data . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.1 Moderate-resolution Imaging Spectroradiometer (MODIS)
data for calculating PRI . . . . . . . . . . . . . . . . . . . . . 31
2.2.2 Effect of correction for surface anisotrophy on photochemical
reflectance index . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.3 Geolocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 Pilot study: Tracking seasonal drought effects on ecosystem light use
efficiency with satellite-based PRI in a Mediterranean forest 35
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2.1 Study site and data . . . . . . . . . . . . . . . . . . . . . . . 38
3.2.2 Benchmark ecosystem light use efficiency . . . . . . . . . . 39
3.2.3 Remote sensing based estimates of light use efficiency . . . 41
3.2.4 Modelling gross primary productivity (GPP) . . . . . . . . . . 43iv Contents
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.1 Comparing LUEs at different time scales . . . . . . . . . . . . 43
3.3.2 Strength of relationship between vegetation index (VI)s and
LUE, absorbed photosynthetically active radiation (aPAR),
and GPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.3 Ability of scaled photochemical reflectance index (sPRI) to
track LUE over time . . . . . . . . . . . . . . . . . . . . . . . 47
3.3.4 Modelling GPP . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.1 Comparing LUEs at different time scales . . . . . . . . . . . . 50
3.4.2 Strength of relationship between VIs and LUE, aPAR, and GPP 50
3.4.3 Ability of sPRI to track LUE over time . . . . . . . . . . . . . . 52
3.4.4 Modelling GPP . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.4.5 General considerations . . . . . . . . . . . . . . . . . . . . . 53
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Remote sensing of light use efficiency in diverse ecosystems with
MODIS-based PRI 55
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2 Data and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1 Selection of study sites . . . . . . . . . . . . . . . . . . . . . 58
4.2.2 In-situ LUE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2.3 Modelling LUE from MODIS based PRI . . . . . . . . . . . . 61
4.2.4 LUE modelled from minimum daily temperature (Tmin),
vapour pressure deficit (VPD) and plant functional type . . . 63
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.1 Are LUEs at times of MODIS overpass representative for the
whole day? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.2 Which MODIS-PRI version suits which setting? . . . . . . . . 65
4.3.3 Can LUE estimation from MODIS-PRI be generalised? . . . . 66
4.3.4 How does LUE modelled from MODIS-PRI compare to other
LUE models? . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3.5 Which influence does the choice of an fraction of absorbed
photosynthetically active radiation (faPAR) product have on
PRI evaluation? . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3.6 Influence of vegetation structure on the PRI signal . . . . . . 70
4.3.7 Sensitivity of the different modelled LUEs to seasonal and
interannual variability . . . . . . . . . . . . . . . . . . . . . . 71
4.4 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . 72
5 Outlook 75
A Appendix 79
A.1 MOD17 GPP model . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
A.2 LUE modelled from PRI . . . . . . . . . . . . . . . . . . . . . . . . . 79
Bibliography 81Contents v
Zusammenfassung 105
Lebenslauf 108CHAPTER 1
Background and motivation
Contents
1.1 Carbon assimilation in terrestrial ecosystems . . . . . . . . 2
1.2 What determines gross primary productivity? . . . . . . . . 3
1.2.1 Mechanistic basis of carbon input into ecosystems . . . . . . 4
1.2.2 Focus: water limitation . . . . . . . . . . . . . . . . . . . . . 5
1.3 Measuring productivity . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Direct measurement . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Measurement with eddy covariance . . . . . . . . . . . . . . . 7
1.4 Ecosystem light use efficiency — How is it constrained? . . 9
1.4.1 How is LUE determined (on a local scale)? . . . . . . . . . . 9
1.4.2 Constraints of ecosystem light use efficiency . . . . . . . . . . 10
1.5 Estimatingprimaryproductivityonregionalandglobalscales 11
1.5.1 Prognostic modelling of gross primary productivity . . . . . . 11
1.5.2 Diagnostic modelling of gross primary productivity—Overview 12
1.5.3 LUE models of primary productivity—focus on MOD17 . . . 12
1.6 Estimating light use efficiency from space . . . . . . . . . . . 17
1.6.1 Estimating LUE with fluorescence measurements . . . . . . . 17
1.6.2 photochemical reflectance index (PRI) as proxy for LUE . . . 18
1.7 Aims of this study . . . . . . . . . . . . . . . . . . . . . . . . . 23
Understanding the continuous exchange of elements among the land, the oceans,
and the atmosphere is one of the big research questions in Earth system science.
Comprehending how these so called biogeochemical cycles function and inter-
act—including their responses to changes in climate and other perturbations—is
crucial for a sustainable future of mankind on planet Earth. To arrive at this under-
standing biotic, biochemical, geochemical and physical aspects have to be taken
into account.