This document analyzes carbon flux estimations in the Arctic based on MODIS vegetation indices. It aims to improve understanding of CO2 dynamics in the Arctic by upscaling satellite data to model Net Ecosystem Exchange (NEE). The study evaluates four MODIS products - MCD15A3, MOD13A1, MOD13Q1, and MYD09GA - to determine which best estimates NEE based on vegetation indices like LAI and NDVI at 12 Arctic sites. Results show that MOD13Q1, with 250m resolution and 16-day temporal resolution, provides the best NEE estimations compared to the other products and field data. Finer spatial resolution is needed to accurately model Arctic landscape heterogeneity and carbon

1. Dept. of Physical Geography and Ecosystem Science, LU
Analysis of Arctic peak-season carbon flux
estimations based on four MODIS vegetation
indices
June 8, 2016

2. Project background
• Pan-Arctic Net Ecosystem Exchange (PANEEx
2015)
• Aim: improve understanding of CO2 dynamics
in the Arctic
• Upscale satellite-derived data to produce NEE
(Net Ecosystem Exchange)

3. Why study C in the Arctic ?
• high-latitude boreal and tundra biomes: sequester a
atmospheric CO2
– 119 Pg of soil organic carbon
• Importance of the Arctic:
– Stable stocks of C (if frozen)
– Heat budget
• Delicate region: Arctic amplification
(Screen & Simmonds, 2010; Serreze et al., 2009)

4. Introduction
• NEE – exchange of C between land and atmosphere
– Uptake, release (-,+)
– Measured in g/m2/yr or µmol m2/s
• Arctic regarded as a net C sink
– future? (McGuire et al., 2010; Kimball et al., 2009)
• Arctic tundra C sequestration:
– Abisko: -1.74 µmol m-2 s-1 (Stoy et al., 2013) Source: AMAP 2012

5. Measurements of C exchange
• Eddy covariance towers
(Baldochi, 2003)
• Gas chambers
(Williams et al., 2006)
• Environmental modelling
(Mbufong,In Prep; Kimball et al., 2009)
Site: Barrow, Alaska, source: SpecNet (http://specnet.info/)

6. • PIRT - Photosyntesis & temp. sensitive (Williams et al., 2006)
• TCF - GPP, soil temperature (Kimball et al., 2009)
• PANEEx - Air temp, LAI, PAR (Mbufong, In Prep)
Examples of C models

7. • PANEEx - Air temp, LAI, PAR
LAI: ~0-2
[m2 / m2]
Air temperature:
~ 7-10 [ ͦC]
PAR: light photon
density [W / m2]

8. Problem statement
• Vegetation properties are important in understanding of
C exchanged in the Arctic
(Mbufong et al., 2015)
• Need for an accurate vegetation proxy (LAI, NDVI)
- Which one, based on what ?

9. Main objectives
• Objective: (1) Reduce knowledge gap between RS and environmental
modelling
(2) Assess suitability of satellite-derived products
• Goal: Model and evaluate Arctic NEE/LAI at 12 sites
• Assumptions:
(i) Vegetation proxies are expected to yield discrepancies in the Arctic
NEE estimations
(ii) The product with the finest spatial resolution is likely to yield most realistic
NEE estimations compared to in situ

10. Product
Spatial
Resolution
Temporal resolution Source
MCD15A3 - LAI 1 km2 4 - day LP DAAC, USGS*
MOD13A1 - NDVI 500 m2 16-day GEE, MODIS Terra
MOD13Q1 - NDVI 250 m2 16-day GEE, MODIS Terra
MYD09GA - NDVI 1 km2 Daily GEE, MODIS Aqua
Methodology, used materials
* LP DAAC – Land Processes Distributed Active Archive Center
• Scope: 2008-2010
• Peak of the growing season: July
• NDVI to LAI conversion for consistency (Van Wijk & Williams, 2005)

11. NEE model Study domain
• Sensitivity analysis: selective
(non-probability) sampling
• Quantitative analysis of “big
data”
• Average daily NEE [µmol m2/s]
(Mbufong et al., 2014)
Air temp: Fcsat
LAI: Rd, α, Fcsat
PAR: PPFD
NEE = − Fcsat + R 𝑑 1 − 𝑒
−α PPFD
𝐹 𝑐𝑠𝑎𝑡+𝑅 𝑑 + 𝑅 𝑑

12. Methodology, used software
• ESRI ArcGIS (vs. 10.2.2 Desktop)
• (Google Inc.)
• Matlab (vs. R2014a, The MathWorks, Inc.)

13. Results, LAI

14. Results, NEE
Slope = 0.29
Intercept = -0.94
r2 = 0.15; rmse = 0.30

15. NEE
Modelled vs. Observed NEE

16. 0 - 0.25
0.25 - 0.5
0.50 - 0.75
0.75 - 1
1.1 - 1.5
1.5 - 2
2.1 - 2.5
2.5 - 3.5
Land fill
Water bodies
0.16
0.87 1.7
1.350.18
0.960.11
1.62
In situ LAI :
~0.24 & ~ 0.63

17. Results summary
• LAI – under & overestimated; 1/4 correlations is
consistently overestimated (slope = 2.45, R2 = 0.8) in
MCD15A3
• LAI – MOD13Q1 shows least overestimation
(slope =1.22, R2 = 0.59, RMSE = 0.23)
• NEE- MCD15A3, statistically insignificant (P>> 0.05)
• NEE – MOD13Q1 & MYD09GA show best estimations
(slope=0.78, R2=0.73; slope= 0.86, R2 = 0.56), p<0.05

18. Discussion – other studies
• Implementation of 250 m NDVI better than 1 km GPP
Schubert et al. (2012)
• Watts (2014)
– used 13Q1 & 13A1 (250 m) compared to EC
(R2 = 0.8, p < 0.05)
– halved the acquisition time by combining 13Q1 & 13A1
(linear interpolation)
• Arctic heterogeneity  500 m and 250 m vegetation indices
may not be sufficient

19. Discussion - methods
• Need for sufficient number of observed
data in global scale(n=12)
• Spatial misalignment of external data
(LAI, PAR)
– Edge effect, miscalculated pixels
• LAI derivation from NDVI (97% of in
situ variation, Abisko) (Van Wijk & Williams, 2005)
• Spectral errors imbedded in LAI
images
• Investigation of finer-resolution
satellites (Sentinel 2 project)
www.sentinel.esa

20. Conclusions
• Three MODIS products generated NEE (p<0.05)
• Best estimation: 250 m 16-day LAI (MOD13Q1)
- Slope = 0.78, R2=0.73
• Spatial resolution is important for result accuracy
- Need for finer scale (30 to 60m)
• Heterogeneity of Arctic landscape can be
modelled via RS; accuracy of NEE is correlated
to the configuration of sat.-derived products

22. Methodology cont.
• Air Temperature
(Ta)

23. Processing PAR
• 00:00
• 03:00
• 06:00
• 09:00
• 12:00
• 15:00
• 18:00
• 21:00
Fine 3-hr resolution creates illumination shadows

24. References
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ecosystems: past, present and future. Glob. Change Biol 9:479-92.
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McClelland, J.W., Melillo, J.M., Peterson, B.J., Prinn, R.G., 2010. An analysis of the carbon balance of the Arctic Basin
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