evaluation of modis gpp product and scaling up gpp over northern australian savannas kasturi devi...
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![Page 1: Evaluation of MODIS GPP product and scaling up GPP over Northern Australian savannas Kasturi Devi Kanniah Jason Beringer Lindsay Hutley Nigel Tapper Xuan](https://reader036.vdocuments.site/reader036/viewer/2022062417/551943fb55034683738b461a/html5/thumbnails/1.jpg)
Evaluation of MODIS GPP product and scaling up GPP
over Northern Australian savannas
Kasturi Devi KanniahJason Beringer Lindsay Hutley
Nigel TapperXuan Zhu
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Objectives
• To validate different versions/collections of MODIS GPP (MOD17) -Collections 4.5, 4.8 and 5
• To validate input parameters used to estimate MODIS
GPP - LAI/fPAR (MOD 15A2), Light Use Efficiency and
meteorological variables (VPD, PAR)
• To estimate GPP using MOD17 algorithm with site
specific values
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Howard Springs• Open woodland savannas forest
50-60% canopy cover.
• Over storey - evergreen trees
• Under storey - by C4 grasses
• Wet season GPP 7-8 g C m-2 day-1
• Dry season GPP– 0.3 to 1.6 g C m-2 day-1
Wet season(Dec-Mac)
Dry season (May-Sept)
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MODIS GPP
Tmin & VPD scalar
Light UseEfficiency
APAR
GPPMOD17A
Max. LUE
fPAR
PAR
NASA DAO/GMAO
BCG model
MOD15A2
NASA DAO/GMAO
Global product, 1 km, 8 day Only useful if its relative accuracy can be determined
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MODIS Collections
Input parameters
Col. 4.5 Col. 4.8 Col.5
fPAR Col.4 Col.4 Col.5
Met (PAR, VPD, Temp)
DAO GMAO GMAO
Maximum light use efficiency (g C MJ-1)
0.80 1.03 1.03
Period 2000-2003
2000-2006
2000- present
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Seasonal GPP pattern• Correct seasonal pattern
• GPP Col. 4.5 & 5 < 4.8
• Col 4.8- good agreement
with tower in the wet
(RPE 1%, IOA 0.72,
RMSE 1 g C m-2 day-1 &
explained 75% variation
in tower GPP.
• Poor performance in the
dry (RPE 31%, RMSE
1.4, IOA 0.59, R2 0.33)
•Col. 4.5 good in the dry (RPE 4%, RMSE 1, IOA 0.72 R2 0.35), but poor
in the wet (RPE -14%, RMSE 1.53, IOA 0.63 and R2 0.46)•Col. 5 underestimated by ~40% in the wet and +10% in the dry
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LAI/fPAR
• Wet season –MODIS ~3.8 vs.
site 2.2
• Dry season LAI -MODIS 1.3
vs. site 0.9
• Wet- MODIS fPAR 0.90 vs. site fPAR 0.67
• Dry- MODIS 0.67 vs. site 0.35
• ~correct LAI & fPAR in Col. 5
• Rapid increase in fPAR from September
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Meteorology
Underestimation of PAR in the wet season-RPE 9% in DAO & 11% in GMAOIn the dry- underestimation of 5-6%Underestimation of VPD scalar in DAO- 4%, but negligible in GMAOIn the dry- underestimation 11% in DAO & 17% in GMAO
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Maximum LUE
• LUE= GPP/APAR
• Site specific max = 1.26
g C MJ-1
• 17% higher than
standard MODIS
algorithm value of 1.03
gCMJ-1 in col. 4.8
• 35% higher than col.
4.5 (0.80 gCMJ-1)
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Source of error
Wet -13%Dry -12%
Wet -7%Dry -15%
Wet 35%Dry 106%
Test 1- MODIS LUETest 2- MODIS meteorologyTest 3- MODIS fPAR
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Algorithm improvements
GPP was recalculated using MOD17 algorithm but with
site specific values
GPP was recalculated using MOD17 algorithm with VPD scalar was replaced with soil moisture index.
– Evaporative Fraction= LE/(LE+H) from flux tower
– EF - indicator of soil or vegetation moisture conditions
because decreasing amounts of energy partitioned into
latent heat flux suggests a stronger moisture limitation
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Improved methods• Improved methods
captured the start of the wet season correctly- used correct fPAR
• Method with VPD still overestimates GPP in the dry season
• Method with EF accurately reduce GPP in the dry season & captured the beginning of the wet season.
Overall these methods reduced RPE by ~50%, RMSE by 42%, increased IOA by 6% compared to Col. 4.8 and explained >90% variation in tower GPP
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Conclusion• MODIS - reasonable estimation of GPP (<12%) - annual basis and
perfect in the wet in Col. 4.8 (1%) and in the dry in Col. 4.5 (4%).
• Main source of error in MODIS- fPAR, and use of VPD as a surrogate
for soil water deficit in the dry season
• Overestimation in fPAR was compensated by relatively low PAR,
VPD scalar and LUE in the wet season.
• In the dry season, VPD scalar & PAR was underestimated, but high
fPAR resulted in the overestimation of GPP
• Col. 5 fPAR accurate but low PAR &LUE- underestimated GPP- LUT
need to be readjusted
• Use of VPD in MOD17 has limitation-arid & semi arid areas
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Future work
• Validation at other locations
• Analyse the spatial & temporal patterns of GPP over NT
using MOD 4.5 & 4.8
• Estimate GPP using fPAR from collection 5 & other site
specific values