Evaluation of MODIS GPP product and scaling up GPP

over Northern Australian savannas

Kasturi Devi KanniahJason Beringer Lindsay Hutley

Nigel TapperXuan Zhu

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

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)

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

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

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

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

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

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)

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

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

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

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

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