Evaluation of MODIS GPP product and scaling up GPP over Northern Australian savannas Kasturi Devi Kanniah Jason Beringer Lindsay Hutley Nigel Tapper Xuan 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 C 4 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 Use Efficiency APAR GPP MOD17A 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. 4.5Col. 4.8Col.5 fPARCol.4 Col.5 Met (PAR, VPD, Temp) DAOGMAO Maximum light use efficiency (g C MJ -1 ) Period 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, R ) 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 GMAO In the dry- underestimation of 5-6% Underestimation of VPD scalar in DAO- 4%, but negligible in GMAO In 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 % 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 LUE Test 2- MODIS meteorology Test 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