%0 Journal Article %1 Kanniah2009 %A Kanniah, K.D. %A Beringer, J. %A Hutley, L.B. %A Tapper, N.J. %A Zhu, X. %D 2009 %J Remote Sensing of Environment %K assimilation drought evapotranspiration firemafs models modis optical remotesensing satellite vegetation wildfire %P - %R DOI: 10.1016/j.rse.2009.04.013 %T Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia %U http://www.sciencedirect.com/science/article/B6V6V-4WBY55G-1/2/d9c954233599d6b48241ef76cfd46c65 %V In Press, Corrected Proof %X In this study, we assessed the accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) GPP (gross primary productivity) Collections 4.5, 4.8 and 5 along with Leaf Area Index (LAI), fraction of absorbed Photosynthetically Active Radiation (fPAR), light use efficiency (LUE) and meteorological variables that are used to estimate GPP for a northern Australian savanna site. Results of this study indicated that the MODIS products captured the seasonal variation in GPP, LAI and fPAR well. Using the index of agreement (IOA), it was found that Collections 4.5 and 4.8 (IOA 0.89 respectively) agreed reasonably well with flux tower measurements between 2001 and 2006. It was also found that MODIS Collection 4.5 predicted the dry season GPP well (Relative Predictive Error (RPE) 4.17%, IOA 0.72 and Root Mean Square Error (RMSE) of 1.05 g C m- 2 day- 1), whilst Collection 4.8 performed better in capturing wet season dynamics (RPE 1.11%, IOA 0.80 and RMSE of 0.91 g C m- 2 day- 1). Although the wet season magnitude of GPP was predicted well by Collection 4.8, an examination of the inputs to the GPP algorithm revealed that MODIS fPAR was too high, but this was compensated by PAR and LUE that was too low. Although LAI and fPAR estimated by Collection 5 were more accurate, GPP for this Collection resulted in a much lower value (RPE 25%) due to errors in other factors. Recalculation of MODIS GPP using site specific input parameters indicated that MODIS fPAR was the main reason for the differences between MODIS and tower derived GPP followed by LUE and meteorological inputs. GPP calculated using all site specific values agreed very well with tower data on an annual basis (IOA 0.94, RPE 6.06% and RMSE 0.83 g C m- 2 day- 1) but the early initiation of the growing season calculated by the MODIS algorithm was improved when the vapor pressure deficit (VPD) function was replaced with a soil water deficit function. The results of this study however, reinforce previous findings in water limited regions, like Australia, and incorporation of soil moisture in a LUE model is needed to accurately estimate the productivity.

@article{Kanniah2009, abstract = {In this study, we assessed the accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) GPP (gross primary productivity) Collections 4.5, 4.8 and 5 along with Leaf Area Index (LAI), fraction of absorbed Photosynthetically Active Radiation (fPAR), light use efficiency (LUE) and meteorological variables that are used to estimate GPP for a northern Australian savanna site. Results of this study indicated that the MODIS products captured the seasonal variation in GPP, LAI and fPAR well. Using the index of agreement (IOA), it was found that Collections 4.5 and 4.8 (IOA 0.89 respectively) agreed reasonably well with flux tower measurements between 2001 and 2006. It was also found that MODIS Collection 4.5 predicted the dry season GPP well (Relative Predictive Error (RPE) 4.17%, IOA 0.72 and Root Mean Square Error (RMSE) of 1.05 g C m- 2 day- 1), whilst Collection 4.8 performed better in capturing wet season dynamics (RPE 1.11%, IOA 0.80 and RMSE of 0.91 g C m- 2 day- 1). Although the wet season magnitude of GPP was predicted well by Collection 4.8, an examination of the inputs to the GPP algorithm revealed that MODIS fPAR was too high, but this was compensated by PAR and LUE that was too low. Although LAI and fPAR estimated by Collection 5 were more accurate, GPP for this Collection resulted in a much lower value (RPE 25%) due to errors in other factors. Recalculation of MODIS GPP using site specific input parameters indicated that MODIS fPAR was the main reason for the differences between MODIS and tower derived GPP followed by LUE and meteorological inputs. GPP calculated using all site specific values agreed very well with tower data on an annual basis (IOA 0.94, RPE 6.06% and RMSE 0.83 g C m- 2 day- 1) but the early initiation of the growing season calculated by the MODIS algorithm was improved when the vapor pressure deficit (VPD) function was replaced with a soil water deficit function. The results of this study however, reinforce previous findings in water limited regions, like Australia, and incorporation of soil moisture in a LUE model is needed to accurately estimate the productivity.}, added-at = {2009-05-26T13:20:38.000+0200}, author = {Kanniah, K.D. and Beringer, J. and Hutley, L.B. and Tapper, N.J. and Zhu, X.}, biburl = {https://www.bibsonomy.org/bibtex/2eb34fe70d7514f1174a826b2435c2bf2/jgomezdans}, description = {ScienceDirect - Remote Sensing of Environment : Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia}, doi = {DOI: 10.1016/j.rse.2009.04.013}, interhash = {67f40e8ba8558b618295adb546adc96b}, intrahash = {eb34fe70d7514f1174a826b2435c2bf2}, issn = {0034-4257}, journal = {Remote Sensing of Environment}, keywords = {assimilation drought evapotranspiration firemafs models modis optical remotesensing satellite vegetation wildfire}, pages = { - }, timestamp = {2009-05-26T13:20:38.000+0200}, title = {Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia}, url = {http://www.sciencedirect.com/science/article/B6V6V-4WBY55G-1/2/d9c954233599d6b48241ef76cfd46c65}, volume = {In Press, Corrected Proof}, year = 2009 }