## Appendix A Additional MODIS Reflectance and Higher Order Products Examined  
In addition to MCD43D, we examined the following MODIS products that are summarized in Table 11: (1) daily Terra MODIS reflectances provided in the MOD09 climate modeling grid (CMG) (0.05\({}^{\circ}\) latitude \(\times\) 0.05\({}^{\circ}\) longitude resolution) product [83; 84; 85; 86] for MODIS Bands 1-7; these data have been adjusted to remove the effects of atmospheric gases and aerosol; data for each grid box were selected on the basis of the low solar zenith angle, minimum Band 3 (blue) reflectance and absence of cloud from Level-3 intermediate files; (2) daily Terra and Aqua MODIS MCD43C4 view angle-corrected nadir BRDF-adjusted reflectances (NBAR) on the CMG; MCD43C4 data were generated similar to MCD43D products described above [87]; (3) angle-adjusted reflectances from the multi-angle implementation of atmospheric correction (MAIAC) applied to Aqua and Terra MODIS (MCD19) and averaged daily on the CMG [88; 89]; Collection 6 MCD19 data on the CMG over Australia are not yet available; (4) monthly NDVI composite (MOD13C2) on the CMG [90]; (5) The MCD15A2H Version 6 MODIS Level 4 fraction of photosynthetically-active radiation (FPAR), product; this is an eight-day composite dataset with 500-m resolution [91; 92]; (6) the MOD17A2H GPP product is an eight-day composite at 500-m resolution based on the LUE concept [14; 93]. We examined each of these datasets in terms of their use to predict GPP as derived from eddy covariance flux tower data, as will be explained in detail below.  
Figure 11 shows a flowchart similar to the one in the main manuscript, but containing all datasets examined in Table 11 in addition to the ones described in the main manuscript.  
The poorest results were obtained using either the MOD17A2H GPP product or the MCD15A2H FPAR (FAPAR) product that drives MOD17A2H. Noted problems in MOD17 GPP, including, but not limited to cloud contamination in MOD15, and proposed solutions have been documented [94; 95; 96; 97]. We obtained significant improvements using the MOD13 composite NDVI product.  
Further improvements were obtained when we processed reflectances using a mean compositing approach [98] as compared with picking a single observation based on a set of criteria, as is common in composite datasets. We found that results with MOD09 reflectances were highly dependent on the quality control filters that were selected. For example, excellent results were achieved when stringent quality control checks were applied including filters for clouds, cloud shadows and fires. However, these checks eliminated data completely from several flux tower sites. Results were degraded using only the basic cloud filtering that appears not to catch all cloud-contaminated pixels. Improvements were possible using MAIAC MCD19 data (where available) as an additional quality filter.  
MAIAC daily results were much more sparse as compared with MCD43 products that are filled in with the 16-day window for data processing. Results with MAIAC and MCD43D angle adjusted reflectances were comparable at the same spatial resolution. The current MAIAC gridded data are at a lower resolution than MCD43D and do not yet have Australia included. We may expect MAIAC MCD19 to perform better at sites with a high probability of cloud and aerosol contamination, such as those in and around Amazonia. There are few of these sites available in FLUXNET 2015 Tier 1. After this initial evaluation, we decided to focus primarily on one reflectance product, MCD43D, as it provides straight-forward quality control and excellent, complete results in a high spatial resolution gridded format.