## 4 Discussion
### Possible Reasons for the Inconsistent Performance of the Four Products
#### 4.1.1 Inconsistency of Model Inputs
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Type** & **MOD16** & **PML-V2** & **BESS** \\ \hline
**Meteorological** & **GMAO-MERRA** (1\({}^{\circ}\times\)1.25\({}^{\circ}\)) & **GLDAS\_21** (0.25\({}^{\circ}\times\)0.25\({}^{\circ}\)) & **NCEP/NCAR** (2.5\({}^{\circ}\times\)2.5\({}^{\circ}\)) \\
**Inputs** & **T\({}_{a}\), \(P_{air}\), RH, \(R_{S\downarrow}\)** & **Prcp, \(T_{a}\), \(P_{air}\), \(R_{S\downarrow}\)** & **\(R_{L\downarrow}\), WS** \\ \hline  & LAI & MOD15A2 (1 km/8 day) & MCD15A3 (500 m/4 day) & MCD15A2 (1 km/8 day) \\  & FPAR & MOD15A2 (1 km/8 day) & - & - \\  & Albedo & MOD43C1\_collection5 & MCD43A3 (500 m/8 day) & MCD43B3 (1 km/16 day) \\  & & (0.05\({}^{\circ}\)/16 day) & - & MCD43B2 (1 km/16 day) \\ Remote & Albedo QC & - & - & MCD43B2 (1 km/16 day) \\ Sensing & Emissivity & - & MOD11A2 (500 m/8 day) & - \\ Inputs & LC & MOD12Q1-UMD & MCD12Q1-IGBP (500 m/year) & MCD12Q1-IGBP (500 m/year) \\  & LST & - & - & MO(Y)D11\_L2 (1 km/5 min) \\  & Aerosol & - & - & MO(Y)D04\_L2 (10 km/5 min) \\  & Water vapor & - & - & MO(Y)D05\_L2 (5 km/5 min) \\  & Cloud & - & - & MO(Y)D06\_L2 (1 km or \\  & Atmospheric & - & - & 5 km/5 min) \\ Profile & - & - & MO(Y)D07\_L2 (5 km/5 min) \\  & FCI & - & - & POLDER 3 (6 km/month) \\ \hline Carbon dioxide & \multicolumn{3}{c}{NOAA-GAMSMMD} \\ concentration & \multicolumn{3}{c}{(global/year)} & 370 ppm \\ \hline  & LAI Leaf Area Index; FPAR: the Fraction of Absorbed Photosynthetically Active Radiation; Albedo QC; BRDF/albedo quality; LC: land cover; LST: land surface temperature; FCI: foliar Clumping Index; \(T_{a}\): air temperature; NOAA-GAMSMMD: NOAA globally averaged marine surface monthly mean data; \(T_{dp}\): dew point temperature; \(P_{air}\): air pressure; Prep: precipitation; RH: relative humidity; \(R_{S\downarrow}\): downward shortwave radiation; \(R_{L\downarrow}\): downward longwave radiation; WS: wind speed. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The major input datasets of MOD16, PML-V2, and BESS. simulating ET. Emissivity is used to calculate longwave radiation and net radiation. The algorithms of MOD16 and PML-V2 are same in calculating net radiation, but different in downward longwave radiation and upward longwave radiation. The downward longwave radiation of MOD16 is calculated by using the Stefan-Boltzman equation, which uses an empirical relationship to calculate atmospheric emissivity based on air temperature, while it directly comes from GLDAS in PML-V2 [62]. Similarly to MOD16, air emissivity is calculated by using a different empirical function related to air temperature in BESS [62; 63]. In terms of upward longwave radiation, surface emissivity is set as a constant of 0.97 in MOD16, while it comes from MOD11A2 in PML-V2. Upward longwave radiation in BESS is calculated using constants of leaf and soil surface emissivity (i.e., 0.98 and 0.94, respectively) and land surface temperature from MOD11 L2 and MYD11 L2. Furthermore, compared with other products, BESS also requires other remote sensing datasets, including clouds, water vapor, aerosols, etc. These datasets are used in the Atmospheric Radiative Transfer Model to obtain downward shortwave radiation. It is worth noting that BESS also uses foliage clumping index rather than LAI to separate the proportion of shade and sunlit leaves. Thirdly, carbon dioxide fertilization is considered in PML-V2, while it is not considered in both MOD16 and BESS. Carbon-dioxide concentration in BESS is set as a constant of 370 ppm, while carbon-dioxide concentration in PML-V2 is derived from the NOAA globally averaged marine surface monthly mean data. We suggest that PML-V2 is more reasonable with regards to this aspect, as carbon dioxide fertilization was proved to increase ET, which is coupled with net primary production [7; 64; 65].