## 2 Study Area and Materials
### Ground Measurements  
The EC data were used to validate the revised SEBS and MODIS GPP models from the TPDC website ([[https://data.tpdc.ac.cn/en/](https://data.tpdc.ac.cn/en/)]([https://data.tpdc.ac.cn/en/](https://data.tpdc.ac.cn/en/)) or [[https://data.tpdc.ac.cn/zh-hans/](https://data.tpdc.ac.cn/zh-hans/)]([https://data.tpdc.ac.cn/zh-hans/](https://data.tpdc.ac.cn/zh-hans/)) accessed on 6 April 2021). The EC measurements of turbulent and CO\({}_{2}\) fluxes at 10 Hz frequency from the Daman superstation site were processed in the midstream of the HRB (Figure 1). The latent heat flux (LE) of continuous 30 min periods in one day was calculated for the daily ET. To fill gaps for the temporal continuous daily ET, a nonlinear regression method was adopted based on the 30 min LE and net radiation (R\({}_{n}\)) data ([PERSON] et al., 2017). However, when there was a loss of more than 50% in one day, the daily ET was abandoned, and the missing data were interpolated according to the relationship between the daily LE and the daily R\({}_{n}\) of other days. [PERSON] et al. (2014, 2017) and [PERSON] et al. (2016) provide more details on the in situ measurements and observation data preprocessing.  
For the daily GPP, the EC systems used directly measure the net ecosystem exchange (NEE) rather than GPP. Thus, GPP is the sum of the NEE and daytime respiration (R\({}_{dt}\)):  
\[\text{GPP}=\text{R}_{dt}-\text{NEE}_{dt}, \tag{1}\]  
where NEE\({}_{dt}\) is daytime NEE. As plants do not photosynthesize at night, nighttime NEE (NEE\({}_{nt}\)) represents nighttime respiration (R\({}_{nt}\)). The effects of temperature on NEE\({}_{nt}\) were described using the following nonlinear regression method ([PERSON] et al., 2017; [PERSON] et al., 2017):  
\[\text{NEE}_{nt}=\text{a}_{0}\times\text{EXP}(\text{b}_{0}\times\text{T}_{a}), \tag{2}\]  
where T\({}_{a}\) is nighttime air temperature. The coefficients a\({}_{0}\) and b\({}_{0}\) were determined based on NEE\({}_{nt}\) and the air temperature was from nonlinear optimization. Thus, R\({}_{dt}\) can be calculated by extrapolating Equation (2) in conjunction with the daytime temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Data Name** & **Start Date** & **End Date** & **Spatial Resolution** & **Temporal Resolution** & **Location** \\ \hline - & All-weather LST (AWLST) & & 1000 m & Daily & HRB \\  & MOD11A1 & & 1000 m & Daily & \\ MODIS & MCD43A3 & & 500 m & Daily & H25V5 \\  & MOD13Q1 & & 250 m & 16 days & \\ Landsat-5 & TM & & 30/120 m & 16 days & \\ Landsat-7 & ETM+ & & 30/60 m & 16 days & P133R033 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of multi-platform satellite remote sensing data.  
In addition, because the CO\({}_{2}\) flux can be underestimated using the EC system for nonturbulent conditions [64], the data included in this study were removed with a friction velocity of more than 0.15 m/s. Nonlinear regression between the measured fluxes and environmental factors was used to fill missing data gaps using a moving window [65; 66]. The daily GPP was synthesized based on half-hours. When more than 20% of a day's data was missing, it was indicated as a missing day. The daily GPP values were calculated using a shallow neural network with meteorological variables. Then, the annual values of the GPP were determined by summing all the daily GPP values.  
The following meteorological data were collected from the Daman superstation: air temperature, wind speed, pressure, relative humidity, rain, soil temperature and moisture profiles, radiation, and surface soil heat flux. These data were measured by constructing in situ stations at intervals of 10 min [61].