## 2 Data and Methods
### Model Development  
#### 2.3.1 Biome-level Simulation  
The RF and XGBost models were implemented in this study and compared to estimate daily NEE in 10 different biomes. To achieve high simulation accuracy, the data need to be pre-processed before further analysis. We merged the subset daily data of EC sites belonging to the same biome to generate hybrid data for each biome. Because daily data were generated by aggregating half-hourly data, further sample screening was performed based on the quality mark of each feature. The quality mark ranges between 0-1, indicating the percentage of observed and good quality gap-filled half-hourly samples per day. To control the data quality of the training features, we excluded the sample when any of the feature's quality mark was less than 0.8. After the process of data cleaning, both of the RF and XGBost models were generated in Python (version 3.7.5) with packages including Numpy (version 1.17.3), Pandas (version 0.25.3), Scikit-learn (version 0.23.2), and Scikit-learn interface for XGBoost.  
For each hybrid data of different biomes, we used the method of \"train_test_split\" to split it into training samples (70%) and test samples (30%) with a \"random_state\" value of 420. The training sample was modeled using the \"randomizedSearchCV\" method with 5-fold cross-validation as well as random search to obtain hyper-parameters for the model. The hyper-parameters used for the two models are listed in Table S1. Taking into account the computational efficiency, the \"n_iter\" parameter in the \"randomizedSearchCV\" method was set to 100 and 200 for RF and XGBoost, respectively. It controls the number of parameter settings that are tried in the model training. After the hyper-parameters are obtained, the best model can be established. The summary of the data processing was outlined in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**ID** & **Site\_ID** & **Site Name** & **Data Start** & **Data End** & **Biome** & **Sample Size** \\ \hline
45 & IT-SR2 & San Rossoe 2 & 2013 & 2014 & ENF & 580 \\
46 & IT-Tor & Torgnon & 2008 & 2014 & GRA & 601 \\
47 & NL-Loo & Loobos & 1996 & 2014 & ENF & 3811 \\  & & ARM USDA UNL OSU Woodward & & & & \\
48 & US-AR2 & Switchgrass 2 & 2009 & 2012 & GRA & 950 \\
49 & US-ARM & ARM Southern Great Plains site-Lamont & 2003 & 2012 & CRO & 1122 \\
50 & US-CRT & Curtice Walter-Berger cropland & 2011 & 2013 & CRO & 421 \\
51 & US-GLE & GLEES & 2004 & 2014 & ENF & 39 \\
52 & US-Goo & Goodwin Creek & 2002 & 2006 & GRA & 1229 \\
53 & US-Me2 & Metolius mature ponderosa pine & 2002 & 2014 & ENF & 1791 \\
54 & US-Me3 & Metolius-second young aged pine & 2004 & 2009 & ENF & 1739 \\
55 & US-Me6 & Metolius Young Pine Burn & 2010 & 2014 & ENF & 1094 \\
56 & US-MMS & Morgan Monroe State Forest & 1999 & 2014 & DBF & 3602 \\
57 & US-NR1 & Niwot Ridge Forest (LTER NWT1) & 1998 & 2014 & ENF & 2066 \\
58 & US-Oho & Oak Openings & 2004 & 2013 & DBF & 1826 \\
59 & US-SRC & Santa Rita Creosote & 2008 & 2014 & MF & 1575 \\
60 & US-SRG & Santa Rita Grassland & 2008 & 2014 & GRA & 2293 \\
61 & US-SRM & Santa Rita Mesquite & 2004 & 2014 & WSA & 3648 \\
62 & US-Svy & Sylvnain Wildermes Area & 2001 & 2014 & MF & 133 \\
63 & US-Ton 1 & Tonzi Ranch & 2002 & 2014 & WSA & 4317 \\
64 & US-UMB & Univ. of Mich. Biological Station & 2000 & 2014 & DBF & 2622 \\
65 & US-Var & Vaira Ranch- Ione & 2000 & 2014 & GRA & 2056 \\
66 & US-WCr & Willow Creek & 1999 & 2014 & DBF & 2671 \\
67 & US-Whs & Walnut Gulch Lucky Hills Shrub & 2007 & 2014 & OSH & 1876 \\
68 & US-Wkg & Walnut Gulch Kendall Grasslands & 2004 & 2014 & GRA & 3569 \\
69 & ZM-Mon & Mongu & 2000 & 2009 & DBF & 550 \\ \hline \end{tabular}  
* Because of the different data filtering methods, the site of US-Ton only modeling in Section 2.3.2, not in Section 2.3.1.  
\end{table}
Table 2: Cont.