## 2 Data and Methodology
### Dataset  
Meteorological factors (precipitation, temperature, wind speed, solar radiation, atmospheric pressure, humidity) and remote sensing data (LAI, land cover) were used to drive the remote sensing-based ecohydrological model. We employed a monthly China Meteorological Forcing Dataset (CMFD) with 0.1\({}^{\circ}\) between 1999 and 2018 as climatic factor inputs ([[https://www.tpdc.ac.cn/zh-hans/data](https://www.tpdc.ac.cn/zh-hans/data)]([https://www.tpdc.ac.cn/zh-hans/data](https://www.tpdc.ac.cn/zh-hans/data)), the last accessed data is on 28 December 2023). This dataset has been widely employed to drive model simulations and data analyses in recent years [37].  
LAI data are the most important remotely sensed driver of model-based simulations. We collected the extensively used GLOBMAP V3.0 LAI products ([38; 39; 40; 3] with 0.08\({}^{\circ}\) from 1999 to 2018 online ([[https://www.resdc.cn/data.aspx?DATAID=336](https://www.resdc.cn/data.aspx?DATAID=336)]([https://www.resdc.cn/data.aspx?DATAID=336](https://www.resdc.cn/data.aspx?DATAID=336)), last accessed date is on 28 December 2023). The original GLOBMAP V3.0 LAI has half months in 1999 and eight-day periods between 2000 and 2018, which were composited into monthly data [41]. Annual dynamic land cover data were provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) with 0.05\({}^{\circ}\) between 2001 and 2018 ([[https://modis.gsfc.nasa.gov/data/dataprod/mod12.php](https://modis.gsfc.nasa.gov/data/dataprod/mod12.php)]([https://modis.gsfc.nasa.gov/data/dataprod/mod12.php](https://modis.gsfc.nasa.gov/data/dataprod/mod12.php)), last accessed date is on 28 December 2023). We adopted the International Geosphere-Biosphere Programme (IGBP) classification method, which includes 17 land-cover types. Because it begins in 2001, the corresponding land cover dataset for 1999 to 2000 was replaced with the 2001 land cover map. We used the monthly albedo calculated by white-sky and black-sky albedo.  
Monthly eddy covariance (EC) records based on the latent heat and net ecosystem exchange at seven flux sites were collected from ChinaFLUX ([[https://www.crenr.org.cn/index.jsp](https://www.crenr.org.cn/index.jsp)]([https://www.crenr.org.cn/index.jsp](https://www.crenr.org.cn/index.jsp)), last accessed date is on 28 December 2023). The seven flux sites included Changbaishan (CBS), Inner Mongolia (NMG), Haibei (HB), Dangxiong (DX), Yucheng (YC), Qianyanzhou (QYZ), and Dinghushan (DHS). To keep the validation period constant over the seven flux sites, we used the period of 2004 to 2009 to calibrate and validate the model. The land where the flux sites were set up covered crops, forest, grass, and shrubs (Table 1). We used proportionality parameters [37; 42] to calculate the monthly GPP and ET.  
We used the spatial distribution of wheat over China [28] to minimize the uncertainty of irrigation when calculating ET. Considering the spatial resolution of the dataset, the remotely sensed LAI, albedo, and raster meteorological datasets from 1999 and 2018 were  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Location** (\({}^{\circ}\)E, \({}^{\circ}\)**N**) & **Land Cover Type** & **Range** \\ \hline CBS & 128.10, 42.40 & Mixed forest (MF) & \\ NMG & 117.45, 43.50 & Grass (GRA) & \\ HB & 101.32, 37.60 & Shrub (SHR) & \\ DX & 91.08, 30.85 & Grass (GRA) & \\ YC & 116.60, 26.95 & Crop (CRO) & \\ QYZ & 115.07, 26.73 & Evergreen needleleaf forest (ENF) & \\ DHS & 112.56, 23.01 & Evergreen broadleaf forest (EBF) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seven flux sites used for model validation.  
resampled to 0.05\({}^{\circ}\) via bilinear interpolation. Due to the deficiency of LAI data in desert and bare lands, we selected only non-desert areas in Mainland China as our study region (Figure 1).