## 2 Study Area and Materials
### Multi-Platform Satellite Remote Sensing Data  
The multi-platform remote sensing data of Terra and Landsat satellites with different spatial and temporal resolutions are listed in Table 1. The daily land surface temperature (LST) level 3 product (MOD1A1) is from the MODIS sensor of the Terra satellite at version 6, which was generated based on the generalized split-window algorithm (GSWA) and has an average 1000 m spatial resolution. This product was validated with an accuracy of 0.6-2.6 K for bare soil land [56].  
Figure 1: Spatial location of study area in Heihe River Basin (HRB) in Northwest China. Standard false-color map of composite 543 bands of Landsat 8 images on 29 September 2015 from the U.S. Geological Survey (USGS) dataset ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) accessed on 6 April 2021) shows scope of study area, with irrigated area and cropland station (Daman superstation (DMS)) in midstream of HRB. In situ Daman superstation observation system includes eddy covariance (EC) and automatic weather station (AWS) system.  
The other MODIS products used include the albedo level 3 product (MCD43A3) at 500 m and the 16-day normalized differential vegetation index (NDVI) level 3 product (MOD13Q1) with 250 m resolution. These MODIS products for the study area from 2010 to 2016 were acquired from the Land Processes Distributed Active Archive Center (LP DAAC, [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) accessed on 6 April 2021). The all-weather LST product was obtained by integrating the MODIS LST product with the AMSR-E/AMSR2 bright temperature product, as detailed in [PERSON] et al. (2017), and it can be downloaded from the National Tibetan Plateau Data Center (TPDC) ([[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) (Table 1).  
The Landsat data with cloud cover below 15% from 2010 to 2016 were acquired from the USGS ([[https://landsat.usgs.gov/](https://landsat.usgs.gov/)]([https://landsat.usgs.gov/](https://landsat.usgs.gov/)) accessed on 6 April 2021) to provide high-resolution information about the LST, albedo, NDVI, and emissivity for quantitative remote sensing. These shortwave reflectance bands and the thermal bands of Landsat data were calibrated and corrected, separately, with the QUAC ([PERSON] et al., 2017) of the ENVI and the Atmospheric Correction Parameter Calculator ([PERSON] et al., 2017) from NASA ([[https://atmcorr.gsfc.nasa.gov/](https://atmcorr.gsfc.nasa.gov/)]([https://atmcorr.gsfc.nasa.gov/](https://atmcorr.gsfc.nasa.gov/)) accessed on 6 April 2021). These model input variables (LST, fractional vegetation cover (FVC), NDVI, emissivity, leaf area index (LAI), and canopy height (HC)) for the ET and GPP models were generated at higher spatial (100 m) and temporal (daily) resolution after fusing the MODIS and Landsat data (Table 1).