## Material and Method
### Data Preprocessing  
Land use in the study area was classified using remote sensing image classification in the study area. _MODIS_ images acquired in May 2017 as a spatial resolution of 0.5 km were sourced for image classification. The classification system developed by the International Geosphere-Biosphere Programme (IGBP) was adopted to classify the research into 17 categories [26;27]. Land-use \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value** & **Land Use** & **Count** & **Area (km-km)** & **Percent (\%)** \\ \hline
1 & Evergreen Needle Leaf Forest & 1934 & 1133.07 & 0.29\% \\
2 & Evergreen Broadleaf Forest & 2168 & 1270.17 & 0.33\% \\
3 & Deciduous Needle Leaf Forest & 5223 & 3060.00 & 0.79\% \\
4 & Deciduous Broadleaf Forest & 18,874 & 11,057.71 & 2.86\% \\
5 & Mixed Forest & 51,534 & 30,192.22 & 7.81\% \\
6 & Closed Shrublands & 7328 & 4293.26 & 1.11\% \\
7 & Open Shrublands & 15,959 & 9349.90 & 2.42\% \\
8 & Woody Savannas & 3822 & 2239.20 & 0.58\% \\
9 & Savannas & 14,820 & 8682.59 & 2.25\% \\
10 & Grasslands & 249,424 & 146,130.04 & 37.82\% \\
11 & Permanent Wetlands & 284 & 166.39 & 0.04\% \\
12 & Croplands & 239,739 & 140,455.89 & 36.35\% \\
13 & Urban and Built-Up & 20,759 & 12,162.08 & 3.15\% \\
14 & Cropland/Natural Vegetation Mosaic & 5192 & 3041.84 & 0.79\% \\
15 & Snow and Ice & 330 & 193.34 & 0.05\% \\
16 & Barren or Sparsely Vegetated & 17,761 & 10,405.64 & 2.69\% \\
17 & Water Bodies & 4384 & 2568.45 & 0.66\% \\ Total & & 659,535 & 386,401.77 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Land cover situation in study area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band Name** & **Centre Wavelength (\(\mu\)m)** & **Spectral Range (\(\mu\)m)** & **Signal Noise Ratio and Irradiance** \\ \hline BLUE & 0.464 & 0.440–0.487 & 177,111 \\ RED & 0.655 & 0.614–0.696 & 598,110 \\ NIR & 0.837 & 0.772–0.902 & 574,106 \\ SWIR & 1.603 & 1.570–1.635 & 72,020 \\ \hline \hline \multicolumn{4}{c}{Radiometric performance} \\ \hline Absolute accuracy (\%) & & \textless{}5 & \\ Inter-channel accuracy (\%) & & \textless{}3 & \\ Stability (\%) & & \textless{}3 & \\ \hline \hline \multicolumn{4}{c}{Geometric performance} \\ \hline  & BLUE:69.69 (49.94) & \\ Geolocation mean accuracy & RED:60.46 (50.78) & \\ (standard deviation) (m) & NIR:61.30 (50.52) & \\ SWIR:61.86 (50.03) & \\ \hline \hline \end{tabular} Data source: [[http://www.vito-eodata.be/](http://www.vito-eodata.be/)]([http://www.vito-eodata.be/](http://www.vito-eodata.be/)).  
\end{table} 
Table 2: Project for On-Board Autonomy (_PROBA-V_) spectral and radiometric characteristics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relative Difference** & **VGT2-VGT1** & _PROBA-V-VGT1_ & _PROBA-V-VGT2_ \\ \hline BLUE & \(-0.13\) & \(1.98\) & \(2.11\) \\ RED & \(-3.20\) & \(-3.91\) & \(-0.74\) \\ NIR & \(0.29\) & \(-0.47\) & \(-0.76\) \\ SWIR & \(-1.37\) & \(-4.65\) & \(-3.37\) \\ _NDVI_ & \(3.68\) & \(3.34\) & \(-0.33\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average relative difference (%) in band reflectance and Normalized Difference Vegetation Index (_NDVI_) between VGT2 (Vegetation sensor 2) and VGT1 (Vegetation sensor 1), _PROBA-V_ and VGT1, and _PROBA-V_ and VGT2. extraction was achieved through the object-oriented classification [28; 29; 30] module in ENVI5.3 software (Exelis Visual Information Solutions, Inc., Broomfield, CO, USA). The geographic coordinates of the three data sources were inconsistent; the _MODIS_ and _PROBA-V_ coordinate system used the World Geodetic System 1984 (WGS84), while the SMAP product was the Cylindrical Equal-area coordinate system. The first step was to re-project the SMAP product from Cylindrical Equal-area to WGS84, and modify the output GeoTIFF file to be ingestible into commonly used GIS applications. After completing the coordinate transformation, the next step is to resample the data so that the three types of data (_MODIS_, _PROBA-V_, and SMAP) are the same pixel size for the model.