\begin{table}
\begin{tabular}{c c c c c c} \hline  
Fortunately, advanced very high resolution radiometer (AVHRR) data, which have a relatively coarse spatial resolution (approximately 8 km \(\times\) 8 km), have provided global satellite observations since 1981 [46]. [PERSON] et al. produced a new series of AVHRR NDVI product suites with a 5 km spatial resolution using the method of temporally continuous vegetation index-based land-surface reflectance reconstruction (VIRR) [47; 48]. Therefore, AGB needs to be upscaled from ground observations to a 5 km spatial resolution. The upscaling methods consist of two fundamental steps [49; 50; 51; 52]: (i) using statistical methods \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Time Horizon** & **Remotely Sensed** & **Models** & **Main Conclusions** & **References** \\ \hline NTP & 2000–2013 & MODIS FPAR/LAI product & Logarithm model & Aqua MODIS FPAR/LAI product may have the highest accuracy in AGB estimation. & [38] \\ \hline TRHR & 2001–2016 & MODIS surface reflectance product & ANN model & ANN model based on grass and NDVI has best accuracy (R\({}^{2}\) = 0.62, RMSE = 589.9 kg/DW ha) in testing set. & [31] \\ \hline Maqu & 2016–2017 & MODIS NDVI and EVI products & Linear model & R\({}^{2}\) of the NDVI models were all greater than those of the EVI models. & [44] \\ \hline NTP & 2000–2014 & MODIS NDVI product & logarithm and exponent models & The slopes of AGB in fenced grasslands and in livestock were \(-\)2.71 and \(-\)0.15 g m\({}^{-2}\) year\({}^{-1}\), respectively. & [39] \\ \hline Shenzha & 2000–2013 & MODIS NDVI product & Exponential model & Annual average AGB was slightly increased and fluctuated between 1800 and 2700 kg/ha, with an average of 2272 kg/ha. & [45] \\ \hline TP & 2000–2014 & MODIS NDVI and EVI products & RF model & Grassland AGB decreased from southeast to northwest with an average value of 77.12 g m\({}^{-2}\) and increasing rate of 0.19 g m\({}^{-2}\) year\({}^{-1}\) in the TP. & [40] \\ \hline TP & 2000–2017 & MODIS NDVI product & RF model & RF model with multiple factors performed best for AGB estimation (R\({}^{2}\) = 0.75, RMSE = 346.06 kg/ha). & [41] \\ \hline NTP & 2004.9 & MODIS NDVI and EVI products & NDVI-based exponential model & Optimal regression model for AGB estimation was NDVI-based exponential models (R\({}^{2}\) = 0.63). & [42] \\ \hline TRHR & 2001–2019 & MODIS NDVI product & HASM & HASM achieved the best results (R\({}^{2}\) = 0.85, RMSE = 29 g m\({}^{-2}\)) and AGB increased by 1 g m\({}^{-2}\) year\({}^{-1}\). & [32] \\ \hline \hline \end{tabular} Note: NTP: northern Tibetan Plateau; EVI: enhanced vegetation index; FPAR: fraction of photosynthetically active radiation; LAI: leaf area index; ANN: artificial neural network; HASM: high accuracy surface model; R\({}^{2}\); coefficient of determination; RMSE: root mean square error; DW: dry weight; hm\({}^{2}\)/ha: hectane.  
\end{table} 
Table 1: _Cont._to establish a relationship between AGB ground observations and explanatory variables, including remotely sensed, meteorological, and ancillary data, and (ii) using gridded remotely sensed, meteorological, and ancillary data to obtain the spatiotemporal pattern of AGB by considering the established relationship. However, there is an obvious problem associated with the spatial representativeness of AGB upscaled from ground observations to a coarse spatial resolution directly [53]. A previous study [10] reported that the random forest (RF) model was used to upscale AGB directly from ground observations to an 8 km spatial resolution in the TP during 1982-2010. Due to the weak spatial representation of ground observations, this upscaling method leads to large uncertainties. Therefore, AGB is required to be upscaled from ground observations to a fine and medium spatial resolution and then from a fine and medium to coarse spatial resolution using multisource remotely sensed data with different spatial resolutions.