## 6 Conclusions  
Based on the AGB upscaling model from GBRT, we generated long-term (1982-2018) grassland AGB datasets with a 5 km spatial resolution by remotely sensed data with different spatial resolutions (MODIS and AVHRR NDVI products), meteorological data (Ta, P, and Rs), ancillary data (elevation), and ground AGB observations in the TRHR. Compared with previous studies, our study can effectively reflect the long-term spatiotemporal variation in AGB during 1982-2018 in the TRHR. Major conclusions are given as follows.  
1. MODIS-derived upscaled AGB with a 1 km spatial resolution based on the GBRT model was validated by AGB ground observations with the best performance (R\({}^{2}\) = 0.76, RMSE = 88.8 g C m\({}^{-2}\), and bias = \(-\)1.6 g C m\({}^{-2}\)).
2. The GBRT model also showed the best validation performance between the AVHRR-derived upscaled and MODIS-derived reference AGB with a 5 km spatial resolution with an R\({}^{2}\) of 0.74, an RMSE of 57.8 g C m\({}^{-2}\), and a bias of \(-\)0.1 g C m\({}^{-2}\).
3. The annual trends of grassland AGB increased by 0.37 g C m\({}^{-2}\) year\({}^{-1}\) on average during 1982-2018, which was mainly attributed to vegetation greening and increasing precipitation.  
In summary, our study successfully generated reliable long-term grassland AGB datasets in the TRHR covering the period of 1982-2018, thereby meeting the need for monitoring the long-term spatiotemporal variation in grassland AGB for grassland utilization decisions and resource management in the TRHR.  
Conceptualization, R.Y., Y.Y. and Q.W.; methodology, R.Y. and Y.Y.; software, R.Y.; validation, Y.Y., H.W., W.T. and Z.Z.; formal analysis, Z.X.; investigation, X.G.; resources, Y.Y.; data curation, J.Y., K.S., X.G. and X.B. writing--original draft preparation, R.Y.; writing--review and editing, R.Y. and Y.Y.; visualization, R.Y.; supervision, H.W.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2016 YFA0600103 and No. 2016 YFA0600102), the National Natural Science Foundation of China (Grant No. 41671331).  
Not applicable.  
Data Availability Statement: Data available on request.  
Acknowledgments: The AGB ground observation data was provided by the Qinghai Eco-Environment Monitoring Center ([[http://www.qhheco.org](http://www.qhheco.org)]([http://www.qhheco.org](http://www.qhheco.org)), accessed on 15 September 2020). MODIS NDVI was obtained online ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)), accessed on 15 September 2020). We would like to thank [PERSON] from Beijing Normal University, China, for providing GLASS AVHRR NDVI product. The authors thank [PERSON] and [PERSON] from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences ([[http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)]([http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)), accessed on 15 September 2020) for providing the China Meteorological Forcing Dataset. UMD-LCC dataset was obtained online ([[http://glcf.umd.edu/data](http://glcf.umd.edu/data)]([http://glcf.umd.edu/data](http://glcf.umd.edu/data)), accessed on 15 September 2020).  
The authors declare no conflict of interest.