); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Yang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Zhang  
3 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.Z)  
Ziping Yang  
1 Qinghai Eco-Environment Monitoring Center, Xining 810007, China; [EMAIL_ADDRESS] (W.T. ); [EMAIL_ADDRESS] (Z.  
quantify the long-term spatiotemporal variation in grassland AGB in the TRHR to improve both the modeling and estimation of the carbon dynamics of grassland ecosystems [5]. Over the past 40 years, ground-based and satellite-based observations have been successfully used to support the efforts of estimating grassland AGB [11; 12; 13; 14; 15]. All ground-based methods and techniques only provide point-based observations that are limited at large spatial scales to show the heterogeneity of AGB [1,16]. Satellite-based observations can rapidly obtain data covering a wide region, so many studies have used various remotely sensed data to estimate grassland AGB at a regional scale [17; 18; 19; 20]. One of the most common methods is to use satellite-based vegetation indices (VIs) [21; 22; 23; 24; 25]. Satellite-based VI data can directly indicate the condition of aboveground plant components [10]. Among the VI data, the normalized difference vegetation index (NDVI) is the most widely used index [26; 27]. Therefore, the MODerate-resolution Imaging Spectroradiometer (MODIS) NDVI product has been widely used to estimate grassland AGB [28; 29]. Table 1 presents a summary of relevant studies focused on estimating grassland AGB based on MODIS products in the TRHR [30; 31; 32], Tibetan Plateau (TP) [33; 34; 35; 36; 37; 38; 39; 40; 41; 42] or related regions [43; 44; 45]. These studies used many linear and nonlinear regression methods, including machine learning methods, to estimate grassland AGB with MODIS products. However, MODIS products cannot be used to estimate AGB before 2000 because of the lack of data. Solely relying on MODIS products has limited the monitoring of spatiotemporal variations in AGB in the TRHR prior to 2000.