## 1 Introduction  
Grasslands are one of the most widespread vegetation types and store one-fifth of the total global carbon in its vegetation and soil [1; 2]; thus, they play an important role in the global carbon cycle. The extensive grasslands in China make up \(\sim\)10% of the total area of grasslands in the world and have been estimated to store 9-15% of the total carbon held in the grasslands worldwide [3]. Northern China's grasslands located in the Tibetan Plateau and Inner Mongolian Plateau, in particular, constitute the majority (more than 70%) of the grasslands in China and represent two significant grassland types worldwide (i.e., alpine and temperate grasslands) [4]. Moreover, they are sensitive to climate changes due to their unique plateau topography, the extreme cold, arid and semi-arid ecological environment and the high soil carbon density [5; 6; 7]. Therefore, a better understanding of the carbon cycle in northern China's grasslands is necessary for it to serve as a sensitivity indicator of regional and global carbon cycles. Ecosystem respiration (RE) is an important outflow component of ecosystem carbon cycle and represents the second largest carbon exchanges between terrestrial ecosystems and the atmosphere [8]. However, as the result of the complex interactions among physical, chemical, and biological processes, \(RE\) varies greatly at different temporal and spatial scales [9]. Extensive studies at regional or global scales suggest that \(RE\) estimates still remains a large uncertainty [10]. Therefore, it is particularly essential but challenging to accurately evaluate the regional \(RE\) in northern China's grasslands for a quantitative assessment of the terrestrial ecosystem carbon budget and its response to future climate changes. Generally, modeling is the commonly adopted method for inferring \(RE\) at a large scale. Remote-sensing models, with relatively simple model structures and high spatiotemporal resolutions, provide promising tools for monitoring regional \(RE\). Currently, with the development of fine-scale remote-sensing technology and long-term regional and global FLUXNET observations, extensive studies have revealed the robust statistical and physiologically meaningful relationships between \(RE\) and satellite products, e.g., the land surface temperature (\(LST\)) [11; 12; 13] and vegetation index (VI) [14; 15; 16]. Based on this information, many semi-empirical satellite-based models were developed and well validated at the plot scale [13; 16; 17; 18; 19]. However, \(RE\) at large scale involves complex spatial and temporal variation processes [20]. Few satellite-based models have an explicit representation of the spatial process of RE. Specifically, the basal respiration rate, an important parameter in \(RE\)models to describe the location-specific respiration rate, is usually set as a constant parameter, which introduces considerable error in the simulated spatial variability of \(RE\)[8; 20; 21]. Therefore, it is critical to identify the controlling mechanism involved in the spatial variations of \(RE\) with more comprehensive data, not just in the temporal variations, thus serving as a valuable basis to better understand patterns of \(RE\) at regional scale and to scale-up from specific sites to vegetation biomes. In addition, both climatic and biotic controls over \(RE\) should be considered comprehensively in \(RE\) simulation [22; 23]. In satellite based models, \(RE\) has often been linked to temperature or plant productively solely. Some studies advanced \(RE\) estimates by accounting for the combined two effects of temperature and plant productivity [13; 15]. The moisture limitation on \(RE\) is often lacking in most of the current satellite-based respiration models, leading to a potential underestimation of drought effects on respiration (e.g., [19; 24]). However, grasslands are one of the most sensitive ecosystems to the alteration of water regimes [25].