## 1 Introduction  
Ecosystem respiration (RE) is the second-largest terrestrial carbon flux after photosynthesis, and plays an important role in the interaction between climate change and carbon dynamics [1, 2]. Minor fluctuations in RE caused by natural or human activity can result in significant changes in atmospheric CO\({}_{2}\) concentrations [2, 3, 4, 5]. Although studies have described the global RE pattern and associated temporal changes [6, 7, 8], there remains considerable uncertainty around the spatiotemporal changes in RE at the regional and biome scales [9], hindering the identification of carbon sinks and source functions in key areas. Efforts have been made to understand how climate change affects spatiotemporal changes in RE, but complex interactions among the physical, chemical, and biological processes of RE present a considerable challenge for researchers [10; 11]. Seasonal changes in grassland RE can be attributed to temperature (Ta) and moisture [2; 12; 13]. Spatial changes in respiration can be attributed to vegetation status, plant productivity, or moisture [12; 14; 15]. However, there have been few analyses of long-term interannual changes in RE. Most of these prior studies have focused on climatic and vegetation factors. Soil factors remain relatively unexplored, including soil organic carbon (SOC) and soil texture (SoifTex). This is because their functions remain poorly understood in the context of complex respiration processes [16; 17; 18]. However, soil factors are important for carbon flux estimation, and soil respiration accounts for a high proportion of ecosystem respiration [19; 20; 21; 22; 23; 24]. Therefore, the important influence of soil factors in the RE estimation model cannot be excluded. To accurately quantify RE, many empirical models [2; 14; 25; 26; 27], semi-empirical models [1; 28], and process models [16; 29] have been developed using data at different temporal and spatial scales. Machine learning (ML) models show promise for solving complex data patterns, owing to their powerful nonlinear regression and classification abilities [30]. ML has been successfully applied in many fields of ecology, including carbon-water flux [2; 31; 32; 33; 34; 35], energy flux [6; 36; 37], and vegetation state [38; 39; 40]. However, the interpretation of the modeling results is often difficult, as the impact of inputs on the output cannot easily be understood through networks in comparison with process-based models [30; 41; 42; 43]. In recent years, the attention mechanism has become one of the main focuses in ML research. Its core principle is to assign greater weight to important information to achieve the purpose of selecting the key information, which improves the interpretability of the results [44; 45; 42]. Combined with powerful neural network models, such as long short-term memory networks (LSTM), the attention mechanism has achieved great success in time series prediction [44; 46; 47]. ML models with a high prediction accuracy provide promising tools for monitoring regional RE. Grasslands play an important role in the terrestrial ecosystem carbon cycle, especially grassland ecosystems in alpine regions and in arid and semi-arid regions, where fragile ecological environments are more likely to be affected by climate change [14; 48]. Grassland is the dominant landscape in China, accounting for 40% of the national land area [2], and is mainly distributed in the Tibetan Plateau (TP) and the Inner Mongolian Plateau (IM) (Figure 1). China's grasslands account for approximately 10% of the global grassland area and form 9-16% of the global grassland carbon pool [49]. Figure 1: Spatial distribution of alpine and temperate grasslands in China [34]. Black triangles denote the 18 flux sites. In this study, we applied the geo-sensory multi-level attention network (Geoman) model developed by [PERSON] et al. [44] to estimate the RE in northern China's grasslands from 2001-2015, at a spatial resolution of 1 km.