Some researchers have recently used more accessible land surface parameters to build spatial [5] or temporal variation models of \(R_{\mathrm{s}}\) to predict \(R_{\mathrm{s}}\) based on empirical models and provide data supporting the carbon budget on a large scale. The application of remote sensing to studies of \(R_{\mathrm{s}}\) based on long-term observations is not easy and has seldom been conducted. Therefore, taking the combination of remote sensing technology and ecology process as the breakthrough point and integrating multi-source data containing in situ measured data and spatial data products to study soil respiration along long time series is not only the demand for the development of remote sensing application, but also an effective way to solve the scale expansion of field observing. As the second largest geographic unit in China, accounting for 70% of the world's loess distribution and the largest loess accumulation area in the world, the Loess Plateau region has formed its own unique climate and topography, within the characteristics of high altitude distribution, is also faced with a lot of ecological function decline such as soil erosion, vegetation destruction caused by human activities, and climatic change. The terrestrial carbon cycle in the Loess Plateau is an important component of the global carbon balance. Sub-alpine meadows are widely distributed in China, which are dominated by perennial herbs. As an important type of grassland ecosystem, ecological investigations of sub-alpine meadows have received the attention of researchers because their ecological functions are far greater than their economic values. In the present study, a typical mountain meadow in the Loess Plateau, which was one of only four meadows in Shanxi province, was investigated. The carbon balance in this high-altitude distributed ecosystem is extremely sensitive to climatic change; therefore, the temporal characteristics of the carbon cycle were investigated to provide basic datasets with actual reference values. Furthermore, applying Moderate Resolution Imaging Spectroradiometer (MODIS) data to the temporal variation model of in situ measured \(R_{\rm s}\) will verify the science significance and feasibility of estimating \(R_{\rm s}\) using remote sensing data. The study was conducted to explore the temporal dynamics in \(R_{\rm s}\) as well as its connections to temperature, soil moisture and vegetation index in a sub-alpine meadow in north China based on field measuring data and spatial data products. Thereafter the dynamic response model of seasonal variations in \(R_{\rm s}\) could be formulated by its driving primary factors based on multi-source data, further quantitively examining the dominant role of temperature in soil respiration in the sub-alpine meadow zone and the potential applicability of spatial data products to estimate \(R_{\rm s}\). The hypotheses of this study were: (1) seasonal variations in \(R_{\rm s}\) would be effectively estimated by its primary driving factors, (2) \(R_{\rm s}\) was most sensitive to temperature variations in study area thus temperature would act as a key role in estimating seasonal variations in \(R_{\rm s}\) in the sub-alpine meadow zone, and (3) MODIS products would be reliable indicators for the quantitative estimation of \(R_{\rm s}\) in a certain statistical sense.