soil to the atmosphere, accounting for approximately 60%-90% of the ecosystem respiration, 40%-60% of the total primary productivity [7], and almost 10 times the amount of CO2 emitted by industrial combustion (5 Pg C yr\({}^{-1}\)) [8; 9]. Moreover, the value of the \"missing carbon sink\" has increased, making it essential to study the response of \(R_{\mathrm{s}}\) to climatic change. Additionally, quantitative observation of \(R_{\mathrm{s}}\) is crucial to accurately estimating the carbon budget of a given area [2; 10]. Soil respiration is a complex process of CO2 emission that involves autotrophic respiration and heterotrophic respiration, including the production of CO2 by plant roots, soil microbes, and soil fauna, and the subsequent emission of CO2 from soil to the atmosphere [11; 12]. Thus, \(R_{\mathrm{s}}\) is affected by a combination of environmental factors including temperature [13], soil water content [14], rainfall events [15; 16], photosynthetic rate, physical and chemical characteristics of soil such as the dissolution of carbonates due to acidification in calcareous and lined acidic soils [11; 17], soil biological properties [18], solar radiation, landform factors [3], and external disturbances [10]. Therefore, these biotic and abiotic elements could potentially provide effective parameter inputs for the modeling of \(R_{\mathrm{s}}\). Predicted dynamic models of \(R_{\mathrm{s}}\) variation based on all related environmental factors affecting \(R_{\mathrm{s}}\) are complex and often constructed according to several key factors controlling the main process. There is considerable evidence suggesting that \(R_{\mathrm{s}}\) is closely related to variations in temperature in most ecological scenarios [5; 19], as well as to the soil moisture in dry conditions [20], and gross primary photosynthesis (GPP) or vegetation indexes [21] in ecosystems with high coverage. Although the effects of these factors on \(R_{\mathrm{s}}\) could not be separated directly, the relative significance of each variable to \(R_{\mathrm{s}}\) differs in various ecosystems. The temperature sensitivity index of \(R_{\mathrm{s}}\) (Q10) has often been used to describe the dependence of \(R_{\mathrm{s}}\) on temperature; however, it is actually not constant at temporal or spatial scales. Rather, its value varies under different temperature conditions [2], especially at lower temperatures. Understanding the variation characteristics of Q10 is a key step in investigation of \(R_{\mathrm{s}}\) characteristics that contributes to the rational estimation of regional carbon budgets. An infrared gas dynamic analysis system is a reliable and convenient field soil carbon dioxide flux observation method within higher measuring accuracy than that of the static method. However, the observed results can only represent the carbon flux characteristics of a specific ecosystem in the particular environment, and it can not be directly applied to other regions. Also, long-time series quantification of \(R_{\mathrm{s}}\) measured in situ remains challenging because of the need to conduct great amounts of field work and the uncertainties regarding different ecological scenarios. Increasing the sample size under the background of global climatic change is critical to accurately investigating the dynamic balance of the carbon cycle. Remote sensing is the only feasible means for large-scale continuous and quantitative observation of ecological indicators at present. It is a low-cost and convenient method of acquiring land surface parameters indirectly, which compound the mixed effects of plants and soil. This technique has been proven to be valid for the estimation of surface parameters (land surface temperature, soil water content, and surface reflectivity), but is less applicable to underground biochemical processes such as \(R_{\mathrm{s}}\)[22; 23].