## 1 Introduction  
Soil respiration (SR) is the main pathway through which carbon exits terrestrial ecosystems [1, 2, 3], changing soil organic carbon (SOC) storage and its allocation in soil [4]. It is the integral part of the ecosystem carbon balance (net ecosystem production, \(NEP=GPP-Re\)), defined as the difference between the gross primary production or photosynthesis (GPP) and the total respiration (\(Re\)), which is the direct indicator of the ecosystem's well being [5, 6], and a useful indicator of plant metabolism [1]. That is why SR--a main component of \(Re\)--should be monitored for estimation of the ecosystem ability to withstand environmental stresses due to adverse changes in the environment or due to climate change [2, 3], and for reporting the annual greenhouse-gas inventory [7]. For the separation of the ecosystem-related and soil-related parameters in \(NEP\), the total respiration (\(Re\)) should be split to ecosystem respiration related to its growth (autotrophic respiration, \(Ra\)) and the soil respiration related to the microbial activity for the SOC_ decomposition (heterotrophic respiration, _Rh_) [8; 9]. This respiration separation is often modeled from the eddy-covariance data [1; 10; 11; 12; 13; 14] that could lead to inconsistent results [15; 16], due to the high variability of the _Ra_ contribution to _Re_ (10%-90%), depending on the seasonality and vegetation types [17]. The direct methods for SR measurements employ operation of soil chambers of the closed type [18; 19]. As a rule, these methods require regular travelling to the site to conduct measurements to assure the adequate representation of SR estimations during the year. This represents the significant labor intensity of the soil-chamber measurement procedure [20]. The quality of these measurements can be altered by the size and the installation of the chambers as well as by the soil heterogeneity [4], introducing high measurement variability, and the presence of snow or vegetation covering the soil surface and interrupting gas exchange [21; 22]. Moreover, such a monitoring on a country level looks unrealistic because of a prohibitively high cost [4]. The viable alternative to the measurement methods is to focus on the modeling of SR including both simple empirical models [14; 23; 24; 25; 26] and more sophisticated process-based dynamical models [4; 27; 28; 29]. A number of research studies focusing on both SR measurements and applications of empirical SR models highlight a strong dependency of SR on the following parameters: (i) temperature [30; 31; 32; 24; 33] due to change in microbial activity; (ii) soil moisture [34; 35; 24; 36; 37] due to change in soil porosity and accessibility of atmospheric oxygen; (iii) precipitation [20; 24; 38; 39; 40; 41; 42] as a simpler way for the soil-moisture estimation; (iv) change of water level in soil [26; 43; 44] blocking below-water-level SOC oxidation; and (v) allocation of above-ground biomass [23; 45]. Several studies link changes in the amount of _SOC_ stored in ecosystem soils to differences in SR [1; 8; 46; 47; 48; 49]. The empirical models usually use one of two temperature sources for parameterization: the soil temperature (\(T_{soil}\)) [23; 26; 31; 32; 33] or the air temperature (\(T_{air}\)) [20; 24; 25; 24]. [PERSON] and [PERSON] (1995) [24] note that the soil-temperature application is more consistent from the Q\({}_{10}\) temperature-coefficient behavior's point of view. They justified it by the direct response of the microbial activity on changes in \(T_{soil}\). On the other hand, [PERSON] and [PERSON] (2022) [42] showed that the Raich-Hashimoto model [25] with the quadratic temperature dependency gives good results when it is parameterized by \(T_{air}\). It should be noted that, as a rule, the choice of \(T_{soil}\) or \(T_{air}\) stays on investigators' judgment, and until now, no analysis on the preferred temperature sources for the model parameterizations for different soil conditions have been conducted.