Soil respiration is a major process of carbon dioxide emission from terrestrial ecosystems to atmosphere. Thus, quantifying the spatiotemporal pattern of soil respiration (R_s) at the regional or continental scale is critical to establish an experimental and theoretical basis for accurate assessment of the global carbon budget.

Three major approaches are available for estimating R_s at the regional scale. The inventory method multiplies the mean R_s rates of each land cover type by its area. Then, the total of the CO₂ emissions from different land covers is taken as the total R_s. The second method calculates the R_s and its spatiotemporal variability using a process-based model. The third uses geostatistical model of R_s, which is constructed based on the relationships between environmental variables and measured soil carbon flux.

Each method has weakness and uncertainties. Relatively, the geostatistical R_s model has simple structure, sound parameterization method, and reasonable results, though it cannot predict R_s changes with climate, nitrogen deposition, etc. It is usually built on the relationships between in situ R_s rates and environmental variables, such as temperature, precipitation, and leaf area index (LAI). In addition, the geostatistical model can validate the process based R_s model by providing independent data on soil CO₂ emissions. Therefore, the geostatistical R_s model is most widely used in the quantification of spatiotemporal variability of R_s at regional scale. Progress has been made on the research of R_s quantification in China over the past decade.

A research group led by professor YU Guirui from Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research(IGSNRR), Chinese Academy of Sciences published an article in Environmental Science and Technology (2010, 44: 6074–6080). The researchers summarize the R_s data measured in China from 1995 to 2004. Based on the data, a new region-scale geostatistical model of soil respiration (GSMSR) was developed by modifying a global scale statistical model. The GSMSR model, which is driven by monthly air temperature, monthly precipitation, and soil organic carbon (SOC) density, can capture 64% of the spatiotemporal variability of soil R_s.

The group evaluated the spatiotemporal pattern of R_s in China using the GSMSR model. The estimated results demonstrate that the annual R_s in China ranged from 3.77 to 4.00 Pg C yr^-1 between 1995 and 2004, with an average value of 3.84 ± 0.07 Pg C yr^-1, contributing 3.92%-4.87% to the global soil CO₂ emission. Annual R_s rate of evergreen broadleaved forest ecosystem was 698 ±11 g C m^-2 yr^-1, significantly higher than that of grassland (439 ±7 g C m^-2 yr^-1) and cropland (555±12 gC m^-2 yr^-1). The contributions of grassland, cropland, and forestland ecosystems to the total R_s in China were 48.38±0.35%, 22.19±0.18%, and 20.84±0.13%, respectively.