Estimation of Soil Respiration by Its Driving Factors Based on Multi-Source Data in a Sub-Alpine Meadow in North China

Sustainability

卷:11

Junxia Yan¹  Hongjian Li¹  Yanan Liang²  Yanpeng Cai² 

[1] Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China;

[2] State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;

关键词: Soil respiration;    seasonal variations;    spatial data products;    model specification;    Q₁₀ values;    sub-alpine meadow;   

DOI  :  10.3390/su11123274

来源: DOAJ

【 摘 要 】

Soil respiration (R_s) in high-altitude areas are normally sensitive to varying climatic conditions. The objective of this research was mainly to explore temporal variations in R_s rates and the corresponding controlling factors for the establishment of appropriate fitting models in a sub-alpine meadow of north China. The data was obtained through field measuring and extraction of the Moderate Resolution Imaging Spectroradiometer (MODIS) in the geographical unit of the study site over the period of 2007 to 2015. The main results were as follows: (1) seasonal variations in R_s rates, soil temperature (T_s), land surface temperature (LST), and normalized difference vegetation index (NDVI) all produced symmetrical bell type patterns, while soil moisture (M_s) showed a fluctuating pattern, (2) a T_s-exponential model could greatly capture seasonal variations of R_s rates in the study site, reflecting the role of temperature as a dominant driving factor in determining R_s temporal variations in alpine meadow areas, (3) there was no significant difference between the performing indicators evaluating the proposed T_s-exponential model and the LST-exponential model. This indicated great potential for applying remote sensing products to estimate seasonal R_s rates and 4) seasonal variations in R_s rates towards temperature sensitivity (Q₁₀) showed a concave curve and dramatically decreased as the temperature increased from −1 to 11 °C. Overall, the results indicated that attention to significant effects of climatic conditions on R_s, particularly in areas of low temperature, should be warranted. Also, applicability of remote sensing products for estimating R_s was reflected and demonstrated.

【 授权许可】

Unknown