Estimating soil respiration using spectral vegetation indices and abiotic factors in irrigated and rainfed agroecosystems

AIMS: Our aims were to identify the primary factors involved in soil respiration (Rₛ) variability and the role that spectral vegetation indices played in Rₛ estimation in irrigated and rainfed agroecosystems during the growing season. METHODS: We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CIgᵣₑₑₙ ₑdgₑ) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for Rₛ estimation. Different statistical models were used to analyze the dependencies of Rₛ on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems. RESULTS: The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in Rₛ. Residual analysis indicated that Rₛ was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of Rₛ should be included in the Rₛ model to describe the growing-season dynamics of Rₛ. Among the three VIs, CIgᵣₑₑₙ ₑdgₑ showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CIgᵣₑₑₙ ₑdgₑ into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of Rₛ. CONCLUSIONS: Our results suggest that spectral vegetation index from remote sensing could be used to estimate Rₛ, which will be helpful for the development of a future Rₛ model over a large spatial scale.