Plant and Soil

, Volume 367, Issue 1, pp 535–550

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

Regular Article

DOI: 10.1007/s11104-012-1488-9

Cite this article as:
Huang, N. & Niu, Z. Plant Soil (2013) 367: 535. doi:10.1007/s11104-012-1488-9



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.


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.


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.


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.


Soil respirationRemote sensingVegetation indexCanopy photosynthesisAgroecosystems

Supplementary material

11104_2012_1488_MOESM1_ESM.doc (28 kb)
ESM 1(DOC 27.5 kb)
11104_2012_1488_MOESM2_ESM.doc (39 kb)
ESM 2(DOC 39 kb)
11104_2012_1488_MOESM3_ESM.doc (30 kb)
ESM 3(DOC 30 kb)
11104_2012_1488_MOESM4_ESM.doc (104 kb)
ESM 4(DOC 104 kb)

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.The State Key Laboratory of Remote Sensing Science, Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina