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Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran

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Abstract

Knowledge of environmental factors controlling soil organic carbon (SOC) stocks can help predict spatial distribution SOC stocks. So, this study was carried out to select the best environmental factors to model and estimate the spatial distribution of SOC stocks in northwestern Iran. Soil sampling was performed at 210 points by multiple conditioned Latin Hypercube method (cLHm) and SOC stocks were measured. Also, environmental factors, including terrain attributes, moisture index, and normalized difference vegetation index (NDVI), were calculated. SOC stocks were modeled using random forest (RF) and partial least squares regression (PLSR) models. Modeling SOC stocks by RF model showed that the efficient factors for estimating the SOC stocks were slope height (slph), terrain surface texture (texture), standardized height (standh), elevation, relative slope position (rsp), and normalized height (normalh). Also, the PLSR model selected standardized height (standh), relative slope position (rsp), slope, and channel network base level (chnl base) to model SOC stocks. In both RF and PLSR methods, the standh and rsp factors were suitable parameters for estimating the SOC stocks. Predicting the spatial distribution of SOC stocks using environmental factors showed that the R2 values for RF and PLSR models were 0.81 and 0.40, respectively. The result of this study showed that in areas with complex land features, terrain attributes can be good predictors for estimating SOC stocks. These predictors allow more accurate estimates of SOC stocks and contribute considerably to the effective application of land management strategies in arid and semiarid area.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Supplementary data associated with this article can be found, in the online version, at https://earthexplorer.usgs.gov/ and https://giovanni.gsfc.nasa.gov/giovanni/.

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Authors and Affiliations

  1. Department of Soil Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

    Kamal Khosravi Aqdam, Nafiseh Yaghmaeian Mahabadi & Hassan Ramezanpour

  2. Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran

    Salar Rezapour

  3. Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

    Zohreh Mosleh

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  1. Kamal Khosravi Aqdam
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  2. Nafiseh Yaghmaeian Mahabadi
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  3. Hassan Ramezanpour
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  4. Salar Rezapour
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  5. Zohreh Mosleh
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Correspondence to Kamal Khosravi Aqdam or Nafiseh Yaghmaeian Mahabadi.

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Khosravi Aqdam, K., Yaghmaeian Mahabadi, N., Ramezanpour, H. et al. Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran. Environ Monit Assess 193, 713 (2021). https://doi.org/10.1007/s10661-021-09502-3

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