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Predicting the spatial distribution of soil mineral particles using OLI sensor in northwest of Iran

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Abstract

Texture is one of the most important soil properties that knowledge of the spatial distribution is essential for land-use planning and other activities related to agriculture and environment protection. So, this study was performed to supply the soil texture spatial distribution using standardized spectral reflectance (ZPC1) index of Landsat 8 satellite images in the northwest of Iran. The soil sampling was performed using a random method in 145 points. Mineral soil particles including clay, silt, and sand were determined, and soil texture was calculated. In this study, Landsat 8 satellite images were used to interpolate the soil texture spatial distribution. In the first step, the principal component analysis (PCA) was obtained. Then, PCA1 was standardized using a z-score (ZPC1), and regression techniques were used to create proper relationships between ZPC1 and the primary soil particles. Then, spatial distribution of soil particles was used to create a spatially distributed map of the soil textural classes. The results showed that the standardization of the first component reduced the standard deviation of PCA1 from 23.6 to 10.8. The results of comparing ZPC1 with soil mineral components showed that with increasing the amounts of soil clay and sand, the ZPC1 value decreases and increases, respectively. The results showed that the ranges of the spatial distribution of clay and sand were similar to the laboratory-measured amounts. The results of texture class prediction using the soil texture triangle showed that the amount of similarity between the measured and predicted classes was 53.79%.

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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. The data of satellite images of Landsat 8 are available in the earth explorer website (https://earthexplorer.usgs.gov/).

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Acknowledgements

The authors of this article would like to thank the soil laboratory of ROZH located in Saqqez city, Kurdistan province of Iran, for collecting, sampling, and analyzing the samples and accepting all expenses of this study.

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Authors and Affiliations

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

    Kamal Khosravi Aqdam

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

    Naser Miran & Farrokh Asadzadeh

  3. College of Agriculture & Natural Resources, Faculty of Soil Sciences, University of Tehran, Tehran, Iran

    Younes Mohammadi Khajelou

  4. Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran

    Mohammad Khosravi Aqdam

  5. 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. Naser Miran
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  3. Younes Mohammadi Khajelou
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  4. Mohammad Khosravi Aqdam
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Correspondence to Kamal Khosravi Aqdam or Naser Miran.

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Khosravi Aqdam, K., Miran, N., Mohammadi Khajelou, Y. et al. Predicting the spatial distribution of soil mineral particles using OLI sensor in northwest of Iran. Environ Monit Assess 193, 377 (2021). https://doi.org/10.1007/s10661-021-09163-2

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