Skip to main content
Log in

Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

The increase in global population, rapid urbanization, and continuous soil degradation have disrupted the balance between food demand and supply. Precision agriculture (PA) is an effective solution for overcoming this challenge. The first step in the PA was to determine the physicochemical attributes of the soil. This research aimed to predict soil chemical properties using Geographic Information Systems (GIS), Remote Sensing (RS), and artificial neural networks (ANN). This research explored soil chemical properties using classical statistics, Geostatistics, ANN, and Spatial Interpolation (SI). Multiple Linear Regression (MLR), Ordinary Least Square Regression (OLS), Group Method of Data Handling (GMDH) Neural Networks, Inverse Distance Weighting (IDW), and Kriging Interpolation were used to model and predict soil chemical properties. These modeling techniques accurately predicted soil organic matter (SOM) and pH. This study compared and evaluated models based on Landsat-8 and Sentinel-2 to predict soil chemical properties. Among the soil chemical properties, SOM was significantly modeled using MLR, with an accuracy of 0.54 Sentinel and 0.39 Landsat. Landsat band-TIRS2 and Sentinel band-9 significantly defined the SOM. The exploratory regression technique predicts SOM by incorporating more variables, such as Landsat bands 10 and 11 and Sentinel bands 1, 2, 5, 9, and 10. OLS regression predicted SOM with an accuracy of 0.55 and 0.54 for Sentinel and Landsat, respectively. Using GMDH Neural Network, a more accurate model was developed incorporating Sentinel bands 10, 9, and 8 and Landsat bands 5, 10, and 11, with the best accuracy of 0.68%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Data will be made available upon reasonable request.

References

Download references

Acknowledgements

This research was collectively assisted by the Department of Geography, Islamia College, Peshawar, School of Civil and Environmental Engineering, National University of Science and Technology, Pakistan, and Petroleum and Mining Engineering Department, Faculty of Engineering, Tishk International University, Erbil, Kurdistan Region, Iraq.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Abdur Raziq or Ayad M. Fadhil Al-Quraishi.

Ethics declarations

Conflicts of interest

Authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbasi, N.T., Zarin, R., Raziq, A. et al. Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics. Model. Earth Syst. Environ. (2024). https://doi.org/10.1007/s40808-024-02050-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40808-024-02050-y

Keywords

Navigation