Abstract
Understanding the distribution of soil properties over the landscape is required for a variety of land resource management applications, modeling, and monitoring practices. The main aim of this research is to conduct a spatial prediction of selected topsoil properties such as soil pH, calcium carbonate (CaCO3); exchangeable sodium percentage (ESP); and cation exchange capacity (CEC) using integrated remotely sensed data and machine learning approach in northwestern Libya. The results indicated that the coefficient of determination (R2) varies from 0.22 to 0.42, the root mean square error (RMSE) ranges between 0.35 and 6.96, and the normalized root mean square error (NRMSE) ranges between 0.12 and 0.26 indicating less residual variance and thus a proper operation of the machine learning model used. Based on these results, it can be concluded that this approach is an effective and valid methodology for modeling and spatial mapping soil properties in this area, and this method could also be applied to other regions with similar characteristics.
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Author Contributions
Hamdi A. Zurqani: conceptualization, methodology, supervision, software, data curation, formal analysis, validation, investigation, writing-original draft, visualization, writing—review and editing, review of analysis. The author has read and agreed to the published version of the manuscript.
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Zurqani, H.A. (2022). Integration of Remotely Sensed Data and Machine Learning Technique for Spatial Prediction of Selected Soil Properties in Northwestern Libya. In: Zurqani, H.A. (eds) Environmental Applications of Remote Sensing and GIS in Libya. Springer, Cham. https://doi.org/10.1007/978-3-030-97810-5_5
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