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Remote Sensing
Volume 14
Issue 3
10.3390/rs14030472
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Article

A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties

by
Ruhollah Taghizadeh-Mehrjardi
1,2,3,
Hossein Khademi
4,
Fatemeh Khayamim
4,*,
Mojtaba Zeraatpisheh
5,6,
Brandon Heung
7 and
Thomas Scholten
1,2,3
1
Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72070 Tübingen, Germany
2
CRC 1070 Resource Cultures, University of Tübingen, 72074 Tübingen, Germany
3
DFG Cluster of Excellence “Machine Learning”, University of Tübingen, 72070 Tübingen, Germany
4
Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran
5
Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China
6
College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
7
Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(3), 472; https://doi.org/10.3390/rs14030472
Submission received: 9 November 2021 / Revised: 10 January 2022 / Accepted: 15 January 2022 / Published: 19 January 2022
(This article belongs to the Special Issue Remote Sensing of Soil Properties)
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Abstract

This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively.
Keywords: spatial modeling; machine learning; remote sensing; model averaging spatial modeling; machine learning; remote sensing; model averaging
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MDPI and ACS Style

Taghizadeh-Mehrjardi, R.; Khademi, H.; Khayamim, F.; Zeraatpisheh, M.; Heung, B.; Scholten, T. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties. Remote Sens. 2022, 14, 472. https://doi.org/10.3390/rs14030472

AMA Style

Taghizadeh-Mehrjardi R, Khademi H, Khayamim F, Zeraatpisheh M, Heung B, Scholten T. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties. Remote Sensing. 2022; 14(3):472. https://doi.org/10.3390/rs14030472

Chicago/Turabian Style

Taghizadeh-Mehrjardi, Ruhollah, Hossein Khademi, Fatemeh Khayamim, Mojtaba Zeraatpisheh, Brandon Heung, and Thomas Scholten. 2022. "A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties" Remote Sensing 14, no. 3: 472. https://doi.org/10.3390/rs14030472

APA Style

Taghizadeh-Mehrjardi, R., Khademi, H., Khayamim, F., Zeraatpisheh, M., Heung, B., & Scholten, T. (2022). A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties. Remote Sensing, 14(3), 472. https://doi.org/10.3390/rs14030472

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