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

Remote Sensing

Thomas Scholten¹  Ruhollah Taghizadeh-Mehrjardi¹  Brandon Heung²  Hossein Khademi³  Fatemeh Khayamim³  Mojtaba Zeraatpisheh⁴ 

[1] Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72070 Tübingen, Germany;Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, Canada;Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran;Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China;

关键词: spatial modeling;    machine learning;    remote sensing;    model averaging;   

DOI  :  10.3390/rs14030472

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown