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Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests

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  • Desheng Wang

    (Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution, Jiangsu Province, Nanjing 213323, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • A-Xing Zhu

    (Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
    State Key Laboratory Cultivation Base of Geographical Environment Evolution, Jiangsu Province, Nanjing 213323, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA)

Abstract

Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM. However, the accuracy of the similarity-based approach is limited, and the performance of random forests is affected by the quality of the feature set. The objective of this study was to present a method for soil mapping by integrating the similarity-based approach and the random forests method. The Heshan area (Heilongjiang province, China) was selected as the case study for mapping soil subgroups. The results of the regular validation samples showed that the overall accuracy of the integrated method (71.79%) is higher than that of a similarity-based approach (58.97%) and random forests (66.67%). The results of the 5-fold cross-validation showed that the overall accuracy of the integrated method, similarity-based approach, and random forests range from 55% to 72.73%, 43.48% to 69.57%, and 54.17% to 70.83%, with an average accuracy of 66.61%, 57.39%, and 59.62%, respectively. These results suggest that the proposed method can produce a high-quality covariate set and achieve a better performance than either the random forests or similarity-based approach alone.

Suggested Citation

  • Desheng Wang & A-Xing Zhu, 2020. "Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests," Land, MDPI, vol. 9(6), pages 1-16, May.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:6:p:174-:d:364652
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    References listed on IDEAS

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    1. United Nations, 2016. "The Sustainable Development Goals 2016," Working Papers id:11456, eSocialSciences.
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