**Using Artificial Neural Networks to Produce High-Resolution Soil Property Maps** Using Artificial Neural Networks to Produce

DOI: 10.5772/intechopen.70705

Zhengyong Zhao, Fan-Rui Meng, Qi Yang and Hangyong Zhu Zhengyong Zhao, Fan-Rui Meng, Qi Yang and

High-Resolution Soil Property Maps

Additional information is available at the end of the chapter Hangyong Zhu

http://dx.doi.org/10.5772/intechopen.70705 Additional information is available at the end of the chapter

Abstract

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50 Advanced Applications for Artificial Neural Networks

High-resolution maps of soil property are considered as the most important inputs for decision support and policy-making in agriculture, forestry, flood control, and environmental protection. Commonly, soil properties are mainly obtained from field surveys. Field soil surveys are generally time-consuming and expensive, with a limitation of application throughout a large area. As such, high-resolution soil property maps are only available for small areas, very often, being obtained for research purposes. In the chapter, artificial neural network (ANN) models were introduced to produce high-resolution maps of soil property. It was found that ANNs can be used to predict high-resolution soil texture, soil drainage classes, and soil organic content across landscape with reasonable accuracy and low cost. Expanding applications of the ANNs were also presented.

Keywords: ANN, soil drainage, soil texture, soil organic carbon, DEM, topography, hydrological index, vertical slope position
