Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data

Miaomiao Ji, Keke Zhang and Qiufeng Wu
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Miaomiao Ji: Northeast Agricultual University, Harbin, China
Keke Zhang: College of Engineering, Northeast Agricultural University, Harbin, China
Qiufeng Wu: College of Science, Northeast Agricultural University, Harbin, China

International Journal of Ambient Computing and Intelligence (IJACI), 2020, vol. 11, issue 3, 66-79

Abstract: Soil temperature, as one of the critical meteorological parameters, plays a key role in physical, chemical and biological processes in terrestrial ecosystems. Accurate estimation of dynamic soil temperature is crucial for underground soil ecological research. In this work, a hybrid model SAE-BP is proposed by combining stacked auto-encoders (SAE) and back propagation (BP) algorithm to estimate soil temperature using hyperspectral remote sensing data. Experimental results show that the proposed SAE-BP model achieves a more stable and effective performance than the existing logistic regression (LR), support vector regression (SVR) and BP neural network with an average value of mean square error (MSE) = 1.926, mean absolute error (MAE) = 0.962 and coefficient of determination (R2) = 0.910. In addition, the effect of hidden structures and labeled training data ratios in SAE-BP is further explored. The SAE-BP model demonstrates the potential in high-dimensional and small hyperspectral datasets, representing a significant contribution to soil remote sensing.

Date: 2020
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