Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

Abstract

AbstractPrevious studies for retrieving soil moisture content (SMC) from visible and near‐infrared hyperspectral data over vegetation‐covered surfaces using spectral unmixing, non‐negative matrix factorization, and albedo/vegetation coverage in trapezoid spaces have required mass spectral preprocessing and offered only limited improvements in prediction accuracy. Recently, deep learning has triggered some improvements in soil properties prediction from hyperspectral data because of its automatic feature extraction and high accuracy. In this study, hyperspectral data in a simulation experiment with different vegetation coverages, SMCs, and soil types were acquired. Deep learning models, one‐dimensional convolutional neural network (1D‐CNN), and long short‐term memory network (LSTM) are proposed to predict SMC. The results showed that two deep learning models achieved excellent predictions (residual prediction deviation [RPD] > 2.5) using the unpreprocessed mixed spectra and partial least squares regression (PLSR) had a good prediction (RPD = 1.88). The 1D‐CNN (R2p = .91) and LSTM (R2p = .90) significantly outperformed PLSR (R2p = .72), which demonstrated that deep learning could improve SMC prediction over partially vegetation‐covered surfaces. However, when only using bare soil spectra, the prediction accuracy was commensurate, whether through the 1D‐CNN, LSTM, or PLSR models; additionally, 1D‐CNN and LSTM had better performance on all mixed spectra than bare soil spectra. These results indicated that deep learning had no advantage on smaller datasets. We also found that SMC prediction with deep learning was affected by vegetation coverage and soil type but was still very good. The 1D‐CNN and LSTM are effective models for predicting SMC with large hyperspectral datasets acquired from complex soil surface conditions.