Soil moisture retrieving using hyperspectral data with the application of wavelet analysis

As a powerful tool, the wavelet transform method has been widely used in feature extraction for hyperspectral data, while few studies are focused on soil moisture retrieving. The objective of this study was to evaluate the effectiveness of discrete wavelet transform (DWT) for soil moisture retrieving. In this study, a total of 78 measurements of soil moisture and hyperspectral data were collected through soil sampling as well as laboratory quantitative control. There were 13 different mother wavelets capable of decomposing hyperspectral data that were recommended, along with six decomposition levels from 5 to 10. The performances of two feature extraction methods namely band selection and DWT were compared, using three indexes, i.e., R ², R _adj and root mean square error (RMSE) introduced to validate soil moisture retrieving results. The experimental results indicated that the wavelet transform method could significantly reduce the dimensionality of hyperspectral data, resulting in a much more effective performance. Among the 78 estimation models using the wavelet transform, there were 42 models superior to band selection, with 24 models yielding good correlations between the predicted soil moisture and the measured ones (R ² ≥ 0.7, RMSE ≤ 0.050, p ≤ 0.05). Furthermore, when the wavelet decomposition level was 9 and the mother wavelets were Daubechies 2, Daubechies 4, Reserve Biorthogonal 3.3 and Biorthogonal 6.8, the retrieving results were optimum. Additionally, the experimental results proved that the wavelet analysis technique was capable of preserving high- and low-frequency spectral information at different decomposition scales, and could correctly reflect the variation of soil moisture. Thus, it would be helpful in further environmental monitoring.