Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

• Published in: Geoderma Vol. 383; p. 114793
• Main Authors: Taghizadeh-Mehrjardi, Ruhollah, Schmidt, Karsten, Toomanian, Norair, Heung, Brandon, Behrens, Thorsten, Mosavi, Amirhosein, S. Band, Shahab, Amirian-Chakan, Alireza, Fathabadi, Aboalhasan, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2021
• Subjects: agricultural productivity; digital elevation models; Digital soil mapping; Iran; Machine learning; prediction; regression analysis; salinity; soil depth; soil restoration; Soil salinity; Support vector regression; topography; topsoil; uncertainty; uncertainty analysis; wavelet; Wavelet transformation; Iran; Support vector regression; Digital soil mapping; Wavelet transformation; Soil salinity; Machine learning
• DOI: 10.1016/j.geoderma.2020.114793
• ISSN: 0016-7061

•Soil salinity was predicted using machine learning algorithms in central Iran.•Wavelet-SVR indicated higher performance compared to the standalone SVR.•The estimated prediction interval for SVR is wider than of W-SVR.•Remote sensing features control the spatial distribution of the salinity widely. The low potential of agricultural productivity in the majority of central Iran is mainly attributed to high levels of soil salinity. To increase agricultural productivity, while preventing any further salinization, and implement effective soil reclamation programs, precise information about the spatial patterns and magnitude of soil salinity is essential. In this study, soil salinity was predicted and mapped using machine learning (ML) and digital soil mapping approaches. Specifically, support vector regression (SVR) was combined with wavelet transformation (W-SVR) of a wide range of environmental covariates derived from a digital elevation model, remote sensing, and climatic data. Predictions of soil salinity were carried out for six standard depth increments (0–5, 5–15, 15–30, 30–60, 60–100, 100–200 cm). Cross-validation was carried out by partitioning the data into 70% used for training the model and 30% for testing the model. Uncertainty of the ML algorithms was quantified using the uncertainty estimation based on local errors and clustering (UNEEC) method. The results indicated that W-SVR performed better in predicting soil salinity for all six depth increments. The differences were most apparent for the lowest soil depth increments where W-SVR resulted in ~1.4 times higher correlation coefficient when compared to the SVR. At lower soil depths increments, covariate importance analysis indicated that topographic derivatives were the most relevant covariates in the models. For topsoil salinity, remote sensing covariates were the most relevant predictors of soil salinity. Regardless of soil depth, climatic predictors were the most important predictors. Uncertainty analysis also indicated that for all depth increments, the estimated prediction interval for SVR obtained by the UNEEC method was wider than that of W-SVR and further indicating the higher performance of W-SVR in comparison to the SVR. The predicted salinity maps showed the highest salinity for soils in the eastern parts of central Iran, which was consistent with the Agro-climatic Zoning of Isfahan Province.