Soil salinity prediction based on scale-dependent relationships with environmental variables by discrete wavelet transform in the Tarim Basin

•The dominant scales of soil salinity variation were 21.77 km and >32 km.•GDVI was the major predictor of soil salinity on salt-affected land.•Accuracy of the predictions with wavelet reconstruction was largely improved.•The predicted performance of WRM was outperform than SMLR. The variation in soil salinity is affected by environmental factors that occur at different scales with varying intensities. It is critical to adequately consider environmental variables under scale effects for digital soil mapping which has been minimally discussed in previous studies. The objectives of this research are to analyze the scale-dependent variability in soil salinity distribution under environmental controlling factors using discrete wavelet transform (DWT) techniques and to compare the differences between the accuracy of soil salinity predictions with and without multiple scale-specific relationships. Thirteen environmental factors related to soil salinity that included influencing environmental factors and indicative environmental factors involving climate, soil, terrain, and vegetation were extracted at 500 m intervals along four transects through farmland and salt-affected land situated at the oasis and oasis-desert ecotones of Xinjiang, China. Each spatial series of soil salinity and environmental variables along the four transects was separated into seven scale components (six details components, namely D1 through D6, and one approximation component, namely A6). A Hilbert transform was used to identify the specific spatial scales of each scale component in the DWT procedure. The results indicate that 21.77 km and >32 km were the dominant scales, which explained approximately 60–80% of the spatial variation of soil salinity throughout the oases. The prediction accuracy with wavelet reconstruction that depended on all the scale components of environmental variables is significantly improved compared with the accuracy of those with the stepwise multiple linear regression method at a single sampling scale. The generalized difference vegetation index (GDVI) was the major predictor of soil salinity on salt-affected land, while evapotranspiration and the terrain ruggedness index (TRI) were the major contributing factors in farmland inside the oases. This study demonstrated that specific scale-dependent relationships can reveal the scale control of soil salinity variation and had the potential to improve the prediction accuracy of soil properties.