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Visible–near-infrared–shortwave-infrared (VNIR–SWIR) spectroscopy is one of the most promising sensing techniques to meet ever-growing demands for soil information and data. To ensure the successful application of this technique in the field, efficient methods for tackling detrimental moisture effects on soil spectra are critical. In this paper, mathematical techniques for reducing or removing the effects of soil moisture content (SMC) from spectra are reviewed. The reviewed techniques encompass the most common spectral pre-processing and algorithms, as well as less frequently reported methods including approaches within the remote sensing domain. Examples of studies describing their effectiveness in the search for calibration model improvement are provided. Moreover, the advantages and disadvantages of the different techniques are summarized. Future research including further studies on a wider range of soil types, in-field conditions, and systematic experiments considering several SMC levels to enable the definition of threshold values for the effectiveness of the discussed methods is recommended.

Mathematical techniques to remove moisture effects from visible–near-infrared–shortwave-infrared soil spectra—review