Prediction of soil texture classes through different wavelength regions of reflectance spectroscopy at various soil depths

Abstract

The demand for quality and low-cost soil information is growing due to the demands of land use planning and precision agriculture. Soil texture is one of the key soil properties, as it determines other vital soil characteristics such as soil structure, water and thermal regime, diversity of living organisms, plant growth, as well as the soil quality in general. It is usually not constant over an area, varying in space and with soil depth. Routine soil texture analysis is, however, time consuming and expensive. Because of this, the success of proximal soil sensing techniques in estimate soil properties using the VIS-NIR-SWIR and MIR regions is increasing. Advantages of soil spectroscopy include time efficiency, economic convenience, non-destructive application and freeing of chemical agents involved. Therefore, the objectives of this study were: (a) to explore the potential of clay, sand and silt prediction using reflectance spectroscopy; (b) assess the performance of predictive models in different spectral regions, i.e. VIS-NIR-SWIR and MIR; (c) assess the effect of different soil depths on predictive models; and finally (d) explain the differences in prediction accuracy in the means of the input data structure. Soil samples were collected at three depths (0–20, 20–40 and 40–60 cm) at 70 sampling sites over a study area located in the State of Rio Grande do Sul (Brazil). The content of soil texture was determined by Pipette method, and soil spectra were obtained with FieldSpec Pro (VIS-NIR-SWIR) and by Alpha Sample Compartment RT (MIR). Cubist regression algorithm was applied to train predictive models in three separate modeling modes differing in spectral region: (i) VIS-NIR-SWIR, (ii) MIR and (iii) VIS-NIR-SWIR plus MIR. The results showed that the combination of all three soil depths led to a more accurate prediction of soil texture compared to subdivided soil depths. This was explained by variability of the data, which was larger for the total dataset than for the depth-specific data. Consequently, we suggested that no precise comparison between different studies can be made without a proper description of the input data. For all-depths models, the MIR calibration obtained the best accuracy, which was explained due to more information comprised in the MIR region against the VIS-NIR-SWIR. The bands that were more important in predicting soil texture in MIR are related to mineralogy, specifically to kaolinite. This study demonstrated that the MIR spectroscopy technique is capable to complement the standard soil particle size analysis, specially where a large number of soil samples need to be treated in a short period of time.