Near infrared spectroscopy as an easy and precise method to estimate soil texture
Soil texture is beside soil organic matter the most fundamental soil parameter affecting most biological, chemical, physical soil properties. However, conventional laboratory analyses of soil texture are time-consuming and expensive, calling for alternative analytical methods. In this study, we tested the performance of near infrared spectroscopy (NIRS) derived texture estimates for a national-scale soil dataset of the German Agricultural Soil Inventory, using memory-based learning and log-ratio transformation. The developed NIRS models had a root mean square error of prediction of 66.1, 55.5, and 18.5 g kg−1 for sand, silt, and clay content, respectively. The lowest relative error (7.5%) was found for clay content, while the relative error for silt content was 11.2% and for sand content 11.1%. Ratio of performance to deviation varied between 4.8 and 6.2 in all cases, indicating excellent model fit. The key to excellent model performance was log-ratio transformation, which allowed all three particle size fractions to be modeled simultaneously while meeting the constraint that all size fractions should add up to 100%. Our NIRS based soil texture estimates outperformed the texture-by-feel method applied to the same set of soils (Vos et al. 2016). NIRS is thus a suitable low-cost analytical method for texture analysis that can be used for large-scale datasets.