Near infrared spectroscopy as an easy and precise method to estimate soil texture

Jaconi, Angelica GND; Vos, Cora GND; Don, Axel GND

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.

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Jaconi, Angelica / Vos, Cora / Don, Axel: Near infrared spectroscopy as an easy and precise method to estimate soil texture. 2019.

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