Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field

Efficient tools to measure within-field spatial variation in soil are important when establishing agricultural field trials and in precision farming. The object of the study was to investigate if a combination of two techniques, principal component analysis (PCA) and geostatistics, could reveal spatial soil variation from near infrared reflectance (NIR) spectroscopy data and thereby replace more conventional, viz. laborious and expensive, soil analyses. NIR spectrum is known to reveal information about important soil chemical, physical and biological properties and has been used in soil science for years. Three soil variables, total carbon (Tot-C), clay content and pH, were used as reference variables. The study was carried out on one site (200×160 m) in eastern Sweden with a Eutric Cambisol soil type where a sampling grid of 20×20 m was established. From the grid nodes, 99 samples were collected to a depth of 10 cm. The soil was analyzed by NIR and the data were decomposed by PCA. The first two principal components (PC 1 and PC 2) explained 85% of the total variance and therefore these two PCs were selected for further assessment of spatial variation by variography and kriging. PC 1 showed the strongest spatial dependence with a range of 148 m and a nugget close to zero. The variogram for PC 1 was robust and the kriging map expressed a clear pattern. The range of spatial correlation varied between the three reference soil variables. Tot-C expressed a low spatial dependence with a high proportion of nugget, whereas clay content and pH expressed spatial dependence at a range of 54 and 46 m, respectively. Neither of the traditional soil variables showed as strong spatial dependence as PC 1 of NIR. The advantage of the NIR-PCA strategy is that the first PCs will capture the spectral bands that express the largest variation regardless of what the NIR bands correlate to and, hence, PC 1 will always explain the variation of the soil properties that in each specific case have the largest influence on the PCA model. In conclusion, the NIR-PCA strategy seems to be an efficient and reliable strategy to use when determining the soil spatial variation in a field.