Title: VNIR SPECTROSCOPY FOR ESTIMATION OF WITHIN-FIELD VARIABILITY IN SOIL PROPERTIES Authors
Submitted to: Global Worshop on High Resolution Digital Soil Sensing and Mapping
Publication Type: Proceedings
Publication Acceptance Date: January 15, 2008
Publication Date: February 5, 2008
Citation: Sudduth, K.A., Kitchen, N.R., Sadler, E.J., Drummond, S.T., Myers, D.B. 2008. VNIR spectroscopy for estimation of within-field variability in soil properties. In: Proceedings of the First Global Worshop on High Resolution Digital Soil Sensing and Mapping. February 5-8, 2008. Sydney, Australia. 2008 CDROM Interpretive Summary: Over the last three decades or more, researchers have estimated soil properties using visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS), with varying results. Using VNIR DRS for estimating soil property variation within fields is particularly challenging, because in many cases the variation in the property of interest may be relatively small and because of the need to deal with spatially correlated data. In this study, we used VNIR DRS to estimate the variability in soil physical and chemical properties within a typical production field in northeast Missouri, USA. Soil samples were obtained to a 15-cm depth on a 30-m grid spacing, plus at a number of random sampling locations. Laboratory analyses were conducted for sand, silt, and clay fractions, organic matter, pH, P, K, Ca, Mg, and cation exchange capacity (CEC). VNIR reflectance of dried and sieved samples was obtained in the laboratory using a spectrometer with a wavelength range from 350 to 2500 nm. Partial least squares (PLS) regression was used to estimate soil properties from spectra, and a regression-kriging approach was used to account for spatial dependence. We found that some soil properties, such as CEC and pH, were well-estimated with PLS regression, while others, such as organic matter, were not. Coupling regression-kriging with PLS regression improved estimates in some cases, but was not as effective as has been reported in some other research studies. More advanced calibration methods should be investigated for their ability to improve within-field VNIR DRS results on these soils.
Technical Abstract: Measuring the variation in soil properties within fields is an important component of precision agriculture. For many soil properties, it is difficult to obtain enough data to accurately characterize their spatial variation, due to the cost of traditional sampling and laboratory analysis. Sensors that can estimate soil properties without the need for sampling are a promising alternative. One technology that has received considerable attention in this regard is optical reflectance sensing in the visible and near infrared (VNIR) wavelength bands. In this study, we examined the use of VNIR reflectance sensing to estimate the variation in soil properties for a typical claypan-soil field in northeast Missouri. We found that some soil properties, such as CEC (cation exchange capacity) and pH, were well-estimated, while others, such as organic matter, were not. Further work with more advanced analysis methods will be needed to obtain better results, particularly for those soil properties like organic matter that did not vary much within the field. The results of this study provide information that instrumentation engineers and researchers should consider as they develop and implement new in-field soil sensing technology.