Measuring Soil Properties in situ using Diffuse Reflectance Spectroscopy Travis H. Waiser, Cristine L. Morgan Texas A&M University, College Station, Texas Heterogeneity of soil hydraulic properties directly influences the quality and quantity of water in streams, reservoirs, and groundwater. However mapping soil properties in the field is difficult, because no instrument exists to directly and rapidly quantify soil properties in situ. Such an instrument would be an asset to hydrologists and resource managers. Justification Material and Methods  72 soil cores were collected over six farm fields in two Texas counties, Erath & Comanche.  6-cm diameter soil cores were cut in half and scanned in situ at 3-cm intervals at moist and air-dry water content with a FieldSpec® Pro FR (Analytical Spectral Devices, Inc.). Scanning a core with Diffuse Reflectance Spectroscopy. Schematic of scanned soil core 3-cm Column 1Column 2  DRS appears to be capable of rapid, reliable, in situ measurements of soil clay content at varying water contents.  Many of the significant wavelengths correspond with clay mineralogy. Conclusions  The pipette method was used to measure particle size distribution of the soil samples.  The spectral data were treated by taking the first derivative.  Partial Least Squares (PLS) regression was used to create a prediction model to convert spectral reflectance to clay content. To calibrate the prediction model, 70% of the soil samples were used and the remaining were used for model validation. A total of 273 soil samples were used. References Chang, C.W., D.A. Laird, M.J. Mausbach and C.R. Hurburgh Near-infrared reflectance spectroscopy-principle components regression analysis of soil properties. Soil Sci. Soc. Am. J. 65: Sudduth, K.A. and J.W. Hummel Soil organic matter, CEC and moisture sensing with a portable NIR spectrophotometer. Trans. ASAE. 36: Results  The PLS regression models for the moist and air-dry soil scans proved to predict soil clay content with a Root Mean Squared Error of 6% and 5% with R 2 -values of 0.84 and 0.88, respectively.  144 of 216 spectra were significant in building the model to predict clay content. The wavelengths in which the uncertainty limits do not cross the zero axis are significant. Objectives Diffuse Reflectance Spectroscopy (DRS) has proven effective in providing rapid prediction of soil properties in the laboratory (Shepard and Walsh, 2002). However, it is still unknown how useful DRS is for in situ analysis of soil properties (Sudduth and Hummel, 1993). The specific objectives of this research are the following: 1.Quantify the benefits and limitations of Visible and Near-Infrared ( nm) Diffuse Reflectance Spectroscopy for measuring clay content of soil in situ. 2.Determine the spectral ranges that influence the clay content model. Material and Methods cont…  The soil samples ranged from 1 to 58% clay content, with a mean of 26% and a standard deviation of 14% clay. Results cont…  Project results will be added to a national soil reflectance library.  A field mounted Diffuse Reflectance Spectroscopy system will now be developed Impact + Column 1 prediction Column 2 prediction ▲ Lab measurement Predicted, % Measured, % ▬ regression line ▬ 1:1 line Clay Content of moist in situ soil + calibration samples validation samples Core prediction in situ with depth Depth (cm) Clay Content (%) Wavelength (nm) b Regression coefficients (b) of wavelengths Clay Minerals Smectite Kaolinite