The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

Abstract

The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared (MIR) reflection from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a costeffective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.

Visible and Near Infrared Spectroscopy in Soil Science

“…11). Kriged maps from predicted data on approximately 1.5 samples per ha improved the accuracy and changed maps radically compared with those kriged from 0.5 samples per ha as conventionally sampled and analyzed with reference methods (Wetterlind et al, 2008b). Figure 11.…”

“…For clay spiked global performed best at one site, local only at another, and at the third site all three methods performed very poorly. A strategy relying on very few calibration samples from the current target area has been suggested (Wetterlind et al, 2008b, Wetterlind et al, 2010. Data from Swedish farms suggest that rationality can be gained with farm-scale calibrations built on 25 samples only, targeted from about 100 ha, with clay and organic matter being predicted with high precision (Fig.…”

“…Calibrations are used to predict the resulting samples and the kriged maps are shown on the right. Adapted from Wetterlind et al (2008b).…”

“…Over the past two decades, research on the use of visible-near infrared (vis-NIR) diffuse reflectance spectroscopy in soil science has increased rapidly (Ben-Dor and Banin, 1995a;Bowers and Hanks, 1965;Brown et al, 2006;Shepherd and Walsh, 2002;Stenberg et al, 1995;Sudduth et al, 1989;Viscarra Rossel and McBratney, 1998;Wetterlind et al, 2008b). The main focus has been on basic soil composition, particularly soil organic matter (SOM), texture, and clay mineralogy, but also nutrient availability and properties such as fertility, structure, and microbial activity.…”

Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems

“…There is need to develop methods that use the minimum number of soil analysis to reduce and minimize cost for preparing SOM maps to support precision agriculture (Wetterlind et al, 2008), quantitative soil-landscape modeling (McKenzie et al, 2000) and global soil C monitoring (Post et al, 2001). …”