The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics

Abstract

SU MMARYNear-infrared reflectance spectroscopy (NIRS) was used for the analysis of soil samples for silt, sand, clay, calcium (Ca), potassium (K), sodium (Na), magnesium (Mg), copper (Cu) and iron (Fe). A total of 332 samples of different soils from Uruguay (South America) were used. The samples were scanned in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance. Cross validation was applied to avoid overfitting of the models. The coefficient of determination in calibration (R 2 cal ) and the standard errors in cross validation (SECV) were 0 . 80 (SECV : 6 . 8), 0 . 84 (SECV : 6 . 0), 0 . 90 (SECV: 3 . 6) in per cent for sand, silt and clay respectively. For both macro and microelements the R 2 cal and SECV were 0 . 80 (SECV : 0 . 1), 0 . 95 (SECV : 2 . 9), 0 . 90 (SECV 0 . 8), for K, Ca, Mg in g/kg respectively, and 0 . 86 (SECV : 0 . 82) and 0 . 92 (SECV : 25 . 5) for Cu and Fe in mg/kg. It was concluded that NIRS has a great potential as an analytical method for soil routine analysis due to the speed and low cost of analysis.

Salt Content Distribution and Paleoclimatic Significance of the Lop Nur “Ear” Feature: Results from Analysis of EO-1 Hyperion Imagery

“…can be estimated using laboratory spectra at different spectral resolutions, with R 2 ≥ 0.88 and RPD > 2. These results are expected and consistent with previous studies which show that Ca 2+ can be reliably estimated from lab measured reflectance spectra [24,40,41]. PLS resulted in good estimation with laboratory spectra for Fe 2+ and Si…”

On-line measurement of soil properties without direct spectral response in near infrared spectral range

“…These results are in line with those found in the literature. Comparing with other studies reported in the literature under laboratory measurement condition (Shepherd and Walsh, 2002;Cohen et al, 2005;Viscarra Rossel and Behrens, 2010), the model performance for pH in the prediction set is among the best models (r 2 = 0.50- (Cozzolino and Moron, 2003;Groenigen et al, 2003;Udelhoven et al, 2003;Wetterlind et al, 2010). Almost the same results as those achieved in this study were reported by Dunn et al (2002) , 2006;Zornoza et al, 2008) and CEC (r 2 = 0.13-0.90; RMSEP = 1.22-10.43 cmol kg À1 ; RPD = 0.60-2.7) (e.g.…”

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

“…Visible and Near-Infrared (VNIR, 400-1200 nm) spectroscopy and Short-Wave-Infrared (SWIR, 1200-2500 nm) spectroscopy are non-destructive, rapid and low-cost methods that differentiate materials based on their reflectance in the wavelength range from 400-2500 nm. VNIR/SWIR spectroscopy was confirmed to be a superior substitute for conventional laboratory analysis of soil chemical properties, such as various forms of carbon [2], N, P, K contents, Cation Exchange Capacity (CEC), pH [3] and, to some Successful predictions of Soil Organic Carbon (SOC) using spectroscopy have been reported [35,36]. Soil water content has also been predicted under both laboratory and in situ conditions [37].…”