Prediction of Soil Carbon Fractions Using a Field Spectroradiometer Equipped with an Illuminating Contact Probe

Abstract

This research compared the accuracy of laboratory reference measurements of soil C and N fractions with soil reflectance spectra acquired using a portable field spectroradiometer with an illuminating contact probe. Soil samples were taken from eight, 1.6 ha watersheds, located in El Reno, Oklahoma on native warm season grasslands and agronomic managements with landform complexes serving as replicates within and among treatments. Soil samples were taken from 0–30-cm. Measurements included total soil organic carbon (TSOC), total soil nitrogen (TSN), residual C of acid hydrolysis (RCAH), and particulate organic matter C (POMC) and N (POMN). Soil reflectance in the 350 to 2500 nm region was correlated with individual laboratory measurements. Each reference dataset was divided into model development data (70%) and model validation data (30%). Calibrated models were applied to validation datasets. Statistical analysis revealed that prediction efficiencies of soil reflectance models were highly quantitative. Coefficients of determination (R2) were near 1 (≥0.90) and ratios of predicted values to the measured standard deviation (RPD) were >2, indicative of good predictive models. The field spectroradiometer enabled us to parameterize soil spatial variability and soil reflectance measurements, reducing the resources required to acquire edaphic measurements.

Comparable Discrimination of Soil Constituents Using Spectral Reflectance Data (400–1000 nm) Acquired with Hyperspectral Radiometry

“…The research reported herein is an extension of our previous work reported in Fortuna et al [15], but correlates the same variables (TSOC, total soil N (TSN), ROC, POMC and particulate soil N (POMN) to the HDR data collected only in the VIS-NIR region of the spectrum. Our analysis also evaluates VIS-NIR HDR data acquired not only from the high-end PSR + 3500 spectroradiometer, but also that from comparatively inexpensive and low S-N-R proximal (low-end) sensors.…”

“…Details of collection and processing of the soil samples used in this study are found in Fortuna et al [15]; therefore, only a brief summary is provided here. The soil samples were collected from three 1.6 ha sized watersheds at the USDA-ARS Grazinglands Research Laboratory, located in El Reno, OK, USA (35 • 32 28 N, 98 • 02 56.5 W).…”

“…The second and third detectors are 256-element, thermo-electrically cooled InGaAs arrays covering the shortwave infrared 1 (SWIR1, 970-1910 nm), and shortwave infrared 2 (SWIR2, 1900-2500 nm) regions, respectively, but the data from these two detectors are not used in this study. Results from using the complete spectrum (UV-SWIR) and full chemometric treatment (spectral smoothing, noise reduction, and derivatives) are reported by Fortuna et al [15]. The VIS-NIR data reported in [15] were parsed from the full UV-SWIR data for reanalysis and comparison of results from the other sensors used in this study.…”

“…Results from using the complete spectrum (UV-SWIR) and full chemometric treatment (spectral smoothing, noise reduction, and derivatives) are reported by Fortuna et al [15]. The VIS-NIR data reported in [15] were parsed from the full UV-SWIR data for reanalysis and comparison of results from the other sensors used in this study. A minimal log (1/R) transformation (where R is the soil reflectance at a given wavelength) of the spectral data was chosen to reflect a probable application of proximal sensing in a real-time, in-situ field setting.…”

“…All of the studies noted here used an expensive, high signal-to-noise ratio (S-N-R). Our previous research, Fortuna et al [15], used HDR UV-SWIR data, acquired via a high S-N-R, portable field spectrometer to predict total whole and ground soil carbon (TSOC) and nitrogen (TSN), particulate soil C and N (POMC, POMN), residual C of acid hydrolysis (RCAH) in both ground and unground dried and sieved soils. Performance metrics from both the equation calibration and validation phases indicated that >90% of the variability in each of the measured variables was accounted for by the proximally sensed data.…”