Abstract
Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.
Highlights
Traditional agriculture applies uniform management to fields without considering the spatial heterogeneity of soil properties and plant growth
Ten different preprocessing techniques were applied to spectral data, and multiple soil properties were subsequently estimated using DECS and partial least squares regression (PLSR)
When averaged across the three datasets, the grand mean prediction R2 was very similar for all preprocessing techniques, ranging from 0.58 to 0.61, with the 30-point Gaussian window smoothing and Standard normal variate (SNV) plus Gaussian performing the best (R2 = 0.61), followed by the 30-point moving average and SNV (R2 = 0.60)
Summary
Traditional agriculture applies uniform management to fields without considering the spatial heterogeneity of soil properties and plant growth. This contributes to potential over-application of chemical inputs such as fertilizer, pesticides, and herbicides, leading to increased environmental risk. Precision agriculture has the potential to improve crop production, prevent excess application of chemical inputs, reduce expenses, and reduce environmental impacts. To reach this goal, site-specific soil properties that affect plant growth and crop production need to be measured to provide a basis for precision agriculture management.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
