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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #360048
Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Improving in-situ estimation of soil profile properties using a multi-sensor probe

Author
item PEI, XIAOSHUAI - China Agricultural University
item Sudduth, Kenneth - Ken
item Veum, Kristen
item LI, MINZAN - China Agricultural University

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2019
Publication Date: 2/27/2019
Publication URL: https://handle.nal.usda.gov/10113/6477321
Citation: Pei, X., Sudduth, K.A., Veum, K.S., Li, M. 2019. Improving in-situ estimation of soil profile properties using a multi-sensor probe. Sensors. 19(5):1011. https://doi.org/10.3390/s19051011.
DOI: https://doi.org/10.3390/s19051011

Interpretive Summary: Collection of in-field data using profile diffuse reflectance spectroscopy (DRS) soil sensors has the potential to provide rapid, high-resolution estimation of soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. Additionally, the ability to collect simultaneous data with additional sensors may allow for improved results. Although much research has been conducted on analysis methods for laboratory-based DRS, few have focused on methods for in-field DRS. Therefore, this study compared multiple sensor data sources, data processing techniques, and calibration modeling approaches for estimation of multiple soil properties with in-field sensors. We identified a combination of processing and modeling approaches that worked best in this data. We also found that there was little improvement in soil property estimation by adding data from additional sensors to DRS data, meaning that in many cases the added complexity of multiple sensors may not be justified. While broader applicability of these results needs to be verified by additional data collection and analysis over more soils, the results of this study provide information that scientists and producers can use to improve in-field sensor based data collection for more informed agroecosystem management.

Technical 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 only for Ca was the improvement more than 5%. 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 modelling techniques for in-situ estimation of profile soil properties.