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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #332157

Research Project: Sustainable Intensification of Grain and Biomass Cropping Systems using a Landscape-Based GxExM Approach

Location: Cropping Systems and Water Quality Research

Title: Profile soil property estimation using a VIS-NIR-EC-force probe

Author
item Cho, Youngin - University Of Missouri
item Sudduth, Kenneth - Ken
item Drummond, Scott

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/15/2017
Publication Date: 6/16/2017
Citation: Cho, Y., Sudduth, K.A., Drummond, S.T. 2017. Profile soil property estimation using a VIS-NIR-EC-force probe. Transactions of the ASABE. 60(3):683-692. doi: 10.13031/trans.12049.

Interpretive Summary: Quantification of profile soil properties such as soil carbon, bulk density, texture, and water content is traditionally accomplished by collection of soil samples in the field and subsequent laboratory analysis. This process is inefficient and may be impractical when measurements are needed at many locations and depths across a field or landscape, for example to develop precision agriculture management plans, to quantify spatial variability in soil quality, or to drive spatially-explicit process-based models. Field-deployable sensors that can collect data at multiple depths in the soil profile are a potential solution to this problem. This research evaluated one such sensor that measures visible (VIS) and near-infrared (NIR) soil reflectance, soil electrical conductivity (ECa) and soil strength to a depth of approximately 1 m. We evaluated its ability to estimate soil carbon, bulk density, and water content for four different central Missouri fields with varying soils. We found that soil carbon and sand and silt contents were well-estimated using VIS-NIR reflectance. Fusion of data from ECa and soil strength sensors with the VIS-NIR data improved results marginally at best, and soil bulk density was not estimated well with any dataset. While this multiple-sensor probe showed some potential, additional data collection and improved analyses are needed to more thoroughly evaluate its ability to quantify profile soil properties. These results will benefit researchers and practitioners interested in applying soil profile sensors to improve the efficiency of quantifying spatially-variable soil properties.

Technical Abstract: Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related those sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density, carbon, water content, and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m followed by collection of soil cores at each site for laboratory measurements. Using only reflectance data, soil bulk density, total organic carbon, and water content were not well-estimated (R2 = 0.32, R2 = 0.67, and R2 = 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in water content estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by Major Land Resource Area (MLRA), fusion of data from all sensors did improve soil texture fraction estimates. The largest improvement compared to VIS-NIR reflectance alone was for MLRA 115B, where estimation errors were reduced by approximately 14 to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties where combination of data from multiple sensors is required.