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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #320190

Title: Monitoring soil health with a sensor fusion approach

item Sudduth, Kenneth - Ken
item Veum, Kristen
item Kitchen, Newell

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/21/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Sensor-based approaches to assessment and quantification of soil health are important to facilitate cost-effective, site-specific soil management. While traditional laboratory analysis is effective for assessing soil health (or soil quality) at a few sites, such an approach quickly becomes infeasible when data is needed at many sites to assess variations across fields and landscapes. In previous work we established the ability of visible, near-infrared (VNIR) diffuse reflectance spectroscopy to estimate multiple soil quality indicators (SQIs) and Soil Management Assessment Framework (SMAF) scores. Soil reflectance analysis was particularly effective for biological SQIs, including organic C, ß-glucosidase, total N, and the biological component score of the SMAF. However, VNIR spectroscopy was unable to accurately quantify important chemical and physical soil quality variables, including bulk density, soil texture fractions, and extractable macronutrients (i.e., P and K). In this research, we investigated the potential of a sensor fusion approach incorporating VNIR spectroscopy, soil apparent electrical conductivity (ECa), and penetration resistance measured by cone penetrometer (i.e., cone index, CI) to quantify biological, chemical, and physical SQIs. Data were obtained from two depths (0-5 and 5-15 cm) at 108 locations within a 10-ha research site encompassing different cropping systems and landscape positions. Spectral data were obtained in the laboratory from collected samples, while CI and ECa data were obtained in situ. Calibration models for SQIs and SMAF scores were developed with partial least squares (PLS) regression. The accuracy of SQI and SMAF estimations using sensor fusion of VNIR, ECa, and CI data were compared to those obtained with VNIR data alone.