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ARS Home » Plains Area » Lincoln, Nebraska » Agroecosystem Management Research » Research » Publications at this Location » Publication #429074
Research Project: Integrated Agricultural Systems for a Resilient Circular Bioeconomy in the Central Plains

Location: Agroecosystem Management Research

Title: Remote Sensing of Soil Organic Carbon in Varied Tillage-Crop Systems

Author
item Zoller, Amy
item Birru, Girma
item Kharel, Tulsi
item Jin, Virginia
item Schmer, Marty
item Freidenreich, Ariel
item WARDLOW, BRIAN - University Of Nebraska
item Kettler, Timothy
item GALA, TEKLEAB - Chicago State University

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/13/2025
Publication Date: 7/15/2025
Citation: Zoller, A.L., Birru, G.A., Kharel, T.P., Jin, V.L., Schmer, M.R., Freidenreich, A.S., Wardlow, B., Kettler, T.A., Gala, T. 2025. Remote Sensing of Soil Organic Carbon in Varied Tillage-Crop Systems. Journal of Environmental Quality. 1-13. https://doi.org/10.1002/jeq2.70060.
DOI: https://doi.org/10.1002/jeq2.70060

Interpretive Summary: Soil organic carbon (SOC) is an important indicator of soil health. A better understanding the spatial and temporal distribution of SOC is critical to protect soil resources. A field-scale study was conducted to evaluate RS of SOC over four varied tillage-crop systems, using two fields (CSP 1 and CSP 2) located in eastern Nebraska and two early spring Sentinel-2 multispectral images as a basis for the four systems. SOC measurements, early spring reflectance bands and band ratio, growing season peak vegetation indices, soils, historic yield and elevation derivatives were combined to develop a RF model. The research highlighted the potential of using early spring images under dry conditions to model SOC under varied tillage-crop systems. The model results for the NT-Soy field were especially encouraging, due to widespread adoption of conservation tillage or no-till practices across the Midwest, US. This analysis also highlighted the complexities of mixed pixels and warrants further investigation to better understand effects of SOC, crop residues and soil properties in satellite-scale remote sensing studies.

Technical Abstract: The use of remote sensing (RS) to estimate soil organic carbon (SOC) in cropland has become increasingly important. Yet, RS estimation of cropland SOC is challenging, particularly when mixed crop residues and soils are present. Our objective was to develop a RS model to estimate SOC under varied tillage-crop systems typical of Midwest, US farming practices and evaluate model performance with respect to each system. Four tillage-crop systems were evaluated: Conventional Till (CT) corn (fall only tillage), CT corn (fall and spring tillage), No-Till (NT) soybean, and NT corn. A Random Forest (RF) model was developed using SOC measurements, Sentinel-2 early spring images (bands and band ratios) and ancillary data (elevation, yield, soils, peak vegetation), and accuracy and most important variables were assessed for each system. The CT corn and NT soybean models predicted SOC with reasonable accuracy (R2=0.65-0.73, n=43-46), with most important variables centering on Sentinel-2 early spring images. The NT corn model, however, underperformed (R2=0.14, n=46), with most important variables centering on ancillary data inputs instead of early spring images. The RF model was also used to map the spatial distribution of SOC over the study area, which showed variability related to human disturbance (historical railroad tracks). This research provided insight to estimation and mapping of SOC in varied tillage-crop systems and highlighted the suitability of early spring RS images to improve results in mixed residue and soils landscapes.