Topographic correction of visible near‐infrared reflectance spectra for horizon‐scale soil organic carbon mapping

• Published in: Soil Science Society of America journal Vol. 88; no. 2; pp. 207 - 223
• Main Authors: Duro, Alyssa M., Hirmas, Daniel R., Ajami, Hoori, Billings, Sharon A., Zhang, Xi, Li, Li, Flores, Alejandro, Moreno, Victoria, Cao, Xiaoyang, Guilinger, James, Oleghe, Ewan, Giménez, Daniel, Gray, Andrew, Sullivan, Pamela L.
• Format: Journal Article
• Language: English
• Published: 01-03-2024
• ISSN: 0361-5995; 1435-0661
• DOI: 10.1002/saj2.20612

Understanding soil organic carbon (SOC) response to global change has been hindered by an inability to map SOC at horizon scales relevant to coupled hydrologic and biogeochemical processes. Standard SOC measurements rely on homogenized samples taken from distinct depth intervals. Such sampling prevents an examination of fine‐scale SOC distribution within a soil horizon. Visible near‐infrared hyperspectral imaging (HSI) has been applied to intact monoliths and split cores surfaces to overcome this limitation. However, the roughness of these surfaces can influence HSI spectra by scattering reflected light in different directions posing challenges to fine‐scale SOC mapping. Here, we examine the influence of prescribed surface orientation on reflected spectra, develop a method for correcting topographic effects, and calibrate a partial least squares regression (PLSR) model for SOC prediction. Two empirical models that account for surface slope, aspect, and wavelength and two theoretical models that account for the geometry of the spectrometer were compared using 681 homogenized soil samples from across the United States that were packed into sample wells and presented to the spectrometer at 91 orientations. The empirical approach outperformed the more complex geometric models in correcting spectra taken at non‐flat configurations. Topographically corrected spectra reduced bias and error in SOC predicted by PLSR, particularly at slope angles greater than 30°. Our approach clears the way for investigating the spatial distributions of multiple soil properties on rough intact soil samples.
Core Ideas
A novel topographic correction method reduced the effect of surface orientation on lab‐based soil reflectance.
A linear empirical approach outperformed two versions of a geometric topographic correction.
Bias and error in soil organic carbon predictions were decreased when topographically corrected reflectance spectra were used.
Topographic correction advances the use of hyperspectral imaging on intact soil samples displaying natural roughness.