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SOIL CHEMISTRY

Free Iron Oxide Determination in Mediterranean Soils using Diffuse Reflectance Spectroscopy

^a Remote Sensing Section, GeoForschungsZentrum Potsdam, Telegrafenberg, 14473 Potsdam, Germany
^b Transportation and GeoInformation Eng. Unit, Faculty of Civil and Environ. Eng., Technion, Haifa 32000, Israel
^c Dep. of Soil Science, Univ. of Almería, 04120-Almería, Spain
^d Institute of Geography, Geomatics Dep., Humboldt-Universität zu Berlin, 10099 Berlin, Germany

^* Corresponding author (nirichter{at}yahoo.de).

Soil Fe oxides occur in almost all soils and reflect different environmental conditions by the high variability of their mineralogy and concentration. Quantitatively determining this important pedogenic indicator enables diffuse reflectance spectroscopy (DRS) based on material-specific absorption characteristics. This paper presents a methodology that directly links free Fe oxide content (Fe_d, citrate-dithionite extractable Fe) with the diagnostic Fe absorption band near 900 nm (Fe-NIR). In addition, we investigated the influence of soil texture on the spectral characteristics and prediction accuracy. We showed that the Fe absorption bands of clay-dominated soil samples were, in general, deeper than sand-dominated samples with comparable Fe_d content. Based on the Fe-NIR absorption depth, we created two texture-dependent Fe_d prediction models, retrieving the best Fe_d estimates for the sand calibrated model (R²_v = 0.87, rel. MSE_v = 13.9%). Due to the high texture variability in sand, silt, and clay fractions of the clay–silt dominated samples, the clay–silt calibrated model produced good predictions (R²_v = 0.70, rel. RMSE_v = 19.0%). The soil texture appeared to have no significant influence on model stability but did affect the prediction accuracy. Constant Fe_d contents were over- and underestimated when applying the texture-dependent models to other texture groups. The texture-independent model was stable and performed well (R²_v = 0.76, rel. RMSE_v = 18.1%). These results are highly relevant to the subsequent spatial assessment of free Fe oxide content as an indicator for soil development from hyperspectral remote sensing data.

Abbreviations: ASD, analytic spectral device • DRS, diffuse reflectance spectroscopy • Fe_d, citrate-dithionite extractable iron • Fe-NIR, iron absorption band in the near infrared spectrum • Fe-VIS, iron absorption band in the visible infrared spectrum • N, total number of samples used for modeling • PLS, partial least square • R, Pearson's product-moment correlation coefficient • RMSE, root mean square error • rel. RMSE, relative root mean square error • SOC, soil organic carbon • Stdev, standard deviation • VIS/NIR, visible and near infrared