Potential of EnMAP Hyperspectral Imagery for Regional-Scale Soil Organic Matter Mapping

Abstract

The emergence of new-generation hyperspectral satellites offers more potential for mapping soil properties. This study presents the first assessment of EnMAP (Environmental Mapping and Analysis Program) hyperspectral imagery for soil organic matter (SOM) prediction and mapping using actual spectral data from 282 soil samples. Different spectral preprocessing techniques, including Savitzky–Golay (SG) smoothing, the second derivative of SG, and Standard Normal Variate (SNV) transformation, were evaluated in combination with embedded feature selection to identify the most relevant wavelengths for SOM prediction. Partial Least Squares Regression (PLSR) models were developed under different pre-treatment scenarios. The best performance was obtained using SNV preprocessing with the top 30 EnMAP bands (wavelengths) selected, giving R2 = 0.68, RMSE = 0.34%, and RPIQ = 1.75. The combination of SNV with feature selection successfully identified significant wavelengths for SOM prediction, particularly around 550 nm in the Vis–NIR region, 1570–1630 nm, and 1600 nm and 2200 nm in the SWIR region. The resulting SOM predictions exhibited spatially consistent patterns that corresponded with known soil–landscape relationships, highlighting the potential of EnMAP hyperspectral data for mapping soil properties despite its limited geographical availability. While these results are promising, this study identified limitations in the ability of PLSR to extrapolate predictions beyond the sampled areas, suggesting the need to explore non-linear modeling approaches. Future research should focus on evaluating EnMAP’s performance using advanced machine learning techniques and comparing it to other available hyperspectral products to establish robust protocols for satellite-based soil monitoring.