Spectral assessment of organic matter with different composition using reflectance spectroscopy

Soil surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental factors, such as CO² budget, soil fertility and sustainability, is soil organic matter (SOM), as well as its sequestration. Soil spectroscopy is a popular method to assess SOM content rapidly in both field and laboratory domains. However, SOM source composition differs from soil to soil, and the use of spectral-based models for quantifying SOM may present limited accuracy when applying a generic approach to SOM assessment. We therefore examined the extent to which the generic approach can assess SOM contents of different origin using spectral-based models. We created an artificial big dataset composed of pure dune sand as a SOM-free background, which was artificially mixed with increasing amounts of different organic matter (OM) sources obtained from commercial compost of different origins. Dune sand has high albedo and yields optimal conditions for SOM detection. This study combined two methods: partial least squares regression for the prediction of SOM content from reflectance values across the 400–2500 nm region and a soil spectral detection limit (SSDL) to judge the prediction accuracy. Spectral-based models to assess SOM content were evaluated with each OM source as well as with a merged dataset that contained all of the generated samples (generic approach). The latter was concluded to have limitations for assessing low amounts of SOM (<0.6%), even under controlled conditions. Moreover, some of the OM sources were more difficult to monitor than others; accordingly, caution is advised when different SOM sources are present in the examined population.