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The importance of monitoring soil properties is constantly increasing among researchers and policy-makers. In this context, it is imperative to identify cost effective and reliable strategies for soil mapping compared to the costlier traditional solutions. A wide range of tools are becoming available that enable better utilization of Earth Observation capabilities to monitor the soil ecosystem. This work is an effort of assessing the potential of Sentinel-2 imagery data for mapping Soil Organic Matter (SOM) contents and investigating the possibilities of its enhancement through ASTER derived information. The rural area around the lake Zazari, located in the Western Macedonia district of Greece, was chosen as study area. Initially, pixel-wise vegetation indices (NDVI and NBR2) were calculated, utilizing a local version of the CEOS Open Data Cube for masking Sentinel-2 bare soil pixels extending a three-year period (2017–2019). The generated mask was used to extract soil spectral signatures at the image level over selected 100 field samples. The resulting time series was expanded through the conjunction of ASTER Thermal InfraRed bands by matching the exact data acquisition dates of two platforms. The conclusive part of the work contains the application of regression modelling to effectively assess soil variables. The local Partial Least Square regression algorithm was chosen, due to its characteristics of performing inherently local predictions. Five-fold cross-validation technique was used for reporting the models’ accuracy, which was assessed through R 2 coefficient, RPIQ ratio and RMSE. The model estimated SOM values among a synthetic bare soil composite image that was acquired over study area’s agricultural fields. Two models were trained and compared; one over Sentinel-2 imagery bands that were used as the predictor variables’ set and a second over an expanded predictor variables’ set, including ASTER thermal bands. The results signified evidence of accuracy increase of SOM content assessment, through spaceborne imagery analysis.
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