Evaluating Different Methods for the Estimation of Bare Soil Surface Reflectance Using Multispectral Satellite Image Time Series

Abstract. Soil degradation poses a significant threat to both food security and climate change. Remote Sensing offers valuable insights for soil monitoring, enabling cost-effective observation across extensive regions and extended periods of time. This study evaluates bare soil reflectance mapping at medium spatial resolution by benchmarking the performance of various compositing approaches, providing an assessment of the contribution of different techniques i.e., simultaneous use of multiple spectral indices, different compositing, masking or thresholding techniques and other parameters of the time series i.e. cloud cover and time range. Focusing on Greece, a Mediterranean country with diverse microclimates and soil types, the study leverages Landsat 8 images spanning from 2015 to 2020 and the LUCAS 2015 database to evaluate the results. A wide range of experiments were conducted to determine the best approach for creating a bare soil reflectance composite (BSC), evaluated based on a) its correlation per spectral band with the spectral reference data and b) its performance in soil organic carbon prediction, serving as an indicator of the BSC’s quality. The study demonstrated that estimating bare soil reflectance from multispectral satellite image time series can be significantly improved through careful selection and optimization of a range of parameters especially over a large and heterogeneous study area. The results offer a strong basis for refining methodologies in bare soil reflectance estimation and provide insightful information for future monitoring efforts.