Unveiling year-round cropland cover by soil-specific spectral unmixing of Landsat and Sentinel-2 time series

Croplands are essential for food security but also impact the environment, biodiversity, and climate. Understanding, monitoring, modeling, and managing these impacts require accurate, comprehensive information on cropland vegetation cover. This study aimed to continuously monitor the state and vegetative processes of cropland, focusing on the assessment of bare soil and its cover with photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at the national level. We employed regression-based unmixing techniques using time series of Sentinel-2 and Landsat imagery to quantify cover fractions of NPV, PV, and soil during the agricultural growing season. Our approach extends existing spectral unmixing methods by incorporating a novel soil-specific unmixing process based on a soil reflectance composite. The extension accounts for variations in the spectral characteristics of soils which is particularly relevant for large-scale monitoring of annually cultivated croplands, as the spectral soil properties can vary considerably at national level and periods of bare soil are frequent. All cover fractions were predicted with mean absolute errors between 0.13 and 0.19. Introducing soil-specific unmixing reduced the mean absolute error of the predictions for soil by 11.3 % and NPV by 15.1 % without compromising PV predictions, particularly benefiting areas with bright soils. These findings demonstrate the efficacy of our method in accurately predicting crop cover throughout the cultivation period and underline the added value of incorporating the soil adjustment into the unmixing workflow. The contributions of this research are twofold: first, it provides essential data for the continuous monitoring of cropland cover, supporting agricultural carbon cycle and soil erosion modeling. Second, it enables further investigation into cropland management practices, such as cover cropping and tillage, through time series analysis techniques. This work underscores the potential of advanced spectral unmixing methods for enhancing agricultural monitoring and management strategies. • Spatially explicit soil-specific unmixing of Sentinel-2 and Landsat time series. • Unique bare soil spectrum identified for each pixel from soil reflectance composite. • Support Vector Regression models trained on synthetic spectral libraries. • Soil-specific predictions improved for NPV and soil, not impaired for PV. • Soil accuracy improvements did not compromise NPV, and vice versa.