Knowledge of total aboveground biomass (AGB) at harvest time, from site to national scales, is critical to understanding the potential for C sequestration and residue utilization in agricultural ecosystems. Cover crop biomass estimation models using an integration of remote sensing and field data represent a useful inversion technique. (Li et al., 2022). The development of remote sensing techniques has broken the temporal and spatial limitations of traditional methods and has become an important monitoring method for wheat AGB at different level.

In this research, we present different approaches to estimating winter wheat biomass in Denmark, using a variety of different datasets and methods, such as observational data of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index2 (EVI2), Ratio Vegetation Index (RVI), Precipitation (mm), Land surface temperature (^oC), as well as crop grain production (statistical data) for the years 2013 – 2023, supplemented with data from national soil mapping (e.g. texture, SOC, pH) and a Digital Elevation Model (DEM for estimating slope, aspect, etc.).

Our results illustrates that the cover crop biomass in Denmark showed significant spatial and interannual variation from 2014 to 2023, with the spatial and temporal distribution characteristics closely related to NDVI. The linear regression between peak biomass and NDVI yielded a R² of 0.78, which could be further improved by also considering other information on soils, fertilization, Vis and landscape features. This information, will allow assessment of the potential of agricultural landscapes to provide estimates of regional availability of residues for the bioeconomy while improving soil organic C stocks.