An automatic approach for validating fractional cover of soils in agricultural fields using UAV and cellphone images

Kurzfassung

Fractional Vegetation and Soil Cover (fCover) is an important land surface parameter especially, in agricultural systems. It provides quantitative cover of photosynthetically active vegetation (PV), non-photosynthetically active (NPV), and bare soil (BS) to serve the data needs for soil parameter modeling, soil erosion monitoring and the identification of land degradation. Further, it supports the observation of the impact of climate-friendly tillage practices on carbon stocks in agricultural systems (farming practices). With the increasing accessibility of hyperspectral data, the mapping of fCover is possible with significant accuracy due to the high spectral information content, especially in the SWIR wavelength region. Thus, large-scale validations are becoming necessary. However, validating fCover is challenging because so far ground data availability is very limited and fragmented. Nevertheless, for operational L3 processors, large-scale validation approaches are necessary and required to provide reliable accuracy and uncertainty measures for the land product. At the same time, the validation approach should be easy to implement, operate globally and should be cost and time-efficient. The idea of this study is to test and enhance a fCover validation method that is suitable to validate the separation between bare soils and non-photosynthetically active vegetation (NPV) with the perspective of transferring this approach in space and time.