Agricultural Land Cover Mapping based on Sentinel-1 Coherence Time-Series

Abstract

The study is aimed at understanding the value of interferometric coherence in mapping regions characterized by a mixture of crops and grasses. The results highlight that a 5% improvement in the classification accuracy can be achieved by using the coherence in addition to the backscatter intensity and by combining VV and VH. It is shown that the largest contribute in class discrimination is brought in winter, when dry vegetation and bare soils can be expected. It was also notably observed that coherence information can enhance the identification of harvesting events in a small but significant number of cases.