Crop changes over one year
As a part of ESA’s SInCohMap project, which is dedicated to exploring innovative methodologies for land-cover and vegetation mapping using Sentinel-1 multitemporal coherence data, the usefulness of coherence time series for crop-type mapping has been demonstrated recently. A year-long time series of data from 2017 combined pairs of images of an agricultural area in Seville, Spain, from the two Sentinel-1 satellites. The images were used to classify 17 different crop types cultivated that year. Coherence was measured by using the pairs of consecutive images, acquired with a separation of six days, and also at two polarisations: VV and VH. The animation shows the presence of white and black rectangles in the cultivation area, according to the absence or presence of growing crops, and how they change along the year. As a side comment, there are some dates in fall (October and November) with a generalised low coherence, but they are due to rain events.
