The European Space Agency has recently highlighted our SInCohMap project, and in particular the research leaded by our colleagues Alejandro Mestre-Quereda and Juan M. López-Sánchez from the University of Alicante. This work, entitled “Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping” has been recently published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing journal.
This work explores the potential use of the interferometric coherence measured with Sentinel-1 satellites as input feature for crop classification. A one-year time-series of Sentinel-1 images acquired over an agricultural area in Spain, in which 17 crop species are present, is exploited for this purpose. Different options regarding temporal baselines, polarization, and combination with radiometric data (backscattering coefficient) are analyzed in the associated pater. The presented results show that both radiometric and interferometric features provide notable classification accuracy when used individually, where the overall accuracy lies between 70% and 80%. It is found that the shortest temporal baseline coherences (6 days) and the use of all available intensity images perform best, hence proving the advantage of the 6-day revisit time provided by the Sentinel-1 constellation with respect to longer revisit times. It is also shown that dual-pol data always provide better classification results than single-pol ones. More importantly, when both coherence and backscattering coefficient are jointly used, a significant increase in accuracy is obtained (greater than 7% in overall accuracies). Individual accuracies of all crop types are increased, and an overall accuracy above 86% is reached. This proves that both features provide complementary information, and that the combination of interferometric and radiometric radar data constitutes a solid information source for this application.