Dataset for "Assessment of interferometric coherence contribution to corn and soybean mapping with Sentinel-1 data time series"

Employed dataset for a research work development. Crop mapping with remote sensing data is a valuable tool for monitoring global food security, economic stability, and environmental conditions. This work aimed to evaluate the added value of Repeat-Pass interferometric coherence to backscatter for soybean and corn mapping, which are the most significant grain crops in Argentina. In addition, it was proposed the analysis of crops’ growth stages SAR information as well as the accuracy of in-season crop classifications. A complete 1-year time series of dual polarization Sentinel-1 (A and B) images was used. High accuracies were obtained with backscatter coefficients for both polarizations (higher than 95%) and interferometric coherence (88%). The work showed that coherence addition to single VV polarization backscatter yielded an improvement of 2-3% in general precision and 2-4 in kappa index score. Such result suggests the added value of interferometric coherence to backscattering coefficient, being a valuable source for single polarization SAR data.