Early-Season Crop Mapping on an Agricultural Area in Italy Using X-Band Dual-Polarization SAR Satellite Data and Convolutional Neural Networks

Early-Season Crop Mapping on an Agricultural Area in Italy Using X-Band Dual-Polarization SAR Satellite Data and Convolutional Neural Networks

Blog Article

Early-season crop mapping provides decision-makers with timely information on crop types and conditions that are crucial for agricultural management.Current satellite-based mapping Vacuum Pipe solutions mainly rely on optical imagery, albeit limited by weather conditions.Very few exploit long-time series of polarized synthetic aperture radar (SAR) imagery.To address this gap, we assessed the performance of COSMO-SkyMed X-band dual-polarized (HH, VV) data in a test area in Ponte a Elsa (central Italy) in January–September 2020 and 2021.

A deep learning convolutional neural network (CNN) classifier arranged with two different architectures (1-D and 3-D) was trained and used to recognize ten classes.Validation was undertaken with in situ measurements from regular field campaigns carried out during satellite overpasses over more than 100 plots each year.The 3-D classifier structure and the combination of HH+VV backscatter provide the best classification accuracy, especially during the first months of each year, i.e.

, 80% already in April 2020 and in May 2021.Overall accuracy above 90% is always marked from June using the 3-D classifier with HH, VV, and HH+VV backscatter.These Kids Watch experiments showcase the value of the developed SAR-based early-season crop mapping approach.The influence of vegetation phenology, structure, density, biomass, and turgor on the CNN classifier using X-band data requires further investigations, along with the relatively low producer accuracy marked by vineyard and uncultivated fields.