Title
Early-Season Crop Mapping on an Agricultural Area in Italy Using X-Band Dual-Polarization SAR Satellite Data and Convolutional Neural Networks
Abstract
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 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 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.
Year
DOI
Venue
2022
10.1109/JSTARS.2022.3198475
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Keywords
DocType
Volume
Convolutional neural network (CNN), COSMO-SkyMed, crop early mapping, deep learning, dual polarization, synthetic aperture radar (SAR), X-band
Journal
15
ISSN
Citations 
PageRank 
1939-1404
0
0.34
References 
Authors
0
11