In this paper, a novel approach is proposed to exploit a time series of COSMO-SkyMed (CSK) HH-VV SAR images to map rice fields and to estimate the sowing dates. The approach relies on multi-polarization features, i.e., the squared modulus of the HH and VV channels and the polarization ratio, extracted from CSK SAR scenes. The key step consists of extracting a rice training signature related to the multipolarization features. This signature allows estimating the sowing date that, at once, is used to refine the rice map obtained by the conventional interpretation of the CSK time series in terms of the scattering mechanisms of the different growing cycles. Experiments, carried out on a time series of 32 CSK images, collected from the Mekong Delta region, South Vietnam, confirm the soundness of the proposed methodology which is shown to provide results comparable to the ones obtained by a literature approach that exploits a similar dataset.

A New Methodology for Rice Area Monitoring with COSMO-SkyMed HH-VV PingPong Mode SAR Data

Mascolo, Lucio;Forino, Giuseppina;Nunziata, Ferdinando;Pugliano, Giovanni;Migliaccio, Maurizio
2019-01-01

Abstract

In this paper, a novel approach is proposed to exploit a time series of COSMO-SkyMed (CSK) HH-VV SAR images to map rice fields and to estimate the sowing dates. The approach relies on multi-polarization features, i.e., the squared modulus of the HH and VV channels and the polarization ratio, extracted from CSK SAR scenes. The key step consists of extracting a rice training signature related to the multipolarization features. This signature allows estimating the sowing date that, at once, is used to refine the rice map obtained by the conventional interpretation of the CSK time series in terms of the scattering mechanisms of the different growing cycles. Experiments, carried out on a time series of 32 CSK images, collected from the Mekong Delta region, South Vietnam, confirm the soundness of the proposed methodology which is shown to provide results comparable to the ones obtained by a literature approach that exploits a similar dataset.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/75756
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