Operational Crop Classification with Saocom L-Band Sar Images in the Pampas Region, Argentina

This work presents the calibration/validation results of the operational classification product of SAOCOM mission, integrating a year's worth of field campaigns during the period 2019-2020 in a central area of the Argentine Pampas region with accuracies varying between 62% and 90% depending on the crop and its phenological phase. Accurate crop classification is a necessary preliminary step for soil moisture estimation in the SAOCOM mission (L-band Synthetic Aperture Radar satellite). For this purpose, reaction-diffusion systems that employ stochastic distances were used to reduce noise in SAR (Synthetic Aperture Radar) images and classify them.

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Published
2024-09-18
DOI
https://doi.org/10.1109/argencon62399.2024.10735849
Primary Topic
Smart Agriculture and AI
Type
article
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article

Operational Crop Classification with Saocom L-Band Sar Images in the Pampas Region, Argentina

Federico Machado, Ignacio G. Pascual, Rodrigo Edrosa, Marc Thiebault
Smart Agriculture and AI
article

Operational Crop Classification with Saocom L-Band Sar Images in the Pampas Region, Argentina

Federico Machado, Ignacio G. Pascual, Rodrigo Edrosa, Marc Thiebault
article en

Abstract

This work presents the calibration/validation results of the operational classification product of SAOCOM mission, integrating a year's worth of field campaigns during the period 2019-2020 in a central area of the Argentine Pampas region with accuracies varying between 62% and 90% depending on the crop and its phenological phase. Accurate crop classification is a necessary preliminary step for soil moisture estimation in the SAOCOM mission (L-band Synthetic Aperture Radar satellite). For this purpose, reaction-diffusion systems that employ stochastic distances were used to reduce noise in SAR (Synthetic Aperture Radar) images and classify them.

National Space Activities Commission (AR)
Zero hunger
Openalex Percentile: Top 89%
Smart Agriculture and AI
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