Using dense time-series of C-Band sar imagery for classification of diverse, worldwide agricultural systems

Dingle Robertson Laura, Davidson Andrew M., McNairn Heather, Hosseini Mehdi, Mitchell Scott W., de Abelleyra Diego, Verón Santiago R., Defourny Pierre, Le Maire Guerric, Planells Milena, Valero Silvia, Ahmadian Nima, Bosch David, Cosh Michael H., Siqueira Paula R.. 2019. Using dense time-series of C-Band sar imagery for classification of diverse, worldwide agricultural systems. In : IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium proceedings. IEEE, GRSS. Yokohama : IEEE, 6231-6234. ISBN 978-1-5386-9155-7 IGARSS 2019, Yokohama, Japon, 28 Juillet 2019/2 Août 2019.

Résumé : Cloudy conditions impede and reduce the utility of optical imagery. With the launch of Sentinel-1A and B, the ongoing availability of RADARSAT-2 imagery, and the expected launch of the RADARSAT Constellation Mission (RCM), dense time series of C-band Synthetic Aperture Radar (SAR) data will now be readily available. For crop classification and mapping, SAR imagery has yet to be used to its full potential and has generally been combined with optical imagery. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. Sets of dense time-series SAR imagery which include RADARSAT-2 and Sentinel-1 data were prepared for this experiment. AAFC's operational Decision Tree (DT) and newly implemented Random Forest (RF) classification methodologies were applied to these SAR only data-stacks, and to optimized, traditional data-stacks of optical/SAR combinations. This paper outlines the results of these dense time-series classifications and how these results were affected by changing numbers of agriculture classes, numbers of available SAR imagery and numbers of training and validation data points for individual crop types. In general, for the dense time-series SAR stacks, overall accuracies of greater than 85%, a typical operational goal, were obtained for 6 of 12 sites. These results have important operational implications for particularly cloudy regions where the availability of optical imagery is limited.

Auteurs et affiliations

• Dingle Robertson Laura, Agriculture and Agri-Food Canada (CAN)
• Davidson Andrew M., Agriculture and Agri-Food Canada (CAN)
• McNairn Heather, Agriculture and Agri-Food Canada (CAN)
• Hosseini Mehdi, Carleton University (CAN)
• Mitchell Scott W., Carleton University (CAN)
• de Abelleyra Diego, INTA (ARG)
• Verón Santiago R., INTA (ARG)
• Defourny Pierre, UCL (BEL)
• Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0002-5227-958X
• Planells Milena, CESBIO (FRA)
• Valero Silvia, CESBIO (FRA)
• Ahmadian Nima, JKI (DEU)
• Bosch David, USDA (USA)
• Cosh Michael H., USDA (USA)
• Siqueira Paula R., University of Massachusetts (USA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/605044/)

Métriques

[ Page générée et mise en cache le 2023-09-14 ]