C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems

Dingle Robertson Laura, Davidson Andrew M., McNairn Heather, Hosseini Mehdi, Mitchell Scott W., de Abelleyra Diego, Verón Santiago R., Le Maire Guerric, Plannells Milena, Valero Silvia, Ahmadian Nima, Coffin Alisa, Bosch David, Cosh Michael H., Basso Bruno, Saliendra Nicanor. 2020. C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems. International Journal of Remote Sensing, 41 (24) : 9628-9649.

Url - jeu de données - Entrepôt autre : https://figshare.com/articles/journal_contribution/C-band_synthetic_aperture_radar_SAR_imagery_for_the_classification_of_diverse_cropping_systems/13176636

Quartile : Q2, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q3, Sujet : REMOTE SENSING

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user's and producer's accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.

Mots-clés Agrovoc : Radar à synthèse d'ouverture, système de culture, surveillance des cultures, imagerie par satellite, Observation satellitaire, cartographie de l'utilisation des terres, cartographie de l'occupation du sol, analyse de séries chronologiques, données spatiales, nuage, conditions météorologiques, télédétection

Classification Agris : F08 - Systèmes et modes de culture
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Auteurs et affiliations

• Dingle Robertson Laura, Agriculture and Agri-Food Canada (CAN) - auteur correspondant
• 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)
• Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0002-5227-958X
• Plannells Milena, CESBIO (FRA)
• Valero Silvia, CESBIO (FRA)
• Ahmadian Nima, JKI (DEU)
• Coffin Alisa, USDA (USA)
• Bosch David, USDA (USA)
• Cosh Michael H., USDA (USA)
• Basso Bruno, MSU (USA)
• Saliendra Nicanor, USDA (USA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/598291/)

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