An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE)

• Autores: Medina Medina, Angel James, Salas López, Rolando, Zabaleta Santisteban, Jhon Antony, Tuesta Trauco, Katerin Meliza, Turpo Cayo, Efrain Yury, Huaman Haro, Nixon, Oliva Cruz, Manuel, Gómez Fernández, Darwin
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2466
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2466; https://doi.org/10.3390/agronomy14030557
• Nivel de acceso: acceso abierto
• Materia: SAR; Rice; Monitoring; Changes; https://purl.org/pe-repo/ocde/ford#4.01.06; SAR (radar); Radar de abertura sintética; Vigilancia; Oryza sativa; Arroz

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.