Improvement of phenological metrics and computational application to identify soybean and corn in the Cerrado biome

• Grant number: 25/00099-4
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: May 01, 2025
• End date: July 29, 2025
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal Investigator: Michel Eustáquio Dantas Chaves
• Grantee: Livia Gabriele Dias Soares
• Supervisor: Lia Barbara Cunha Barata Duarte
• Host Institution: Faculdade de Ciências e Engenharia. Universidade Estadual Paulista (UNESP). Campus de Tupã. Tupã , SP, Brazil
• Institution abroad: Universidade do Porto (UP), Portugal
• Associated to the scholarship: 24/08083-7 - Identification of soybean and corn crops in the Brazilian Cerrado via satellite images, BP.IC

Abstract

Food security is a pressing global issue, emphasized in international conferences and the Sustainable Development Goals (SDGs). The Cerrado, a biome vital to Brazil's biodiversity, is increasingly threatened by agricultural expansion and the growing demand for commodities like soybeans and corn. To address these challenges, innovative strategies are essential to balance ecosystem service provision with legal and sustainable agricultural production, aligning with international commitments. Remote sensing - including spectral indices and phenological metrics - is a pivotal technology for reducing uncertainties in crop identification, mapping, and monitoring. This proposal aims to secure an international internship to enhance phenological metrics, thereby improving the accuracy of land use and land cover classifications in the Cerrado. The methodology involves a spectrotemporal analysis of Landsat 8/OLI data, focusing on spectral indices related to biomass, plant water content, and soil reflectance. Phenological metrics will be integrated into the processing workflow, alongside the development of open-source Geographic Information System (GIS) applications and quality control protocols for sample data. The project seeks to improve the distinction between soybean and corn crops by creating qualitative sample banks and scalable methods, in collaboration with the University of Porto, Portugal. By advancing scientific knowledge and contributing to global food security, this study aligns with SDGs 2, 9, 12, and 13, offering solutions that combine innovation with sustainability.