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Orientador(es)
Resumo(s)
Agricultural soils play a crucial role in food security and climate change, as the process of accumulation and stabilization of soil organic carbon simultaneously improves the physicochemical properties, such as water retention, aggregate stability, or plant nutrition. For these reasons, knowing the amount of Soil Organic Carbon (SOC) is important to manage climate alarm, but the sampling and subsequent laboratory analyses that are traditionally used for this purpose are expensive and time-consuming, which leads to explore alternative non-degrading and environmentally friendly methods. Remote Sensing (RS) arises in this sense, seeking to reduce time consumption and cost . However, the amount of data needed for this type of evaluation requires advanced computer methods for the elaboration of SOC predict models . A Literature Review (LR) was conducted with the aim of finding tools, advances, and gaps in the literature. Using the PRISMA method, 30 articles that used RS and Artificial Intelligence (AI) to estimate SOC were selected to LR .The keywords and synonyms chosen for this research were: “Deep learning” and “Neural network”; “Remote sensing”; “Soil organic carbon” and “Organic matter”. The period covered by this work is from 2021 to August 2023.
Descrição
Palavras-chave
Soil organic carbon Remote sensing Artificial intelligence Deep learning Neural networks
Contexto Educativo
Citação
Lima, Arthur A. J.; Hernandez Hernandez, Zulimar; Lopes, Júlio Castro; González, Antonio; Lopes, Rui Pedro; Figueiredo, Tomás de (2024). A literature review on the use of ESA satellite data for soil organic carbon estimation models with artificial intelligence tools. In ESA Symposium on Earth Observation for Soil Protection and Restoration. Frascati.