| Grant number: | 24/16515-4 |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| Start date: | February 01, 2025 |
| End date: | December 31, 2025 |
| Field of knowledge: | Physical Sciences and Mathematics - Geosciences |
| Principal Investigator: | Michel Eustáquio Dantas Chaves |
| Grantee: | Gustavo Henrique Vieira Barros |
| Host Institution: | Faculdade de Ciências e Engenharia. Universidade Estadual Paulista (UNESP). Campus de Tupã. Tupã , SP, Brazil |
Abstract Crop production estimates based on interviews and questionnaires applied to rural producers cause uncertainties that compromise food security. Therefore, improving accuracy is essential. Remote sensing techniques, for example, make it possible to monitor the phenological development of crops via satellite images. Currently, space agencies allow free access and use of image collections from space missions, such as Landsat, from the National Aeronautics and Space Administration (NASA). Such collections allow for the monitoring of different surface targets as long as interpretation keys related to the identification of each agricultural target are applied based on inherent characteristics, such as shape, spectrality, and phenological cycle. The present proposal aims to integrate time series of Landsat 8/Operational Land Imager (OLI) images, field data, agricultural calendars, and Machine Learning algorithms to characterize and monitor sugarcane crops crops in the State of São Paulo, the largest producer in Brazil. To this end, we will use the Google Earth Engine (GEE) cloud computing platform, which has tools for such integration and allows the creation of banks of representative samples and maps derived from agricultural monitoring. We will assess the 2020/2021, 2021/2022 and 2022/2023 harvests. As a result, we seek to generate sample banks for the biome and increase the assertiveness of classification methods, which aligns with Sustainable Development Goals (SDGs) 2, 7, 12, 13, and 15, entitled "Zero Hunger and Sustainable Agriculture", "Responsible Consumption and Production", "Responsible Consumption and Production", "Combating climate change" and "Life on Earth", respectively. | |
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