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Use of dense Sentinel-2/MSI time series and machine learning algorithms to improve crop monitoring in the Cerrado biome

Grant number: 21/07382-2
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): January 01, 2022
Effective date (End): August 03, 2023
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal Investigator:Ieda Del'Arco Sanches
Grantee:Michel Eustáquio Dantas Chaves
Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovação (Brasil). São José dos Campos , SP, Brazil


Endowed with unique natural resources and agricultural production, the Cerrado is a strategic biome for Brazil. However, the dichotomy between conservation and production raises concerns associated with land use and land cover changes. Agricultural production is intense, especially in the MATOPIBA, a territorial geographic reality rich in edaphoclimatic characteristics and technology for high yields. However, as the area and volumes of production and exports grow, the biodiversity loss intensifies climate change and generates economic consequences. With changes in the global consumption profile, less tolerant to products derived from illegally deforested areas, Brazilian exports have suffered boycott threats in the international market. The agricultural sector has been considered a villain of deforestation and needs to improve the monitoring and traceability of the supply chain. Precise land use and land cover information subsidize it and climate change, food security, and agricultural dynamics policies, among others. However, solving it still is a challenge. Due to data scarcity or models disconnected from the field's reality, accurate analyzes only occur in the post-harvest period, inhibiting the detailed and updated change detection. This proposal aims to develop a method to improve agricultural mapping in the Cerrado from machine learning and time series analysis derived from analysis-ready data composed of data from the Sentinel-2/MultiSpectral Instrument mission, sensitive to subtle variations in vegetation, in a multidimensional data cube architecture in space, time, and properties. It is expected to propose a within-season agricultural monitoring system and generate accurate information at the level of crops and natural vegetation phytophysiognomies. (AU)

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Scientific publications (7)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MATAVELI, GUILHERME; DE OLIVEIRA, GABRIEL; CHAVES, MICHEL E. D.; DALAGNOL, RICARDO; WAGNER, FABIEN H.; IPIA, ALBER H. S.; SILVA-JUNIOR, CELSO H. L.; ARAGAO, LUIZ E. O. C.. Science-based planning can support law enforcement actions to curb deforestation in the Brazilian Amazon. CONSERVATION LETTERS, v. 15, n. 6, p. 9-pg., . (21/07382-2, 19/25701-8, 16/02018-2, 19/21662-8)
CHAVES, MICHEL E. D.; SANCHES, IEDA D.; ADAMI, MARCOS. Brazil needs juridical security to recover agri-environmental epistemic sovereignty. LAND USE POLICY, v. 132, p. 4-pg., . (21/07382-2)
CHAVES, MICHEL E. D.; MATAVELI, GUILHERME; ZU ERMGASSEN, ERASMUS; ARAGAO, RAFAELA B. DE A. B.; ADAMI, MARCOS; SANCHES, IEDA D.. Reverse the Cerrado's neglect. NATURE SUSTAINABILITY, v. N/A, p. 2-pg., . (21/07382-2, 23/03206-0, 19/25701-8, 16/02018-2, 20/15230-5)
ESCOBAR-SILVA, ELTON VICENTE; BOURSCHEIDT, VANDOIR; DAUGHTRY, CRAIG S. T.; KINIRY, JIM R.; BACKES, ANDRE R.; CHAVES, MICHEL E. D.. A general grass growth model for urban green spaces management in tropical regions: A case study with bahiagrass in southeastern Brazil. URBAN FORESTRY & URBAN GREENING, v. 73, p. 12-pg., . (21/07382-2, 18/12428-9, 17/24038-8)
PARREIRAS, TAYA CRISTO; BOLFE, EDSON LUIS; DANTAS CHAVES, MICHEL EUSTAQUIO; SANCHES, IEDA DEL'ARCO; SANO, EDSON EYJI; VICTORIA, DANIEL DE CASTRO; BETTIOL, GIOVANA MARANHAO; VICENTE, LUIZ EDUARDO. Hierarchical Classification of Soybean in the Brazilian Savanna Based on Harmonized Landsat Sentinel Data. REMOTE SENSING, v. 14, n. 15, p. 22-pg., . (19/26222-6, 21/07382-2)
MATAVELI, GUILHERME; CHAVES, MICHEL; GUERRERO, JOAO; ESCOBAR-SILVA, ELTON VICENTE; CONCEICAO, KATYANNE; DE OLIVEIRA, GABRIEL. Mining Is a Growing Threat within Indigenous Lands of the Brazilian Amazon. REMOTE SENSING, v. 14, n. 16, p. 13-pg., . (21/07382-2, 21/11435-4, 19/25701-8)
CHAVES, MICHEL E. D.; SANCHES, IEDA D.. Improving crop mapping in Brazil's Cerrado from a data cubes- derived Sentinel-2 temporal analysis. REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, v. 32, p. 13-pg., . (21/07382-2)

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