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Use of remote sensing images time series for monitoring Brazilian Agriculture

Grant number: 16/23750-3
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): January 01, 2017
Effective date (End): December 31, 2018
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal Investigator:Gilberto Camara Neto
Grantee:Michelle Cristina Araujo Picoli
Home Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brasil). São José dos Campos , SP, Brazil
Associated research grant:14/08398-6 - E-Sensing: big earth observation data analytics for land use and land cover change information, AP.ESCIENCE.TEM
Associated scholarship(s):17/19812-6 - Agricultural productivity and growth in Brazil: direct and indirect environmental impacts, BE.EP.PD

Abstract

Brazil is one of the largest agricultural producers in the world and a leading producer of biofuels. However, the use of remote sensing images to provide estimates of crop yield is still limited. This is due to the limitations of current data analysis methods, which focuses on processing a single image. The expectation of this project is that methods of "big analytics data" can significantly improve the use of satellite image in the generation of information about crop yield in Brazil. This project will focus in the specification and validation activities on methods for agricultural monitoring using big earth observation data. These methods should be based on analysis of satellite image time series. The tasks to be performed are: (a) detection of planted area of soybeans, maize and sugarcane, rice and wheat crops in selected areas, using methods that process large scale satellite image time series; (b) detailed assessment of big earth observation data analytics for agricultural mapping. In this project will development analytical methods for detecting large agricultural areas in Brasil, with the specific tasks of mapping land cover associated to soybeans, maize and sugarcane. The project results will be compare with ground truth data, that will be acquired, and with results from IBGE (Brazil's Census Bureau). The methods for agricultural monitoring should be developed in the R language and work with data stored in the SciDB array database. (AU)

Scientific publications
(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)
PICOLI, MICHELLE C. A.; RORATO, ANA; LEITAO, PEDRO; CAMARA, GILBERTO; MACIEL, ADELINE; HOSTERT, PATRICK; SANCHES, IEDA DEL'ARCO. Impacts of Public and Private Sector Policies on Soybean and Pasture Expansion in Mato Grosso-Brazil from 2001 to 2017. LAND, v. 9, n. 1 JAN 2020. Web of Science Citations: 0.
MACIEL, ADELINE MARINHO; CAMARA, GILBERTO; VINHAS, LUBIA; ARAUJO PICOLI, MICHELLE CRISTINA; BEGOTTI, RODRIGO ANZOLIN; FERREIRA GOMES DE ASSIS, LUIZ FERNANDO. A spatiotemporal calculus for reasoning about land-use trajectories. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, v. 33, n. 1, p. 176-192, 2019. Web of Science Citations: 3.
ARAUJO PICOLI, MICHELLE CRISTINA; CAMARA, GILBERTO; SANCHES, IEDA; SIMOES, ROLF; CARVALHO, ALEXANDRE; MACIEL, ADELINE; COUTINHO, ALEXANDRE; ESQUERDO, JULIO; ANTUNES, JOAO; BEGOTTI, RODRIGO ANZOLIN; ARVOR, DAMIEN; ALMEIDA, CLAUDIO. Big earth observation time series analysis for monitoring Brazilian agriculture. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, v. 145, n. B, p. 328-339, NOV 2018. Web of Science Citations: 13.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.