CBERS data cubes for land use and land cover mapping in the Brazilian Cerrado agricultural belt

ABSTRACT

The agricultural frontier expansion in the Cerrado biome made Brazil a leader in commodity exports and is changing its landscape. Hence, efforts to accurate land use and land cover (LULC) monitoring in this region are strategic, due to its role in Brazil’s food, environmental, and economic security policy. Thinking on planning and technical sovereignty in the spatial sector, the China–Brazil Earth Resources Satellite (CBERS) Program was launched to provide useful data for decision-makers to manage the Brazilian territory independently of external policies. Their data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are largely applied in deforestation monitoring by remote sensing specialists but less applied than data from other image providers for machine learning-based LULC mapping due to the small number of spectral bands and limitations related to clouds and shadows detection. However, with advances in orbital data analysis, data cubes enabled storing and accessing large spatio-temporal analysis-ready data. Within this scope, the Brazil Data Cube Project (BDC) creates multidimensional data cubes from orbital sensors’ data for all Brazilian territory. We applied BDC CBERS-4/WFI data cubes to generate LULC classifications for the Extremo Oeste Baiano agricultural belt correspondent to the 2017/2018 and 2019/2020 harvest periods, at two levels of detail: broad and crop type, incorporating ground truth samples, crop calendar knowledge, and vegetation indices to a dense time series analysis approach. Overall Accuracies were equal to 0.87 and 0.89 for broad, and 0.91 and 0.94 for crop type classifications. The results indicate CBERS-4/WFI data cubes as a useful tool for improving crop monitoring in the Cerrado biome based on machine learning.

Acknowledgements

The authors thank the responsible staff for the CBERS Program and the Brazil Data Cube Project. Specifically, Ieda Sanches and Michel Chaves acknowledge the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) for her Research Productivity Fellowship and his Research Fellowship, respectively.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. For more details, see http://brazildatacube.org/

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) - Finance Code 001, under Research Fellowship 88887.351470/2019-00 (M.E.D.C.), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), under Research Productivity Fellowship (PQ - 303012/2018-8) (I.D.S.), and the Environmental Monitoring of Brazilian Biomes Project (Brazil Data Cube), funded by the Amazon Fund through the financial collaboration of the Brazilian Development Bank (BNDES) and the Foundation for Science, Technology and Space Applications (FUNCATE), Process 17.2.0536.1 (A.R.S. and J.G.F.)