The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B5-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 251–256, 2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 251–256, 2020

  24 Aug 2020

24 Aug 2020


I. D. Sanches1, R. Q. Feitosa2, B. Montibeller3, P. M. Achanccaray Diaz2, A. J. B. Luiz4, M. D. Soares2, V. H. R. Prudente1, D. C. Vieira1, L. E. P. Maurano1, P. N. Happ2, J. Chamorro2, and L. V. Oldoni1 I. D. Sanches et al.
  • 1National Institute for Space Research, São José dos Campos, SP, Brazil
  • 2Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil
  • 3University of Tartu, Tartu, Estonia
  • 4Brazilian Agricultural Research Corporation, Jaguariúna, SP, Brazil

Keywords: Optical Images, SAR Images, Tropical area, Crop Recognition, Random Forest, Fully Convolutional Recurrent Networks

Abstract. Applying remote sensing technology to map and monitor agriculture and its impacts can greatly contribute for the proper development of this activity, promoting efficient food, fiber and energy production. For that, not only remote sensing images are needed, but also ground truth information, which is a key factor for the development and improvement of methodologies using remote sensing data. While a variety of images are current available, inclusive cost-free images, field reference data is scarcer. For agricultural applications, especially in tropical regions such as Brazil, where the agriculture is very dynamic and diverse (recent agricultural frontiers, crop rotations, multiple cropping systems, several management practices, etc.), and cultivated over a vast territory, this task is not trivial. One way of boosting the researches in agricultural remote sensing is to stimulate people to share their data, and to foster different groups to use the same dataset, so distinct methods can be properly compared. In this context, our group created the LEM Benchmark Database (a project funded by the ISPRS Scientific Initiative project - 2017) from the Luiz Eduardo Magalhães (LEM) municipality, Bahia State, Brazil. The database contains a set of pre-processed multitemporal satellite images (Landsat-8/OLI, Sentinel-2/MSI and SAR band-C Sentinel-1) and shapefiles of agricultural fields with their correspondent monthly land use classes, covering the period of one Brazilian crop year (2017–2018). In this paper we present the first results obtained with this database.