Title
Campo Verde Database: Seeking to Improve Agricultural Remote Sensing of Tropical Areas
Date Issued
01 March 2018
Access level
metadata only access
Resource Type
journal article
Author(s)
Del'Arco Sanches I.
Feitosa R.Q.
Dias Soares M.
Barreto Luiz A.J.
Schultz B.
Pinheiro Maurano L.E.
University of Rio de Janeiro
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing, and crop rotation, makes agriculture highly dynamic. In this letter, we present the Campo Verde agricultural database. The purpose of creating and sharing these data is to foster advancement of remote sensing technology in areas of tropical agriculture, primarily the development and testing of methods for crop recognition and agricultural mapping. Campo Verde is a municipality of Mato Grosso state, localized in the Cerrado (Brazilian Savanna) biome, in central west Brazil. Soybean, maize, and cotton are the primary crops cultivated in this region. Double cropping systems are widely adopted in this area. There is also livestock and forestry production. Our database provides the land-use classes for 513 fields by month for one Brazilian crop year (between October 2015 and July 2016). This information was gathered during two field campaigns in Campo Verde (December 2015 and May 2016) and by visual interpretation of a time series of Landsat-8/Operational Land Imager (OLI) images using an experienced interpreter. A set of 14 preprocessed synthetic aperture radar Sentinel-1 and 15 Landsat-8/OLI mosaic images is also made available. It is important to promote the use of radar data for tropical agricultural applications, especially because the use of optical remote sensing in these regions is hindered by the high frequency of cloud cover. To demonstrate the utility of our database, results of an experiment conducted using the Sentinel-1 data set are presented.
Start page
369
End page
373
Volume
15
Issue
3
Language
English
OCDE Knowledge area
Agricultura
Subjects
Scopus EID
2-s2.0-85040918861
Source
IEEE Geoscience and Remote Sensing Letters
ISSN of the container
1545598X
Sponsor(s)
Manuscript received October 23, 2017; revised November 22, 2017; accepted December 29, 2017. Date of publication January 23, 2018; date of current version February 23, 2018. This work was supported by the “Science without Borders” CNPq/CAPES Program under Project 402.597/2012-5. (Corresponding author: Raul Queiroz Feitosa.) I. Del’Arco Sanches and L. E. Pinheiro Maurano are with the National Institute for Space Research, São José dos Campos 12227-010, Brazil (e-mail: ieda.sanches@inpe.br; luis.maurano@inpe.br).
Sources of information:
Directorio de Producción Científica
Scopus