Abstract
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.
Highlights
Brazil is a world leader in food production, and agriculture is historically one of the principal bases of the country’s economy, providing food, fiber and sugarcane ethanol
Iii) Based on information collected in situ, together with optical remote sensing time series images (Sentinel-2/Multi Spectral Instrument (MSI) and LANDSAT-8/Operational Land Imager (OLI)) and NDVI profiles (MODIS/TERRA), an experienced interpreter created monthly field references maps covering one Brazilian crop year (June 2017 – June 2018)
His work hypothesis is that the data generated by the Multi Spectral Instrument (MSI)/Sentinel-2, the Operational Land Imager (OLI)/Landsat-8 and the Wide-Field Imager (WFI)/CBERS-4 sensors can be integrated in the same time series after harmonization procedures, and that the higher frequency of observations can be used to improve the classification of crop types and yield estimation
Summary
Brazil is a world leader in food production, and agriculture is historically one of the principal bases of the country’s economy, providing food, fiber and sugarcane ethanol. The great territorial extension of Brazil, the regional diversity of physical aspects such as climate, soil, vegetation cover and water availability, and the dynamism of Brazilian agriculture (multiple harvests, new agricultural frontiers, diversity of management practices, etc.), represent a challenge for monitoring the agricultural activity (Formaggio and Sanches, 2017) In this scenario, remote sensing can be very useful, since it provides a synoptic view of the surface and repeatability of covering (systematic monitoring) throughout the development of the entire crops’ cycles along the whole crop year. The lack of ground truth samples to train and test methodologies is a major obstacle for the successful use of remote sensing, especially for agricultural applications (targets are dynamic – Sanches et al, 2019) This task can be time consuming and costly and demands the intervention of agricultural specialists for the field data collection. The strengths of LEM database are threefold: i) it provides information from an important tropical agricultural area (West of Bahia State, Brazil); ii) the reference data covers an entire crop year 2017/2018 (monthly land use classes are provided); iii) it is a free available database
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
