Abstract
Abstract. The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.
Highlights
IntroductionBenchmarks are important to make different approaches comparable so that promising strategies can be identified
Benchmarks are important to make different approaches comparable so that promising strategies can be identified.Fostering the creation of public test beds for new algorithms is a major ISRPS strategic policy (Chen et al, 2016)
This paper presents the results of an experiment, which explores part of the data available in Luís Eduardo Magalhães (LEM) database (C-band Sentinel-1 and Sentinel2/MSI images) for crop mapping
Summary
Benchmarks are important to make different approaches comparable so that promising strategies can be identified. Fostering the creation of public test beds for new algorithms is a major ISRPS strategic policy (Chen et al, 2016). Some benchmark datasets have been delivered with ISPRS support (e.g., Nex et al, 2015; Rottensteiner et al, 2014). Several subsequent works were carried out on the basis of such datasets. Most of them refer to urban areas. There is only one public multi-temporal, multi-sensor dataset devoted to the assessment of automatic methods for agricultural land-cover classification (Sanches et al, 2018)
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