LEM+ dataset: For agricultural remote sensing applications

Remote sensing allows obtaining information on agriculture regularly with non-invasive measurement approaches. Field data is crucial for adequate agricultural monitoring by remote sensing. However, public available field data are scarce, mainly in tropical regions, where agriculture is highly dynamic. The present publication aims to support the reduction of this gap. The LEM+ dataset provides information monthly about 16 land use classes for 1854 fields from October 2019 to September 2020 (one Brazilian agricultural year) from Luís Eduardo Magalhães (LEM) and other municipalities in the west of Bahia state, Brazil. The reference data were collected in two fieldworks (March 2020 - first crop season, and August 2020 - second crop season). The boundaries of the fields visited in situ were delimited using Sentinel-2 false color compositions (near infrared - red - green) at 10 m spatial resolution. The land use classes were labeled monthly based on information collected in situ (agricultural land use and photographs) and by visual interpretation of Sentinel-2 false color composition (near infrared - shortwave infrared - red) and MODIS/Terra (Normalized Difference Vegetation Index) time series. The dataset can be useful for the development of new pattern recognition methods for agricultural land use mapping and monitoring, comparison of different classification methods, and optical and SAR remote sensing time series analysis. This dataset contributes to complement previous initiatives [1], [2] to make tropical agriculture field reference data publicly available.