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A B S T R A C TRemote sensing applications in agriculture are presented as a very promising reality, but research is still needed for the correct use of spectral data. The objective of this study was to evaluate the spectral-temporal patterns of eleven wheat cultivars (Triticum aestivum L.). The experiment was conducted in Cascavel, PR, in the year 2014. With the help of the GreenSeeker and FieldSpec 4 terrestrial sensors, spectral signatures were determined and the temporal profiles of the Normalized Difference Vegetation Index (NDVI) were created. Statistical differences between wheat cultivars were analysed using analysis of variance (ANOVA) and Scott-Knott test. Grain yields obtained with INSEY (In-Season Estimate of Yield) factors were correlated. NDVI normalized by degree-days accumulated from the Feekes growth stages 2 and 8 showed to be more consistent in the estimation of grain yield, explaining approximately 70% of the variation. At the Feekes stage 10.1, wheat cultivars presented different spectral patterns in the near and medium infrared bands. This suggests that these spectral bands can be used to differentiate wheat cultivars.

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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.

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