@Article{OldoniSanPicCovFro:2020:AgReSe,
author = "Oldoni, Lucas Volochen and Sanches, Ieda Del'Arco and Picoli,
Michelle Cristina Ara{\'u}jo and Covre, Renan Moreira and Fronza,
Jos{\'e} Guilherme",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "LEM+ dataset: for agricultural remote sensing applications",
journal = "Data in Brief",
year = "2020",
volume = "33",
pages = "e106553",
keywords = "Field reference data, Time series analysis, Remote sensing, Double
crop system, Tropical agriculture.",
abstract = "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 Lu{\'{\i}}s Eduardo Magalh{\~a}es (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.",
doi = "10.1016/j.dib.2020.106553",
url = "http://dx.doi.org/10.1016/j.dib.2020.106553",
issn = "2352-3409",
label = "lattes: 2456184661855977 2 OldoniSanPicCovFro:2020:AgReSe",
language = "en",
targetfile = "oldoni_lem.pdf",
urlaccessdate = "28 abr. 2024"
}