@Article{BreunigGDDPSDC:2020:DeMaZo,
author = "Breunig, F{\'a}bio Marcelo and Galv{\~a}o, L{\^e}nio Soares and
Dalagnol da Silva, Ricardo and Dauve, Carlos Eduardo and Parraga,
Adriane and Santi, Ant{\^o}nio Luiz and Della Flora, Diandra
Pinto and Chen, Shuisen",
affiliation = "{Universidade Federal de Santa Maria (UFSM)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Fazenda Vila Morena} and
{Universidade Estadual do Rio Grande do Sul (UERGRS)} and
{Universidade Federal de Santa Maria (UFSM)} and {Universidade
Federal da Grande Dourados (UFGD)} and {Guangzhou Institute of
Geography}",
title = "Delineation of management zones in agricultural fields using
cover–crop biomass estimates from PlanetScope data",
journal = "International Journal of Applied Earth Observation and
Geoinformation",
year = "2020",
volume = "85",
pages = "e102004",
month = "Mar.",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura
sustent{\'a}vel}",
keywords = "Precision agriculture, Remote sensing, Biomass, Satellite, Machine
learning, Crop yield.",
abstract = "Several methods have been proposed to delineate management zones
in agricultural fields, which can guide interventions of the
farmers to increase crop yield. In this study, we propose a new
approach using remote sensing data to delineate management zones
at three farm sites located in southern Brazil. The approach is
based on the hypothesis that the measured aboveground biomass
(AGB) of the cover crops is correlated with the measured cash-crop
yield and can be estimated from surface reflectance and/or
vegetation indices (VIs). Therefore, we used seven different
statistical models to estimate AGB of three cover crops (forage
turnip, white oats, and rye) in the season prior to cash-crop
planting. Surface reflectance and VIs were used as predictors to
test the performance of the models. They were obtained from high
spatial and temporal resolution data of the PlanetScope (PS)
constellation of satellites. From the time series of 30 images
acquired in 2017, we used the PS data that matched the dates of
the field campaigns to build the models. The results showed that
the satellite AGB estimates of the cover crops at the date of
maximum VI response at the beginning of the flowering stage were
useful to delineate the management zones. The cover-crop AGB
models that presented the highest coefficient of determination
(R-2) and the lowest root mean square (RMSE) in the validation and
test datasets were Support Vector Machine (SVM), Cubist (CUB) and
Stochastic Gradient Boosting (SGB). For most models and cover
crops, the Enhanced Vegetation Index (EVI) and the Normalized
Difference Vegetation Index (NDVI) were the two most important AGB
predictors. At the date of maximum VI at the beginning of the
flowering stage, the correlation coefficients (r) between the
cover-crop AGB and the cash-crop yield (soybean and maize) ranged
from +0.70 for forage turnip to +0.78 for rye. The fuzzy
unsupervised classification of the cover-crop AGB estimates
delineated two management zones, which were spatially consistent
with those obtained from cash-crop yield. The comparison between
both maps produced overall accuracies that ranged from 61.20% to
68.25% with zone 2 having higher cover-crop AGB and cash-crop
yield than zone 1 over the three sites. We conclude that satellite
AGB estimates of cover crops can be used as a proxy for generating
management zone maps in agricultural fields. These maps can be
further refined in the field with any other type of method and
data, whenever necessary.",
doi = "10.1016/j.jag.2019.102004",
url = "http://dx.doi.org/10.1016/j.jag.2019.102004",
issn = "0303-2434",
language = "en",
targetfile = "breunig_delineation.pdf",
urlaccessdate = "20 set. 2024"
}