@Article{PicoliMaDuScCoHeRo:2019:SuDrDe,
author = "Picoli, Michelle Cristina Ara{\'u}jo and Machado, Pedro Gerber
and Duft, Daniel Garbellini and Scarpare, F{\'a}bio Vale and
Corr{\^e}a, Simone Toni Ruiz and Hernandes, Thayse Aparecida
Dourado and Rocha, Jansle Vieira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade de S{\~a}o Paulo (USP)} and {Universidade de
S{\~a}o Paulo (USP)} and {Washington State University} and
{Universidade de S{\~a}o Paulo (USP)} and {Laborat{\'o}rio
Nacional de Ci{\^e}ncia e Tecnologia do Bioetanal (CTBE)} and
{Universidade Estadual de Campinas (UNICAMP)}",
title = "Sugarcane drought detection through spectral indices derived
modeling by remote\‑sensing techniques",
journal = "Modeling Earth Systems and Environment",
year = "2019",
volume = "5",
pages = "1679--1688",
keywords = "Drought stress · Climatological soil–water balance (CSWB) ·
Monitoring · Satellite images.",
abstract = "Several indices based on satellite images have been explored to
monitor agricultural drought. Despite the existence of some
drought indices, no drought monitoring system for sugarcane
exists. In this sense, drought detection could be useful tool to
quantify losses and help with action plans. This study
investigates the Landsat image potential for sugarcane drought
detection by assessing the relationship between vegetation and
agricultural drought indices (normalized diference vegetation
index (NDVI), vegetation condition index (VCI), normalized
diference water index (NDWI), global vegetation moisture index
(GVMI), and normalized diference infrared index (NDII)). Two new
indices combining near-infrared (NIR) and short-wave infrared
(SWIR) bands are proposed for sugarcane drought detection. All
indices were individually and collectively compared with soil
water defcit and water surplus, simulated by the climatological
soilwater balance (CSWB) model. A signifcant correlation between
spectral indices and water balance results, specifcally for NDVI
and VCI indices (~30%), was observed. The drought detection system
identifcation was developed by cluster analysis classifying the
pixels into three distinct groups (drought, intermediate drought,
and non-drought) to later be used in the discriminant analysis.
This methodology showed to have an accuracy rate of 65%. However,
the discriminant analysis approach was better suited for sugarcane
drought monitoring when compared with individual spectral
indices.",
doi = "10.1007/s40808-019-00619-6",
url = "http://dx.doi.org/10.1007/s40808-019-00619-6",
issn = "2363-6203",
language = "en",
targetfile = "picoli_sugarcane.pdf",
urlaccessdate = "18 abr. 2024"
}