@Article{CarvalhoUSAMSCM:2020:DrMoBa,
author = "Carvalho, Mairon {\^A}nderson Cordeiro Correa de and Uliana,
Eduardo Morgan and Silva, Demetrius David da and Aires, Uilson
Ricardo Ven{\^a}ncio and Martins, Camila Aparecida da Silva and
Sousa Junior, Marionei Fomaca de and Cruz, Ibraim Fantin da and
Mendes, M{\'u}cio Andr{\'e} dos Santos Alves",
affiliation = "{Universidade Federal de Mato Grosso (UFMT)} and {Universidade
Federal de Mato Grosso (UFMT)} and {Universidade Federal de
Vi{\c{c}}osa (UFV)} and {Universidade Federal de Vi{\c{c}}osa
(UFV)} and {Universidade Federal do Esp{\'{\i}}rito Santo
(UFES)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal de Mato Grosso (UFMT)} and {Universidade
Federal de Mato Grosso (UFMT)}",
title = "Drought Monitoring Based on Remote Sensing in a Grain-Producing
Region in the Cerrado-Amazon Transition, Brazil",
journal = "Water",
year = "2020",
volume = "12",
number = "12",
pages = "e3366",
keywords = "agricultural planning, soybean, climate risk, natural disaster,
water resource management.",
abstract = "Drought is a natural disaster that affects a countrys economy and
food security. The monitoring of droughts assists in planning
assertive actions to mitigate the resulting environmental and
economic impacts. This work aimed to evaluate the performance of
the standardized precipitation index (SPI) using rainfall data
estimated by orbital remote sensing in the monitoring of
meteorological drought in the CerradoAmazon transition region,
Brazil. Historical series from 34 rain gauge stations, in addition
to indirect measurements of monthly precipitation obtained by
remote sensing using the products CHIRPS-2.0, PERSIANN-CDR,
PERSIANN-CCS, PERSIANN, GPM-3IMERGMv6, and GPM-3IMERGDLv6, were
used in this study. Drought events detected by SPI were related to
a reduction in soybean production. The SPI calculated from the
historical rain series estimated by remote sensing allowed
monitoring droughts, enabling a high detailing of the spatial
variability of droughts in the region, mainly during the soybean
development cycle. Indirect precipitation measures associated with
SPI that have adequate performance for detecting droughts in the
study region were PERSIANN-CCS (January), CHIRPS-2.0 (February and
November), and GPM-3IMERGMv6 (March, September, and December). The
SPI and the use of precipitation data estimated by remote sensing
are effective for characterizing and monitoring meteorological
drought in the study region.",
doi = "10.3390/w12123366",
url = "http://dx.doi.org/10.3390/w12123366",
issn = "2073-4441",
label = "lattes: 7771542353189747 6 CarvalhoUSAMSCM:2020:DrMoBa",
language = "en",
targetfile = "carvalho_drought.pdf",
urlaccessdate = "11 maio 2024"
}