Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil
Drought is a natural disaster that affects a country’s 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) u...
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doaj-04ed7f4a3d024006af0294d5d9ed31412020-12-01T00:04:30ZengMDPI AGWater2073-44412020-11-01123366336610.3390/w12123366Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, BrazilMairon Ânderson Cordeiro Correa de Carvalho0Eduardo Morgan Uliana1Demetrius David da Silva2Uilson Ricardo Venâncio Aires3Camila Aparecida da Silva Martins4Marionei Fomaca de Sousa Junior5Ibraim Fantin da Cruz6Múcio André dos Santos Alves Mendes7Programa de Pós-Graduação em Recursos Hídricos, Universidade Federal de Mato Grosso, Cuiabá 78060-900, BrazilInstituto de Ciências Agrárias e Ambientais, Universidade Federal de Mato Grosso, Sinop 78557-267, BrazilDepartamento de Engenharia Agrícola, Universidade Federal de Viçosa, Viçosa 36570-900, BrazilPrograma de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil;Departamento de Engenharia Rural, Universidade Federal do Espírito Santo, Alegre 29500-000, BrazilPrograma de Pós-Graduação em Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais, São José dos Campos 12227-010, BrazilDepartamento de Engenharia Sanitária e Ambiental, Universidade Federal de Mato Grosso, Cuiabá 78060-900, BrazilInstituto de Ciências Agrárias e Ambientais, Universidade Federal de Mato Grosso, Sinop 78557-267, BrazilDrought is a natural disaster that affects a country’s 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 Cerrado–Amazon 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.https://www.mdpi.com/2073-4441/12/12/3366agricultural planningsoybeanclimate risknatural disasterwater resource management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mairon Ânderson Cordeiro Correa de Carvalho Eduardo Morgan Uliana Demetrius David da Silva Uilson Ricardo Venâncio Aires Camila Aparecida da Silva Martins Marionei Fomaca de Sousa Junior Ibraim Fantin da Cruz Múcio André dos Santos Alves Mendes |
spellingShingle |
Mairon Ânderson Cordeiro Correa de Carvalho Eduardo Morgan Uliana Demetrius David da Silva Uilson Ricardo Venâncio Aires Camila Aparecida da Silva Martins Marionei Fomaca de Sousa Junior Ibraim Fantin da Cruz Múcio André dos Santos Alves Mendes Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil Water agricultural planning soybean climate risk natural disaster water resource management |
author_facet |
Mairon Ânderson Cordeiro Correa de Carvalho Eduardo Morgan Uliana Demetrius David da Silva Uilson Ricardo Venâncio Aires Camila Aparecida da Silva Martins Marionei Fomaca de Sousa Junior Ibraim Fantin da Cruz Múcio André dos Santos Alves Mendes |
author_sort |
Mairon Ânderson Cordeiro Correa de Carvalho |
title |
Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil |
title_short |
Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil |
title_full |
Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil |
title_fullStr |
Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil |
title_full_unstemmed |
Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil |
title_sort |
drought monitoring based on remote sensing in a grain-producing region in the cerrado–amazon transition, brazil |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2020-11-01 |
description |
Drought is a natural disaster that affects a country’s 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 Cerrado–Amazon 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. |
topic |
agricultural planning soybean climate risk natural disaster water resource management |
url |
https://www.mdpi.com/2073-4441/12/12/3366 |
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