The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina

[EN]In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by theArgentinian National Meteorological and Hydrological Services are based on field precipitation data, su...

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Authors: Salvia, Mercedes, Sánchez Martín, Nilda, Piles, María, Ruscica, Romina, González Zamora, Ángel, Roitberg, Esteban, Martínez Fernández, José
Format: article
Status:Published version
Publication Date:2021
Country:España
Institution:Universidad de Salamanca (USAL)
Repository:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/160555
Online Access:http://hdl.handle.net/10366/160555
Access Level:Open access
Keyword:Agricultural drought detection
Soil moisture agricultural drought index (SMADI)
Standardized precipitation evapotranspiration index (SPEI)
Standardized precipitation index (SPI)
Standardized soil moisture anomalies (SSMA)
2508.13 Humedad del Suelo
2506.16 Teledetección (Geología)
2509.01 Meteorología agrícola
id ES_346738564f8a9f57bbd39d0213abbafd
oai_identifier_str oai:gredos.usal.es:10366/160555
network_acronym_str ES
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repository_id_str
spelling The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in ArgentinaSalvia, MercedesSánchez Martín, NildaPiles, MaríaRuscica, RominaGonzález Zamora, ÁngelRoitberg, EstebanMartínez Fernández, JoséAgricultural drought detectionSoil moisture agricultural drought index (SMADI)Standardized precipitation evapotranspiration index (SPEI)Standardized precipitation index (SPI)Standardized soil moisture anomalies (SSMA)2508.13 Humedad del Suelo2506.16 Teledetección (Geología)2509.01 Meteorología agrícola[EN]In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by theArgentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). In this article, we explored the performance of the satellite-based soil moisture agricultural drought index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the standardized soil moisture anomalies (SSMA), SPI and SPEI (at one-month and three-month temporal scales), using the AgriculturalMinistry’s drought emergency database as a benchmark. The performances were analyzed in terms of the suitability of each index to be included in an early warning system for agricultural droughts, including true positive rate (TPR), and both false positive and false negative rates. In our experiments, SMADI showed the best overall performance, with the highest TPR and F1-score, and the second best false positive rate (FPR), positive predictive value, and overall accuracy. SMADI also showed the largest difference between TPR and FPR. SSMA showed the lowest FPR, but also the lowest TPR, making it not useful for an alert system. Furthermore, field precipitation-based indices, yet simple and widely used, showed not to be suitable indicators for detection of agricultural drought for Argentina, neither in the one-month nor in the three-month scale.Argentinean Agencia Nacional de Promoción Científica y Tecnológica Spanish Ministry of Science, Innovation and Universities Castilla y León Government European Regional Development FundIEEE202420242021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/160555reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)PICT 2017-1406ESP2017-89463-C3-3-RRTI2018-096765-A-100CLU-2018-04Attribution- 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1605552026-06-07T06:28:51Z
dc.title.none.fl_str_mv The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
title The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
spellingShingle The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
Salvia, Mercedes
Agricultural drought detection
Soil moisture agricultural drought index (SMADI)
Standardized precipitation evapotranspiration index (SPEI)
Standardized precipitation index (SPI)
Standardized soil moisture anomalies (SSMA)
2508.13 Humedad del Suelo
2506.16 Teledetección (Geología)
2509.01 Meteorología agrícola
title_short The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
title_full The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
title_fullStr The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
title_full_unstemmed The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
title_sort The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina
dc.creator.none.fl_str_mv Salvia, Mercedes
Sánchez Martín, Nilda
Piles, María
Ruscica, Romina
González Zamora, Ángel
Roitberg, Esteban
Martínez Fernández, José
author Salvia, Mercedes
author_facet Salvia, Mercedes
Sánchez Martín, Nilda
Piles, María
Ruscica, Romina
González Zamora, Ángel
Roitberg, Esteban
Martínez Fernández, José
author_role author
author2 Sánchez Martín, Nilda
Piles, María
Ruscica, Romina
González Zamora, Ángel
Roitberg, Esteban
Martínez Fernández, José
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Agricultural drought detection
Soil moisture agricultural drought index (SMADI)
Standardized precipitation evapotranspiration index (SPEI)
Standardized precipitation index (SPI)
Standardized soil moisture anomalies (SSMA)
2508.13 Humedad del Suelo
2506.16 Teledetección (Geología)
2509.01 Meteorología agrícola
topic Agricultural drought detection
Soil moisture agricultural drought index (SMADI)
Standardized precipitation evapotranspiration index (SPEI)
Standardized precipitation index (SPI)
Standardized soil moisture anomalies (SSMA)
2508.13 Humedad del Suelo
2506.16 Teledetección (Geología)
2509.01 Meteorología agrícola
description [EN]In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by theArgentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). In this article, we explored the performance of the satellite-based soil moisture agricultural drought index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the standardized soil moisture anomalies (SSMA), SPI and SPEI (at one-month and three-month temporal scales), using the AgriculturalMinistry’s drought emergency database as a benchmark. The performances were analyzed in terms of the suitability of each index to be included in an early warning system for agricultural droughts, including true positive rate (TPR), and both false positive and false negative rates. In our experiments, SMADI showed the best overall performance, with the highest TPR and F1-score, and the second best false positive rate (FPR), positive predictive value, and overall accuracy. SMADI also showed the largest difference between TPR and FPR. SSMA showed the lowest FPR, but also the lowest TPR, making it not useful for an alert system. Furthermore, field precipitation-based indices, yet simple and widely used, showed not to be suitable indicators for detection of agricultural drought for Argentina, neither in the one-month nor in the three-month scale.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/160555
url http://hdl.handle.net/10366/160555
dc.relation.none.fl_str_mv PICT 2017-1406
ESP2017-89463-C3-3-R
RTI2018-096765-A-100
CLU-2018-04
dc.rights.none.fl_str_mv Attribution- 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution- 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405815467671552
score 15.812429

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