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...
| Autores: | , , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2021 |
| País: | España |
| Recursos: | Universidad de Salamanca (USAL) |
| Repositório: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/160555 |
| Acesso em linha: | http://hdl.handle.net/10366/160555 |
| Access Level: | Acceso aberto |
| Palavra-chave: | 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 |
| Resumo: | [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. |
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