ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-3/W2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3/W2-2020, 53–58, 2020
https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-53-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3/W2-2020, 53–58, 2020
https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-53-2020

  29 Oct 2020

29 Oct 2020

EVALUATION OF THE SOIL MOISTURE AGRICULTURAL DROUGHT INDEX (SMADI) AND PRECIPITATION-BASED DROUGHT INDICES IN ARGENTINA

M. M. Salvia1, N. Sánchez2, M. Piles3, A. Gonzalez-Zamora2, and J. Martínez-Fernández2 M. M. Salvia et al.
  • 1Quantitative Remote Sensing Group, Instituto de Astronomía y Física del Espacio (IAFE, UBA/CONICET), Intendente Güiraldes 2160, Ciudad Universitaria, C1428EGA, CABA, Argentina
  • 2Instituto Hispano Luso de Investigaciones Agrarias (CIALE). University of Salamanca. Duero 12, 37185 Villamayor, Salamanca, Spain
  • 3Image Processing Laboratory, IPL, Universitat de València, 46980 Valencia, Spain

Keywords: soil moisture, precipitation, agricultural drought, SMOS, SMADI, SPI, SPEI

Abstract. Agricultural drought is one of the most critical hazards with regard to intensity, severity, frequency, spatial extension and impact on livelihoods. This is especially true for Argentina, where agricultural exports can represent up to 10% of gross domestic product (GDP), and where drought events for 2018 led to a decrease of nearly 0.5% of GDP. In this work, we investigate the applicability of the Soil Moisture Agricultural Drought Index (SMADI) for detection of droughts in Argentina, and compare its performance with the use of two well-known precipitation-based indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation- Evaporation Index (SPEI). SMADI includes satellite-based information of soil moisture, surface temperature and vegetation greenness, and was designed to capture the hydric stress on the soil-vegetation ensemble. Results indicate that SMADI has greater capabilities for agricultural drought detection than SPI and SPEI: it was able to recognize more than 83% of the registered emergencies, correctly classifying 75% of them as extreme droughts, and outperforming SPI and SPEI in all the analyzed metrics.