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Theoretical and Applied Climatology

, Volume 136, Issue 1–2, pp 203–220 | Cite as

Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices

  • Andreia F. S. RibeiroEmail author
  • Ana Russo
  • Célia M. Gouveia
  • Patrícia Páscoa
Original Paper

Abstract

The response of two rainfed winter cereal yields (wheat and barley) to drought conditions in the Iberian Peninsula (IP) was investigated for a long period (1986–2012). Drought hazard was evaluated based on the multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) and three remote sensing indices, namely the Vegetation Condition (VCI), the Temperature Condition (TCI), and the Vegetation Health (VHI) Indices. A correlation analysis between the yield and the drought indicators was conducted, and multiple linear regression (MLR) and artificial neural network (ANN) models were established to estimate yield at the regional level. The correlation values suggested that yield reduces with moisture depletion (low values of VCI) during early-spring and with too high temperatures (low values of TCI) close to the harvest time. Generally, all drought indicators displayed greatest influence during the plant stages in which the crop is photosynthetically more active (spring and summer), rather than the earlier moments of plants life cycle (autumn/winter). Our results suggested that SPEI is more relevant in the southern sector of the IP, while remote sensing indices are rather good in estimating cereal yield in the northern sector of the IP. The strength of the statistical relationships found by MLR and ANN methods is quite similar, with some improvements found by the ANN. A great number of true positives (hits) of occurrence of yield-losses exhibiting hit rate (HR) values higher than 69% was obtained.

Notes

Acknowledgements

Ana Russo and Andreia Ribeiro thank FCT for grants SFRH/BPD/99757/2014 and PD/BD/114481/2016, respectively. The authors are also sincerely thankful to Carlos C. DaCamara (Instituto Dom Luiz) for his valuable suggestions and support, and to two anonymous reviewers for their constructive comments.

Funding information

This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014).

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto Dom LuízFaculdade de Ciências da Universidade de LisboaLisbonPortugal
  2. 2.Instituto Português do Mar e da AtmosferaLisbonPortugal

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