Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time series

Authors

  • V. Egea-Cobrero Universidad de Sevilla
  • V.F. Rodríguez-Galiano Universidad de Sevilla https://orcid.org/0000-0002-5422-8305
  • E. Sánchez-Rodríguez Universidad de Sevilla
  • M.A. García-Pérez Universidad de Sevilla

DOI:

https://doi.org/10.4995/raet.2018.8891

Keywords:

remote sensing, MTCI, model, wheat, yield, time series

Abstract

There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP"”Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC"”Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R2 =0.81 and R2 =0.57, respectively).

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Author Biographies

V. Egea-Cobrero, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

V.F. Rodríguez-Galiano, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

E. Sánchez-Rodríguez, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

M.A. García-Pérez, Universidad de Sevilla

Geografía Física y Análisis Geográfico Regional

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Published

2018-06-29

Issue

Section

Research articles