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Wheat yield estimation from NDVI and regional climate models in Latvia

Astrid Vannoppen, Anne Gobin, Lola Kotova, Sara Top (UGent) , Lesley De Cruz (UGent) , Andris Vīksna, Svetlana Aniskevich, Leonid Bobylev, Lars Buntemeyer, Steven Caluwaerts (UGent) , et al.
(2020) REMOTE SENSING. 12(14).
Author
Organization
Abstract
Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.
Keywords
General Earth and Planetary Sciences, yield estimation, NDVI, regional climate model, winter wheat, spring wheat, Latvia, PROBA-V, ALARO-0, REMO, weather impact, EUROPEAN WHEAT, VEGETATION, TEMPERATURE, VALIDATION, PREDICTION, STRESS

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Citation

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MLA
Vannoppen, Astrid, et al. “Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia.” REMOTE SENSING, vol. 12, no. 14, 2020, doi:10.3390/rs12142206.
APA
Vannoppen, A., Gobin, A., Kotova, L., Top, S., De Cruz, L., Vīksna, A., … Termonia, P. (2020). Wheat yield estimation from NDVI and regional climate models in Latvia. REMOTE SENSING, 12(14). https://doi.org/10.3390/rs12142206
Chicago author-date
Vannoppen, Astrid, Anne Gobin, Lola Kotova, Sara Top, Lesley De Cruz, Andris Vīksna, Svetlana Aniskevich, et al. 2020. “Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia.” REMOTE SENSING 12 (14). https://doi.org/10.3390/rs12142206.
Chicago author-date (all authors)
Vannoppen, Astrid, Anne Gobin, Lola Kotova, Sara Top, Lesley De Cruz, Andris Vīksna, Svetlana Aniskevich, Leonid Bobylev, Lars Buntemeyer, Steven Caluwaerts, Rozemien De Troch, Natalia Gnatiuk, Rafiq Hamdi, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, and Piet Termonia. 2020. “Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia.” REMOTE SENSING 12 (14). doi:10.3390/rs12142206.
Vancouver
1.
Vannoppen A, Gobin A, Kotova L, Top S, De Cruz L, Vīksna A, et al. Wheat yield estimation from NDVI and regional climate models in Latvia. REMOTE SENSING. 2020;12(14).
IEEE
[1]
A. Vannoppen et al., “Wheat yield estimation from NDVI and regional climate models in Latvia,” REMOTE SENSING, vol. 12, no. 14, 2020.
@article{8670277,
  abstract     = {Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.},
  articleno    = {2206},
  author       = {Vannoppen, Astrid and Gobin, Anne and Kotova, Lola and Top, Sara and De Cruz, Lesley and Vīksna, Andris and Aniskevich, Svetlana and Bobylev, Leonid and Buntemeyer, Lars and Caluwaerts, Steven and De Troch, Rozemien and Gnatiuk, Natalia and Hamdi, Rafiq and Reca Remedio, Armelle and Sakalli, Abdulla and Van De Vyver, Hans and Van Schaeybroeck, Bert and Termonia, Piet},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keywords     = {General Earth and Planetary Sciences,yield estimation,NDVI,regional climate model,winter wheat,spring wheat,Latvia,PROBA-V,ALARO-0,REMO,weather impact,EUROPEAN WHEAT,VEGETATION,TEMPERATURE,VALIDATION,PREDICTION,STRESS},
  language     = {eng},
  number       = {14},
  pages        = {20},
  title        = {Wheat yield estimation from NDVI and regional climate models in Latvia},
  url          = {http://dx.doi.org/10.3390/rs12142206},
  volume       = {12},
  year         = {2020},
}
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