4.7 Article

Estimating Farm Wheat Yields from NDVI and Meteorological Data

期刊

AGRONOMY-BASEL
卷 11, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/agronomy11050946

关键词

yield estimation; NDVI; winter wheat; Triticum aestivum; Belgium; weather impact

资金

  1. European Union's Horizon 2020 Research and Innovation Programme [818346]
  2. H2020 Societal Challenges Programme [818346] Funding Source: H2020 Societal Challenges Programme

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Models based on NDVI for winter wheat yield demonstrate that crop yield is more influenced by environmental factors rather than vegetation indices themselves.
Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R-2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

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