Quantifying hail damage in crops using Sentinel-2 imagery

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DOI

https://doi.org/10.3390/rs14040951

Langue de publication
Anglais
Date
2022-02-16
Type
Article
Auteur(s)
  • Ha, Thuan
  • Shen, Yanben
  • Duddu, Hema
  • Johnson, Eric
  • Shirtliffe, Steven J.
Éditeur
MDPI

Résumé

Hailstorms are a frequent natural weather disaster in the Canadian Prairies that can cause catastrophic damage to field crops. Assessment of damage for insurance claims requires insurance inspectors to visit individual fields and estimate damage on individual plants. This study computes temporal profiles and estimates the severity of hail damage to crops in 54 fields through the temporal analysis of vegetation indices calculated from Sentinel-2 images. The damage estimation accuracy of eight vegetative indices in different temporal analyses of delta index (pre-and post-hail differences) or area under curve (AUC) index (time profiles of index affected by hail) was compared. Hail damage was accurately quantified by using the AUC of 32 days of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Water Index (NDWI), and Plant Senescence Radiation Index (PSRI). These metrics were well correlated with ground estimates of hail damage in canola (r = −0.90, RMSE = 8.24), wheat (r = −0.86, RMSE = 12.27), and lentil (r = 0.80, RMSE = 17.41). Thus, the time-series changes in vegetation indices had a good correlation with ground estimates of hail damage which may allow for more accurate assessment of the extent and severity of hail damage to crop land.

Sujet

  • Cultures,
  • Télédétection,
  • Temps (Météorologie)

Mots-clés

  • Crops--Effect of hail on,
  • Agriculture--Remote sensing,
  • Sentinel-2 (Artificial satellite),
  • Time-series analysis

Droits

Creative Commons Attribution 4.0 International (CC BY 4.0)

Pagination

1-17

Évalué par les pairs

Yes

Identifiants

ISSN
2072-4292

Article

Titre de la revue
Remote Sensing
Volume de la revue
14
Numéro de revue
4
Numéro de l'élément
951
Date d'acceptation
2022-02-15
Date de soumission
2022-01-19

Référence(s)

Ha, T., Shen, Y., Duddu, H., Johnson, E., & Shirtliffe, S. J. (2022). Quantifying hail damage in crops using Sentinel-2 imagery. Remote Sensing, 14(4), Article 951. https://doi.org/10.3390/rs14040951

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Collection(s)

Agricultural practices, equipment, and technology

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