Quantifying hail damage in crops using Sentinel-2 imagery
Quantifying hail damage in crops using Sentinel-2 imagery
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Full item details
- creativework.keywords - en
- Crops--Effect of hail on
- Agriculture--Remote sensing
- Sentinel-2 (Artificial satellite)
- Time-series analysis
- creativework.keywords - fr
- Cultures--Effets de la grêle sur
- Agriculture--Télédétection
- Sentinel-2 (Satellites artificiels)
- Série chronologique
- dc.contributor.author
- Ha, Thuan
- Shen, Yanben
- Duddu, Hema
- Johnson, Eric
- Shirtliffe, Steven J.
- dc.date.accepted
- 2022-02-15
- dc.date.accessioned
- 2025-01-24T14:25:38Z
- dc.date.available
- 2025-01-24T14:25:38Z
- dc.date.issued
- 2022-02-16
- dc.date.submitted
- 2022-01-19
- dc.description.abstract - en
- 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.
- dc.identifier.citation
- 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
- dc.identifier.doi
- https://doi.org/10.3390/rs14040951
- dc.identifier.issn
- 2072-4292
- dc.identifier.uri
- https://open-science.canada.ca/handle/123456789/3343
- dc.language.iso
- en
- dc.publisher - en
- MDPI
- dc.rights - en
- Creative Commons Attribution 4.0 International (CC BY 4.0)
- dc.rights - fr
- Creative Commons Attribution 4.0 International (CC BY 4.0)
- dc.rights.uri - en
- https://creativecommons.org/licenses/by/4.0/
- dc.rights.uri - fr
- https://creativecommons.org/licenses/by/4.0/deed.fr
- dc.subject - en
- Crops
- Remote sensing
- Weather
- dc.subject - fr
- Cultures
- Télédétection
- Temps (Météorologie)
- dc.subject.en - en
- Crops
- Remote sensing
- Weather
- dc.subject.fr - fr
- Cultures
- Télédétection
- Temps (Météorologie)
- dc.title - en
- Quantifying hail damage in crops using Sentinel-2 imagery
- dc.type - en
- Article
- dc.type - fr
- Article
- local.acceptedmanuscript.articlenum
- 951
- local.article.journalissue
- 4
- local.article.journaltitle - en
- Remote Sensing
- local.article.journalvolume
- 14
- local.pagination
- 1-17
- local.peerreview - en
- Yes
- local.peerreview - fr
- Oui
- local.requestdoi
- No
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