Development of a Multi-Scale Tomato Yield Prediction Model in Azerbaijan Using Spectral Indices from Sentinel-2 Imagery

This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme “Support to Development of a Rural Business Information System (RBIS)”, and the performance of the updated crop-specific yield predi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-09, Vol.14 (17), p.4202
Main Authors: Psiroukis, Vasilis, Darra, Nicoleta, Kasimati, Aikaterini, Trojacek, Pavel, Hasanli, Gunay, Fountas, Spyros
Format: Article
Language:English
Subjects:
Agribusiness
Agricultural production
Agriculture
Azerbaijan
crop modelling
Crops
Decision making
Estimates
Farms
Food supply
Growing season
Growth models
Natural gas prices
Performance evaluation
Prediction models
processing tomato
Regression models
Remote sensing
Satellite imagery
Sentinel-2
Specific yield
Tomatoes
Vegetation
Vegetation index
yield prediction
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Summary:This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme “Support to Development of a Rural Business Information System (RBIS)”, and the performance of the updated crop-specific yield prediction models and their generated predictions at regional and national levels are presented. The model was built using Sentinel-2 satellite imagery to obtain cumulative values of six (6) selected vegetation indices (VIs). The data were collected on five (5) different dates for processing tomato fields in the Khachmaz region of Azerbaijan during summer 2021 (June to August) at 10- to 13-day intervals. In addition, a targeted field sampling campaign was conducted on selected Khachmaz pilot fields towards the end of the growing season to assess the potential of Sentinel-2 data to determine yield variability in tomato fields. Finally, actual recorded yields were collected at the field level to build the yield prediction regression model and evaluate its performance at different spatial scales, ranging from single field to national level, as well as under different data availability scenarios (number of consecutive Sentinel-2 images used). The results showed a high degree of correlation between all implemented VIs and processing tomato yield, with a coefficient of determination of up to 0.89 for the NDVI, providing valuable information for future estimates of tomato production across multiple spatial scales. The developed prediction model could also be used in the agri-food sector for national yield estimates to support policy and regulatory decisions at the national level.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14174202