Drought assessment in paddy rice fields using remote sensing technology towards achieving food security and SDG2
Bibliographic Data
| ID | 21295840 |
|---|---|
| Authors | Hadi Shams Esfandabadi (0000-0002-7005-1204, Islamic Azad University, South Tehran Branch Department of Geomatics Engineering), Mohsen Ghamary Asl (0000-0001-5586-5085, Islamic Azad University, South Tehran Branch Department of Geomatics Engineering), Zahra Shams Esfandabadi (0000-0001-9320-4712, Politecnico di Torino Department of Environment, Land and Infrastructure Engineering (DIATI)), Sneha Gautam (0000-0002-2978-844X, Karunya University), Meisam Ranjbari (0000-0002-2666-9154, University of Turin Department of Economics and Statistics “Cognetti de Martiis”) |
| Year | 2022 |
| Volume | 124 |
| Issue | 12 |
| Pages | 4219-4233 |
| Publication date | 2022-11-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | British Food Journal (JOURNAL) |
| Journal identifiers | ISSN: 0007-070X • E-ISSN: 1758-4108 |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/bfj-08-2021-0872 |
| OpenAlex | W4205608476 |
| Language | EN |
| References cited | 54 |
Purpose This research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield. Design/methodology/approach A three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data. Findings Rice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCImin and VCImin are found to be the most proper indices to predict rice yield. Practical implications The results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances. Originality/value This study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI
Agricultural economics · Agricultural engineering · Agricultural science · Agriculture · Agronomy · Business · Economics · Enhanced vegetation index · Food security · Geography · Index (typography) · Leaf area index · Normalized Difference Vegetation Index · Paddy field · Vegetation (pathology) · Vegetation Index · Yield (engineering) · Climate change impacts on agriculture · Computer Science · Engineering · Environmental Science · Remote Sensing in Agriculture · Rice Cultivation and Yield Improvement
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| Citation velocity | historical |
|---|---|
| Highly cited | No |