Drought assessment in paddy rice fields using remote sensing technology towards achieving food security and SDG2

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.

Automated summary

This research focuses on monitoring vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and proposes the best index to predict rice yield. The study applies a three-step methodology and provides maps of rice paddies and vegetation indices. The findings suggest that the Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), as well as VHI combining TCImin and VCImin, are the most suitable indices for predicting rice yield. The results have practical implications for sustainable agriculture, food safety, and rice market management. The study also contributes to mapping vegetation indices for rice paddies in northern Iran and proposing different calculation methods for VHI to describe rice yield.