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Hail disaster remote sensing monitoring model based on a multilevel grid and risk assessment
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DOI：10.1080/01431161.2026.2648098.png)
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Abstract
En 中文
Hail has occurred frequently and has been the main meteorological disaster causing significant losses to local agricultural production in Honghe since 1961. The purpose was to accurately obtain the spatial distribution and periodic variation characteristics of hail disasters and subsequently reduce the losses caused by hail disasters. The monthly cumulative hail count and daily hail event observation data from 125 meteorological stations in Yunnan Province from 1961 to 2022, hail records, disaster investigation data, meteorological data from 218 hail prevention operation stations, and corresponding multi-source remote sensing satellite data from 2009 to 2022 were used. The Ross-Li model was used to normalize the multi-source remote sensing satellite data, and the spatial-temporal adaptive reflectance fusion model was used to reconstruct the satellite remote sensing data. We propose a hail remote sensing monitoring model based on a multilevel grid and the hail identification index RNDVI_M. The model was applied to monitor the range of hail disasters, with a maximum relative error of 9.08%, average error of 5.62%, and standard deviation of 1.66%. Spatial overlay analysis and spatial correlation analysis were used to quantitatively analyse hail frequency in different disaster-prone environments, such as landform types, terrain undulations, slopes, and terrain types at the level of cultivated land plots. A hail disaster risk assessment model was proposed to calculate the hail risk spatial distribution characteristics, which can be used for the rational adjustment of crop planting structures and the layout of artificial hail suppression operation stations. The wavelet power spectrum analysis method was used to analyse the periodic variation characteristics of hail; its primary period was approximately 4 years, and the sub-period was approximately 22 years. The results are highly important for the rational arrangement of agricultural production and prevention of hail disasters.
Keywords:
Hail disaster remote sensing monitoring
hail disaster risk assessment
hail identification index RNDVI_M
multilevel grid
wavelet power spectrum analysis
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