A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Hailstorms have increased in frequency and intensity over the past decade causing substantial losses agriculture. Since hailstorms often hit a wide area with no detectable pattern, relying on traditional field-based methods to assess crop damage is difficult. The aim of this study was develop test new four-stage normalized vegetation index approach for detecting severity hailstorm sugarcane plants large estate south eastern Zimbabwe. following six spectral indices were computed period before after event extent sugarcane: Green Chlorophyll Index (GCI); Normalized Difference Vegetation (NDVI); Senescent (NDSVI); Red Edge (RECI); Tillage (NDTI); Modified Soil Adjusted (MSAVI2). Then, differences each separately difference maps are reported as delta (Δ) indices. results show that within one week even two weeks hailstorm, ΔNDTI, ΔNDVI ΔRECI consistently able detect characterise damage. When used partial least squares-discriminant analysis (PLS-DA), ΔNDTI performed best mapping throughout estate. discriminate three different levels an overall accuracy 90% Kappa value 0.85. Combined these imply using multi-spectral datasets fortnight promising tool generating reliable information about by hailstorms. Such spatially explicit useful creating customised insurance packages sensitive incurred farmer.