Suitability of Satellite Imagery for Surveillance of Maize Ear Damage by Cotton Bollworm (<i>Helicoverpa armigera</i>) Larvae

Abstract

The cotton bollworm (Helicoverpa armigera, Lepidoptera: Noctuidae) poses significant risks to maize. Changes in the maize plant, such as its phenology, influence the short-distance movement and oviposition of cotton bollworm adults and, thus, the distribution of the subsequent larval damage. We aim to provide an overview of future approaches to the surveillance of maize ear damage by cotton bollworm larvae based on remote sensing. We focus on finding a near-optimal combination of Landsat 8 or Sentinel-2 spectral bands, vegetation indices, and maize phenology to achieve the best predictions. The study areas were 21 sweet and grain maze fields in Hungary in 2017, 2020, and 2021. Correlations among the percentage of damage and the time series of satellite images were explored. Based on our results, Sentinel-2 satellite imagery is suggested for damage surveillance, as 82% of all the extremes of the correlation coefficients were stronger, and this satellite provided 20–64% more cloud-free images. We identified that the maturity groups of maize are an essential factor in cotton bollworm surveillance. No correlations were found before canopy closure (BBCH 18). Visible bands were the most suitable for damage surveillance in mid–late grain maize (|rmedian| = 0.49–0.51), while the SWIR bands, NDWI, NDVI, and PSRI were suitable in mid–late grain maize fields (|rmedian| = 0.25–0.49) and sweet maize fields (|rmedian| = 0.24–0.41). Our findings aim to support prediction tools for cotton bollworm damage, providing information for the pest management decisions of advisors and farmers.

Keywords

• integrated pest management
• Sentinel-2
• Landsat 8
• maize
• damage prediction
• pest detection