Mapping Insect-Proof Screened Citrus Orchards Using Sentinel-2 MSl Time-Series Images

Abstract

Citrus is a crucial agricultural commodity of the hilly subtropical regions of southern China. Attempts in recent years to combat the destructive disease Huanglongbing (HLB) have led to citrus orchards being covered with insect-proof screens (IPS). Understanding which citrus orchards are covered by IPS is crucial for regional water and soil conservation, as well as control of plastic pollution. However, monitoring of orchards is complicated by IPS spectral interference in remotely sensed image classification. Here, an optimal feature combination scheme is developed and tested for mapping citrus orchards that use IPS. Seasonal Sentinel-2 images from 2021 were used to define indices for vegetation, plastic mulch, red edge, and texture. These were combined with topographic and land surface temperature using random forest classification to determine optimal feature discrimination combinations for orchards in Xunwu County, Jiangxi Province. Results show: (1) significantly higher visible light reflectance from IPS orchards ensures spectral discrimination between IPS covered and uncovered orchards. (2) After feature optimization, the seasonal spectral band has the highest accuracy (86%) in single feature classification. The addition of conventional indices and topographic-temperature features improves classification to 92%. (3) Xunwu County had 460 km2 of citrus orchard cover in 2021, with 88 km2 (19%) of that total being covered with IPS. Our method effectively and accurately maps citrus orchards with or without IPS coverage at 10 m resolution. The effective monitoring of large-scale IPS in other regions can now support the development of local and regional sustainable agricultural policies.

Keywords

• citrus orchards
• Sentinel-2
• image classification
• insect-proof screen
• random forest
• feature optimization