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1 Pair Water Proof Leg Gaiters - Adjustable Oxford Cloth Leg Covers for Hiking Skiing Snow Sand Protection Breathable Outdoor Gear in Saudi Arabia

Green

SAR 102

Brand
MOPFOL
Weight
359 g
ColorGreen
1 +

Special Features

  • Water proof Protection: Engineered with 600D Oxford fabric and taped seams to block water and snow ensuring dry feet during rainy hikes or snowy mountain adventures.
  • Adjustable Secure Fit: Features bottom straps for custom tightening around various boot types providing a firm grip that stays in place during vigorous activities.
  • Breathable Comfort: Designed with airflow technology to release heat and reduce water buildup keeping your legs comfortable during extended outdoor use.
  • Versatile Outdoor Use: Ideal for hiking climbing skiing and traversing through mud sand or debris-filled terrain offering reliable leg protection.
  • Complete Package: Includes one pair of black gaiters and a mesh storage bag for convenient carrying and organization on your travels.

Description

Water proof leg gaiters provide reliable protection for outdoor activities. These adjustable boot covers protector against snow, sand, and debris during hiking, climbing, and skiing adventures.
The breathable design ensures comfort while maintaining durability in challenging environments.
Secure the gaiters around your lower legs and footwear. Adjust the bottom straps for a custom fit over various shoe types.
The water proof construction avoid during rain or snow exposure. Use the storage mesh bag for convenient transport after your outdoor journey.
Material: Oxford Cloth
Color Options: Black, Green
Flattened Dimensions: 40cm(15.75in) length x 52cm(20.47in) width
Diameter: 15cm(5.91in)
Maximum Leg Circumference: 47cm(18.50in)
Product Weight: 355g
Package includes:
2 x Leg Gaiters
1 x Storage Mesh Bag
Notes:
1.The real color of the item may be slightly different from the pictures shown on website caused by many factors such as brightness of your monitor and light brightness.
2. Please allow slight manual measurement deviation for the data.

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