Establishing a Pre-Launch Validation Framework for the CAS500-4 Vegetation Index: Sentinel-2 Utilization and Ground Observation Network Expansion Strategy
This study aims to establish a pre-launch validation framework for the Vegetation Index (VI) products of the Compact Advanced Satellite 500-4 (CAS500-4), scheduled for launch in 2026.To this end, Sentinel-2, internationally recognized for its high quality, was adopted as proxy satellite data, and a ground observation network covering South Korea's forest environments was established.All evaluation processes adhered to the Land Product Validation (LPV) protocol of the Committee on Earth Observation Satellites (CEOS) and the Quality Assurance Framework for Earth Observation (QA4EO), ensuring consistent objectivity and transparency in validation.Initial validation results using multispectral sensors from the established ground network (8 sites) showed high correlations (R > 0.8) between Sentinel-2 Normalized Difference Vegetation Index (NDVI) and ground measurements in deciduous broadleaf and mixed forests, whereas notably lower correlations were observed in evergreen coniferous forests.These findings confirmed the operational readiness of the validation system.Furthermore, given that the current eight sites are insufficient to represent the geographical heterogeneity of forests nationwide, the necessity of expanding the network to an optimal 31 sites using geostatistical techniques was proposed.The standardized framework and the multi-platform (Tower-Unmanned Aerial Vehicle [UAV]-Satellite) triple-collocation strategy established in this study will serve as a key foundation for securing the reliability and maximizing the utility of future CAS500-4 data.
Authors
- Byung Oh Yoo (ORCID: https://orcid.org/0000-0002-3778-5185)
- Minji Seo (ORCID: https://orcid.org/0000-0003-4883-9289)
- Joongbin Lim
- Cheonggil Jin
- Cheolho Lee
Institutions
- Korea Forest Service (KR)
- National Institute of Environmental Research (KR)
Publication Details
- Journal
- Korean Journal of Remote Sensing
- Published
- 2026-02-28
- DOI
- https://doi.org/10.7780/kjrs.2026.42.1.11
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of Forest Science