상세정보
학술지
Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite
- 발행일
- 201601
- 출처
- Journal of Sensors, v.2016, pp.1-8
- ISSN
- 1687-725X
- 출판사
- Hindawi Publishing
- 초록
- The purpose of this study was to optimize a composite method for the Geostationary Ocean Color Imager (GOCI), which is the first geostationary ocean color sensor in the world. Before interpreting the sensitivity of each composite with ground measurements, we evaluated the accuracy of bidirectional reflectance distribution function (BRDF) performance by comparing modeled surface reflectance from BRDF simulation with GOCI-measured surface reflectance according to composite period. The root mean square error values for modeled and measured surface reflectance showed reasonable accuracy for all of composite days since each BRDF composite period includes at least seven cloud-free angular sampling for all BRDF performances. Also, GOCI-BRDF-adjusted NDVIs with four different composite periods were compared with field-observation NDVI and we interpreted the sensitivity of temporal crop dynamics of GOCI-BRDF-adjusted NDVIs. The results showed that vegetation index seasonal profiles appeared similar to vegetation growth curves in both field observations from crop scans and GOCI normalized difference vegetation index (NDVI) data. Finally, we showed that a 12-day composite period was optimal in terms of BRDF simulation accuracy, surface coverage, and real-time sensitivity.
- KSP 제안 키워드
- Angular sampling, Color sensor, Composite method, Field observations, Geostationary ocean color imager, Ground measurements, Growth curves, Real-Time, Root mean square(RMS), Simulation accuracy, Surface coverage