Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite
Joint Authors
Yeom, Jong-Min
Han, Kyung-Soo
Kim, Sang-il
Ahn, Do-Seob
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-23
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Sang-il& Ahn, Do-Seob& Han, Kyung-Soo& Yeom, Jong-Min. 2016. Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite. Journal of Sensors،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1110586
Modern Language Association (MLA)
Kim, Sang-il…[et al.]. Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite. Journal of Sensors No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1110586
American Medical Association (AMA)
Kim, Sang-il& Ahn, Do-Seob& Han, Kyung-Soo& Yeom, Jong-Min. Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1110586
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110586