Spectral Information Adaptation and Synthesis Scheme for Merging Cross-Mission Ocean Color Reflectance Observations from MODIS and VIIRS

Kaixu Bai, Ni Bin Chang, Chi Farn Chen

研究成果: 雜誌貢獻期刊論文同行評審

17 引文 斯高帕斯(Scopus)

摘要

Obtaining a full clear view of coastal bays, estuaries, lakes, and inland waters is challenging with single satellite sensor observations due to cloud impacts. Cross-mission sensors provide the synergistic opportunity to improve spatial and temporal coverage by merging their observations; however, discrepancies originating from the instrumental, algorithmic, and temporal differences should be eliminated before merging. This paper presents the Spectral Information Adaptation and Synthesis Scheme (SIASS) for generating cross-mission consistent ocean color reflectance by merging 2012-2015 observations from Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite over Lake Nicaragua in Central America, where the cloud impact is salient. The SIASS is able to not only eliminate incompatibilities for matchup bands but also reconstruct spectral information for mismatched bands among sensors. Statistics indicate that the average monthly coverage of a merged ocean color reflectance product over Lake Nicaragua is nearly twice that of any single-sensor observation. Results show that SIASS significantly improves consistency among cross-mission sensors by mitigating prominent discrepancies. In addition, reconstructed spectral information for those mismatched bands help preserve more spectral characteristics needed to better monitor and understand the dynamic aquatic environment. The final implementation of SIASS to map the chlorophyll-aconcentration demonstrates the efficacy of SIASS in bias correction and consistency improvement. In general, SIASS can be applied to remove cross-mission discrepancies among sensors to improve the overall consistency.

原文???core.languages.en_GB???
文章編號7177067
頁(從 - 到)311-329
頁數19
期刊IEEE Transactions on Geoscience and Remote Sensing
54
發行號1
DOIs
出版狀態已出版 - 1 1月 2016

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