干旱区生态环境知识资源中心(Arid): Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies

Arid

DOI	10.3390/rs13061075
	Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies
	Cao, Changyong; Zhang, Bin; Shao, Xi; Wang, Wenhui; Uprety, Sirish; Choi, Taeyoung; Blonski, Slawomir; Gu, Yalong; Bai, Yan; Lin, Lin; Kalluri, Satya
通讯作者	Cao, CY (corresponding author), NOAA, NESDIS, Ctr Satellite Applicat & Res, College Pk, MD 20740 USA.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:6
英文摘要	Suomi NPP has been successfully operating since its launch on 28 October 2011. As one of the major payloads, along with microwave and infrared sounders (Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS)), and ozone mapping/profiling (OMPS) instruments, the Visible Infrared Imaging Radiometer Suite (VIIRS) has performed for well beyond its mission design life. Its data have been used for a variety of applications for nearly 30 environmental data products, including global imagery twice daily with 375 and 750 m resolutions, clouds, aerosol, cryosphere, ocean color and sea-surface temperature, a number of land products (vegetation, land-cover, fire and others), and geophysical and social economic studies with nightlights. During the early days of VIIRS operational calibration and data production, there were inconsistencies in both algorithms and calibration inputs, for several reasons. While these inconsistencies have less impact on nowcasting and near real-time applications, they introduce challenges for time series analysis due to calibration artifacts. To address this issue, we developed a comprehensive algorithm, and recalibrated and reprocessed the Suomi NPP VIIRS radiometric data that have been produced since the launch. In the recalibration, we resolved inconsistencies in the processing algorithms, terrain correction, straylight correction, and anomalies in the thermal bands. To improve the stability of the reflective solar bands, we developed a Kalman filtering model to incorporate onboard solar, lunar, desert site, inter-satellite calibration, and a deep convective cloud calibration methodology. We further developed and implemented the Solar Diffuser Surface Roughness Rayleigh Scattering model to account for the sensor responsivity degradation in the near infrared bands. The recalibrated dataset was validated using vicarious sites and alternative methods, and compared with independent processing from other organizations. The recalibrated radiometric dataset (namely, the level 1b or sensor data records) also incorporates a bias correction for the reflective solar bands, which not only addresses known calibration biases, but also allows alternative calibrations to be applied if so desired. The recalibrated data have been proven to be of high quality, with much improved stability (better than 0.3%) and accuracy (by up to 2%). The recalibrated radiance data are now available from 2012 to 2020 for users and will eventually be archived on the NOAA CLASS database.
英文关键词	Suomi NPP VIIRS recalibration viirs reprocessing radiometric consistency radiometric stability accuracy Kalman filtering SRRS model WUCD DNB reprocessing VIIRS reprocessing system
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000651968300001
WOS关键词	REFLECTIVE SOLAR BANDS ; ON-ORBIT CALIBRATION ; STABILITY ; DNB ; TERRESTRIAL ; RESOLUTION ; NOAA-20 ; AQUA ; IMPROVEMENTS ; CLIMATE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351499
作者单位	[Cao, Changyong; Kalluri, Satya] NOAA, NESDIS, Ctr Satellite Applicat & Res, College Pk, MD 20740 USA; [Zhang, Bin; Shao, Xi; Wang, Wenhui; Uprety, Sirish; Bai, Yan; Lin, Lin] Univ Maryland, Cooperat Inst Satellite Earth Syst Studies CISESS, CISESS, College Pk, MD 20740 USA; [Choi, Taeyoung; Blonski, Slawomir; Gu, Yalong] Global Sci & Technol, College Pk, MD 20740 USA
推荐引用方式 GB/T 7714	Cao, Changyong,Zhang, Bin,Shao, Xi,et al. Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies[J],2021,13(6).
APA	Cao, Changyong.,Zhang, Bin.,Shao, Xi.,Wang, Wenhui.,Uprety, Sirish.,...&Kalluri, Satya.(2021).Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies.REMOTE SENSING,13(6).
MLA	Cao, Changyong,et al."Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies".REMOTE SENSING 13.6(2021).