干旱区生态环境知识资源中心(Arid): Determination of the Key Comparison Reference Value from Multiple Field Calibration of Sentinel-2B/MSI over the Baotou Site

Arid

DOI	10.3390/rs12152404
	Determination of the Key Comparison Reference Value from Multiple Field Calibration of Sentinel-2B/MSI over the Baotou Site
	Gao, Caixia; Liu, Yaokai; Liu, Jinru; Ma, Lingling; Wu, Zhifeng; Qiu, Shi; Li, Chuanrong; Zhao, Yongguang; Han, Qijin; Zhao, Enyu; Qian, Yonggang; Wang, Ning
通讯作者	Liu, YK
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:15
英文摘要	Field calibration is a feasible way to evaluate space-borne optical sensor observations via natural or artificial sites on Earth's surface with the aid of synchronous surface and atmospheric characteristic data. Since field calibration is affected by the coupled effects of surface and atmospheric characteristics, the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, making it difficult to determine the consistency of these multiple calibration results. In view of this, by assuming that the radiometric performance is invariant during field calibration and the calibration samples are independent of each other, the surface-atmosphere invariant Key Comparison Reference Value (KCRV) is essentially derived from various calibration results. As the number of calibration samples increases, the uncertainty in the KCRV should decrease, and the KCRV should approach the "true" value. This paper addresses a novel method for estimating a weighted average value from multiple calibration results that can be used to compare each calibration result, and this value is accepted as the KCRV. Furthermore, this method is preliminarily applied to the field calibration of the Multispectral Instrument (MSI) onboard the Sentinel-2B satellite via the desert target at the Baotou site, China. After employing a chi-squared test to verify that 12 calibration samples are independent from each other, the KCRV of the 12 calibration samples at the Baotou site is derived, which exhibits much lower uncertainty than a single sample. The results show that the KCRVs of the relative differences between the simulated and observed at-sensor reflectance are 3.75%, 5.11%, 6.09%, and 5.03% for the four bands of Sentinel-2B/MSI, respectively, and the corresponding uncertainties are 1.84%, 1.87%, 1.90%, and 1.93%. It is noted that the KCRV uncertainty obtained with only 12 calibration samples is reduced significantly, and in the future, more samples in other instrumented sites will be used to validate this method thoroughly.
英文关键词	field calibration key comparison reference value Monte Carlo uncertainty analysis
类型	Article
语种	英语
开放获取类型	DOAJ Gold
收录类别	SCI-E
WOS记录号	WOS:000567095500001
WOS关键词	SURFACE TEMPERATURE PRODUCT ; SOLAR SPECTRAL IRRADIANCE ; SOLSPEC SPECTROMETER ; ATLAS ; NM
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326122
作者单位	[Gao, Caixia; Liu, Yaokai; Liu, Jinru; Ma, Lingling; Qiu, Shi; Li, Chuanrong; Zhao, Yongguang; Qian, Yonggang; Wang, Ning] Chinese Acad Sci, Key Lab Quantitat Remote Sensing Informat Technol, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; [Wu, Zhifeng] Natl Inst Metrol, Beijing 100029, Peoples R China; [Han, Qijin] China Ctr Resources Satellite Data & Applicat, Beijing 100094, Peoples R China; [Zhao, Enyu] Dalian Maritime Univ, Coll Informat Sci & Technol, Dalian 116026, Peoples R China
推荐引用方式 GB/T 7714	Gao, Caixia,Liu, Yaokai,Liu, Jinru,et al. Determination of the Key Comparison Reference Value from Multiple Field Calibration of Sentinel-2B/MSI over the Baotou Site[J],2020,12(15).
APA	Gao, Caixia.,Liu, Yaokai.,Liu, Jinru.,Ma, Lingling.,Wu, Zhifeng.,...&Wang, Ning.(2020).Determination of the Key Comparison Reference Value from Multiple Field Calibration of Sentinel-2B/MSI over the Baotou Site.REMOTE SENSING,12(15).
MLA	Gao, Caixia,et al."Determination of the Key Comparison Reference Value from Multiple Field Calibration of Sentinel-2B/MSI over the Baotou Site".REMOTE SENSING 12.15(2020).