Abstract
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.
Highlights
The ability to detect and quantify changes in Earth’s environment and its global energy balance depends on satellite sensors that can provide calibrated, consistent measurements of Earth’s surface features [1]
The 12 samples in 2018 are used to calibrate Sentinel-2B/Multispectral Instrument (MSI), and a Key Comparison Reference Value (KCRV) weighted by uncertainty is derived to evaluate the consistency of these samples
Since the field calibration is affected by the coupled effects of surface and atmospheric characteristics, the relative differences acquired under different surface and atmospheric conditions differ from each other and have different uncertainties, making it difficult to compare the consistency among multiple samples
Summary
The ability to detect and quantify changes in Earth’s environment and its global energy balance depends on satellite sensors that can provide calibrated, consistent measurements of Earth’s surface features [1]. In 2014, the CEOS Working Group on Calibration & Validation (WGCV) initiated Radiometric Calibration Network (RadCalNet) activities to provide satellite operators with SI-traceable TOA spectrally resolved reflectance to aid in the radiometric calibration of optical imaging sensors from a coordinated network of instrumented land-based test sites [6]. This project aims to reduce the error using automated instruments and nominal standard data processing systems to increase the number of matchups between in situ measurements and space sensor observations and to reduce the overall uncertainties. Combined field radiometric calibration results could be expressed in the form of a single radiometric calibration
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