Vicarious Radiometric Calibration of Ocean Color Bands for FY-3D/MERSI-II at Lake Qinghai, China

Chen, Shengli; Zheng, Xunhua; Li, Xin; Wei, Wei; Du, Shenda; Guo, Fuxiang

doi:10.3390/s21010139

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…NSMC provides the MERSI-II L1 calibration coefficients, which were calibrated in January 2019. Due to the influence of instrument attenuation and atmospheric environment factors, the calibration ability of calibration parameters deteriorates, which makes the radiation correction deviation and the product accuracy not as high as that of the satellite launch [48]. It is necessary to evaluate the absolute radiometric calibration accuracy of MERSI-II's visible light bands for the purpose of the possible influences of calibration uncertainties on WLR products.…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Zhang,

Shi,

et al. 2023

Remote Sensing

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In this paper, a new simultaneous retrieval method of the SIRAW algorithm is introduced and carried out on FY3D/MERSI-II satellite images to obtain the aerosol optical thickness (AOT) and normalized water-leaving radiance (WLR) over the ocean. In order to improve the operation efficiency of SIRAW, a machine learning solver is developed to improve the speed of forward radiative transfer computation during retrieval. Ground-based measurement data from AERONET-OC and satellite products from VIIRS are used for comparative verification. The results show that the retrieved AOT and WLR from SIRAW are both in good agreement with those of AERONET-OC and VIIRS. Further, considering the degradation of the MERSI sensor, a new calibration scheme on 412 nm and 443 nm is adopted and an evaluation is carried out. Inter-comparison of derived WLR between MERSI and VIIRS indicates that the new calibration scheme could effectively improve the WLR retrieval accuracy of MERSI with better consistency to the official data of VIIRS. Therefore, this paper confirms that a simultaneous retrieval scheme combined with effective calibration coefficients can be used for high-precision retrieval of real aerosol and water-leaving radiation.

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Section: Discussionmentioning

confidence: 99%

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Zhang,

Shi,

et al. 2023

Remote Sensing

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“…Since instruments degrade after launch due to out-gassing when the instrument leaves the atmosphere, aging of the optics, contamination of optical parts in orbit, and exposure of optical parts, detectors, and electronics to space radiation, satellite platforms are often equipped with onboard calibration devices (Kang et al, 2010;Delwart and Bourg, 2011;Okuyama et al, 2018;Xiong et al, 2020). Indirect, so-called "vicarious" methods are also used, either alternatively (in the absence of onboard calibrators) or to check the onboard device (Fougnie et al, 1999(Fougnie et al, , 2007Martiny et al, 2005;Hlaing et al, 2014;Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of cross-calibrating ocean-color sensors in polar orbit using an intermediary geostationary sensor of reference

Tan

Frouin

Murakami

2023

Front. Remote Sens.

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A generic methodology is presented to cross-calibrate satellite ocean-color sensors in polar orbit via an intermediary geostationary sensor of reference. In this study, AHI onboard Hiwamari-8 is used as the intermediary sensor to cross-calibrate SGLI onboard GCOM-C and MODIS onboard Aqua and Terra (MODIS-A and MODIS-T) after system vicarious calibration (SVC). Numerous coincidences were obtained near the Equator using 3 days of imagery, i.e., 11 May 2018, 22 January 2019, and 25 January 2020. Spectral matching to AHI spectral bands was first performed for a wide range of angular geometry, aerosol conditions, and Case 1 waters using a single band or multiple bands of SGLI, MODIS-A and MODIS-T, yielding root mean square differences of 0.1–0.7% in the blue and green and 0.7%–3.7% in the red depending on the band combination. Limited by the inherent AHI instrument noise and the system vicarious calibration of individual polar-orbiting sensors, cross-calibration was only performed for equivalent AHI bands centered on at 471, 510, and 639 nm. Results show that MODIS-A and MODIS-T are accurately cross-calibrated, with cross-calibration ratios differing by 0.1%–0.8% in magnitude. These differences are within or slightly outside the estimated uncertainties of ±0.6% to ±1.0%. In contrast, SGLI shows larger cross-calibration differences, i.e., 1.4%, 3.4%, and 1.1% with MODIS-A and 1.5%, 4.6%, and 1.5% with MODIS-T, respectively. These differences are above uncertainties of ±0.8–1.0% at 471 and 510 nm and within uncertainties of ±2.3% and ±1.9% at 639 nm. Such differences may introduce significant discrepancies between ocean-color products generated from SGLI and MODIS data, although some compensation may occur because different atmospheric correction schemes are used to process SGLI and MODIS imagery, and SVC is based on the selected scheme. Geostationary sensors with ocean color capability have potential to improve the spectral matching and reduce uncertainties, as long as they provide imagery at sufficient cadence over equatorial regions. The methodology is applicable to polar-orbiting optical sensors in general and can be implemented operationally to ensure consistency of products generated by individual sensors in establishing long-term data records for climate studies.

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“…MERSI-2 is one of the main payloads onboard China's newest polar-orbiting meteorological satellite Fengyun (FY)-3D [30]. It has 25 spectral bands, including nine visible (VIS) bands, eight NIR bands, four mid-infrared (MIR) bands and four far-infrared (FIR) bands.…”

Section: Introductionmentioning

confidence: 99%

Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET

Xie

Hou

et al. 2021

Remote Sensing

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The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from Aerosol Robotic Network. The validation results show that the P905, P936 and fused PWV datasets have relatively large systematic errors (−0.10, −0.11 and −0.07 g/cm2), whereas the systematic error of the P940 dataset (−0.02 g/cm2) is very small. According to the overall accuracy of these four PWV datasets by our assessments, they can be ranked in descending order as P940 dataset, fused dataset, P936 dataset and P905 dataset. The root mean square error (RMSE), relative error (RE) and percentage of retrieval results with error within ±(0.05+0.10∗PWVAERONET) (PER10) of the P940 PWV dataset are 0.24 g/cm2, 0.10 and 76.36%, respectively. The RMSE, RE and PER10 of the P905 PWV dataset are 0.38 g/cm2, 0.15 and 57.72%, respectively. In order to obtain a clearer understanding of the accuracy of these four MERSI-2 PWV datasets, we compare the accuracy of these four MERSI-2 PWV datasets with that of the widely used MODIS PWV dataset and AIRS PWV dataset. The results of the comparison show that the accuracy of the MODIS PWV dataset is not as good as that of all four MERSI-2 PWV datasets, due to the serious overestimation of the MODIS PWV dataset (0.40 g/cm2), and the accuracy of the AIRS PWV dataset is worse than that of the P940 and fused MERSI-2 PWV datasets. In addition, we analyze the error distribution of the four PWV datasets in different locations, seasons and water vapor content. Finally, the reason why the fused PWV dataset is not the one with the highest accuracy among the four PWV datasets is discussed.

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Vicarious Radiometric Calibration of Ocean Color Bands for FY-3D/MERSI-II at Lake Qinghai, China

Cited by 8 publications

References 32 publications

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

Feasibility of cross-calibrating ocean-color sensors in polar orbit using an intermediary geostationary sensor of reference

Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET

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