Radiometric Characteristics of KOMPSAT-3 Multispectral Images Using the Spectra of Well-Known Surface Tarps

Yeom, Jong-Min; Hwang, Jisoo; Jin, Cheonggil; Lee, Dong−Han; Han, Kyung-Soo

doi:10.1109/tgrs.2016.2574902

Cited by 9 publications

(12 citation statements)

References 24 publications

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“…The instruments were fixed on a tripod to measure the atmospheric information, which typically results in a significant standard deviation of approximately 0.23% according to Microtops II ® user guide (version 2.43); however, this can change according to user skill or atmospheric conditions. The 6S model used to determine the TOA radiances for the KOMPSAT-3 and -3A requires the AOD to be in the 550 nm channel [11,21]; however, this does not exist in the Sunphotometer. In this study, the Ångström exponent [45] was used to estimate the AOD in the 550 nm channel.…”

Section: Microtops II ® Sunphotometer and Ozonometer Measurement Of Amentioning

confidence: 99%

“…Table 3 shows the measured values of water vapor (g cm −2 ), total ozone (cm-atm), and AOD in 550 nm used as the atmospheric input parameters of the 6S model. The 6S model used to determine the TOA radiances for the KOMPSAT-3 and -3A requires the AOD to be in the 550 nm channel [11,21]; however, this does not exist in the Sunphotometer. In this study, the Ångström exponent [45] was used to estimate the AOD in the 550 nm channel.…”

Section: Microtops II ® Sunphotometer and Ozonometer Measurement Of Amentioning

confidence: 99%

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Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

Yeom

Hwang

et al. 2018

Remote Sensing

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Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 6S model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec ® 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II ® Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R 2 , values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users.

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Section: Microtops II ® Sunphotometer and Ozonometer Measurement Of Amentioning

confidence: 99%

Section: Microtops II ® Sunphotometer and Ozonometer Measurement Of Amentioning

confidence: 99%

Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

Yeom

Hwang

et al. 2018

Remote Sensing

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“…Table 1. Specifications of the Korea Multi-Purpose Satelllite-2 (KOMPSAT-2) and Korea Multi-Purpose Satelllite-3 (KOMPSAT-3) satellites and imaging sensors [26][27][28].…”

Section: High-resolution Satellite Imagementioning

confidence: 99%

“…Date of launch 28 Four images were successively acquired during 14-15 August 2014 (Table 2). From the time gap between the local mean time on ascending nodes of the satellites, the image acquisition times within a day were not identical, so the images acquired in a day from different satellites enabled them to be compared with each other.…”

Section: Kompsat-2 Kompsat-3mentioning

confidence: 99%

A Feasibility Study of Sea Ice Motion and Deformation Measurements Using Multi-Sensor High-Resolution Optical Satellite Images

Hyun

Kim

2017

Remote Sensing

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Sea ice motion and deformation have generally been measured using low-resolution passive microwave or mid-resolution radar remote sensing datasets of daily (or few days) intervals to monitor long-term trends over a wide polar area. This feasibility study presents an application of high-resolution optical images from operational satellites, which have become more available in polar regions, for sea ice motion and deformation measurements. The sea ice motion, i.e., Lagrangian vector, is measured by using a maximum cross-correlation (MCC) technique and multi-temporal high-resolution images acquired on 14-15 August 2014 from multiple spaceborne sensors on board Korea Multi-Purpose Satellites (KOMPSATs) with short acquisition time intervals. The sea ice motion extracted from the six image pairs of the spatial resolutions were resampled to 4 m and 15 m yields with vector length measurements of 57.7 m root mean square error (RMSE) and −11.4 m bias and 60.7 m RMSE and −13.5 m bias, respectively, compared with buoy location records. The errors from both resolutions indicate more accurate measurements than from conventional sea ice motion datasets from passive microwave and radar data in ice and water mixed surface conditions. In the results of sea ice deformation caused by interaction of individual ice floes, while free drift patterns of ice floes were delineated from the 4 m spatial resolution images, the deformation was less revealing in the 15 m spatial resolution image pairs due to emphasized discretization uncertainty from coarser pixel sizes. The results demonstrate that using multi-temporal high-resolution optical satellite images enabled precise image block matching in the melting season, thus this approach could be used for expanding sea ice motion and deformation dataset, with an advantage of frequent image acquisition capability in multiple areas by means of many operational satellites.

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“…In this study, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model [16] was used to simulate the amount of radiance observed by the sensor at the top of atmosphere (TOA) level. The radiometric calibration method of KOMPSAT-3A is based on the previous KOMPSAT-3 vicarious algorithm [17], but the input data processing for the 6S radiative transfer model was improved. We carefully processed the input data, such as laboratory-based bidirectional reflectance distribution function (BRDF) measurements of each of the tarp samples, with a hyperspectral gonioreflectometric instrument for BRDF correction, and multifilter rotating shadowband radiometer (MFRSR) measurements for atmospheric constituent parameterization.…”

Section: Introductionmentioning

confidence: 99%

Initial Radiometric Characteristics of KOMPSAT-3A Multispectral Imagery Using the 6S Radiative Transfer Model, Well-Known Radiometric Tarps, and MFRSR Measurements

et al. 2017

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On-orbit radiometric characterization of the multispectral (MS) imagery of the Korea Aerospace Research Institute (KARI)'s Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which was launched on 25 March 2015, was conducted to provide quantitative radiometric information about KOMPSAT-3A. During the in-orbit test (IOT), vicarious radiometric calibration of KOMPSAT-3A was performed using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model. The characteristics of radiometric tarps, the atmospheric optical depth from multi-filter rotating shadowband radiometer (MFRSR) measurements, and sun-sensor-geometry were carefully considered, in order to calculate the exact top of atmosphere (TOA) radiance received by KOMPSAT-3A MS bands. In addition, the bidirectional reflectance distribution function (BRDF) behaviors of the radiometric tarps were measured in the laboratory with a two-dimensional hyperspectral gonioradiometer, to compensate for the geometry discrepancy between the satellite and the ASD FieldSpec ® 3 spectroradiometer. The match-up datasets between the TOA radiance and the digital number (DN) from KOMPSAT-3A were used to determine DN-to-radiance conversion factors, based on linear least squares fitting for two field campaigns. The final results showed that the R 2 values between the observed and simulated radiances for the blue, green, red, and near-infrared (NIR) bands, are greater than 0.998. An approximate error budget analysis for the vicarious calibration of KOMPSAT-3A showed an error of less than 6.8%. When applying the laboratory-based BRDF correction to the case of higher viewing zenith angle geometry, the gain ratio was improved, particularly for the blue (1.3%) and green (1.2%) bands, which exhibit high sensitivity to the BRDF of radiometric tarps during the backward-scattering phase. The calculated gain ratio between the first and second campaigns showed a less than 5% discrepancy, indicating that the determined radiometric characteristics of KOMPSAT-3A are reliable and useful to the user group for quantitative applications.

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Radiometric Characteristics of KOMPSAT-3 Multispectral Images Using the Spectra of Well-Known Surface Tarps

Cited by 9 publications

References 24 publications

Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

A Feasibility Study of Sea Ice Motion and Deformation Measurements Using Multi-Sensor High-Resolution Optical Satellite Images

Initial Radiometric Characteristics of KOMPSAT-3A Multispectral Imagery Using the 6S Radiative Transfer Model, Well-Known Radiometric Tarps, and MFRSR Measurements

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