&lt;title&gt;Optical characteristics of China Radiometric Calibration Site for Remote Sensing Satellite Sensors (CRCSRSSS)&lt;/title&gt;

Hu, Xiuqing; Zhang, Yuxiang; Liu, Zhiquan; Guangshun, Zhang; Huang, Yibin; Qiu, Kangmu; Wang, Yongkuan; Zhang, Lijun; Zhu, Xunbing; Rong, Zhiguo

doi:10.1117/12.416994

Cited by 19 publications

(6 citation statements)

References 2 publications

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“…It is uniformly covered by gravel and protects the soil of the Gobi Desert from the wind erosion. The precipitation of the Dunhuang test site is low, with a yearly average of 34 mm and a relative maximum in July [25]. No vegetation is present at the Dunhaung test site, and the aerosol loading is typically low.…”

Section: Test Sitementioning

confidence: 99%

Validation of the Calibration Coefficient of the GaoFen-1 PMS Sensor Using the Landsat 8 OLI

Gao

et al. 2016

Remote Sensing

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Abstract:The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager (OLI). Two image pairs, one obtained over the Dunhuang test site and the other over the Golmud test site, were used in this paper. Two spectral band adjustment factors (SBAF), given as the radiance SBAF and reflectance SBAF, were applied to correct the differences in the respective images caused by the relative spectral responses of the PMS sensor and OLI. Uncertainties in the SBAF values arising from atmospheric parameters and the absence of a measured ground spectrum were analyzed in this paper. The results show that the average relative differences of top-of-atmosphere radiance and reflectance values for each band between the PMS sensor and OLI images are 2%-5% for most bands, after SBAF correction with a measured ground spectrum or fitted spectrum. It is demonstrated that the OLI image can be used to validate the calibration coefficient of the PMS sensor, even if the image pairs are not imaged on the same day.

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Section: Test Sitementioning

confidence: 99%

Validation of the Calibration Coefficient of the GaoFen-1 PMS Sensor Using the Landsat 8 OLI

Gao

et al. 2016

Remote Sensing

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“…The site is on an alluvial fan of the Dang River with a uniform area of 25 km 2 with sparsely distributed vegetation. The study site is in the center of the field area, with an area of 400 m × 400 m and an altitude of 1229 m. The Dunhuang site is constructed of black, gray, and white gravel with a diameter from 0.2 to 8.0 cm 19 . According to the meteorological information acquired at Dunhuang weather station, the atmospheric model ranges from the desert to the continental type.…”

Section: Sensor and Site Overviewmentioning

confidence: 99%

Long-term cross calibration of HJ-1A CCD1 and Terra MODIS reflective solar bands

Liu

Shi

Gao

et al. 2021

Sci Rep

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Since its launch on September 6, 2008, HJ-1A has been in the orbit for 13 years. The CCD1 sensor on the HJ-1A has four reflected solar bands. Since the calibration frequency is limited to the annual site calibration, cross-calibration is an effective method to improve the calibration frequency. In this paper, we use 420 image pairs of HJ-1A CCD1 and Terra MODIS over the Dunhuang test site for gains calculation, where we take MODIS as the reference sensor. The spectral band adjustment factors (SBAFs) for cross-calibration are then calculated to compensate for the spectral mismatch. The cross-calibration results are also validated by the field calibration results. From 2008 to 2019, a total of six campaigns have been cross-calibrated on the same day. The gain difference between the site calibration and cross-calibration is less than 3%. The long-term cross-calibration results further indicate that due to the adjustment of HJ-1A CCD gain state in October 2009, an abrupt change occurred 405 days after launch. After 12 years of on-orbit operation, the attenuation rate has reached 23.51%, 21.89%, 8.11%, and 13.37%, respectively by the end of 2019 based on the cross-calibration results.

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“…The local atmosphere is dry with low aerosol loading, which is beneficial for the calibration experiments. The atmospheric aerosol characteristics at the site are typical of a rural continental location, although some larger particles have been observed, possibly originating from sand dunes located to the northwest [10,11,28,29].…”

Section: Calibration Sitementioning

confidence: 99%

“…Therefore, in-orbit vicarious calibration must be used to transform the satellite data into meaningful physical information. Previous studies used reflectance-, irradiance-, and radiance-based techniques [2,3] to successfully calibrate satellites such as the SPOT HRV [4], Landsat TM/ETM [3,5,6], Airborne Visible and Infrared Spectrometer [7], EO-1 Hyperion [8,9], and FY [10,11], MISR [12], Landsat OLI [13], CBERS-4 [14], and many other optical remote sensors [15]. The reflectance-and irradiance-based methods have been compared with cross-calibration methods to derive the calibration coefficients for the BJ-1 microsatellite [16].…”

Section: Satellite Characteristic Descriptionmentioning

confidence: 99%

Vicarious Radiometric Calibration of the Hyperspectral Imaging Microsatellites SPARK-01 and -02 over Dunhuang, China

Zhang

Chen

et al. 2018

Remote Sensing

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Two wide-swath hyperspectral imaging microsatellites, SPARK-01 and -02, were launched on 22 December 2016. Radiometric calibration coefficients were determined for these two satellites via a calibration experiment performed from the end of February to the beginning of March 2017 at the high-altitude, homogenous Dunhuang calibration site in the Gobi Desert in China. In-situ measurements, including ground reflectance, direct transmittance, diffuse-to-global irradiance ratio, and radiosonde vertical profile, were acquired. A unique relative calibration procedure was developed using actual satellite images. This procedure included dark current computation and non-uniform correction processes. The former was computed by averaging multiple lines of long strip imagery acquired over open oceans during nighttime, while the latter was computed using images acquired after the adjustment of the satellite yaw angle to 90 • . This technique was shown to be suitable for large-swath satellite image relative calibration. After relative calibration, reflectance, irradiance, and improved irradiance-based methods were used to conduct absolute radiometric calibrations in order to predict the top-of-atmosphere (TOA) radiance. The SPARK-01 and -02 satellites passed over the calibration site on 7 March and 28 February 2017, during which time fair and non-ideal weather occurred, respectively. Thus, the SPARK-01 calibration coefficient was derived using reflectance-and irradiance-based methods, while that of SPARK -02 was derived using reflectanceand improved irradiance-based methods. The sources of calibration uncertainty, which include aerosol-type assumptions, transmittance measurements, water vapor content retrieval, spectral wavelength shift and satellite image misregistration, were explored in detail for different calibration methods. Using the reflectance and irradiance-based methods, the total uncertainty for SPARK-01 was estimated to be 4.7% and 4.1%, respectively, in the <1000 nm spectral range. For SPARK-02, total uncertainties of 8.1% and of 5.9% were estimated using the reflectance-and improved irradiance-based methods, respectively. The calibration methods were also verified using MODIS images, which confirmed that the calibration accuracies were within the expected range. These in-situ measurements, analyses, and results provide a basis for in-orbit radiometric calibration of the SPARK-01 and -02 satellites. These experiments strongly support the use of diffuse-to-global ratio measurements in in-situ vicarious calibration experiments and the addition of spectrally continuous measurements for direct transmittance, which is important for hyperspectral satellite sensors.

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<title>Optical characteristics of China Radiometric Calibration Site for Remote Sensing Satellite Sensors (CRCSRSSS)</title>

Cited by 19 publications

References 2 publications

Validation of the Calibration Coefficient of the GaoFen-1 PMS Sensor Using the Landsat 8 OLI

Validation of the Calibration Coefficient of the GaoFen-1 PMS Sensor Using the Landsat 8 OLI

Long-term cross calibration of HJ-1A CCD1 and Terra MODIS reflective solar bands

Vicarious Radiometric Calibration of the Hyperspectral Imaging Microsatellites SPARK-01 and -02 over Dunhuang, China

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