scholarly journals Improved NOAA-20 Visible Infrared Imaging Radiometer Suite Day/Night Band Image Quality by Upgraded Gain Calibration

2021 ◽  
Vol 13 (13) ◽  
pp. 2509
Author(s):  
Yalong Gu ◽  
Slawomir Blonski ◽  
Wenhui Wang ◽  
Sirish Uprety ◽  
Taeyoung Choi ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
High Gain ◽  
Radiometric Calibration ◽  
Earth Remote Sensing ◽  
Calibration Process ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Gain Stage ◽  
Orbit Data ◽  
Band Image

Due to complex radiometric calibration, the imagery collected by the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (Suomi-NPP) and the NOAA-20 follow-on satellite is subject to artifacts such as striping, which eventually affect Earth remote sensing applications. Through comprehensive analysis using the NOAA-20 VIIRS DNB prelaunch-test and on-orbit data, it is revealed that the striping results from flaws in the calibration process. In particular, a discrepancy between the low-gain stage (LGS) Earth view (EV) gain and the onboard calibrator solar diffuser view gain makes the operational LGS gain coefficients of a few aggregation modes and detectors biased. Detector nonlinearity at low radiance level also induces errors to the mid-gain stage (MGS) and high-gain stage (HGS) gain through the biased gain ratios. These systematic errors are corrected by scaling the operational LGS gains using the factors derived from the NOAA-20 VIIRS DNB prelaunch test data and by adopting linear regression for evaluating the gain ratios. Striping in the NOAA-20 VIIRS DNB imagery is visibly reduced after the upgraded gain calibration process was implemented in the operational calibration.

scholarly journals Calibration Method of Low-Light Sensor Based on Bridge Lights

2016 ◽  
Vol 33 (6) ◽  
pp. 1123-1134 ◽  
Author(s):  
Shuo Ma ◽  
Wei Yan ◽  
Yunxian Huang ◽  
Jun Jiang ◽  
Shensen Hu ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Calibration Method ◽  
High Gain ◽  
Hangzhou Bay ◽  
Radiometric Calibration ◽  
Low Light ◽  
Invariant Surface ◽  
Vicarious Calibration ◽  
Calibration Methods ◽  
Gain Stage

AbstractMany quantitative uses of the nighttime imagery provided by low-light sensors, such as the day–night band (DNB) on board the Suomi–National Polar-Orbiting Partnership (SNPP), have emerged recently. Owing to the low nighttime radiance, low-light calibration at night must be investigated in detail. Traditional vicarious calibration methods are based on some targets with nearly invariant surface properties under lunar illumination. However, the relatively stable light emissions may also be used to realize the radiometric calibration under low light. This paper presents a low-light calibration method based on bridge lights, and Visible Infrared Imaging Radiometer Suite (VIIRS) DNB data are used to assess the proposed method. A comparison of DNB high-gain-stage (HGS) radiances over a 2-yr period from August 2012 to July 2014 demonstrates that the predictions are consistent with the observations, and the agreement between the predictions and the observations is on the order of −2.9% with an uncertainty of 9.3% (1σ) for the Hangzhou Bay Bridge and −3.9% with an uncertainty of 7.2% (1σ) for the Donghai Bridge. Such a calibration method based on stable light emissions has a wide application prospect for the calibration of low-light sensors at night.

T2SL digital focal plane arrays for earth remote sensing applications

2019 ◽  
Author(s):  
Sarath D. Gunapala ◽  
Sir (Don) B. Rafol ◽  
David Z. Ting ◽  
Alexander Soibel ◽  
Arezou Khoshakhlagh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
Earth Remote Sensing ◽  
Sensing Applications ◽  
Remote Sensing Applications

T2SL meta-surfaced digital focal plane arrays for Earth remote sensing applications

2019 ◽  
Author(s):  
Sarath D. Gunapala ◽  
Sir B. Rafol ◽  
David Z. Ting ◽  
Alexander Soibel ◽  
Arezou Khoshakhlagh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
Earth Remote Sensing ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4453 ◽  
Author(s):  
Mamaghani ◽  
Salvaggio
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Laboratory Method ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
End User ◽  
Sensing Applications ◽  
A Value ◽  
Remote Sensing Applications

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

Status and calibration of the MODIS airborne simulator for earth remote sensing applications

1995 ◽  
Author(s):  
Steven E. Platnick ◽  
Michael D. King ◽  
G. Thomas Arnold ◽  
John E. Cooper ◽  
Liam E. Gumley ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Earth Remote Sensing ◽  
Sensing Applications ◽  
Remote Sensing Applications
Sign in / Sign up

Export Citation Format

Share Document