An integrated method to improve the GOES Imager visible radiometric calibration accuracy

2015 ◽  
Vol 164 ◽  
pp. 103-113 ◽  
Author(s):  
Fangfang Yu ◽  
Xiangqian Wu
2019 ◽  
Vol 11 (6) ◽  
pp. 707 ◽  
Author(s):  
Qiyue Liu ◽  
Tao Yu ◽  
Hailiang Gao

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.


2008 ◽  
Author(s):  
Thomas S. Pagano ◽  
Hartmut H. Aumann ◽  
Rudolf Schindler ◽  
Denis Elliott ◽  
Steve Broberg ◽  
...  

2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Fangfang Yu ◽  
Xiangqian Wu ◽  
Hyelim Yoo ◽  
Haifeng Qian ◽  
Xi Shao ◽  
...  

2008 ◽  
Author(s):  
Ryoichi Imasu ◽  
Naoko Saitoh ◽  
Yosuke Niwa ◽  
Hiroshi Suto ◽  
Akihiko Kuze ◽  
...  

2014 ◽  
Author(s):  
A. Abdul Abdul Latiff ◽  
D. P. Ghosh ◽  
Z. Tuan Harith

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