Landsat 9 Operational Land Imager2 (OLI2) enhanced on-orbit linearity characterization

https://doi.org/10.1117/12.2595912(2021)

引用 0|浏览2
暂无评分
摘要
Like Landsat 8, Landsat 9 will carry an Operational Land Imager (OLI), a visible to short-wave infrared pushbroom sensor. Referred to as OLI-2, this sensor has 8 spectral bands with about 7000 detectors and one panchromatic band with about 14000 detectors. Maintaining uniformity of the data products is a challenge, particularly at the low end of the response’s dynamic range, as the on-board diffuser provides a high signal level. Water color analyses in coastal and inland areas imaged by Landsat, are one of the more demanding science applications of the OLI data, in part due to the low signal levels. OLI data are serving such demanding science applications due to its excellent noise, stability and calibration characteristics. A new set of on-orbit calibration collects planned for OLI-2 are aimed at enhancing the linearity characterization; these collects should help in keeping uniform imagery through a wide dynamic range. Improved pre-launch characterization of the OLI-2 radiometric response non-linearity led to the idea of using two sets of on-orbit collects to confirm the stability of non-linearity correction across a wider range of response. Collects will be taken where the detector integration time is varied while looking at a constant target: a bright solar diffuser panel and a dimmer internal lamp. Both objects are integral parts of the existing OLI calibration assembly subsystem, but only the diffuser was used in this manner on Landsat 8 OLI. Using pre-launch test data, we demonstrate how the combination of these two sets of data collects will assist in confirming the non-linearity correction is maintained to be under 0.5% across much of the dynamic range. If the on-orbit results show changes, then with such data we should be able to refine the calibration parameters until they minimize the residual errors in the non-linearity throughout the dynamic range.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要