RTS Effect Detection in Sentinel-4 Data.
Earth Observing Systems XXII(2017)
Airbus Def & Space
Abstract
The future ESA Earth Observation Sentinel-4/UVN is a high resolution spectrometer intended to fly on board a Meteosat Third Generation Sounder (MTG-S) platform, placed in a geostationary orbit. The main objective of this optical mission is to continuously monitor the air quality over Europe in near-real time. The Sentinel-4/UVN instrument operates in three wavelength bands: Ultraviolet (UV: 305-400 nm), Visible (VIS: 400500 nm) and Near-infrared (NIR: 750-775 nm). Two dedicated CCD detector have been developed to be used in the Focal Plane Subsystems (FPS), one for the combined UV and VIS band, the other covering the NIR band. Being a high resolution spectrometer with challenging radiometric accuracy requirements, both on spectral and spatial dimensions, an effect such the Random Telegraph Signal (RTS) can represent a relevant contribution for the complete system accuracy. In this work we analyze the RTS effect on data acquired during the FPS testing campaign with qualification models for the Sentinel-4/UVN detectors. This test campaign has been performed in late 2016. The strategy for the impact assessment of RTS is to measure the effect at room temperature and then to extrapolate the results to the at instrument operational temperature. This way, very-long lasting data acquisitions could be avoided since the RTS frequency is much lower at cryogenic temperatures. A reliable technique for RTS effect detection has been developed in order to characterize the signal levels amplitude and occurrence frequencies (flipping rate). We demonstrate the residual impact of the RTS on the global In-Orbit Sentinel-4/UVN instrument performance and products accuracy.
MoreTranslated text
Key words
Copernicus Programme,Sentinel-4,Random Telegraph Signal,Imaging spectrometer,CCD,Detector,Testing,Radiation effects
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined