Exploration of the potential for using Spark-I observations to derive atmospheric parameters. Issue 6 (19th March 2018)

• Record Type: Journal Article
• Authors: Chen, Xi; Liu, Yi; Yang, Dongxu; Zhou, Jinsong; Guo, Chongbin; Zhang, Yonghe; Cai, Zhaonan
• Is Part Of: International journal of remote sensing. Volume 39:Issue 6(2018)
• Page Start: 1853
• Page End: 1870
• Publication Date: 2018-03-19
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1416694
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: As part of a low-cost nanosatellite constellation, Spark-I, a hyperspectral resolution instrument with 149 channels covering blue to near-infrared, has been successfully launched on 22 December 2016. In this study, we try to explore its potential to derive atmospheric parameters according to optimal estimation theory. From simulated measurements, we estimated the information content described as degrees of freedom for signal (DFS) and error reduction of typical aerosols, including dust, soot, sea salt, and sulphate, as well as water vapour (H2 O), nitrogen dioxide (NO2 ), surface pressure, and Sun-induced fluorescence (SIF). Based on the results, only the H2 O column, aerosol optical depth (AOD), and surface albedo could be derived with error reduction of 20%, 40%, and near 100%, respectively. The retrieval of aerosol was strongly correlated with surface albedo, especially dust, with a DFS of 0.3–0.9 due to surface variations. After investigating the impact of the oxygen and H2 O absorption bands on the aerosol information content, we recommend retrieving aerosol characteristics using full channels, rather than sub-band channels. The more fraction one type of aerosol is, the larger information about it we can get. Different AOD results in 0.3–0.8 aerosol DFS change and solar zenith angle influences less. The information of atmospheric gases was sensitive to both signal-to-noise ratio (SNR) and spectral resolution due to their particular absorption patterns. However, improving only the SNR by double or more could allow for the derivation of SIF emissions, assuming the a priori estimation is accurate.