End-to-end performance simulator for green house gas observation sensor

For evaluation of system design validity of earth observation sensors in the early development phase, recently many endto-end performance simulators to predict final product accuracy based on sensor hardware design as well as data analysis algorithm were developed. Nevertheless, these performance simulators are very complicated because of most earth observation satellite projects have become too huge, as a result, it is hard to grasp the whole picture of the simulator. We are planning to develop generic end-to-end performance simulator. Its basic strategy is to make the simulator to be simple and to be able to explain the whole system including the relation between instrumental design and the final product, and not to introduce outside complicated black-box model. We started with fundamental mathematics which describe sensor performance and retrieval algorithm. A performance simulator of a Fourier Transform Spectrometer (FTS) applied for green house observation like Greenhouse Gases Observing SATellite (GOSAT) was constructed as a model case. Key performance of the sensor for determination of CO2 column averaged mole fractions (XCO2) during retrieval process are determination accuracy of the instrumental line shape (ILS) and Signal to Noise Ratio (SNR). The ILS was calculated based on mathematical models with instrumental design parameters of GOSAT, and then expected synthetic atmospheric absorption spectrum obtained by the sensor was simulated based on the ILS and an atmospheric forward model. The atmospheric forward model was based on Lambert-Beer law together with the solar irradiance and CO2 absorption cross section database with assumed XCO2, as well as vertical profile of background atmospheric temperature and pressure from meteorological model. Retrieved XCO2 was simulated with its accuracy based on various ILS determination accuracy and SNR. We also applied this simulator to on-orbit data of GOSAT and retrieved XCO2 from Level-1B spectra during period of April 1-30, 2021. As a result, the mean XCO2 during the period of interest agreed with the mean XCO2 of Level-2 products provided by the National Institute for Environmental Studies (NIES) by a difference of 0.5 ppm. In this way, we not only constructed the end-to-end simulator from scratch, but also evaluated the validity of the output products by the actual on-orbit data.

[1]  Ilse Aben,et al.  Determination of the TROPOMI-SWIR instrument spectral response function , 2018, Atmospheric Measurement Techniques.

[2]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[3]  H. Tanimoto,et al.  First Concurrent Observations of NO2 and CO2 From Power Plant Plumes by Airborne Remote Sensing , 2021, Geophysical Research Letters.

[4]  J. Kauppinen,et al.  Fourier Transforms in Spectroscopy , 2001 .

[5]  Calle Isaac Newton.  ARCHEO-E2E: A Reference Architecture for Earth Observation end-to-end Mission Performance Simulators , 2012 .

[6]  H. Gavin.  The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems , 2019 .

[7]  Hyuk Park,et al.  BIBLOS for simulation of the Earth observation missions , 2020, Other Conferences.

[8]  Hartmut Boesch,et al.  Retrieving XCO2 from GOSAT FTS over East Asia Using Simultaneous Aerosol Information from CAI , 2016, Remote. Sens..

[9]  Luis Guanter,et al.  S2eteS: An End-to-End Modeling Tool for the Simulation of Sentinel-2 Image Products , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Hermann Kaufmann,et al.  EeteS: An end-to-end image simulation tool applied to THE EnMAP hyperspectral mission , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[11]  W. Linwood Jones,et al.  Conical-scanning active/passive microwave remote sensor computer simulation , 2007, SPIE Defense + Commercial Sensing.

[12]  Figen S. Oktem,et al.  Image Chain Simulation for Earth Observation Satellites , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Tatsuya Yokota,et al.  Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite , 2010 .

[14]  Luis Alonso,et al.  Design of a satellite end-to-end mission performance simulator for imaging spectrometers and its application to the ESA's FLEX/Sentinel-3 tandem mission , 2015, SPIE Optical Engineering + Applications.

[15]  David M. Rider,et al.  Preflight Spectral Calibration of the Orbiting Carbon Observatory , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[16]  G. Thuillier,et al.  The Solar Spectral Irradiance from 200 to 2400 nm as Measured by the SOLSPEC Spectrometer from the Atlas and Eureca Missions , 2003 .

[17]  Hartmut Boesch,et al.  The CarbonSat End-to-End Simulator , 2016 .