KEYWORDS: Signal to noise ratio, Sensors, Signal processing, Satellites, Digital signal processing, Design and modelling, Image processing, Process control, Spectroscopy, Quantum efficiency
Regarding crop production, which is the basis for food security, improving yields, using fewer materials through the appropriate use of nitrogen fertilizers and pesticides, and protecting the environment have become important global issues. This is part of the Green Food System Strategy announced by Japan's Ministry of Agriculture, Forestry, and Fisheries (MAFF) in May 2021. In this context, we plan to launch a miniature satellite with a hyperspectral sensor to observe Canopy Nitrogen Content (CNC) and Solar-Induced Fluorescence (SIF) in the mid-to the late 2020s. A miniature hyper spectrometer with a wide spectral range of 400 nm to 1700 nm and a narrow spectral resolution of 2nm to 10 nm, with a relatively medium Ground Sampling Distance (GSD) of 70m and Signal-to-Noise Ratio (SNR) of approximately 130 is currently under consideration. System optimization, such as the trade-off between the GSD and SNR under mass and envelope constraints, and the introduction of cutting-edge technologies, such as visible-enhanced InGaAs detectors, are both critical to the realization of specific mission objectives. A feasibility study and preliminary payload design are presented in this thesis.
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.
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