The GF-5 satellite equipped with Greenhouse Gases Monitoring Instrument was launched successfully on May 9,2018. The load adopts the principle of spatial heterodyne spectroscopy for super-resolution spectroscopy. Before launch, the research team has carried out a lot laboratory calibration work and designed an on-board calibration system to ensure the accuracy and stability of data quantification to the greatest extent. The on-orbit calibration is closer to the spectral characteristics of the load on-orbit observation data, which can be complementary to the laboratory and on-board calibration. The premise of site calibration is to select a suitable and effective radiation calibration field. For the spatial heterodyne interference greenhouse gas load with large field of view, non-imaging, and hyperspectral characteristics, this paper studies the surface characteristics, radiation characteristics and atmospheric environmental characteristics of the site, put forward the screening conditions and evaluation methods of site calibration radiation field that meet the characteristics of GMI. Based on the established screening criteria, the selection of GMI calibration sites has been carried out on a global scale and multiple effective calibration sites have been obtained. Observation data that meets the calibration requirements have been collected within the scope of the site, which is the basis for the next step of site determination. The acquisition of standard coefficients has laid the foundation.
The atmospheric Greenhouse gas Monitoring Instrument (GMI) on the Gaofen-5 (GF-5) satellite measures the reflected light from the sun in the near-infrared band and retrieves the concentration of major greenhouse gases such as CO2 and CH4 in the atmosphere. Because GMI does not image ground and lacks the coaxial visible light load, the captured spatial heterodyne spectral image cannot be registered with the control points like traditional remote sensing images. It can only rely on the calibrated location system on the ground for positioning, and there may be deviations in-orbit operation. This paper uses multi-source remote sensing data to design a location registration algorithm for GMI after it is in orbit. First, a region with a large continuous coastline is selected as the registration site, and the supervised learning algorithm is used to extract coastline data with high precision from the multi-spectral image of the China-Brazil Earth Resources Satellite4 (CBERS-04) as the spatial reference. Secondly, GMI O2 band data and DPC polarization multi-spectral data are used for joint cloud detection to filter out the pixels contaminated by the cloud. Finally, according to the spectral change characteristics near the coastline of the GMI route, the observation point located on the coastline is judged, the coordinates of the point are compared with the actual coastline point coordinates, and the coordinate offset is calculated to fit the position correction parameters. The results of experiments using on-orbit data show that the algorithm in this paper can better correct the GMI location error, provide a reference for the design of the correction algorithm of the same type of instrument, and propose improvements to the design of the next generation of related instruments.
We developed an algorithm (named GMI_XCO2) to retrieve the global column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) for greenhouse-gases monitor instrument (GMI) and directional polarized camera (DPC) on the GF-5 satellite. This algorithm is designed to work in cloudless atmospheric conditions with aerosol optical thickness (AOT)<0.3. To quantify the uncertainty level of the retrieved XCO2 when the aerosols and cirrus clouds occurred in retrieving XCO2 with the GMI short wave infrared (SWIR) data, we analyzed the errors rate caused by the six types of aerosols and cirrus clouds. The results indicated that in AOT range of 0.05 to 0.3 (550 nm), the uncertainties of aerosols could lead to errors of −0.27% to 0.59%, −0.32% to 1.43%, −0.10% to 0.49%, −0.12% to 1.17%, −0.35% to 0.49%, and −0.02% to −0.24% for rural, dust, clean continental, maritime, urban, and soot aerosols, respectively. The retrieval results presented a large error due to cirrus clouds. In the cirrus optical thickness range of 0.05 to 0.8 (500 nm), the most underestimation is up to 26.25% when the surface albedo is 0.05. The most overestimation is 8.1% when the surface albedo is 0.65. The retrieval results of GMI simulation data demonstrated that the accuracy of our algorithm is within 4 ppm (∼1%) using the simultaneous measurement of aerosols and clouds from DPC. Moreover, the speed of our algorithm is faster than full-physics (FP) methods. We verified our algorithm with Greenhouse-gases Observing Satellite (GOSAT) data in Beijing area during 2016. The retrieval errors of most observations are within 4 ppm except for summer. Compared with the results of GOSAT, the correlation coefficient is 0.55 for the whole year data, increasing to 0.62 after excluding the summer data.
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