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Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments.
- Source :
-
Sensors (14248220) . Nov2016, Vol. 16 Issue 11, p1818. 16p. - Publication Year :
- 2016
-
Abstract
- The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km² and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO²) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO² in the flight area using the limited flight test data and the approximate surface of XCO², which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO² in the flight test area, which takes the approximate surface of XCO² as its driving field and the XCO² observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO² were constructed with HASM based on the flight's observations. The results showed that the mean XCO² in the flight test area is about 400 ppm and that XCO² over urban areas is much higher than in other places. Compared with OCO-2's XCO², the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO² surface based on the flight test of the TanSat instruments fell within an expected and acceptable range. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 16
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 119760666
- Full Text :
- https://doi.org/10.3390/s16111818