Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments.

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.

A comparison of satellite observations with the XCO2 surface obtained by fusing TCCON measurements and GEOS-Chem model outputs.

Ground observations can capture CO2 concentrations accurately but the number of available TCCON (Total Carbon Column Observing Network) sites is too small to support a comprehensive analysis (i.e. validation) of satellite observations. Atmospheric transport models can provide continuous atmospheric CO2 concentrations in space and time, but some information is difficult to generate with model simulations. The HASM platform can model continuous column-averaged CO2 dry air mole fraction (XCO2) surface taking TCCON observations as its optimum control constraints and an atmospheric transport model as its driving field. This article presents a comparison of the satellite observations with a HASM XCO2 surface obtained by fusing TCCON measurements with GEOS-Chem model results. We first verified the accuracy of the HASM XCO2 surface using six years (2010-2015) of TCCON observations and the GEOS-Chem model XCO2 results. The validation results show that the largest MAE of bias between the HASM results and observations was 0.85ppm and the smallest MAE was only 0.39ppm. Next, we modeled the HASM XCO2 surface by fusing the TCCON measurements and GEOS-Chem XCO2 model results for the period 9/1/14 to 8/31/15. Finally, we compared the GOSAT and OCO-2 observations with the HASM XCO2 surface and found that the global OCO-2 XCO2 estimates more closely resembled the HASM XCO2 surface than the GOSAT XCO2 estimates.

Surface modeling of soil antibiotics.

Large numbers of livestock and poultry feces are continuously applied into soils in intensive vegetable cultivation areas, and then some veterinary antibiotics are persistent existed in soils and cause health risk. For the spatial heterogeneity of antibiotic residues, developing a suitable technique to interpolate soil antibiotic residues is still a challenge. In this study, we developed an effective interpolator, high accuracy surface modeling (HASM) combined vegetable types, to predict the spatial patterns of soil antibiotics, using 100 surface soil samples collected from an intensive vegetable cultivation area located in east of China, and the fluoroquinolones (FQs), including ciprofloxacin (CFX), enrofloxacin (EFX) and norfloxacin (NFX), were analyzed as the target antibiotics. The results show that vegetable type is an effective factor to be combined to improve the interpolator performance. HASM achieves less mean absolute errors (MAEs) and root mean square errors (RMSEs) for total FQs (NFX+CFX+EFX), NFX, CFX and EFX than kriging with external drift (KED), stratified kriging (StK), ordinary kriging (OK) and inverse distance weighting (IDW). The MAE of HASM for FQs is 55.1 µg/kg, and the MAEs of KED, StK, OK and IDW are 99.0 µg/kg, 102.8 µg/kg, 106.3 µg/kg and 108.7 µg/kg, respectively. Further, RMSE simulated by HASM for FQs (CFX, EFX and NFX) are 106.2 µg/kg (88.6 µg/kg, 20.4 µg/kg and 39.2 µg/kg), and less 30% (27%, 22% and 36%), 33% (27%, 27% and 43%), 38% (34%, 23% and 41%) and 42% (32%, 35% and 51%) than the ones by KED, StK, OK and IDW, respectively. HASM also provides better maps with more details and more consistent maximum and minimum values of soil antibiotics compared with the measured data. The better performance can be concluded that HASM takes the vegetable type information as global approximate information, and takes local sampling data as its optimum control constraints.

[Effects of China future land use change on aboveground vegetation biomass].

Land use change has significant effects on vegetation biomass via altering ecosystem structure. By adopting a spatially explicit land use change model, this paper simulated the spatiotemporal pattern of land use change in China till 2030, based on the historical scenario (in this scenario, the land use trend in 1988-2005 was extrapolated to obtain the area of each land use type in the future) and the planned scenario (in this scenario, the area of each land use type in the future was based on the national scale land use planning). On the basis of this simulation and using a biomass density approach, the spatial pattern of vegetation biomass change in China was estimated. The simulation showed that under the historical scenario, the forest area would be decreased but the forest age would be in adverse, and accordingly, the forest biomass density would have an increase. Till 2030, the overall vegetation biomass in China would be 14619 Tg, with an increase of 251.19 Tg as compared to the situation in 2005. Under the planned scenario, the forest area would be increased, and the overall vegetation biomass in 2030 would be 15468 Tg, with an increase of 1100 Tg as compared to the situation in 2005. In the planned scenario, the planted forest area would be larger while the forest age would be younger, resulting in a much lower vegetation biomass density in 2030 than that in the historical scenario, and thus, the China's vegetation in the planned scenario would have a higher potential to act as a carbon sink.

Relationship between species diversity and ecotope diversity.

The relationship between species diversity and ecotope diversity has long been debated. But these debates seem meaningless because most of them were based on different definitions. In this paper, diversity has two components: richness based on the total number and evenness based on the relative abundance. Species diversity is distinguished into individual-counting diversity and biomass-based diversity. Ecotope diversity is divided into individual ecotope-counting diversity and ecotope-area based diversity. Under this definition, we make a comprehensive investigation into Dongzhi tableland of Loess Plateau by cooperating with local technicians. We find that individual-counting diversity is significantly correlated with biomass-based diversity in grassland ecosystems; individual ecotope-counting diversity and ecotope-area based diversity have a significant correlation. Therefore, it is unnecessary to divide species diversity into individual-counting diversity and biomass-based diversity in grassland ecosystems and to distinguish ecotope diversity into individual ecotope-counting and ecotope-area based diversity for the issues that have no special requirement for accuracy. But the analyses of the investigation data demonstrate that species diversity has no significant correlation with ecotope diversity.