[Show abstract][Hide abstract] ABSTRACT: Methane (CH4) is regarded as one of the most important greenhouse gases due to its radiative forcing. Therefore, in running climate models, it is important to have accurate estimates of CH4 concentrations at an appropriate scale. Although great efforts have recently been undertaken to quantify atmospheric CH4 concentrations based on extensive ground-based measurements, it is still difficult to obtain the spatial variations of the CH4 volume mixing ratio (VMR) on a regional scale. This study analyses the spatial variations of CH4 VMRs in China, based on the retrieved CH4 data from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) spectra. The results showed that the spatial distribution of CH4 VMRs presented decreasing gradients from south-east to north-east and the lowest CH4 concentrations were located on the Qinghai-Tibet Plateau. Paddy fields were the main sources of CH4 in China, as shown through spatial analysis. Natural wetlands and population also contributed to CH4 VMRs. Plant, climate and soil properties presented a strong positive influence on CH4 concentrations, which could be used to interpret the spatial variations. Stepwise multiple regression modelling results showed that temperature, normalized difference vegetation index (NDVI) and soil total nitrogen could explain 76.9% of the differences in CH4 throughout China, and the average difference between the retrieved and the modelled methane concentrations was 14 ppb.
International Journal of Remote Sensing 02/2011; 32(3):833-847. DOI:10.1080/01431161.2010.517804 · 1.65 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Freshwater environments contribute more than 20% of total CH4 flux to the atmosphere, and the lakes are one of the major natural sources of atmospheric CH4, mainly in boreal lakes. So, we present and analyze the temporal and spatial variations of CH4 VMRs over Lake Baikal, the only freshwater body, the unique large and high latitude lake, based on the retrieved monthly CH4 data from SCIAMACHY spectra. The result showed that monthly CH4 concentrations in atmosphere extended between 1744.780ppb and 1818.495ppb. The value in March was a relative higher value, and then increased from April to August. After this growing, the CH4 concentration decreased to September. But, there was an increased trend in October. Overall, the temporal pattern liked the ocean pattern during the growing season, and was in line with permafrost in other time. Moreover, the spatial distribution of CH4 VMRs showed that the values were higher in North Lake Baikal, followed by South, Center. The crucial controlling factor for the seasonal variation of CH4 concentration and spatial difference was both temperature, the amount of organic matter in the sediment and stability of hydrate. This exerted a direct influence on diatom and CH4 source plus (hydrate). Furthermore, the monthly CH4 concentrations by air transport were few for the size of basin over Lake Baikal and mountain barriers. So the source over Lake Baikal was itself. Based the average annual mean (8 months) atmospheric CH4 concentration and the area of the Lake Baikal (31494Km2), the annual CH4 flux (at all atmospheric altitude levels) was roughly estimated to be 40.03Mg yr (-1) (mean) during the period (March-October, 2003).
The 18th International Conference on Geoinformatics: GIScience in Change, Geoinformatics 2010, Peking University, Beijing, China, June, 18-20, 2010; 01/2010
[Show abstract][Hide abstract] ABSTRACT: Methane (CH4) is regarded as one of the most important greenhouse gases due to its radiative forcing. Since the SCIAMACHY instrument on ENVISAT was in orbit, CH4 measurements at a regional scale became available. However, the spatial resolution of 0.5 deg latitude × 0.5 deg longitude omits many detailed spatial variations. The present study aimed to improve the spatial resolution of the retrieved atmospheric CH4 concentrations with the aid of the normalized difference vegetation index (NDVI) and land surface temperatures (LST) from MODIS with the spatial resolution of 0.05 deg latitude × 0.05 deg longitude. The gridded CH4 concentrations were firstly converted into point files, which were then divided into training and testing groups. Three methods of Ordinary Kriging, Regression Kriging, and Co-Kriging were used to simulate the spatial distribution of CH4 concentrations. The accuracy assessment showed that the Co-Kriging method combing with NDVI obtained the lowest mean predication error and root mean square prediction error. Thus, the spatially distributed atmospheric CH4 with the resolution of 0.05 deg latitude ×0.05 deg longitude was acquired.
Proceedings of SPIE - The International Society for Optical Engineering 10/2009; 7498. DOI:10.1117/12.832425 · 0.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: With the rapid development of remote sensing technology, the means of accessing to remote sensing data become increasingly abundant, thus the same area can form a large number of multi-temporal, different resolution image sequence. At present, the fusion methods are mainly: HPF, IHS transform method, PCA method, Brovey, Mallat algorithm and wavelet transform and so on. There exists a serious distortion of the spectrums in the IHS transform, Mallat algorithm omits low-frequency information of the high spatial resolution images, the integration results of which has obvious blocking effects. Wavelet multi-scale decomposition for different sizes, the directions, details and the edges can have achieved very good results, but different fusion rules and algorithms can achieve different effects. This article takes the Quickbird own image fusion as an example, basing on wavelet transform and HVS, wavelet transform and IHS integration. The result shows that the former better. This paper introduces the correlation coefficient, the relative average spectral error index and usual index to evaluate the quality of image.
Proceedings of SPIE - The International Society for Optical Engineering 01/2009; DOI:10.1117/12.832960 · 0.20 Impact Factor