Explore the vast range of titles available.

Long-term highly accurate surface soil moisture data of TP （Tibetan Plateau） are important to the research of Asian monsoon and global atmospheric circulation. However, due to the sparse in-situ networks, the lack of soil moisture observations has se- riously hindered the progress of climate change researches of TP. Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E （Advanced Microwave Scanning Radiometer for EOS）, we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distri- bution and its multi-year changing trend in area of TP. Compared to the in-situ observations, the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated. The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E. The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed. The results show that the soil mois- ture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP. Based on the new soil moisture product, we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP. From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP, we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.

The poor distribution of meteorological stations results in a limited understanding of the precipitation pattern in the Tianshan Mountains. The spatial patterns of precipitation over the mid Tianshan Mountains were characterized based on the TRMM 3B43 monthly precipitation data. By comparing satellite estimates with observed data, it shows that TRMM 3B43 data underestimate the precipitation in mountain region. Regression models were developed to improve the TRMM 3B43 data, using geographic location and topographic variables extracted from DEM using GIS technology. The explained variance in observed precipitation was improved from 64% （from TRMM 3B43 products alone） to over 82% and the bias reduced by over 30% when location and topographic variables were added. We recalculated all the TRMM 3B43 monthly precipitation grids for the period 1998 to 2009 using the best regression models, and then studied the variation patterns of precipitation over the mid Tianshan Mountains. The results are well explained by a general understanding of the patterns of precipitation and orographic effects. This indicated that the Tianshan Mountains strongly influences the amount and distribution of precipitation in the region This is highlighted by the confinement of the precipitation maxima to the windward （northern slope）. And complex vertical changes in theprovenance and distribution of precipitation, like that a negative increasing rate of precipitation in the vertical direction exists in the north but does not in south. The results have also revealed large gradients and different patterns in seasonal precipitation that are not simply related to elevation, the distribution of precipitation may also be affected by other seasonal factors such as the sources of moist air, wind direction and temperature.

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature （Ta） and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature （Ts） to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years （2001 to 2007） are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta （R2 ranging from 0.62 to 0.90 and standard error between 2.25℃ and 3.23℃）. Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62℃ （in January, the coldest month） and 17.29℃ （in July, the warmest month）, and for the warm season （May-September）, it is from 13.1℃ to 17.29℃. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0℃ isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.

合成孔径雷达差分干涉(InSAR)测量技术是最近30年来发展迅速的空间对地观测技术。相对传统测量技术,该技术具有精度高、覆盖范围广、不受天气状况影响等优势,已在区域性沉降监测、灾害监测评估、能源资源勘查等非常广阔的领域展现了它的应用潜力。SAR卫星影像质量不够高是制约该技术精度提升和应用范围扩展的一大因素。