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Snow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change ...

Fractional vegetation cover (FVC) is an important parameter in the study of ecosystem balance, soil erosion, and climate change and is often used to evaluate and monitor vegetation degradation and desertification. Remote sensing provides the only feasible way to estimate FVC at regional and global scales. In the present study, an empirical model of FVC estimation is developed for central Tibetan Plateau (TP) based on the relationships between vegetation indices from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and corresponding field measurements derived from digital camera, which is followed by in-depth analysis on the spatial distribution of vegetation coverage using proposed method. Study shows that a linear relationship exists between vegetation coverage from the field observation and MODIS NDVI with coefficient of determination of R2 = 0.90, which is slightly better than MODIS SAVI performance with R2 = 0.89 and is an optimal regression model for FVC estimation. Vegetation coverage ranges 20–90% in the most part of central TP, presenting moderate to high as a whole, and generally decreases from east to west with strong regional differences due to discrepancies in land-cover types, plant species, topography and water resources availability, and so on.

The middle reaches of Yarlung Zangbo River (YR) and its two tributaries is a region in central Tibet Autonomous Region where the industrial and agricultural economy is most concentrative and developed. Remote sensing of land surface parameters has significant meaning for crop growth monitoring, crop yield assessment and disaster monitoring. It also has great theoretical importance for understanding energy and water cycle in local area and even for surrounding regions. In this paper, four scenes data in 2003 under nearly clear weather conditions are selected as the spring, summer, autumn and winter cases. Different algorithms for AVHRR and MODIS data will be applied to retrieve land surface temperature (LST), albedo, Normalized Difference Vegetation Index (NDVI), vegetation coverage, emissivity, and water vapour content, respectively. The derived LST, albedo are validated with field measurements and MODIS products. The estimations are in good agreement with in-situ measurements, with MODIS retrievals being slightly better than those of AVHRR. This means that the adopted remote sensing retrieval algorithms are not only applicable but also simple for the study area. As called the "water vapour inflow corridor", the valleys of YR and its two tributaries are proved to have higher LST and larger atmospheric water vapour content. Spatiotemporal variations of land surface parameters in the middle reaches of YR and its two tributaries can also be clearly identified.