Qinghai Lake basin and the lake have undergone significant changes in recent decades. We examine MODIS-derived grassland vegetation and snow cover of the Qinghai Lake basin and their relations with climate parameters during 2001 to 2010. Results show: (1) temperature and precipitation of the Qinghai Lake basin increased while evaporation decreased; (2) most of the grassland areas improved due to increased temperature and growing season precipitation; (3) weak relations between snow cover and precipitation/vegetation; (4) a significantly negative correlation between lake area and temperature (r=−0.9, p<0.05); and (5) a positive relation between lake level (lake-level difference) and temperature (precipitation). Compared with Namco Lake (located in the inner Tibetan Plateau) where the primary water source of lake level increases was the accelerated melt of glacier/perennial snow cover in the lake basin, for the Qinghai Lake, however, it was the increased precipitation. Increased precipitation explained the improvement of vegetation cover in the Qinghai Lake basin, while accelerated melt of glacier/perennial snow cover was responsible for the degradation of vegetation cover in Namco Lake basin. These results suggest different responses to the similar warming climate: improved (degraded) ecological condition and productive capacity of the Qinghai Lake basin (Namco Lake basin).
Snow is one of the most important components of the cryosphere. Remote sensing of snow focuses on the retrieval of snow parameters and monitoring of variations in snow using satellite data. These parameters are key inputs for hydrological and atmospheric models. Over the past 30 years, the field of snow remote sensing has grown dramatically in China. The 30-year achievements of research in different aspects of snow remote sensing in China, especially in (1) methods of retrieving snow cover, snow depth/snow water equivalent, and grain size and (2) applications to snowmelt runoff modeling, snow response on climate change, and remote sensing monitoring of snow-caused disasters are reviewed/summarized. The importance of the first remote sensing experiment on snow parameters at the upper reaches of the Heihe River Basin, in 2008, is also highlighted. A series of experiments, referred to as the Cooperative Observation Series for Snow (COSS), focus on some key topics on remote sensing of snow. COSS has been implemented for 3 years and will continue in different snow pattern regions of China. The snow assimilation system has been established in some regions using advanced ensemble Kalman filters. Finally, an outlook for the future of remote sensing of snow in China is given.
Taking Nam Co basin as an example, we examine the interrelationship among vegetation growth, lake expansion, snow cover, and climate change, based on meteorological data and multisource remote sensing datasets. Results show that the climate has become warmer and wetter during the period of time from 1961 to 2010, with rates of +0.04°C/year (P<0.001) for annual mean temperature and +1.66 mm/year (P=0.007) for annual precipitation, while the snow-covered index experienced a decreasing trend (−31.94 km2·day/year, P=0.129) from 2003 to 2010. In response, the vegetation growth was deteriorative in most parts of the basin. Conversely, both the lake’s area and water level increased (+2.15 km2/year and +0.12 m/year, respectively). Although the enhanced vegetation index in the basin negatively correlates well with the lake’s area (R2=0.75, P=0.001), the correlation shows gradual decrease as distance away from the lake’s shoreline, from 25 km (zone A), to 25–50 km (zone B), and to 50–95 km (zone C). Two main factors might have contributed to this: (1) lake expansion buried grassland vegetation in zone A and (2) more gravel buildup and soil erosion due to runoff from snow melted water in zone A than in zones B and C. This study provides a scientific basis for the evaluation of changes in alpine grassland, lake, snow cover, and their responses to climate change.
We describe and validate an improved endmember extraction method to improve the fractional snow-cover mapping based on the algorithm for fast autonomous spectral endmember determination (N-FINDR) maximizing volume iteration algorithm and orthogonal subspace projection theory. A spectral library time series is first established by choosing the expected spectra information using prior knowledge, and the fractional snow cover (FSC) is then retrieved by a fully constrained least squares linear spectral mixture analysis. The retrieved fractional snow-cover products are validated by the FSC derived from Landsat imagery. Our results indicate that the improved algorithm can obtain the endmember information accurately, and the retrieved FSC has better accuracy than the MODIS standard fractional snow-cover product (MOD10A1).