Latent heat flux is the main indicator of regional water-heat balance and plays an important role in drought monitoring and water resource management. Here, we attempt to estimate latent heat flux using a two-source energy balance model (TSEB). The decomposition algorithm of soil surface temperature and vegetation canopy temperature is discussed, and it is a key factor for calculating the latent heat flux in the TSEB model. Temperature decomposition was conducted using two methods: one is based on a simple linear relationship between the canopy temperature and directional radiation temperature and the other is based on soil latent heat flux expressed by the Priestley–Taylor formula. Then, the soil temperature was estimated using the soil latent heat flux. The estimation of the surface heat flux was based on the soil and vegetation canopy temperatures. The results show that the Priestley–Taylor formula method provided more accurate estimates of the latent heat flux than the linear relation method, and the reliability and precision were improved. The root-mean-squares error of the former method decreased by 38.8% compared with the latter method. The TSEB model was used to estimate the surface heat flux, and it was feasible for monitoring drought in typical drought-prone regions.
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.
Snowline altitude (SLA) is the most sensitive indicator for monitoring climatic behavior among all the cryosphere elements. In this study, the snowline and SLA over the Tibetan plateau (TP) during 2001 to 2013 are extracted using the cloud-removed MODIS daily fractional snow cover (FSC) products combined with digital elevation model (DEM), and the spatiotemporal changes of SLA and their response to the changing temperature are examined. The proposed MODIS-based SLA-extracting methodology includes cloud removal from MODIS FSC data, the determination of the snowline and SLA, and the establishment of the snowline altitude field (SLAF). Results show that the SLA in the interior of the TP is obviously higher than the peripheral mountainous area due to the complex terrain. There is no obvious trend of SLA change during the examined period although a strong seasonal and interannual variability of SLA is discovered. The interannual fluctuation of SLA in the snowmelt period can be explained by the high-positive correlations between the SLA and temperature. The MODIS-based SLA-extracting method described has a good application potential in SLA monitoring for other regions.
The complex terrain, shallow snowpack, and cloudy conditions of the Tibetan Plateau (TP) can greatly affect the reliability of different remote sensing (RS) data, and available station data are scarce for simulating and validating the snow distribution. Aiming at these problems, we design a synthesis method for simulating the snow distribution in the TP where the snow is patchy and shallow in most regions. Different RS data are assimilated into the SnowModel, using the ensemble Kalman filter method. The station observations are used for the validation of assimilated snow depth. To avoid the scale effect during validation, we design a random sampling comparison method by constructing a subjunctive region near each station. For years 2000 to 2008, the root-mean-square error of the assimilated results are in the range [0.002 m, 0.008 m], and the range of Pearson product-moment correlation coefficients between the in situ observations and the assimilated results are in the range [0.61, 0.87]. The result suggests that the snow depletion curve is the most important parameter for the simulation of the snow distribution in ungauged regions, especially in the TP where the snow is patchy and shallow.
Snow cover changes over the Tibetan plateau (TP) are examined using moderate resolution imaging spectroradiometer (MODIS) daily fractional snow cover (FSC) data from 2001 to 2011 as well as in situ temperature data. First, the accuracy of the MODIS FSC data under clear sky conditions is evaluated by comparing with Landsat 30-m observations. Then we describe a cloud-gap-filled (CGF) method using cubic spline interpolation algorithm to fill in data gaps caused by clouds. Finally, the spatial and temporal changes of snow cover are analyzed on the basis of the MODIS-derived snow-covered area and snow-covered days (SCD) data. Results show that the mean absolute error of MODIS FSC data under clear sky condition is about 0.098 over the TP. The CGF method is efficient in cloud reduction (overall mean absolute error of the retrieved FSC data is 0.092). There is a very high inter-annual and intra-seasonal variability of snow cover in the 11 years. The higher snow cover corresponds well with the huge mountains. The accumulation and melt periods of snow cover vary in different elevation zones. About 34.14% (5.56% with a significant decline) and 24.75% (3.9% with a significant increase) of the study area presents declining and increasing trend in SCD, respectively. The inter-annual fluctuation of snow cover can be explained by the high negative correlations observed between the snow cover and the in situ temperature, especially in some elevations of February, April, May, August, and September.
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