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Abstract:The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and the Water Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and available methods is indispensable for the local environment evaluation and engineering constructions planning. Most of the previous permafrost maps have employed traditional mapping method based on field surveys and borehole investigation data. However their accuracy is limited because it is extremely difficulties in obtaining mass data in the high-altitude and cold regions as the QTP; moreover, the mapping method, which would effectively integrate many factors, is still facing great challenges. With the rapid development of remote sensing technology in permafrost mapping, spatial data derived from the satellite sensors can recognize the permafrost environment features and quantitatively estimate permafrost distribution. Until now there is no map indicated permafrost presence/absence on the QTP that has been generated only by remote sensing data as yet. Therefore, this paper used permafrost-influencing factors and examined distribution features of each factor in permafrost regions and seasonally frozen ground regions. Then, using the Decision Tree method with the environmental factors, the 1 km resolution permafrost map over the QTP was obtained. The result shows higher accuracy compared to the previous published map of permafrost on the QTP and the map of the glaciers, frozen ground and deserts in China, which also demonstrates that making comprehensive use of remote sensing technology in permafrost mapping research is fast, macro and feasible. Furthermore, this result provides a simple and valid method for further permafrost research.

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

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