| In this paper,we focus on the background of global climate change and the phenomenon of whether or not there is a receding trend of climate warming in recent years,and select land surface temperature as an indicator to assess climate warming,and analyze the spatiotemporal characteristics of surface temperature based on two spatial scales,namely global scale and local scale.The global scale focuses on the trends and characteristics of land surface temperature,while the local scale focuses on the relationship between wind power and land surface temperature in the practical application of this new energy sector.Based on the global scale,the analysis of the land surface temperature itself consists of two parts: the trend analysis and the decomposition of the change characteristics.The land surface temperature data from MODIS sensors MOD11A1 and MYD11A1 are used as source data,and the instantaneous temperatures of the four moments in the daily scale are arithmetically averaged to obtain the daily average temperature data,and aggregated step by step to obtain the monthly average temperature,seasonal average temperature and annual average temperature data,and finally the seasonal average temperature and annual average temperature are used for the study and analysis.(1)In the trend analysis section,MannKendall significance test,Theil-Sen trend estimation,reference validation of onedimensional linear regression fitting,calculation of Theil’s tau correlation coefficient,and superposition analysis of significance and monotonicity are used to finally obtain the spatiotemporal trends of surface temperature;(2)In the feature decomposition section,empirical orthogonal function(EOF),north significance test,decomposition of surface temperature spatio-temporal set,obtaining effective spatial modalities as well as time series,and finally obtaining the spatio-temporal variation characteristics of surface temperature.Based on the local scale,to explore the relationship between the impact of new energy applications on the surface temperature,a wind farm in Republican County,Hainan Tibetan Autonomous Region,Qinghai Province,China is used as the study area,and the source data are MOD11A2 and MYD11A2,which are inverse surface temperature data from MODIS sensors,and the instantaneous temperatures of four moments are retained and aggregated into monthly and seasonal temperatures respectively,and finally the seasonal temperature is used for the study By removing the temporal and spatial backgrounds,the impact of wind farms is extracted from the surface temperature trend itself,and the impact on the surrounding area is further explored.The main conclusions of the study are as follows.(1)The proportion of areas showing an increase in the annual change of land surface temperature is about 47.36%,of which the significant increase is about 20.38%,while the decrease is about 19.58%,of which the significant decrease is about 0.65%,indicating that the warming trend is relatively stronger than the cooling trend,and the significant warming trend is strongest in summer,stronger in spring,followed by autumn,and weakest in winter among the seasonal changes.(2)For the spatial and temporal decomposition of land surface temperature,the effect is better in spring and autumn,with cumulative variance contributions of 69.24% and 67.69%through the north significance test,respectively,and less well in summer and winter,with cumulative variance contributions of 31.32% and 37.17% through the north significance test,respectively.(3)Wind farms have a warming effect on land surface temperature and it is more obvious in spring and winter,and the warming effect on the surrounding areas subsides with distance. |
