Spatio-temporal Variations And Driving Factors Of Land Surface Temperature In China Based On Reconstructed Remote Sensing Data

In recent years,many extreme weather and disasters have occurred in China with the intensification of global climate change,and the abnormal changes in land surface temperature(LST)have posed a huge threat to the stability of the climate environment and agricultural production.LST,an important indicator to evaluate the earth's environment,has great influence on material cycle,ecosystem balance and the production and life of human beings.In the context of global climate change,accurately revealing the characteristics of the LST temporal and spatial changes and studying the driving factors of these changes are important for further understanding the formation mechanism of LST and coping with global climate change,and have great research value for many works including meteorological disaster warning,agriculture production and ecological restoration.This study,based on the deficiency of pixel information and low precision of MODIS LST data due to cloud and rain weather,used a data restoration model to reconstruct higher-quality LST data of China from 2002 to 2018.On this basis,the temporal and spatial changes of China LST on different time dimensions were analyzed from multiple scales such as the whole area,local area and single pixel,and the areas of abnormal LST change were studied emphatically.Finally,this study combined various kinds of data such as the land surface,atmosphere,ocean,and social and economic development to explore the driving factors of the temporal and spatial changes of LST.(1)The data restoration model constructed based on ground stations,neighboring pixels,and altitude has effectively improved the data quality of MODIS LST.The coverage of reconstructed monthly LST data is more complete,and the data accuracy reaches about 1.5K.(2)From 2002 to 2018,LST in China showed a fluctuating growth trend at a rate of 0.008?/ year,with a large range of inter-annual variation.There are some abnormal warming trends in many regions including the central part of Inner Mongolia,east of the Taihang Mountains in the North China Plain,south of the Qilian Mountains in the Qinghai-Tibet Plateau,Chengdu Plain and the Yangtze River Delta region,and the average warming rate reached 0.08?/ year.The LST change trends under different time dimensions have obvious spatial difference.(3)Among these four parameters selected in this study,precipitation and vegetation have the strongest influence on the interannual variation of LST,followed by soil moisture and atmospheric water vapor.There is a significant positive correlation between vegetation quantity and LST in cold areas such as high altitude regions and high latitude regions,while the increase of precipitation and soil moisture will lead to a significant cooling trend in most areas of China.(4)The spatial distribution of LST in China is significantly affected by seven factors including latitude,longitude,altitude,NDVI,soil moisture,atmospheric water vapor and precipitation.However,the influence degree of the seven variables on the LST is different in different regions.The regression model based on these seven parameters can be used to effectively simulate the local LST.The overall simulation accuracy is about 2K,and some areas can be less than 1K.(5)El Nino-La Nina,NAO and IOBW play an important driving role in the interannual variation of LST in China,and there is a certain time lag in these driving effects.El Nino-La Nina has a significant influence on the LST over nearly 40% of China,which is mainly manifested in the El Nino event leading to a significant warming trend in some areas including the most of North China Plain,western Tarim Basin,most of Qinghai-Tibet Plateau and southwest China.The La Nina event had the opposite effects.The influence of NAO on LST has a reverse relationship in western and eastern regions.NAO of positive year will lead to a cooling trend in western China(northern Xinjiang,Qinghai-Tibet Plateau,Yunnan-Guizhou Plateau),and lead to a warming trend in northeast China and the eastern part of North China Plain.The warming of the Indian Ocean(IOBW>0)will lead to a significant warming in Southwest China and Hainan Island,while the Inner Mongolia Plateau and some parts of the Northeast Plain will show a cooling trend.The lag time of El Nino-La Nina's impact on LST is relatively long,with an average lag time close to 8 months.The lag effect of IOBW(5 months)and NAO(3 months)on LST is relatively weak.The application of the influence relationship and hysteresis effect of the three climatic patterns on LST can provide effective references for the early warning of meteorological disasters in many regions of China.(6)Social and economic factors including urban expansion,population growth and infrastructure construction are important driving forces for the significant warming of some regions in China,such as along the Beijing-Guangzhou Railway in the North China Plain and in some large transport hub cities including Beijing,Shanghai,Zhengzhou,Xi'an and Chengdu.