Spatial Heterogeneity Of Seasonal Land LST Under The Background Of Multi-source Drivers

From a global perspective,the impact of LST on air pollution,ecological damage and human quality of life is increasingly strong.Due to the continuous construction of urban and rural areas,the"blue-green space"of the underlying surface is gradually replaced by construction and hardening,which leads to the rise of LST.In recent years,the problems of living,ecology and production caused by the rise of LST have gradually emerged,seriously affecting human life safety.Therefore,a deep understanding of the driving factors of LST has become an urgent problem.Jiangsu Province is a coastal province in the east of China.It is not only prosperous in culture and rich in resources,but also one of the leading provinces in economic development.This paper takes Jiangsu Province as an example to explore the driving factors of land LST at county and district scale in Jiangsu Province.This study has an in-depth understanding of the nature,climate and meteorology,ecological environment and social economy of Jiangsu Province,using multi-source data such as land use,social economy,POI,air quality and meteorology,etc.OLS,GWR,MGWR,kernel density analysis,kriging interpolation and landscape index analysis were used to excavate the driving factors of spatial heterogeneity of land LST in Jiangsu Province in 2018.The main research contents and results are as follows:(1)In this paper,the seasonal division rules in meteorology are adopted for the seasonal division of the data with diurnal degree in the full text.A total of 46 driving factors of varying and non-varying types were sorted out.Land LST and driving factors were calculated and connected,so as to explore the seasonal driving force of land LST.While ensuring the reliability of experimental data,the fitting degree of experimental results was improved.(2)Spatial pattern analysis of land LST and driving factors showed that the temperature in the south was higher than that in the north in spring,summer and autumn,and the area of the middle temperature region was larger than that in the north.Variable and non-variable factors showed spatial heterogeneity due to the interaction of natural and human factors.(3)The OLS regression model was used to screen the driving factors.It was proved that 20driving factors from six aspects passed the zero test and multicollinearity test(VIF<7.5).The autocorrelation results show that the LST distribution in four seasons is significantly positive spatial autocorrelation.Local autocorrelation shows that the distribution of LST has spatial heterogeneity and instability.Compared with the output parameters of the traditional GWR model and the MGWR model,the R²,Adj-R²,AICC and the sum of squares of residuals of the MGWR model were better,and the fitting effect was 0.900 in spring.0.751 in summer;In autumn,it was 0.827;It was 0.950 in winter.The results show that the driving factors selected in this paper have the highest fitting degree in winter.(4)The driving factors of nature,social economy,air quality,human activities,meteorology and landscape used in this paper are analyzed.The results show that:on the whole,there are great differences in driving factors of four seasons,and different driving factors regulate LST through different driving effects in different seasons.In terms of driving factors,relative humidity,POI and O₃ are the main driving factors for LST in spring.Relative humidity,population density and POI were the main factors in summer.In autumn,the main factors are O₃,sunshine duration and NO₂.In winter,sunshine hours,NO₂ and O₃ are the main factors.The paper has 36 pictures,11 tables,and 85 references.