Feedback and contribution of vegetation, air temperature and precipitation to land surface temperature in the Yangtze River Basin considering statistical analysis

Land surface temperature(LST),especially day-night LST difference(LSTd-LSTn),is a key variable for the stability of terrestrial ecosystems,affected by vegetation and climate change.Quantifying the contribution and feedback of vegetation and climate to LST changes is critical to developing mitigation strategies.Based on LST,Normalized vegetation index(NDVI),land use(LU),air temperature(AT)and precipitation(Pre)from 2003 to 2021,partial correlation was used to analyze the response of LST to vegetation and climate.The feedback and contribution of both to LST were further quantifed by using spatial linear relationships and partial derivatives analysis.The results showed that both interannual LST(LSTy)and LSTd-LSTn responded negatively to vegetation,and vegetation had a negative feedback effect in areas with significantly altered.Vegetation was also a major contributor to the decline of LSTd-LSTn.With the advantage of positive partial correlation area of 94.99%,AT became the main driving factor and contributor to LSTy change trend.Pre contributed negatively to both LSTy and LSTd-LSTn,with contributions of-0.004℃/y and-0.022℃/y,respectively.AT played a decisive role in LST warming of YRB,which was partially mitigated by vegetation and Pre.The present research contributed'to,the,detection,of LST changes and improved understanding of the driving mechanism.

Future variation of land surface temperature in the Yangtze River Basinbasedon CMIP6 model

In recent years,past changes in global and regional land surface temperatures(LST)have been well studied,however,future LST changes have been largely ignored owing to data limitations.In this study,three climate variables of CMIP6,namely air temperature(AT),precipitation(Pre),and leaf area index(LAl),were spatially corrected using the Delta downscaling method.On this basis,by combining MODIS LST,elevation,slope and aspect,a random forest(RF)model was built to calculate the LST from 2022 to 2100.The absolute variability(AV)and Mann-Kendall(M-K)tests were used to quantitatively detect interannual and seasonal LST changes in different Shared Socioeconomic Pathways(SSPs)scenarios.The results showed that the AV value increased successively from SSP1-2.6 to SSP2-4.5 and then to SSP5-8.5.Compared with the base period(2003-2021),the increment in interannual,spring,summer and autumn LST during 2022-2100 was mainly between 1 and 2°℃under threescenarios.The interannual and seasonal LST were spatially characterized by significant warming over large areas,and the increasing was the fastest under SSP5-8.5.These results indicate that,in the future,LST will increase further over large areas,especially in winter.