Air Temperature Estimation with MODIS Data over the Northern Tibetan Plateau

Time series of MODIS land surface temperature(Ts) and normalized difference vegetation index(NDVI) products,combined with digital elevation model(DEM) and meteorological data from 2001 to 2012,were used to map the spatial distribution of monthly mean air temperature over the Northern Tibetan Plateau(NTP). A time series analysis and a regression analysis of monthly mean land surface temperature(Ts) and air temperature(Ta) were conducted using ordinary linear regression(OLR) and geographical weighted regression(GWR). The analyses showed that GWR,which considers MODIS Ts,NDVI and elevation as independent variables,yielded much better results [RAdj2> 0.79; root-mean-square error(RMSE) =0.51℃–1.12℃] associated with estimating Tacompared to those from OLR(RAdj2= 0.40-0.78; RMSE = 1.60℃–4.38℃).In addition,some characteristics of the spatial distribution of monthly Taand the difference between the surface and air temperature(Td) are as follows. According to the analysis of the 0℃ and 10℃ isothermals,Tavalues over the NTP at elevations of 4000–5000 m were greater than 10℃ in the summer(from May to October),and Tavalues at an elevation of3200 m dropped below 0℃ in the winter(from November to April). Taexhibited an increasing trend from northwest to southeast. Except in the southeastern area of the NTP,T d values in other areas were all larger than 0℃ in the winter.

MODIS-based estimation of air temperature of the Tibetan Plateau

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.

基于MODIS数据的青藏高原气温与增温效应估算

利用2001-2007年MODIS地表温度数据、137个气象观测台站数据和ASTERGDEM数据,采用普通线性回归分析方法(OLS)及地理加权回归分析方法(GWR),研究了高原月均地表温度与气温的相关关系,最终选择精度较高的GWR分析方法,建立了高原气温与地表温度、海拔高度的回归模型。各月气温GWR回归模型的决定系数(AdjustedR2)都达到了0.91以上(0.91～0.95),标准误差(RMSE)介于1.16～1.58℃;约70%以上的台站各月残差介于-1.5～1.5℃之间,80%以上的台站的残差介于-2～2℃之间。根据该模型,估算了青藏高原气温,并在此基础上,将高原及周边地区7月份月均气温转换到4500m和5000m海拔高度上,对比分析高原内部相对于外围地区的增温效应。研究结果表明:(1)利用GWR方法,结合地面台站的观测数据和MODISTs、DEM等,对高原气温估算的精度高于以往普通回归分析模型估算的精度(RMSE=2～3℃),精度可以提高到1.58℃;(2)高原夏半年海拔5000m左右的高山区气温能达到0℃以上,尤其是7月份,海拔4000～5500m的高山区的气温仍能达到10℃左右,为山地森林的发育提供了温度条件,使高原成为北半球林线分布最高的地方;(3)高原的增温效应非常突出,初步估算,在相同的海拔高度上高原内部气温要比外围地区高6～10℃。