基于Landsat8的地表温度反演算法研究——以滇池流域为例

2013年2月11日Landsat 8在加州范德堡空军基地发射升空,其携带的热红外传感器为反演地表温度提供了一种新的数据,但目前尚没有针对Landsat 8热红外波段反演地表温度的算法。针对Landsat 8第10波段特征,对现有反演地表温度的单窗算法进行了参数修正,得到了用Landsat 8第10波段反演地表温度的单窗算法系数。为了评价修正后算法的精度,用MODTRAN模拟地表温度为20、30和40℃时大气水汽含量分别为1.0、1.5、2.0和2.5g·cm-2传感器高度处的热辐射值,再将模拟数据用修正后的单窗算法反演地表温度,结果表明:地表温度越低、大气水汽含量越低,误差越小;模拟结果的平均误差为0.74℃。说明基于Landsat 8第10波段用修正后的单窗算法反演地表温度是可行的,该方法可为地表温度反演提供一种途径。最后以滇池流域为例,基于2013年4月20日的Landsat 8热红外数据反演了滇池流域的地表温度,并分析了滇池流域地表温度的分布特征。

Study of Land Surface Temperature Retrieval based on Landsat 8-With the Sample of Dianchi Lake Basin

February 11,2013,Landsat 8 was successfully launched in Vandenberg Air Force Base,California.The thermal infrared sensor can provide a new data source which used to retrieve land surface temperature,but there is no corresponding retrieval algorithm for it until now.According to USGS,given larger uncertainty in the Band 11 values,users should work with TIRS Band 10 as a single spectral band and should not attempt a split\|window correction by both TIRS Band 10 and 11.In this paper,we revised coefficients of the mona\|window algorithm to retrieve land surface temperature(LST) using Landsat 8 thermal infrared data (band 10),so that the new Landsat thermal infrared data could be effectively used.To validate the revised algorithm,the land surface temperature 20 ℃,30 ℃,40 ℃ and water vapor content 1,1.5,2,2.5 g·cm^-2 were assumed,then the thermal radiance on the sensor were simulated respectively using MODTRAN atmospheric simulation program,and then retrieved the LST using the improved mona\|window algorithm.The result shows that a lower land surface temperature and lower water vapor content can keep a low error,and the average error is 0.74 ℃,which indicate that the revised algorithm able to provide an accurate LST retrieval from Landsat 8 thermal data.In the last,this paper retrieved the land surface temperature of Dianchi Lake Basin based on an image collected on April 20,2013,the result fit well with the actual conditions,and then analysed its distribute characteristic.