摘要
为了反演出全天各个时刻的地表温度数据,在不考虑大气水汽含量和地表比辐射率的情况下,首先建立"风云2号"C星2个热红外通道数据相对于MODIS地表温度数据之间的回归方程,反演出各个时刻的5 km空间分辨率的地表辐射温度;然后根据地表温度最大值和最小值出现的时间,将反演的地表辐射温度降尺度到1 km空间分辨率,在同时刻、同尺度的前提下,通过对"风云2号"C星数据反演得到的5 km空间分辨率地表辐射温度和MODIS数据升尺度得到的5 km空间分辨率地表温度以及它们的空间分布进行对比分析,发现二者的空间分布格局趋于一致;最后,结合植被覆盖分类图,求出不同植被覆盖类型区域在"风云2号"C星(1 km空间分辨率)影像和MODIS(1 km空间分辨率)影像上的平均地表辐射温度,计算得到的绝对误差为1.95 K,相对误差为10.7%。反演结果表明,以土壤和植被为主体的地表面,用该方法得到的地表辐射温度与MODIS地表温度的最大误差在2 K之内。
In order to invert the land surface temperature data of a whole day,the authors first used data for MODIS land surface temperature and the same passing time of the two infrared channels of FY-2C satellite to obtain the regression coefficient by linear fitting of each pixel without considering atmospheric water vapor content and land surface emissivity,and then inverted the land surface radiation temperature with the spatial resolution of 5 km per hour of the day.Based on the transit time,the maximum and minimum surface temperatures appeared,which were downscaled to the spatial resolution of 1 km.Comparing the distribution of the 5 km spatial resolution land surface radiation temperature inverted from FY-2C data with the distribution of MODIS 5 km spatial resolution land surface temperature at the same time and same scale,the authors found that their spatial distributions are similar.Finally,the authors calculated the average 1 km spatial resolution surface radiation temperature inverted from FY-2C remote sensing data and MODIS 1 km spatial resolution land surface temperature in the regions with different vegetation types in combination with the vegetation cover classification map,and the results suggest that the absolute error is 1.95 K and the relative error is 10.7%,which means that the error of the land surface radiation temperature inverted by the method and the land surface temperature is below 2 K when the main body of the land surface is covered with soil and vegetation.
出处
《国土资源遥感》
CSCD
2011年第4期14-19,共6页
Remote Sensing for Land & Resources
基金
国家自然科学基金项目(编号:408711704
0801141和41101329)
中国科学院百人计划项目共同资助