摘要:
地表温度(land surface temperature,LST)是地面能量平衡等模型中的重要参数之一.高时间分辨率的遥感LST可通过降尺度处理实现空间分辨率的提高,这对详细的LST时空分布监测具有重要意义.以北京市为研究区,选择Landsat8 OLI/TIRS数据,通过改进的单窗(improved mono-window,IMW)算法反演LST作为验证数据,在计算归一化差值植被指数(normalized difference vegetation index,NDVI)和归一化差值建筑指数(normalized differ-ence built-up index,NDBI)等多种遥感指数并模拟至1000 m空间分辨率的基础上,联合空间分辨率为1000 m的MODIS/LST产品,利用随机森林(random forest,RF)模型实现LST(100 m空间分辨率)降尺度,并与多因子回归方法和基于植被指数的LST锐化算法(TsHARP)2种常用降尺度方法进行对比.实验结果表明:以模拟Landsat/LST作为降尺度数据源,RF方法降尺度LST的均方根误差(root-mean-square,RMSE)为2.01 K,与多因子回归方法和TsHARP算法相比,精度分别提高了0.16 K和0.44 K;针对MODIS/LST降尺度时,RF方法的RMSE为2.29 K,与多因子回归方法和TsHARP算法相比,精度分别提高了0.42 K和0.50 K;针对不同地表类型,RF算法降尺度效果不同,其中高植被覆盖区表现最优,RMSE为1.81 K;城镇表面因其空间异质性,RMSE则达到了2.75 K.
Abstract:
Land surface temperature(LST)is an important parameter in the model of energy balance of the earth surface. The enhanced spatial resolution of high temporal resolution of remote sensing surface temperature can be realized by downscaling algorithm,which is of great significance for monitoring the spatial and temporal distribution of the LST. In this paper,Beijing City was taken as the study area,and the LST with 100 m spatial resolution was retrieved by using Landsat8 OLI/TIRS data through improved mono-window(IMW)algorithm,which was used as validation data. Besides,the normalized difference vegetation index(NDVI),normalized difference built-up index (NDBI)and other remote sensing index were calculated and simulated to the spatial resolution of 1 000 m, which was united with the MODIS/LST with the spatial resolution of 1 000 m to be input into the random forest(RF)model to acquire downscaled LST(100 m). Meanwhile,the downscaled results retrieved by RF algorithm were compared with the two commonly used methods of downscaling,multi factor regression method and LST sharpening algorithm based on vegetation index(TsHARP). The results show that,with the simulated Landsat/LST as the data source, the RMSE of downscaling LST retrieved by RF was 2.01 K,and the RMSE was improved by 0.16 K and 0.44 K compared with the multi factor regression method and TsHARP algorithm respectively. For the MODIS/LST, the RMSE of downscaling LST retrieved by RF was 2.29 K, and the RMSE was improved by 0.42 K and 0.50 K compared with multi factor regression method and TsHARP algorithm respectively. For different land surface types, the effects of RF downscaling algorithm are different. The effect of high vegetation coverage area is the best, and the RMSE is 1.81 K. Due to the spatial heterogeneity of the urban surface, the RMSEhas reached a maximum of 2.75 K.
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