Fan Xiwei, Nie Gaozhong, Wu Hua, Tang Bo-Hui
Opt Express. 2018 Feb 19;26(4):4148-4165. doi: 10.1364/OE.26.004148.
Studies indicated that a root mean square error (RMSE) of 3.7 K was found if dust aerosol was not considered in the traditional land surface temperature (LST) retrieval algorithm. To reduce the influence of dust aerosol on LST estimation, a three-channel algorithm is proposed using MODIS channels 29, 31, and 32 with model coefficients irrelevant to the aerosol optical depth (AOD). Compared with actual and estimated LSTs, the RMSEs are 1.8 K and 1.6 K for dry and wet atmospheres, respectively, when the AOD is 1.0. Sensitivity analyses considering instrument noise, land surface emissivity uncertainties, and the algorithm error itself show that the LST errors are 2.5 K and 1.7 K for dry and wet atmospheres, respectively, when the AOD is 1.0. Finally, some in situ measured LSTs at the Jichanghuangmo, Huazhaizi, and Yingke sites in northwest China are taken as referenced LST values and compared with the MODIS LST products MOD11_L2/MYD11_L2 and those estimated with the proposed method. The results show that the proposed method can improve the LST retrieval accuracy from 1.4 K to 2.2 K in dust aerosol atmospheres.
研究表明，如果在传统的陆地表面温度（LST）反演算法中不考虑沙尘气溶胶，均方根误差（RMSE）为3.7K。为了减少沙尘气溶胶对LST估算的影响，提出了一种利用中分辨率成像光谱仪（MODIS）第29、31和32波段的三通道算法，其模型系数与气溶胶光学厚度（AOD）无关。当AOD为1.0时，与实际LST和估算LST相比，干燥和潮湿大气条件下的RMSE分别为1.8K和1.6K。考虑仪器噪声、陆地表面发射率不确定性以及算法误差本身的敏感性分析表明，当AOD为1.0时，干燥和潮湿大气条件下的LST误差分别为2.5K和1.7K。最后，将中国西北鸡场荒漠、花寨子和营科站点的一些现场实测LST作为参考LST值，与MODIS的LST产品MOD11_L2/MYD11_L2以及用所提方法估算的LST值进行比较。结果表明，所提方法在沙尘气溶胶大气条件下可将LST反演精度提高1.4K至2.2K。
