Zhang Xiaoyu, Wang Chenguang, Zhao Hong, Lu Zehui
Opt Express. 2017 Oct 30;25(22):27210-27224. doi: 10.1364/OE.25.027210.
Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. Nevertheless, on the regional scale, the actual weather is cloudy for half a year in most regions. Therefore, receiving all-weather LST from thermal-infrared remote sensing is necessary and urgent. In this paper, an approach with multi-temporal and spatial neighboring-pixels in combination with diurnal solar radiation and surface temperature evolution is proposed to estimate daytime all-weather LST using FY-2D data. Evaluation of the accuracy of the algorithm is performed against the simulated data and the in situ measurements. The root mean square error (RMSE) between the actual and estimated LSTs under cloud-free conditions is approximately 1.84 K for the simulated data, while the RMSE of LST under cloud-free conditions varies from 3.42 to 5.1 K for the in situ measurement, and RMSE of LST under cloudy sky is approximately 7 K. The results indicate that the new algorithm is practical for retrieving the daytime all-weather LST at high-temporal resolution without any auxiliary field measurement, although some uncertainties exist.
地表温度（LST）是陆气系统相互作用中的一个关键参数。然而，在区域尺度上，大多数地区实际天气半年时间都是多云的。因此，从热红外遥感获取全天候LST既必要又迫切。本文提出一种结合多时相和空间相邻像元以及昼夜太阳辐射和地表温度演变的方法，利用风云二号D星数据估算白天全天候LST。针对模拟数据和实地测量数据对算法精度进行了评估。对于模拟数据，无云条件下实际LST与估算LST之间的均方根误差（RMSE）约为1.84K，而对于实地测量数据，无云条件下LST 的RMSE在3.42至5.1K之间变化，有云天空下LST的RMSE约为7K。结果表明，尽管存在一些不确定性，但新算法对于在无任何辅助实地测量的情况下以高时间分辨率反演白天全天候LST是可行的。
