引用本文: 王爱辉, 杨英宝, 潘鑫, 胡解君德. 顾及时空特征的FY-4A云覆盖像元地表温度重建模型[J]. 武汉大学学报 ( 信息科学版), 2021, 46(6): 852-862.
WANG Aihui, YANG Yingbao, PAN Xin, HU Jiejunde. Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 852-862.
 Citation: WANG Aihui, YANG Yingbao, PAN Xin, HU Jiejunde. Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 852-862.

## Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics

• 摘要: 热红外遥感为地表温度(land surface temperature, LST)时空全局快速获取提供了有效手段, 但目前已有的地表温度产品未估算云覆盖像元地表温度, 如何估算地表温度产品中空值像元的地表温度, 得到无缝的地表温度数据, 是热红外遥感的研究难点。针对该问题, 提出了一种顾及时空特征的LST重建模型, 该模型首先在时间域对LST空值进行重建, 然后在空间域对LST空值进行重建, 最后采用Savitzky-Golay滤波器对重建的LST数据进行平滑去噪, 实现LST的空值重建。以黑河流域为研究区域, 以风云四号A星(Fengyun 4A, FY-4A)数据为例, 计算了该模型在不同天气条件下的重建精度, 并分析了不同空值区域大小对重建结果的影响。结果表明, 所提方法能解决晴空和多云天气下有空值像元的LST重建问题, 一天内LST连续空值数目为1~31时, 重建的均方根误差为0.405~1.915 K, 决定系数R2为0.952~0.989;与传统的昼夜温度变化模型相比较, 该模型不受有效LST像元数量和LST分布时刻的影响。

Abstract:
Objectives  Resorting to remote sensing technology, land surface temperature (LST) data of high spatial and temporal resolution can be conveniently acquired. However, due to the influence of bad weather conditions such as cloud and rain, there are many null values in those LST data, being an obstacle to the further application of LST data.
Methods  A novel null reconstruction model of LST based on spatial and temporal characteristics is proposed.Firstly, the reconstruction of partial null values of LST data is realized by weighting the effective records of the same period within contiguous days in the time domain, followed the intrinsic assumption that the changes of LST data in adjacent time periods are similar. Secondly, the reconstruction of other null valued pixels are reconstructed in the spatial domain, based on the assumption that adjacent similar pixels have similar LST. The similar pixels are searched by the normalized vegetation index (NDVI). In addition, due to the influence of extreme weather conditions, the above two steps are repeated several times until all null pixels are entirely reconstructed. Finally, Savitzky-Golay (S-G) filter is employed to remove the noise of the reconstructed LST data.
Results  Compared to the in-situ data, the root mean squared errors (RMSE) of the LST data of all the FY-4A before and after reconstruction are 4.805 K and 6.969 K, respectively. Their determination coefficients (R2) are 0.842 and 0.605, respectively. Under clear sky conditions, RMSEs of FY-4A LST data before and after reconstruction are 3.392 K and 5.016 K, respectively, and their R2 are 0.948 and 0.874, respectively. Under cloudy weather conditions, RMSE and R2 of reconstructed LST data are 5.053 K and 0.726, respectively. Under rainy weather conditions, RMSE and R2 of reconstructed LST data are 7.872 K and 0.313, respectively. Compared with the original LST data of FY-4A, the RMSE of the reconstructed data ranges 0.483-0.507 K and the R2 ranges 0.846-0.976 for the null value regions of different sizes in spatial domain. RMSE ranges 0.405-1.915 K and R2 ranges 0.952-0.989 for invalid LST time series of different sizes in time domain.
Conclusions  The proposed null reconstruction model of LST can accomplish effective reconstruction not only for clear weather, but also for cloudy weather of long time series. Excluding the error of FY-4A LST data, RMSE of reconstruction results reaches 2.171 K. When the number of LST valid pixels is very small, the invalid LST can also be effectively reconstructed by the proposed model, which is tough for the diurnal temperature cycle (DTC) model.

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