数字农科院2.0

Evaluating FY3C-VIRR reconstructed land surface temperature in cloudy regions

文献类型： 外文期刊

作者： Yongqian Wang;Dejun Zhang;Liang Sun;Shiqi Yang;Tang Shihao;Yanghua Gao;Qinyu Ye;Hao Zhu

作者机构：

关键词： drought; FY-3; land surface temperature reconstruction; remotely sensed daily land surface temperature reconstruction model (rsdast)

期刊名称： European Journal of Remote Sensing

ISSN： 1129-8596

年卷期： 2021 年 54 卷 1 期

页码：

收录情况： JCR（2021版）

摘要： Missing values in land surface temperature (LST) data are often observed in the cloud-sheltered area, thereby seriously limiting the spatiotemporal continuity of LST. In this work, Remotely Sensed Daily Land Surface Temperature Reconstruction model(RSDAST) is used to gap-fill the pixels sheltered by clouds in FY-3 C/VIRR LST. Result shows that the cloud pixels in VIRR and MODIS original LST (OLST) product can be reconstructed accurately, but the reconstruction accuracy of MODIS LST is better compared to VIRR based on the RSDAST model. In addition, the reconstruction accuracy of VIRR and MODIS LST decreases with the increase in cloud coverage, and the reduction of the reconstruction accuracy of VIRR LST is larger than that of MODIS data. The number of effective dry-wet edge fitted by VIRR RLST/NDVI scatterplot was higher than that of OLST/NDVI, and the number of clear sky pixels in Reconstructed TVDI (RTVDI) images increased significantly, indicating that the RSDAST expands the temporal resolution and spatial coverage of infrared remote sensing data under cloudy conditions. Moreover, in DOY150–DOY243, the correlation between RTVDI and soil moisture is better than that of Original TVDI (OTVDI), indicating that the RSDAST improves the monitoring ability of soil moisture in these conditions. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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