Reconstruction of Hourly FY-4A AGRI Land Surface Temperature under Cloud-Covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 10; p. 1777
• Main Authors: Li, Yuxin, Zhu, Shanyou, Zhang, Guixin, Xu, Wenjie, Jiang, Wenhao, Xu, Yongming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial satellites in remote sensing; cloud-covered pixels; Clouds; Datasets; Earth temperature; Energy; Environmental aspects; Errors; Filtration; FY-4A; Heat; Identification and classification; Land surface temperature; Land Surface Temperature (LST); Machine learning; Mathematical models; Measurement; Meteorological satellites; Methods; Net radiation; Normalized difference vegetative index; Outliers (statistics); Radiation; random forest regression; Reconstruction; Remote sensing; Savitzky-Golay filtering; Skin; Surface energy; Surface properties; Temporal resolution; Weather stations; Wind; China; Heilongjiang China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16101777

Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution.