{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:16Z","timestamp":1760144716251,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fengyun Application Pioneering Project","award":["FY-APP-2022.0204","42171101","42271351"],"award-info":[{"award-number":["FY-APP-2022.0204","42171101","42271351"]}]},{"name":"Natural Science Foundation of China","award":["FY-APP-2022.0204","42171101","42271351"],"award-info":[{"award-number":["FY-APP-2022.0204","42171101","42271351"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013G 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 \u22121.13\u20130.39 K, mean absolute errors (MAE) of 1.46\u20132.4 K, and root mean square errors (RMSE) of 1.77\u20133.2 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.<\/jats:p>","DOI":"10.3390\/rs16101777","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T06:53:49Z","timestamp":1715928829000},"page":"1777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Reconstruction of Hourly FY-4A AGRI Land Surface Temperature under Cloud-Covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8599-6075","authenticated-orcid":false,"given":"Yuxin","family":"Li","sequence":"first","affiliation":[{"name":"Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Shanyou","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Guixin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Wenjie","family":"Xu","sequence":"additional","affiliation":[{"name":"Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Wenhao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4032-8759","authenticated-orcid":false,"given":"Yongming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112803","DOI":"10.1016\/j.rse.2021.112803","article-title":"Monitoring and Characterizing Multi-Decadal Variations of Urban Thermal Condition Using Time-Series Thermal Remote Sensing and Dynamic Land Cover Data","volume":"269","author":"Xian","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2006.09.001","article-title":"Detection of Geothermal Anomalies Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared Images at Bradys Hot Springs, Nevada, USA","volume":"106","author":"Coolbaugh","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2013.12.008","article-title":"The New VIIRS 375 m Active Fire Detection Data Product: Algorithm Description and Initial Assessment","volume":"143","author":"Schroeder","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1016\/j.rse.2006.11.033","article-title":"Monitoring Root-Zone Soil Moisture through the Assimilation of a Thermal Remote Sensing-Based Soil Moisture Proxy into a Water Balance Model","volume":"112","author":"Crow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111594","DOI":"10.1016\/j.rse.2019.111594","article-title":"Evolution of Evapotranspiration Models Using Thermal and Shortwave Remote Sensing Data","volume":"237","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, W., Tan, J., Ma, M., Li, X., She, X., and Song, Z. (2019). An Effective Similar-Pixel Reconstruction of the High-Frequency Cloud-Covered Areas of Southwest China. Remote Sen., 11.","DOI":"10.3390\/rs11030336"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2017.04.008","article-title":"A Framework for the Retrieval of All-Weather Land Surface Temperature at a High Spatial Resolution from Polar-Orbiting Thermal Infrared and Passive Microwave Data","volume":"195","author":"Duan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1109\/TGRS.2019.2892417","article-title":"A Method Based on Temporal Component Decomposition for Estimating 1-Km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, P., Yin, Z., Yang, H., Wu, Y., and Ma, X. (2019). Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11030300"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3390\/rs1020333","article-title":"Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data","volume":"2","author":"Neteler","year":"2010","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/LGRS.2013.2263553","article-title":"Reconstruction of Time-Series MODIS LST in Central Qinghai-Tibet Plateau Using Geostatistical Approach","volume":"10","author":"Ke","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112566","DOI":"10.1016\/j.rse.2021.112566","article-title":"Cloudy-Sky Land Surface Temperature from VIIRS and MODIS Satellite Data Using a Surface Energy Balance-Based Method","volume":"263","author":"Jia","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27037","DOI":"10.1029\/2000JD900318","article-title":"A Generalized Algorithm for Retrieving Cloudy Sky Skin Temperature from Satellite Thermal Infrared Radiances","volume":"105","author":"Jin","year":"2000","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5228","DOI":"10.1109\/JSTARS.2017.2760202","article-title":"A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis","volume":"10","author":"Das","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1109\/TGRS.2018.2810208","article-title":"Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial\u2013Temporal\u2013Spectral Deep Convolutional Neural Network","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111931","DOI":"10.1016\/j.rse.2020.111931","article-title":"Reconstruction of Daytime Land Surface Temperatures under Cloud-Covered Conditions Using Integrated MODIS\/Terra Land Products and MSG Geostationary Satellite Data","volume":"247","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0002-1571(81)90105-9","article-title":"A Model for Diurnal Variation in Soil and Air Temperature","volume":"23","author":"Parton","year":"1981","journal-title":"Agric. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1029\/1998JD200005","article-title":"Interpolation of Surface Radiative Temperature Measured from Polar Orbiting Satellites to a Diurnal Cycle: 1. Without Clouds","volume":"104","author":"Jin","year":"1999","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4061","DOI":"10.1029\/1999JD901088","article-title":"Interpolation of Surface Radiative Temperature Measured from Polar Orbiting Satellites to a Diurnal Cycle: 2. Cloudy-Pixel Treatment","volume":"105","author":"Jin","year":"2000","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2016.06.019","article-title":"Consistent Land Surface Temperature Data Generation from Irregularly Spaced Landsat Imagery","volume":"184","author":"Fu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2014.04.022","article-title":"A Generic Framework for Modeling Diurnal Land Surface Temperatures with Remotely Sensed Thermal Observations under Clear Sky","volume":"150","author":"Huang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"852","article-title":"Land Surface Temperature Reconstruction Model of FY-4A Cloudy Pixels Considering Spatial and Temporal Characteristics","volume":"46","author":"Wang","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5003917","DOI":"10.1109\/TGRS.2022.3227074","article-title":"Reconstruction of Hourly All-Weather Land Surface Temperature by Integrating Reanalysis Data and Thermal Infrared Data From Geostationary Satellites (RTG)","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"The 30 m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"044004","DOI":"10.1088\/1748-9326\/5\/4\/044004","article-title":"Land Surface Skin Temperature Climatology: Benefitting from the Strengths of Satellite Observations","volume":"5","author":"Jin","year":"2010","journal-title":"Environ. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1080\/01431161.2018.1466082","article-title":"The Impact of the Terrain Effect on Land Surface Temperature Variation Based on Landsat-8 Observations in Mountainous Areas","volume":"40","author":"He","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1002\/2017JD027903","article-title":"Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis","volume":"123","author":"Jia","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_28","unstructured":"Copernicus Climate Change Service (C3S) (2019). C.C.C. C3S ERA5-Land Reanalysis. Copernic. Clim. Chang. Serv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.rse.2018.12.008","article-title":"A Practical Method for Reducing Terrain Effect on Land Surface Temperature Using Random Forest Regression","volume":"221","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1777\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:43:52Z","timestamp":1760107432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":32,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101777"],"URL":"https:\/\/doi.org\/10.3390\/rs16101777","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,5,17]]}}}