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Open AccessArticle

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

by Yunfei Zhang 1,2, Yunhao Chen 1,2,*, Yang Li 1,2, Haiping Xia 1,2 and Jing Li 1,2
1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2610; https://doi.org/10.3390/rs11222610
Received: 18 September 2019 / Revised: 4 November 2019 / Accepted: 5 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))
The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China’s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research. View Full-Text
Keywords: land surface temperature; MODIS; Bayesian Maximum Entropy; interpolation land surface temperature; MODIS; Bayesian Maximum Entropy; interpolation
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MDPI and ACS Style

Zhang, Y.; Chen, Y.; Li, Y.; Xia, H.; Li, J. Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method. Remote Sens. 2019, 11, 2610.

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