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

Abstract

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.

A Simple Real LST Reconstruction Method Combining Thermal Infrared and Microwave Remote Sensing Based on Temperature Conservation

“…The TMTC method combined microwave AMSR2 LST with thermal infrared MODIS LST and utilized the clear-sky LST obtained in previous studies to obtain the daily seamless real LST [25]. The method was divided into two steps: The first step involved filling in the orbital gaps and missing values in the microwave data AMSR2 LST.…”

Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

“…Cloud contamination, therefore, has strong impacts on the availability and the quality of LST products [37]. Methods regarding LST reconstruction for cloudy regions, including the Harmonic Analysis of Time Series (HANTS) algorithm [38], Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) model [39], Bayesian Maximum Entropy (BME) method [40], and Random Forest (RF) regression approach [41], have been investigated by considering neighboring pixels and nearby dates based on normalized difference vegetation index, enhanced vegetation index, normalized difference water index, solar radiation factor, albedo, elevation, slope, longitude, and latitude. Results showed that all methods for reconstructing LSTs for cloudcovered regions were highly accurate compared with original LST pixels and produced spatial and temporal patterns of LST that were consistent with neighboring clear-sky areas [39,41].…”

Editorial for the Special Issue “Remote Sensing Monitoring of Land Surface Temperature”

“…The combination of the state-of-the-art in the thermal infrared (TIR) domain [1][2][3] with the recent advances in the capabilities provided by operating and new satellites [4][5][6][7][8][9][10], UAVbased [11] or aerial remote sensing are boosting the use of land surface temperature (LST) in a variety of research fields [5,8,9,11,12]. LST plays a key role in soil-vegetation-atmosphere processes and becomes crucial in the estimation of surface energy flux exchanges, actual evapotranspiration, or vegetation and soil properties [8,9].…”

“…Published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation [1][2][3][4], improving long-term consistency in satellite LST [5][6][7], downscaling LST [8][9][10], LST applications [11,12] and land surface emissivity research [13,14].…”

“…The applicability of remote sensing LSTs is sometimes compromised in areas that are very frequently covered with clouds. Aware of this issue, Zhang et al [6] and Yoo et al [7] introduced approaches for the gap-filling of MODIS LST data, by reconstructing 1 km clear-sky LST using Bayesian methods [6] or random forest machine learning [7]. This strategy can improve the applicability of LSTs in a variety of research and practical fields.…”