High spatial resolution land surface temperature (LST) has widespread applications in many fields. Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is a crucial means of obtaining such data. UAV thermal cameras typically need to capture numerous images to create a comprehensive TIR mosaic covering the target region, which is then converted into an LST map. However, LST can change rapidly over time, leading to temporal inconsistencies within the LST map, thereby affecting subsequent analysis and decision-making. Although reducing the UAV flight duration can minimize such inconsistencies, most practical applications cannot meet this requirement. Therefore, acquiring the time information of UAV TIR mosaic pixels is essential for developing temporal normalization methods and for assessing temperature data quality. Here, we propose a so-called equivalent acquisition time estimation for mosaics (EATEMs) method, designed to estimate the equivalent acquisition time (EAT) of UAV TIR mosaic pixels. This method integrates principles of UAV photogrammetry and image fusion. In our experiments, the estimated time map accurately reflects the UAV’s flight path and landing situation. Additionally, evaluation results based on ground-measured data indicate that the estimated time has an uncertainty of less than 5 min when there is a good linear relationship between LST and time. The more significant the linear relationship, the smaller the uncertainty. These promising results demonstrate the potential of the EATEM method in addressing issues related to temporal variations in UAV TIR remote sensing.

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST Downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the Geographically Weighted Regression (GWR) and Random Forest (RF), a new downscaling method (i.e., WGWR) considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat-8 TIRS and Terra ASTER LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the RMSEs can be reduced by 0.34 K (0.26 K) in Zhangye and 0.22 K (0.1 K) in Beijing. Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based onin-situLST indicates that the downscaled LST based on WGWR has better agreement with thein-situLST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.

It is urgently needed to obtain the hourly near-real-time all-weather land surface temperature (NRT-AW LST) for immediately monitoring the disaster and environmental changes. Nevertheless, studies on estimating hourly NRT-AW LST are in the preliminary stage. In this study, we proposed a Spatio-TEmporal Fusion (STEF) method for fusing the reanalysis dataset derived from China Land Surface Data Assimilation System (CLDAS) and thermal infrared (TIR) data derived from the Chinese Fengyun-4A (FY-4A) geostationary satellite to estimate the hourly NRT-AW LST with 0.04° resolution. STEF method can produce NRT-AW LST without relying on the data after the target moment. STEF is tested in the Tibetan Plateau. Validation results on DOY 215-366 of 2020 indicate that STEF has good accuracy: RMSEs (MBEs) under clear-sky, cloudy-sky, and all-weather conditions vary from 2.74 K (-1.06 K) to 3.77 K (0.14 K), from 3.31 K (-1.40 K) to 4.46 K (-0.22 K), and from 3.10 K (-1.11 K) to 3.87 K (-0.22 K), respectively. STEF method can improve the accuracies of FY-4A LST, and RMSEs are reduced by about 0.77 K to 1.82 K. The NRT-AW LSTs estimated by STEF have better accuracies than CLDAS LSTs under all-weather conditions. The SETF also exhibited similar results in 2021. We believe that the proposed STEF method can meet the requirements of NRT-AW LST estimation and contributes to improving the timeliness of region monitoring and related parameter estimations.

Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3 km MSG DSR data was spatially downscaled to 30 m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3 km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation within-situmeasurements also revealed the significant improvement in terms of the coefficient of determination (R2) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/m2). In general, the proposed downscaling method in this study show good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.

Glacial debris flows are a common geological hazard in theglacial region of the Tibetan Plateau. This study analyzed the relationship between land surface temperature (LST) and glacial debris flow in the southeastern part of the Tibetan Plateau. LST showed a year-to-year upward trend, which was more pronounced in the glacial region, throughout the study area. After analyzing the causes of eight glacial debris flows, we found that the sudden increase of LST and the long-term high LST in the early period are the main causes besides the rainfall. The results of the study show that LST can be an effective parameter for monitoring and forecasting glacial debris flows.

Microwave land surface emissivity (MLSE) at the K band plays a key role in driving geophysical parameters, such as land surface temperature (LST). However, satellite-based MLSE currently is hard to be quickly retrieved in clear skies since the time cost is high in removing atmospheric contributions. In this letter, one clear-sky atmospheric profile dataset, including a wide range of precipitable water vapor (PWV) values, was constructed using the Thermodynamic Initial Guess Retrieval database for analyzing numerical relationships between PWV and atmospheric parameters. Then, a simplified algorithm was developed for accurately retrieving instantaneous K-bandMLSEs (18.7 and 23.8 GHz) under clear skies, which can significantly save the time of atmospheric correction. The sensitivity analysis shows that PWV is a key factor affecting MLSE estimation at 23.8 GHz, and the brightness temperature (BT) uncertainty has a greater impact on MLSE estimation than LST. Additionally, with LST derived from the Moderate-resolution Imaging Spectroradiometer, BT from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), and the ERA5 reanalysis PWV in 2008, the proposed algorithm was respectively applied in Europe and the United States for presenting its applicability at a station scale and regional scale. The actual sounding profile and global AMSR-E MLSE product were used as validation datasets. Results indicate the simplified approach has a good performance with RMSEs less than 0.02 in the site and regional validations. Whereas, there are some apparent overestimations in estimating clear-skies MLSEs, especially for 23.8 GHz. We believe the proposed approach is promising for retrieving other parameters.

Thermal infrared (TIR) land surface temperature (LST) products derived from geostationary satellites have a high temporal resolution in a diurnal cycle, but they have many missing values under cloudy-sky conditions. Therefore, it is pressing to obtain all-weather LST (AW LST) with a high temporal resolution by filling the gap of TIR LST. In this study, a method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly AW LST. Then, taking the Tibetan Plateau, which is a focus of climate change as a case, RTG was applied to the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation based on thein-situLST shows that the accuracy of the AW LST is better than the FY-4A LST and CLDAS LST under clear-sky, cloudy-sky, and all-weather conditions. The mean RMSEs are 3.02 K for clear-sky conditions, 3.94 K for cloudy-sky conditions, and 3.57 K for all-weather conditions. Uncertainty and coarse resolution of the original FY-4A and CLDAS data affect the accuracy of the obtained AW LST. The results of the LST time series comparison also show that the reconstructed AW LST is consistent within-situLST. The reconstructed AW LST also has good image quality and provides reliable spatial patterns. RTG is practical in obtaining high temporal resolution AW LST from the Chinese FY-4A to satisfy related applications. It can also be extended to other geostationary satellites and reanalysis datasets.