Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

On the basis of preceding study of microwave (MW) land surface temperature (LST) downscaling, this letter proposed an all-weather LST fusion method based on random forest (RF) and evaluated it using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) LSTs in four areas of China that represent different landscapes. The results show that the RF method can effectively avoid the problem of oversmoothing patterns derived by the widely used Bayesian maximum entropy (BME) method and obtained LSTs more consistent with reality. Taking MODIS LST in the Yunnan–Guizhou Plateau (YGP) region and the border of Shanxi Province and Henan Province (BSH) region as a reference, the accuracy of RF method improved up to 13% and 11% compared with those of BME method under different cloud proportions. Taking field observations in the Heihe River Basin (HRB) and the Naqu area as references, the accuracy of RF-derived LST under cloudy conditions is basically consistent with that of MODIS LST in clear sky, differing by only 0.004–0.067 K. Due to the introduction of environmental variables, the performance of RF method is more stable than that of the BME method under different cloud proportions. In summary, RF is promising for fusing MW and thermal infrared (TIR) LSTs.

To extend the time span of the microwave (MW) land surface temperature (LST) dataset in China, this study proposed an optimized empirical algorithm for Advanced Microwave Scanning Radiometer 2 (AMSR2) LST retrieval based on the algorithm for its predecessor, the AMSR-Earth Observing System (AMSR-E). A modified comprehensive classification system of environmental variables (CCSEV) that considered the impact of landform, landcover, atmospheric conditions, and solar radiation on the variation of LST was first constructed, and the LST for each class in the CCSEV was then retrieved through stepwise regression using the brightness temperature in different AMSR2 channels. The results indicate that the annual RMSE of the AMSR2 LST, compared to the reference Moderate Resolution Imaging Spectroradiometer (MODIS) LST from 2012 to 2020, varies between 3.26 K and 3.61 K in the daytime and 2.76 K and 2.96 K in the nighttime, respectively. The RMSE of the AMSR2 LST compared to the field measurements at the sites of the Beidahe river basin and Naqu regions varies between 4.16 K and 5.26 K in the daytime and 2.4 K and 5.17 K in the nighttime. The accuracy is relatively low in the warmer months and daytime due to the stronger solar radiation, and is also relatively low in western China due to the dominate highly fluctuating topography and barren and arid landcover. Generally, the accuracy of the AMSR2 LST is comparable with that of the AMSR-E LST retrieved by the predecessor algorithm, which facilitates coherent long-term applications using AMSR series LST datasets.

Lake-terminating glaciers have some peculiar behaviors compared to land-terminating glaciers, but in-depth observation is still limited regarding their formation, which is crucial for understanding the glacier–lake interaction. Here, the long-term evolutions of Tanymas Lake and the Fedchenko Glacier were investigated based on Landsat images, Google Earth imagery, KH-9 images, glacier surface elevation and velocity change datasets, and meteorological records. The results indicate that Tanymas Lake is both an ice-contact proglacial lake and an ice-dammed lake. It covered an area of 1.10 km² in September 2022, and it is one of the largest glacial lakes in Pamir and even in HMA. The initial basin of Tanymas Lake is a moraine depression in Tanymas Pass, and the blocked dam is the Tanymas-5 Glacier and its terminal moraine. Tanymas Lake was in an embryonic stage before August 2005, in a formation and expansion stage from August 2005 to September 2018, and in a new expansion stage after September 2018. In this process, the Tanymas terminus of the Fedchenko Glacier also transformed from a land terminus to a partial lake terminus, and then to a complete lake terminus. The formation of Tanymas Lake is associated with the accumulation of glacial meltwater and the blockage of drainage, while the slow expansion of Tanymas Lake is related to the cold climate and slight glacier mass loss of Central Pamir. In the coming decades, with the accelerated mass loss of the Tanymas terminus of the Fedchenko Glacier, the area, depth, and water storage of Tanymas Lake will continue to increase, accompanied by the growing GLOF risk.

The Tibetan Plateau contains a large number of mountain glaciers with clean surfaces, where englacial debris is generally entrained by the ice flow and exposed at the glacier margins. The long-term observation on one of the typical clean surface glaciers (the Qiyi Glacier, northern Tibetan Plateau) suggests an early emergence of englacial debris on its transport pathway, with accelerated surface melting from the mid-2000s onwards. Given that the englacial debris layers of the tongue part of Qiyi Glacier are approximately parallel to the glacier surface, the continuing melting might be expected to result in the rapid expansion of exposed debris. Compared with the clean surface ice, debris cover at the same elevation reduced glacier mass loss by ~25.4% during a hydrological year (2020–2021), indicating that the early emergence of englacial debris can protect the glacier from climate warming with prolonged life expectance. As such, future glacial runoff will then reach its peak earlier and be followed by a gentler decreasing trend than model projections with constant clean surface ice. These findings imply that the emerging debris on clean surface glacier may mitigate the glacial-runoff risk, which has so far been neglected in projections of future water supplies.

The ice surface temperature (IST) derived from thermal infrared remote sensing is crucial for accurately monitoring ice or snow surface temperatures in the polar region. Generally, the remote sensing IST needs to be validated by the in situ IST to ensure its accuracy. However, due to the limited availability of in situ IST measurements, previous studies in the validation of remote sensing ISTs are scarce in the Antarctic ice sheet. This study utilizes ISTs from eight broadband radiation stations to assess the accuracy of the latest-released Moderate Resolution Imaging Spectroradiometer (MODIS) IST and Visible Infrared Imager Radiometer Suite (VIIRS) IST products, which were derived from two different algorithms, the Split-Window (SW-based) algorithm and the Temperature–Emissivity Separation (TES-based) algorithm, respectively. This study also explores the sources of uncertainty in the validation process. The results reveal prominent errors when directly validating remote sensing ISTs with the in situ ISTs, which can be attributed to incorrect cloud detection due to the similar spectral characteristics of cloud and snow. Hence, cloud pixels are misclassified as clear pixels in the satellite cloud mask during IST validation, which emphasizes the severe cloud contamination of remote sensing IST products. By using a cloud index (n) to remove the cloud contamination pixels in the remote sensing IST products, the overall uncertainties for the four products are about 2 to 3 K, with the maximum uncertainty (RMSE) reduced by 3.51 K and the bias decreased by 1.26 K. Furthermore, a progressive cold bias in the validation process was observed with decreasing temperature, likely due to atmospheric radiation between the radiometer and the snow surface being neglected in previous studies. Lastly, this study found that the cloud mask errors of satellites are more pronounced during the winter compared to that in summer, highlighting the need for caution when directly using remote sensing IST products, particularly during the polar night.