Soil moisture is one of the fundamental variables in land–atmosphere interactions, hydrological processes and vegetation dynamics. Accurate soil moisture information is of great significance for environmental and climate studies. In recent years, automatic monitoring stations have been increasingly deployed owing to their advantages of high-frequency and standardized measurements. However, their measurements suffer from non-negligible biases, limiting the reliability of automatic soil moisture datasets. To address these challenges, this study conducts a comprehensive validation and machine-learning-based correction of three automatic soil moisture systems: the automatic soil moisture station, the CRS-2000C regional soil moisture measurement system, and the soil temperature–moisture monitoring system at the Xilinhot National Climate Observatory, China. Using manual measurements as reference, the observations from the three automatic stations were validated. All the automatic soil moisture measurements revealed substantial biases, especially in deeper soil layers. To reduce the biases, an ensemble correction framework that employed generalized additive model to integrate Cubist, Random Forest, XGBoost, and CatBoost models was developed for layer-wise soil moisture correction. Five-fold cross-validation was applied to evaluate correction performance. After correction, the accuracy of all stations improved significantly, with R2 increasing by 0.075–0.289, mean absolute error decreasing by 0.012–0.057 m3/m3, and root mean square error decreasing by 0.013–0.075 m3/m3, with particularly pronounced improvements in deeper layers. This study highlights the necessity of correcting automatic soil moisture observations and provides an effective framework for the correction.

Uncrewed aerial vehicles (UAVs) provide an effective tool for fine-scale land surface temperature (LST) mapping, but satellite-based algorithms are unsuitable due to their much lower flight altitudes. Currently, few studies have focused on UAV-based LST retrieval methods, and most either neglect atmospheric influences or rely on atmospheric radiative transfer simulations using local atmospheric profiles, limiting general applicability. This article develops a generalized UAV-based LST retrieval algorithm for UAV thermal cameras integrating the widely used FLIR Tau 2 thermal core. The relationships between atmospheric transmittance, upwelling, and downwelling atmospheric radiance with near-surface meteorological parameters and UAV height were analyzed using simulations based on thermodynamic initial guess retrieval profiles. Generalized empirical equations were then developed to estimate these atmospheric parameters from near-surface air temperature, humidity, and UAV height. Correction equations were also developed to minimize uncertainties in the conversion between temperature and radiance induced by the equivalent wavelength of the broad UAV thermal band. Additionally, band-specific land surface emissivity for six major land cover types was provided to facilitate calculation. Subsequently, LST can be retrieved by removing atmosphere and emissivity influences. The algorithm was validated using both simulated datasets and field observations, achieving mean absolute errors of 0.33 and 1.49 K, respectively. Sensitivity analysis demonstrated the robustness against noise in near-surface meteorological variables and emissivity. This article proposes a generalized, easy-to-use, and accurate UAV-based LST retrieval algorithm, offering valuable technical support for high-resolution thermal environment monitoring.

Thermal infrared (TIR) remote sensing provides an effective means of mapping near-surface air temperature (Ta) at large scales. However, cloud coverage introduces substantial data gaps, posing a considerable challenge for producing all-weather Ta datasets. This study proposed a two-step framework, termed Kernel-based Temporal Filling and Bias Correction (KTF-BC), to reconstruct all-weather remotely sensed Ta. In the first stage, a kernelbased temporal filling method was developed to estimate the theoretical clear-sky Ta for cloud-covered pixels. In the second stage, a bias correction model was constructed to adjust these theoretical estimates toward the actual Ta under cloudy conditions. The proposed framework was applied across China from 2019 to 2023 to generate spatially complete daily mean Ta at 1-km resolution. Validation against meteorological stations under cloudy conditions demonstrated consistently high accuracy, with R2 values up to 0.99, mean absolute errors (MAEs) ranging from 0.91 to 0.95 degrees C, root mean square errors (RMSEs) ranging from 1.21 to 1.26 degrees C, and biases close to 0 degrees C. The method effectively captured fine-scale thermal heterogeneity and demonstrated robust performance across varying cloud conditions and surface environments. This study provides a practical and reliable solution for reconstructing all-weather Ta from satellite observations.

Accurate, high-resolution gridded air temperature forecasts are essential, particularly in mountainous areas with complex terrain. This study proposes a two-stage processing framework DOWN + BC to downscale Global Forecast System (GFS) temperature forecasts and correct their bias. The approach first employs a random forest (RF) model to geographically downscale 3-hourly 0.25° GFS forecasts to a 30 m resolution (DOWN), followed by bias correction (BC) using a first-order adaptive Kalman filter (AKF). The accuracy of the DOWN + BC-processed forecasts was evaluated against both automatic weather station (AWS) observations and high-resolution air temperature fields derived from an extreme gradient boosting model (XGB-derived). The results indicate that (1) the DOWN step effectively refines the spatial detail of temperature distribution, though it yields limited improvement in accuracy compared to the raw GFS forecasts; (2) the combined DOWN + BC method substantially enhances forecast accuracy. At AWS locations, the root mean square error (RMSE) of GFS forecasts decreased by 37.84% in January 2020 and 41.16% in July 2023. Relative to the XGB-derived temperature distribution, RMSE was reduced by 47.27% and 33.79% for the respective periods.

High-resolution near-surface air temperature (Ta) data are important for studies on human life and plant. Remote sensing offers an effective means to acquire spatially continuous Ta over large areas. However, several critical issues have been overlooked in current remote sensing-based Ta estimation, including temporal inconsistencies of land surface temperature (LST), spatial variability over large-scale area, and limitations of single-algorithm approaches. This study developed a three-step ensemble framework to address these issues and map high-accuracy daily average Ta from remote sensing data. This framework consists of temporal normalization, zone-based modeling, and ensemble integration. First, Terra/MODIS LST was temporally normalized using ERA5 reanalysis data to eliminate the uncertainty caused by differences in observation times. Then, the whole study area was divided into subzones, and nine base models were developed in each zone using machine learning (ML) methods to estimate Ta. Finally, the Ta estimation results from the base models were integrated using five ensemble methods to develop an optimal integration strategy for mapping Ta. The results showed that temporal normalization effectively reduced the time difference in LST. Zone-based modeling exhibited enhanced performance compared to holistic modeling strategy. The stacking-based ensemble model outperformed each of the base models. Among them, the generalized additive model (GAM)-based ensemble model achieved highest accuracy of Ta estimation, with R2 of 0.99 across all years, mean absolute errors (MAEs) ranging from 0.73℃ to 0.79℃, and root mean square errors (RMSEs) ranging from 0.99℃ to 1.07℃ from 2019 to 2023. This work provides a valuable reference for accurately mapping Ta from remote sensing data, especially at large scales.

Wind erosion is one of the most severe environmental problems in arid and semi-arid regions, posing a serious threat to ecological security and human settlements. Afforestation is widely acknowledged as a practical strategy for mitigating wind erosion. However, quantitative assessments of the relationship between forest restoration and wind erosion control remain limited, particularly over long temporal scales and at fine spatial resolutions. This study takes Duolun County, Inner Mongolia, as a representative case to examine the role of large-scale forest restoration in controlling wind erosion. Specifically, land use dynamics from 1985 to 2024 were mapped using a time series of Landsat imagery to identify forest expansion. Then, the Revised Wind Erosion Equation (RWEQ) was applied to simulate the spatiotemporal variations in wind erosion and sand fixation. Finally, a scenario-based framework contrasting forested and non-forested conditions was used to isolate and quantify the contribution of forest restoration to wind erosion control. Results showed that forest cover increased significantly from 3.95% to 36.19% over the past 40 years, with expansion primarily concentrated in the central desertified regions and the northern hilly areas. Sand fixation increased from 8.70×105 t to 8.20×106 t, with an average annual growth of 9.06×104 t/year. Spatially, growth rates were more pronounced in the central and northern regions than in the south. Ecological restoration programs contributed substantially to wind erosion control, with their attributable sand fixation increasing from near zero to 6.61×105 t, with an average annual rate of 8.21×103 t/year. These findings provide new insights into the role of large-scale forest restoration in enhancing sand fixation and mitigating wind erosion.

The rapid urbanization changes the underlying surface in urban areas, and consequently modifies the local thermal environment, impacting the health and comfort of residents. Due to the pronounced spatial heterogeneity of surface temperature, both ground-based observations and satellite remote sensing cannot well capture the spatial details of microscale thermal environment. Unmanned aerial vehicle (UAV) thermal infrared remote sensing, with the advantage of high spatial resolution, provides data support for microscale thermal environment monitoring. This study explored the potential of UAV-derived land surface temperature (LST) in analyzing microscale thermal environments. Aerial observations were conducted five times throughout a hot summer day at the central campus of Nanjing University of Information Science and Technology (NUIST), China. A comprehensive LST retrieval method was developed to derive LST from UAV thermal infrared remote sensing data. The UAV-retrieved LST was validated against the synchronous ground observations, achieving high overall accuracy with a coefficient of determination (R2) of 0.96 and a mean absolute error (MAE) of 1.41 degrees C. The UAVderived LST maps showed high spatial variability during the daytime and relatively lower variability at night. The spatial distribution of LST was closely related to land cover, with artificial surfaces exhibiting higher temperatures than natural surfaces, particularly during the daytime. Different land covers also exhibited distinct intraday variations of LST, with significant differences observed in daily maximum LST and peak times. This study provides a technical reference for deriving LST from UAV and offers detailed perspectives of the spatiotemporal characteristics of the microscale thermal environment.

Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of statistical downscaling models across different scales warrants further investigation. In this study, a stepwise downscaling method is proposed, employing multiple linear regression (MLR), Cubist regression tree, random forest (RF), and extreme gradient boosting (XGBoost) models to downscale the 3-hourly ERA5-Land reanalysis air temperature data at the resolution of 0.1° to that of 30 m. A comparative analysis was performed to evaluate the accuracy of downscaled ERA5-Land air temperature results obtained from the stepwise and the direct downscaling methods, based on observed air temperatures at meteorological stations and the spatial distribution of air temperature estimated by a remote sensing method. In addition, variations in the importance of driving factors across different spatial scales were examined. The results indicate that the stepwise downscaling method exhibits higher accuracy than the direct downscaling method, with a more pronounced performance improvement in winter. Compared with the direct downscaling method, the RMSE value of the MLR, Cubist, RF, and XGBoost models under the stepwise downscaling method were reduced by 0.48 K, 0.38 K, 0.48 K, and 0.50 K, respectively, at meteorological station locations. In terms of spatial distribution, the stepwise downscaling results demonstrate greater consistency with the estimated spatial distribution of air temperature, and it can capture air temperature variations across different land surface types more accurately. Furthermore, the stepwise downscaling method is capable of effectively capturing changes in the importance of driving factors across different spatial scales. These results generally suggest that the stepwise downscaling method can significantly improve the accuracy of air temperature downscaled from reanalysis data by adopting multiple resolutions as the intermediate downscaling process.

Heat extremes become increasingly frequent and severe, posing adverse risks to public health and environment. Previous research on extreme heat mostly used meteorological observations or reanalysis data, which cannot well capture detailed spatial patterns. This study developed a seamless air temperature (Ta) dataset from remote sensing data to characterize the spatio-temporal variations of heat extremes in the Yangtze River Delta (YRD) from 2001 to 2023. First, the daily maximum Ta of cloud-free pixels was estimated through machine learning algorithms from MODIS land surface temperature (LST) and other remote sensing data. Then, gaps in the estimated Ta caused by cloud cover were filled using the Temporal Fourier Analysis (TFA) method, generating a seamless daily maximum Ta dataset. The remotely sensed Ta achieved an overall MAE of 1.11 °C. Based on the remotely sensed Ta, six heat indices were calculated to characterize heat extremes, including heat days (HTD), effective accumulated high temperature (EAHT), heatwave frequency (HWF), cumulative heatwave days (HWD), maximum heatwave duration (HWMD) and average heatwave duration (HWAD). Heat extremes occurred frequently in the YRD, with obvious spatial variability. Southern basins experienced intense heat with high frequency and duration, while southern mountains and northern areas experienced weaker heat extremes. Urban areas have substantially more intense heat events than suburbs, attributed to urban heat island effect. 2022 recorded the most severe heat, with notable events also in 2013 and 2003. This study provides valuable insights into heat events in the YRD and serves as a reference for remote sensing research on heat events.

The Microwave Land Surface Emissivity (MLSE) atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation. A ten-day/month synthesized FengYun-3D MLSE atlas (New_FY3D) was constructed by the two global MLSE daily product datasets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), which were retrieved from the same FY-3D MicroWave Radiation Imager (MWRI) Level-1 brightness temperature (BT) data from 2021 to 2022, respectively. Then, a set of global MLSE label samples based on the New_FY3D, including 14 surface geophysical parameters, was obtained for an instantaneous global MLSE simulation at a 0.10° spatial resolution by adopting the extreme gradient boosting (XGBoost) machine learning method. Finally, the FengYun-3F (FY-3F) MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System (ARMS) based on the above different MLSE products were evaluated. The results show that the New_FY3D atlas performs well, and the BT simulation at the top of atmosphere is better than that of FY-3D1, FY-3D2, and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves) atlas. Surface roughness, vegetation coverage, land cover type, and snow cover are vital parameters for MLSE simulation. The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz. The average simulation determination coefficients (R2) under clear-sky and cloud-sky conditions are 0.925 and 0.901, respectively, and the average root-mean-square errors (RMSEs) are 0.018 and 0.021, respectively. Large simulation errors occur in permanent wetland, ice and snow, and urban and built-up areas. With a standard deviation of 6.6 K, the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.

As an important greenhouse gas (GHG) in the atmosphere, carbon dioxide (CO2) has a great impact on global climate change. Accurate knowledge of the spatiotemporal variations of CO2 is of great significance for understanding the carbon cycle and evaluating the effectiveness of carbon emission reduction. In recent years, several satellites with CO2 sensors have been launched and a series of atmospheric CO2 concentration products have been developed using different retrieval algorithms. This study validated nine satellite XCO2 products derived from Greenhouse gases Observing SATellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory-2 (OCO-2), and OCO-3: including ACOS-GOSAT, NIES-GOSAT, BESD-GOSAT, OCFP-GOSAT, SRFP-GOSAT, EMMA, GOSAT-2, OCO-2, and OCO-3 XCO2. The remotely sensed XCO2 products were compared with the XCO2 observations from six Total Carbon Column Observing Network (TCCON) stations in East Asia for validation. The results showed that the OCO-2 XCO2 product outperformed other products, with the highest R2 of 0.94 and the lowest MAE of 1.24 ppm. The ACOS-GOSAT and EMMA-GOSAT XCO2 products also showed favorable accuracies, both achieving R2 of 0.93 and corresponding MAE values of 1.29 and 1.31 ppm, respectively. The GOSAT-2 XCO2 product showed the poorest accuracy, with an R2 of 0.77 and a mean absolute error of 3.28 ppm. There was a significant overestimation of the bias-uncorrected GOSAT-2 XCO2 product in East Asia, and it indicated that bias correction must be performed for this XCO2 product. The accuracy of TCCON XCO2 was not consistent with remotely sensed XCO2 at different stations. The RJ, JS, AN, and TK TCCON stations generally showed better agreements between satellite estimates and TCCON observations, except for the GOSAT-2 XCO2 product.

With frequent extreme heat events (EHEs), rapid urbanization, and uneven social development, comprehensive assessment of heat-related health risks is important for tolerating hot weather. This paper proposed a quantitative method for assessing heat-related health risks at the grid scale. A combination of multisource remote sensing and socio-economic data was utilized to develop an integrated heat health risk index (HRI) considering three dimensions of heat hazards, exposure, and vulnerability in the Yangtze River Delta (YRD), China. Compensating for the limitations of land surface temperature (LST) and station data, daily air temperatures were retrieved to calculate heat hazard index. Gridded population density data were developed using nighttime light data to calculate the exposure index. By combining them and other indicators, an HRI map of the YRD was developed. Furthermore, the spatial heterogeneity and dominant factors of the heat health risk were examined. The results showed that 13.2 % of the areas in the YRD were in high and medium high risk, while 58.6 % were in low and medium low risk. The high-risk areas were primarily concentrated in the Shanghai-Hangzhou Bay urban agglomeration, suggesting synergy between increased exposure and hazards in these metropolitan areas. The high-risk areas were predominantly dominated by hazard/exposure (H-E) and exposure/vulnerability (E-V), accounting for 27.2 % and 21.9 % of the YRD's total area respectively. This study contributes to the identification of areas vulnerable to heat stress and provides references for optimizing heat risk management strategies.

Accurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) microwave radiation imager in 2022, two global MLSE daily product datasets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), were obtained by using one-dimensional variational method and microwave radiative transfer method, respectively. Based on the global spatiotemporal consistency assessment, a high-quality daily MLSE training dataset for the Tibetan Plateau was selected from the two datasets. Then, ten land surface parameters from routine observation were chosen as input features to the RF model to simulate the MLSE under all-sky conditions in the Tibetan Plateau. The results show that both FY-3D1 and FY-3D2 MLSE datasets are comparable to the international mainstream MLSE products in quality, while the clear sky FY-3D1 is likely to be better than the clear/cloudy FY-3D2 MLSE. Land surface roughness, vegetation optical thickness, normalized vegetation index, and land cover type are identified as the most important factors affecting MLSE in the Tibetan Plateau. The RF model can effectively simulate the MLSE in the frequency range of 10.65-89.0 GHz under all-sky conditions. The coefficients of determination (R-2) for horizontal polarization and vertical polarization range from 0.86 (10.65 GHz) to 0.91 (18.7 GHz) and from 0.60 (10.65 GHz) to 0.74 (89.0 GHz), respectively. The root mean square errors for horizontal polarization and vertical polarization range from 0.017 (23.8 GHz) to 0.023 (10.65 GHz) and from 0.016 (10.65 GHz) to 0.019 (89.0 GHz), respectively. These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE.

The Yangtze River Delta (YRD) region is one of China's most urbanized areas, which has typical non-uniformity features of the underlying surface and complicated underlying surface structures that even manifest at the sub-kilometer scale. This research investigates the influence of subgrid nonuniformity on the simulation of the meteorological environment in the YRD region utilizing high-resolution land use data. The Noah_mosaic/Noah land surface scheme is used to perform a high-resolution sensitivity test with or without taking subgrid nonuniformity into account during the numerical simulation of summer 2020 and 2022 based on the WRF (Weather Research and Forecasting model) model, respectively. Results demonstrate that, in contrast to the Noah scheme, the Noah_mosaic scheme's simulation results have a stronger correlation with observations and a smaller inaccuracy, suggesting that it is more capable of accurately representing the intricate physical processes that occur beneath the surface of the urban agglomeration region. The suburban area becomes warmer and drier and the urban area gets colder and wetter after taking into account the subgrid nonuniformity. This difference is more pronounced at night, particularly in the temperature field, where the average temperature change is -0.14 degrees C/0.53 degrees C during the night and -0.04 degrees C/0.05 degrees C during the day in the urban/suburban area. Non-uniformity in simulation results can reach up to 400 m altitude, resulting in an increase in instability in the suburban atmosphere, particularly at night. At the same time, the body temperature rises by approximately 0.27 degrees C in almost all areas of land at night. In addition, it is found that when the proportion of buildings in the grid is less than 50%, except the daily minimum temperature T-min,, the meteorological elements change greatly with the increase of the proportion of buildings, and the growth rates of T-mean, and T-max reach 0.29 degrees C/10% and 0.55 degrees C/10% respectively, whereas the growth rates of T-mean, and T-max are only 0.1 degrees C/10% and 0.06 degrees C/10%, which suggests that the future development of small towns in low building density areas will have a greater impact on the meteorological environment.

Nature reserves play a key role in the conservation of habitats, species and ecosystems in China. However, the issue of light pollution within these reserves has not received well recognition. Whereas previous studies have investigated light pollution in protected areas by remote sensing, the influence of the atmospheric scattered light from neighborhoods was ignored by using only direct light emissions. This study mapped the nighttime all-sky brightness from NPP/VIIRS data to quantify light pollution over the national nature reserves in China from 2013 to 2022. Based on the remotely sensed sky brightness, the spatio-temporal variations of sky brightness were analyzed. The mean sky brightness across all nature reserves was 0.63 mcd/m2 and the mean trend was 0.0189 mcd/m2/a, indicating obvious and accelerating light pollution. Furthermore, the sky brightness around the nature reserves was estimated and compared with the reserves. In general, the surrounding regions showed higher sky brightness and brightening trends, suggesting a certain but insufficient protective effect on the sky quality of nature reserves. This study provides a comprehensive understanding of the light pollution conditions in the national nature reserves in China and serves as a valuable reference for future assessments of light pollution using remote sensing data.

Land cover is an important variable for climate, hydrology, and ecology studies. With the availability of various high-resolution global land cover (GLC) products, conducting a comprehensive assessment on their accuracy and consistency is important. In this study, we compared the performance of three latest 10-m-resolution GLC products, which include FROMGLC10 in 2017, ESA's Worldcover10 in 2020, and ESRIGLC10 in 2020, and three latest 30-m-resolution GLC products, which include FROMGLC30 in 2017, GLC_FCS30 in 2020, and Globeland30 in 2020, in China. The consistency of these products was investigated in terms of spatial consistency and area consistency. Though the six GLC products demonstrate similar overall distribution patterns, their detailed spatial distributions are quite different, especially for the three 10-m-resolution products. Evidently, the cropland, forest, grassland, and bareland exhibited high inconsistencies than the other types. The classification accuracy of the six GLC products was also quantitatively assessed based on a visual-interpretation-based reference dataset. FROMGLC10 exhibits the highest overall accuracy of 65.57%, followed by FROMGLC30 (64.96%) and Worldcover10 (62.74%). ESRIGLC10 (49.79%) exhibits the lowest accuracy. The accuracies of shrubland, wetland, and tundra were relatively low. This study provides a valuable reference for selecting appropriate GLC products for potential users.

The Tibetan Plateau (TP), the Third Pole of the world, has experienced significant warming over the past several decades. Previous studies have mostly relied on station-observed air temperature (Ta), reanalysis data, and remotely sensed land surface temperature (LST) to analyze the warming trend over the TP. However, the uneven distribution of stations, the poor spatial resolution of reanalysis data, and the differences between LST and Ta may lead to biased warming rates. This paper first maps Ta over the TP from 2001 to 2020 based on multi-source remote sensing data, and then quantifies the spatio-temporal variations of remotely sensed Ta and elevation dependent warming (EDW) of this region. The monthly mean Ta is estimated using machine learning (ML) method year by year, and its accuracy is validated based on station-observed Ta. The coefficient of determination (R2 ranges from 0.97 to 0.98 and the mean absolute error (MAE) ranges from 1.01 to1.04 °C. The remotely sensed Ta is used to analysis warming trend and EDW over the TP. The overall warming trend of the TP during 2001–2020 is 0.17 ℃/10a, and warming mainly distributed in the eastern TP, central TP and western Kunlun Mountains. Among the four seasons, autumn shows the most significant warming, tripling the annual warming rate. Winter shows a significant cooling trend, with the warming rate of -0.18 ℃/10a. The study also reveales the existence of EDW at both the annual and seasonal scales. This paper suggests the potential of remotely sensed Ta in global warming study, and also provides an improved understanding of climate warming over the TP.

For the aroma enhancement research of heated cigarettes, it is worth exploring whether tobacco can be pyrolyzed into pyrolysis liquids containing a large number of volatile aroma components. In this study, tobacco pyrolysis liquids were prepared in subcritical/supercritical ethanol, and their applications in the aroma enhancement of heated cigarettes were investigated. The optimal conditions of supercritical liquefaction reactions were determined by optimizing the reaction time, liquid/solid mass ratio and temperature conditions. Moreover, the effect of supercritical liquefaction conditions on volatile aroma components in tobacco pyrolysis liquids was investigated by GC-MS. The results indicated that the reaction temperature had the most significant impact on the tobacco pyrolysis reaction, and higher reaction temperature promoted the pyrolysis conversion of tobacco, resulting in enhanced tobacco conversion and a high content of volatile components in the tobacco pyrolysis liquid. The optimal reaction conditions for the preparation of tobacco pyrolysis liquid were found to be a temperature of 220°C, a liquid/solid mass ratio = 15, and a 2-h reaction time. Meanwhile, the content of ester compounds and nicotine in the tobacco pyrolysis liquid increased significantly with the increase of reaction temperature. Sub/supercritical ethanol treatment significantly destroyed the surface structure of tobacco, and the degree of tobacco depolymerization increased when temperature rised. The analysis of aroma compounds in the smoke of heated cigarettes indicated that the tobacco pyrolysis liquid could significantly increase the release of aromatic substances and has a significant aroma-enhancing effect. This article proposed and prepared tobacco pyrolysis liquid in subcritical/supercritical ethanol and explored its potential application in the aroma enhancement of heated cigarettes, offering a new route for flavor enhancement technology for this type of product.

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–G 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 −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 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.