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The air temperature changes in the Palu MW7.5 earthquake in Indonesia on 28 September 2018 were analyzed, based on the additive tectonic stress caused by celestial tidal-generating forces (ATSCTF) and air temperature data from the National Center for Environmental Prediction (NCEP). This paper explored the variation characteristics of three-dimensional stratified of air temperature caused by seismic activity and the coupling relationship between air temperature changes and the tidal force. The background information for air temperature calculation was obtained from the tidal force changes, and the air temperature increment method was used to study the temperature evolution process of different periods in the study area. The results found that the tidal force acting on the critical state earthquake faults may be an important external factor inducing earthquakes, and there was indeed a significant air temperature increase anomaly during the Palu MW7.5 earthquake. The paper also summarized the abnormal characteristics of air temperature caused by seismic activity: the air temperature closer to the land's surface has greater anomaly amplitude and a wider anomaly area than that of the upper air. [ABSTRACT FROM AUTHOR]

The paper took Guang'an City in Sichuan Province of China as a research region, and it built an evaluation index system based on 14 evaluation indices according to four criteria, namely, ecological background, ecological structure, ecological benefits, and ecological stress. The Delphi algorithm and entropy weight (a combination of subjective and objective methods) were used to calculate the weight value of each evaluation index, and an ideal point model was used to calculate the ideal point value. The ideal point ecological grade was classified, and the principal component analysis method was used to obtain the main control factors of each year, which revealed the relationship between the ecological distribution of ideal points and the environmental impact factor, with the changing characteristics of the ecology in the research region according to the hotspot model of the spatial differentiation of land ecological quality as the main control factor. Lastly, the paper also analyzed the land ecological quality of Guangan City in 2000, 2005, 2010, and 2015. The study results show that the overall land ecological quality in Guang'an City generally increased from 2005 to 2015. The proportion of forest land area and the temperature were the most important main control factors. The air temperature and the land ecological quality were positively correlated. [ABSTRACT FROM AUTHOR]

Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts. [ABSTRACT FROM AUTHOR]

The land surface temperature obtained from remote sensing was widely used in the simulation of permafrost mapping instead of air temperature with the rapid development of remote sensing technology. The land surface freezing and thawing index (LFI and LTI), which is commonly regarded as the ground surface freezing and thawing index (GFI and GTI), can produce certain errors in the simulation of permafrost distribution on the Qinghai–Tibet Plateau. This paper improved the accuracy of the thermal condition of the surface soil in the Qinghai–Tibet Engineering Corridor (QTEC) by calculating the LFI (or LTI) and N-factors. The environmental factors affecting the spatial distribution of the GFI and GTI were detected by the GeoDetector model. Finally, the multiple linear relationships between the GFI (or GTI) and the environmental factors were established. The results from 25 monitoring sites in the QTEC show that the Nf (ratio of GFI to LFI) is 1.088, and the Nt (ratio of GTI to LTI) is 0.554. The explanatory power of the interaction between elevation and latitude for the GFI and GTI is 79.3% and 85.6%, respectively. The multiple linear regression model with six explanatory variables established by GFI (or GTI) has good accuracy. This study can provide relatively accurate upper boundary conditions for the simulation of permafrost distribution in the QTEC region. [ABSTRACT FROM AUTHOR]

Satellite thermal infrared remote sensing has received worldwide attention in earthquake-precursors exploration. Meanwhile, it has also encountered great controversy due to the lack of quantitative interpretation of the observations, despite the existing qualitative physical mechanisms being able to greatly help us understand thermal infrared anomalies. Here, we report a potential mechanism to quantitatively analyze co-seismic thermal infrared anomalies based on temperature change caused by stress variation through theoretical, experimental, and field investigations. This paper firstly deduces theoretically the temperature variation during elastic deformation of rock on the basis of the thermodynamic theory. Secondly, three laboratory experiments on rock samples are conducted to verify the theoretical estimates of the temperature changes caused by stress variations using an infrared camera with the spectral range of 8~12 μm. Thirdly, a mechanical model on thrust faults is built to evaluate the co-seismic temperature drop as a result of thrust faulting. The model shows that the co-seismic temperature drop in rocks should be in the order of 0.18 K. This variation in rock temperature may cause a change in heat equivalent to changes in shallow atmospheric temperatures of 3.0–6.0 K, which is in accordance with the temperature anomalies observed by satellite thermal infrared remote sensing. In addition, the temperature change caused by crustal stress variation may involve a large spatial scale, covering the whole focal area, which has characteristics of regional distribution and is conducive to satellite observation. Therefore, a quantitative explanation of the satellite thermal infrared seismic anomaly mechanism can be given via the temperature change caused by crustal stress variation. [ABSTRACT FROM AUTHOR]

Rapid urbanisation is affecting people in different ways, with some becoming more vulnerable to the impacts of climate change. Africa's cities are projected to be home to nearly 60% of the continent's population by 2050. In conjunction with climate change, these cities are experiencing critical environmental challenges, including changes in the urban thermal environment. Urban areas generally exhibit significantly higher air and surface temperatures than their surrounding rural areas, resulting in urban heat islands. However, little has been done to synthesise existing knowledge and identify the key research gaps in this area, particularly in Africa. This paper focuses on the combined effects of urbanisation and climate change on the urban thermal environment in Africa, and provides a comprehensive review of results, major advances and the dominant direction of research. Our review of 40 publications from peer-reviewed journals from 2000 to 2021 revealed that South Africa, Ethiopia and Nigeria were most frequently studied, and satellite imagery-based data and analysis were used predominantly. Results from a few studies have shown the practical implications for urban land-use planning, informal settlement management, human wellbeing and productivity, energy use, air pollution and disease spread. Integrated approaches, strengthening planning institutions, and early warning systems are proposed to address climate change. Low-income groups are emphasised in efforts to help people cope with heat stress. Solutions based on land use and land cover dynamics and blue–green infrastructure are mentioned but are in need of further research. Cities with similar patterns of urbanisation, geographies and climate conditions could benefit from multi-disciplinary research collaboration to address the combined impacts of rapid urbanisation and climate change. [ABSTRACT FROM AUTHOR]

This paper aims to better understand why there was a global warming pause in 2000–2015 and why the global mean surface temperature (GMST) has risen again in recent years. We present and statistically analyze substantial time-series observed datasets of global lower-stratospheric temperature (GLST), troposphere–stratosphere temperature climatology, global land surface air temperature, GMST, sea ice extent (SIE) and snow cover extent (SCE), combined with modeled calculations of GLSTs and GMSTs. The observed and analyzed results show that GLST/SCE has stabilized since the mid-1990s with no significant change over the past two and a half decades. Upper-stratospheric warming at high latitudes has been observed and GMST or global land surface air temperature has reached a plateau since the mid-2000s with the removal of natural effects. In marked contrast, continued drastic warmings at the coasts of polar regions (particularly Russia and Alaska) are observed and well explained by the sea-ice-loss warming amplification mechanism. The calculated GMSTs by the parameter-free quantum-physics warming model of halogenated greenhouse gases (GHGs) show excellent agreement with the observed GMSTs after the natural El Niño southern oscillation and volcanic effects are removed. These results have provided strong evidence for the dominant warming mechanism of anthropogenic halogenated GHGs. The results also call for closer scrutiny of the assumptions made in current climate models. [ABSTRACT FROM AUTHOR]

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities. [ABSTRACT FROM AUTHOR]

This paper presents the results of long-term temperature monitoring at the Yakutsk and Zeleny Lug stations, which are experimental sites, for the thermal state of valley permafrost landscapes under the conditions of modern climate warming. An analysis of the long-term data from meteorological stations in the region clearly showed one of the highest trends of increase in the mean annual air temperature in the north of Russia. Here, we established quantitative regularities in the long-term variability of the ground temperature at the bottom of the active layer and at zero amplitude. The dynamics of the ground temperature of the layer of zero amplitude during climate warming indicate the thermal stability of permafrost. The main regulating factor of the thermal state of grounds in permafrost landscapes is short-term fluctuations in the regime of snow accumulation. Active layer thickness is characterized by low interannual variability, weak climate warming responses, and insignificant trends. The results of studies of the thermal regime of soils can be extended to the same types of valley landscapes in the Lena River, and are a reliable basis for predicting heat transfer in natural and disturbed landscapes. [ABSTRACT FROM AUTHOR]

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature ( T air ) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis ( p < 0.05 ) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between T air and LST in rural (HWN, HWF, HWD, r > 0.90 ) and peri-urban (HWM, HWA, r > 0.65 ) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability. [ABSTRACT FROM AUTHOR]

Daily time series with continuous records of mean air and soil temperature from 127 meteorological stations—fairly distributed over the country—were used to compute monthly temperature trends, as well as changes in the timing of the first and the last frost days over Romania since 1961. Results show that the frequency of the number of days with daily temperature averages below 0 °C in case of air and soil surface temperature is stable for most months, except for January, when (for both soil and air temperature), the number of days with a temperature below 0 °C is decreasing in the majority of the stations. The occurrence of the first day with (mean air and soil surface) temperatures below 0 °C, presents a delay in the south, south-east, and west, and an earlier occurrence in eastern and central regions. The occurrence of the last day with a mean air and soil surface temperature below 0 °C shows a stable trend for most stations (except for some small areas in the north, south-east and south-west of Romania). The regime of the land temperature is more stable, due to the physical characteristics of the soil, compared to the more versatile atmosphere. Linkages between thermal parameters and large-scale atmospheric circulation are also discussed. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is a key variable in global climate studies. Global climate models such as ERA5 and CMIP6 predict various parameters at coarse spatial resolution (>9 km). As a result, local phenomena such as the urban heat islands are not reflected in the model's outputs. In this study, we address this limitation by downscaling the resolution of ERA5 (9 km) and CMIP6 (27 km) Ta to 1 km, employing two different machine learning algorithms (XGBoost and Deep Learning). Our models leverage a diverse set of features, including data from satellites (land surface temperature and normalized difference vegetation index), from ERA5 and CMIP6 climate models (e.g., solar and thermal radiation, wind), and from digital elevation models to develop accurate machine learning prediction models. These models were rigorously validated against observations from 98 meteorological stations in the East Mediterranean (Israel) using a standard cross-validation technique as well as a leave-one-group-out on the station ID evaluation methodology to avoid overfitting and dependence on geographic location. We demonstrate the sensitivity of the downscaled Ta to local land cover and topography, which is missing in the climate models. Our results demonstrate impressive accuracy with the Deep Learning-based models, obtaining Root Mean Squared Error (RMSE) values of 0.98 °C (ERA5) and 1.86 °C (CMIP6) for daily Ta and 2.20 °C (ERA5) for hourly Ta. Additionally, we explore the impact of the various input features and offer an extended application for future climate predictions. Finally, we propose an enhanced evaluation framework, which addresses the problem of model overfitting. This work provides practical tools and insights for building and evaluating Ta downscaling models. The code and data are publicly shared online. [ABSTRACT FROM AUTHOR]

The accuracy of Land Surface Temperature (LST) products retrieved from satellite data in mountainous-coastal areas is not well understood. This study presents an analysis of the spatial and temporal variability of the differences between the LST and in situ observed air and surface temperatures (ISTs) for the southeastern slope of Lake Baikal's surroundings. The IST was measured at 12 ground observation sites located on the southeastern macro-slope of the Primorskiy Ridge (Baikal, Russia) within an elevation range of 460–1656 m above sea level from 2009 to 2021. LST was estimated using 617 Landsat (7 and 8) images from 2009–2021, taking into account brightness temperature, surface emissivity and vegetation cover fraction. The comparison of the LST from satellite data and the IST from ground observation showed relatively high differences, which varied depending on the season and site type. A neural network was suggested and calibrated to improve the LST data. The corrected remote-sensed temperature was found to reproduce the IST very well, with mean differences of about 0.03 °C and linear correlation coefficients of 0.98 and 0.95 for the air and surface IST. [ABSTRACT FROM AUTHOR]

Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth's surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (Ta) have different physical meanings and are measured with different techniques, LST has often been successfully employed to estimate Ta. For this reason, in this work, we estimated Ta from LST MODIS collection 6 (C6) and used other predictor variables. Daily mean Ta was calculated from Spanish State Meteorological Agency (AEMET) stations data on the Livingston and Deception Islands, and from the PERMASNOW project stations on Livingston Island; both islands being part of the South Shetland Islands (SSI) archipelago. In relation to our previous work carried out in the study area with collection 5 (C5) data, we obtained higher R2 values (R2CV = 0.8, in the unique model with Terra daytime data) and lower errors (RMSECV = 2.2 °C, MAECV = 1.6 °C). We corroborated significant improvements in MODIS C6 LST data. We analyzed emissivity as a possible factor of discrepancies between C5 and C6, but we did not find conclusive results, therefore we could not affirm that emissivity is the factor that causes differences between one collection and another. The results obtained with the applied filters indicated that MODIS data can be used to study Ta in the area, as these filters contribute to the reduction of uncertainties in the modeling of Ta from satellites. [ABSTRACT FROM AUTHOR]

Land surface temperature plays an important role in the water cycle and surface energy balance. Using data collected by a vorticity covariance tower from 2010 to 2022, the relative threshold method and TRM method were employed to study the land–atmosphere exchange of water and the heat flux of rubber forest ecosystems under heatwave and non-heatwave conditions. The results show that the latent heat flux, sensible heat flux, and incoming and outgoing radiation increase from non-heatwave to heatwave conditions. In addition, the multi-year average LST was 6.7 °C higher under HW conditions than under non-HW conditions at the 99% confidence level. Further attribution analysis demonstrates that heatwave-induced land surface temperature change is mainly governed by atmospheric factors rather than by land surface factors. Specifically, radiative forcing shows the largest positive contribution, which is partly offset by the negative contributions of air temperature and relative humidity. In particular, the contributions of radiative forcing, air temperature, relative humidity, and atmospheric pressure to LST were 14.70 K, −4.76 K, −5.86 K, and −0.04 K, respectively. Moreover, surface resistance contributed to LST by 2.42 K, aerodynamic resistance by −0.23 K, and soil heat flux by −0.91 K. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data. [ABSTRACT FROM AUTHOR]

The current application of local climate zones (LCZs) often ends with (inter)zonal comparation of land surface temperature (LST) or air temperature (AT). LST evaluation employs an enhanced concept of LCZs together with cluster analysis for LCZs grouped based on LST. The paper attempts to combine them into a complex approach derived from the case study on a medium-sized Central European city (Hradec Králové, the Czech Republic). In particular, the paper addresses the following. (i) The relation of LST and AT, when the daily course of temperature profile ranging clear off the surface up to 2 m was fitted by a rational 2D function. The obtained equation enables derivation of the AT from LST and vice versa. (ii) The differences in thermal response of LCZs based on LST or AT, where the highest average LST and average maximum LST show LCZs 10, 2, 3 and 8, i.e., with a significant proportion of artificial surfaces. The cluster of LCZs with a significant representation of vegetation, LCZs 9, B, D, A and G, have significantly lower LST. (iii) The contribution of LCZs to understanding of LST/AT relation and whether their specific relation could be expected in particular LCZs, when subsequent interaction assessment of LST and AT revealed statistically their significant correlation in LCZs for certain cases. [ABSTRACT FROM AUTHOR]

The study of the response of vegetation phenology in the Qinghai Tibet Plateau to various climatic variables is paramount to unveiling the reaction of alpine ecosystems to worldwide climate alterations. Nonetheless, the lagged and cumulative effects of various climatic variables on vegetation phenology in the Qinghai Tibet Plateau remain unclear. Therefore, based on MODIS NDVI data, we extracted vegetation phenological parameters from 2001 to 2020, including the start of the vegetation growing season (SOS) and the end of the vegetation growing season (EOS), and then analyzed the response mechanisms of vegetation phenology to pre-seasonal air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) in the Qinghai Tibet Plateau on the basis of an investigation of the lag and cumulative effects. The results showed that: (1) the multiyear mean values of the SOS mainly occurred from 120 to 160 days, accounting for 86.17% of the study area, while the multiyear mean values of the EOS were mainly concentrated between 260 and 280 days, accounting for 77.05% of the study area; (2) air temperature (T), precipitation (P), and daytime and nighttime land surface temperatures (DLST, NLST) had different degrees of lagging effects on the SOS and the EOS. Among them, the time lag effect of precipitation on vegetation phenology was more pronounced; (3) different climatic variables had distinct cumulative effects on vegetation phenology. In contrast to the insignificant cumulative effects of temperature and nighttime surface temperature on the SOS and the EOS, the cumulative effects of precipitation and daytime land surface temperature on the SOS were more pronounced than those on the EOS; (4) the SOS and air temperature, precipitation, and NLST were mainly negatively correlated, in which the proportion of the negative correlation between SOS and NLST was up to 68.80%, and SOS and DLST were mainly positively correlated with a positive correlation proportion of 73.27%, EOS and air temperature, precipitation, and NLST were positively correlated with a positive correlation proportion of EOS and precipitation of up to 71.52%, and EOS and DLST were mainly negatively correlated with a negative correlation ratio of 55.87%. [ABSTRACT FROM AUTHOR]

It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products. [ABSTRACT FROM AUTHOR]

Accurate, seamless, and long-term land surface temperature (LST) data sets are crucial for investigating climate change and agriculture production. However, factors like cloud contamination have led to invalid values in the LST product, which has restricted the application of the LST dataset. Therefore, the reconstruction of LST products is challenging, and it is attracting widespread attention. This study compared the performance of different algorithms (XGBoost, GBDT, RF, POLY, MLR) and different training sets (using only good-quality pixels or using both good-quality and other-quality pixels) in the estimation of missing pixels in the LST data, obtaining a seamless daily 1 km LST dataset of MODIS Terra-day, Aqua-day, Terra-night, and Aqua-night data for Zhejiang Province and its surrounding areas from 2000 to 2022. The results demonstrated that the performance of machine-learning models is significantly better than that of linear models, and among the five models, XGBoost performed the best, with an RMSE of less than 1 °C. The Wilcoxon test between the reconstructed LST and the true LST values revealed that including both good-quality and other-quality pixels for reconstruction resulted in a 33% increase in the number of days with non-significant differences compared with using only good-quality pixels. Moreover, the reconstructed nighttime LST has a lower RMSE compared with the reconstructed daytime LST, and the RMSE of the reconstructed LST on the Terra satellite is lower than the RMSE of the reconstructed LST on the Aqua satellite. The RMSEs for the reconstructed LSTs are 0.50 °C, 0.61 °C, 0.36 °C, and 0.39 °C, corresponding to Terra-day, Aqua-day, Terra-night, and Aqua-night for images with coverage reaching 70%, 0.65 °C, 0.83 °C, 0.49 °C, respectively, and 0.52 °C for images with coverage less than 70%. The accuracy of the reconstructed LSTs using our proposed framework outperforms the existing reconstruction methods. The 1 km daily seamless LST products can be applied in various fields, such as air temperature estimation, climate change, urban heat island, and crop temperature stress monitoring. [ABSTRACT FROM AUTHOR]

Irrigation has excellent potential for altering surface characteristics and the local climate. Although studies using site observations or remote sensing data have demonstrated an irrigation cooling effect (ICE) on the air temperature (Tem) and land surface temperature (LST), it is difficult to eliminate other stress factors due to different backgrounds. We characterized the irrigation effect as the differences (Δ) of LST and DCT (DCT = LST − Tem) between irrigated and adjacent non-irrigated areas. An improved method was proposed to detect it over the North China Plain (NCP) based on satellite observations. We also investigated the effects of irrigation on Tem, precipitation, NDVI, and ET, and explored the relationships between them. The results show that irrigation induced a decrease in the daytime/nighttime LST and DCT (−0.13/−0.09 and −0.14/−0.07 °C yr−1), Tem (−0.023 °C in spring), and precipitation (−1.461 mm yr−1), and an increase in NDVI (0.03 in spring) and ET (0.289 mm yr−1) across the NCP. The effect on nighttime LST and NDVI increased by 0.04 °C 10 yr−1 and 0.003 10 yr−1, and that on ET weakened by 0.23 mm 10 yr−1 during 2000–2015. The ICE on the LST had evident spatiotemporal heterogeneity, which was greater in the daytime, in the spring, and in the northern area of the NCP (dry–hot conditions). The daytime ICE in the NCP and northern NCP was 0.37 and 0.50 °C during spring, respectively, with the strongest ICE of 0.60 °C in Henan; however, the ICE was less evident (<0.1 °C) in the southern NCP throughout the year. The ΔNDVI, ΔET, and ΔTem were the main factors driving ICE, explaining approximatively 22%, 45%, and 25% of the daytime ICE, respectively. For every unit of these measures that was increased, the daytime ICE increased by about 7.3, 4.6, and 1.5 °C, respectively. This study highlights the broad irrigation effect on LST, ET, NDVI, and the climate, and provides important information for predicting climate change in the future. The improved method is more suitable for regions with uneven terrain and a varying climate. [ABSTRACT FROM AUTHOR]

Most of the existing studies on the increase in air temperature (AT) in industrial neighborhoods (UIs) approach the subject from the analysis of the land surface temperature (LST). Therefore, the objective of this study was to analyze, in addition to LST, the variables of air temperature, relative and specific humidity, wind speed and direction, sky view factor and the albedo of the material surfaces, and to verify which of them has a greater impact on the urban microclimate of the UIs of two cities, Sintra/PT and Uberlândia/BR. To develop this analysis, representative sections of industrial urban areas in the previously mentioned cities were selected and computational simulations were carried out with the ENVI-met software to obtain results related to the studied variables. The results of the simulations, analyzed using multivariate analysis, showed that even though the Udia UI has materials with lower albedo (−45%), lower percentages of vegetation (−20%) and lower WS (−40%) than the Sin UI, the AT inside it may be lower than in the unshaded surroundings around 1.3 °C. For Sin UI, a difference in WS of −1.9 m/s, compared to the control points, caused a peak of +1.5 °C in the industrial environment at 13 h, contrary to what happened in Udia UI. [ABSTRACT FROM AUTHOR]

The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) has a critical impact on the energy balance of land surface processes and ecosystem stability. Meanwhile, LST is controlled by multiple factors at the surface, resulting in heterogeneity of its spatial distribution. To understand the drivers of LST spatial heterogeneity and their contributions, the effects of air temperature, normalized difference vegetation index (NDVI), soil moisture, net surface radiation, precipitation, aerosol optical depth (AOD), evapotranspiration, water vapor, digital elevation model (DEM), climate type, and land cover type on LST spatial heterogeneity was analyzed in this study with GeoDetector. The results showed that the explanatory ability of air temperature to impact the spatial heterogeneity of LST was the largest in each year with a mean value of 0.74, followed by water vapor with a mean value of 0.7, and the driving effect of the factors on LST showed an increasing trend year by year. However, the land cover type did not have an effect on the spatial heterogeneity of LST for the univariate analysis in this study. In addition, the interaction analysis indicated that the spatial distribution of LST was jointly driven by all the driving factors. Among them, air temperature had the strongest interaction with other factors, with the strength of the effect in the range of 0.73–0.8. In terms of the highly sensitive area of LST for each driver, AOD has the largest driving area, accounting for 15.8% of the total area, followed by WV, TA, and ET at about 11%, and the remaining variables are less than 10%. During the study period, the area of the highly sensitive region of LST for each factor showed an overall decreasing trend, indicating that the influence of the driving factors on LST will be stronger and more concentrated. Generally, this study provides meaningful understanding of the spatial heterogeneity of LST since 2003 and provides a scientific reference for coping with climate change, analyzing surface environmental patterns, and protecting ecological environment. [ABSTRACT FROM AUTHOR]

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques. [ABSTRACT FROM AUTHOR]

Air temperature ( T a ) is a common meteorological element involved in many fields, such as surface energy exchange and water circulation. Consequently, accurate T a estimation is essential for the establishment of hydrological, climate, and environmental models. Unlike most studies concerned with the estimation of daily T a from land surface temperature, this study focused on the estimation of instantaneous T a from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profile products aboard the Terra and Aqua satellites. The applicability of various estimation methods was examined in two regions with different geomorphological and climate conditions, North and Southwest China. Specifically, the spatiotemporal trend of T a under clear sky conditions can be reflected by the atmospheric profile extrapolation and average methods. However, the accuracy of T a estimation was poor, with root mean square error (RMSE) ranging from 3.5 to 5.2 °C for North China and from 4.0 to 7.7 °C for Southwest China. The multiple linear regression model significantly improved the accuracy of T a estimation by introducing auxiliary data, resulting in RMSE of 1.6 and 1.5 °C in North China and RMSE of 2.2 and 2.3 °C in Southwest China for the Terra and Aqua datasets, respectively. Since atmospheric profile products only provide information under clear sky conditions, a new multiple linear regression model was established to estimate the instantaneous T a under cloudy sky conditions independently from atmospheric profile products, resulting in RMSE of 1.9 and 1.9 °C in North China and RMSE of 2.5 and 2.8 °C in Southwest China, for the Terra and Aqua datasets, respectively. Finally, instantaneous T a products with high accuracy were generated for all-weather conditions in the study regions to analyze their T a spatial patterns. The accuracy of T a estimation varies depending on MODIS datasets, regions, elevation, and land cover types. [ABSTRACT FROM AUTHOR]

Peatland fires in Central Kalimantan emit thick smoke and large amounts of greenhouse gases and have an impact on the environment globally, but studies on fire weather have not been carried out due to lack of diurnal weather data. The aim of this study is to identify the fire weather conditions during active fires that is needed to mitigate future occurrences of peat fires in Indonesia. The available diurnal weather data was used to analyze the fire weather conditions. Based on meteorological data on active fires (11 days), there was a significant increase in air temperature due to the sea breeze that started blowing in the morning. The average values for the 11-day period around 15:00 are a maximum air temperature of 36 °C, minimum humidity of 37%, wind speed of 21 km h−1, and a rate of increase of 2.7 °C h−1 from 8:00. The difference in sea and land temperatures causes strong winds to blow and triggers an increase in land temperatures. The results of this report can help predict fire activity at high temperatures in the future based on global warming predictions made by other researchers. The rapid rate of increase in air temperature from the morning will be useful in anticipating fires in Central Indonesia. [ABSTRACT FROM AUTHOR]

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen's slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales. [ABSTRACT FROM AUTHOR]

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites. [ABSTRACT FROM AUTHOR]

The distinction between satellite-based land surface temperature (LST) and air temperature has become an increasingly important part of managing urban heat islands. While the preponderance of urban heat research relies on LST, the emergence of a growing infrastructure of publicly available consumer oriented, ground-based sensor networks has offered an alternative for characterizing microscale differences in temperatures. Recent evidence suggests large differences between LST and air temperatures, yet discerning the reason for these differences between satellite-derived measurements of urban heat islands (UHI) and ground-based measurements of air temperature remains largely unresolved. In this study, we draw on an unusually robust and spatially exhaustive dataset of air temperature in two distinct bioclimates—Portland, Oregon, USA and Washington, D.C., USA—to evaluate the role of land cover on temperature. Our findings suggest that LST in highly built environments is consistently higher than recorded air temperatures, at times varying upwards of 15-degree Celsius, while forested areas contain between 2.5 and 3.5-degree Celsius lower temperatures than LST would otherwise indicate. Furthermore, our analyses points to the effects of land use and land cover features and other geophysical processes may have in determining differences in heat measurements across the two locations. The strength of the present analyses also highlights the importance of hyperlocal scales of data used in conjunction with coarser grain satellite derived data to inform urban heat assessments. Our results suggest a consistent pattern in both study areas, which can further our capacity to develop predictive models of air temperature using freely available descriptions of LST. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations. [ABSTRACT FROM AUTHOR]

Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill-posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this study, a fully coupled framework was developed to robustly retrieve NSAT from thermal remote sensing data, integrating physical, statistical, and deep learning methods (PS-DL). Based on physical derivation, the optimal combinations of remote sensing bands were chosen for building the inversion equations to retrieve NSAT, and deep learning was used to optimize the calculations. Multisource data (physical model simulations, remote sensing data, and assimilation products) were used to establish the training and test databases. The NSAT retrieval accuracy was enhanced using the land surface temperature (LST) and land surface emissivity (LSE) as prior knowledge. The highest mean absolute error (MAE) and root-mean-square error (RMSE) of the retrieved NSAT data were 0.78 K and 0.89 K, respectively. In a cross-validation against the China Meteorological Forcing Dataset (CMFD), the MAE and RMSE were 1.00 K and 1.29 K, respectively. The actual inversion MAE and RMSE for the optimal band combination were 1.21 K and 1.33 K, respectively. The proposed method effectively overcomes the limitations of traditional methods as the inversion accuracy is enhanced by adding the information of atmospheric water vapor and more bands, and the applicability (portability) of the algorithm is enhanced using LST and LSE as prior knowledge. This model can become a general inversion paradigm for geophysical parameter retrieval, which is of milestone significance because of its accuracy and the ability to allow deep learning for physical interpretation. [ABSTRACT FROM AUTHOR]

The use of vegetation is one of the effective methods to combat the increasing Urban Heat Island (UHI). However, vegetation is steadily decreasing due to urban pressure and increased water stress. This study used air temperature measurements, humidity and an innovative advanced earth system analysis to investigate, at daytime, the relationship between green surfaces, built-up areas and the surface urban heat island (SUHI) in Marrakesh, Morocco, which is one of the busiest cities in Africa and serves as a major economic centre and tourist destination. While it is accepted that UHI variation is generally mitigated by the spatial distribution of green spaces and built-up areas, this study shows that bare areas also play a key role in this relationship. The results show a maximum mean land surface temperature difference of 3.98 °C across the different city neighbourhoods, and bare ground had the highest correlation with temperature (r = 0.86). The correlation between the vegetation index and SUHI is decreasing over time, mainly because of the significant changes in the region's urban planning policy and urban growth. The study represents a relevant overview of the factors impacting SUHI, and it brings a new perspective to what is known so far in the literature, especially in arid climate areas, which have the specificity of large bare areas playing a major role in SUHI mitigation. This research highlights this complex relationship for future sustainable development, especially with the challenges of global warming becoming increasingly critical. [ABSTRACT FROM AUTHOR]

The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics. [ABSTRACT FROM AUTHOR]

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes. [ABSTRACT FROM AUTHOR]

Meteorologically observed air temperature (Ta) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of Ta estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration. [ABSTRACT FROM AUTHOR]

This study focuses on the determination and examination of both the Land Surface Temperature (LST) and the atmospheric temperature in the city of Catania Sicily (Italy), through freely available satellite remote sensing images from the Sentinel-2 and MODIS missions. Satellite images were processed as raster data in free and open-source GIS environments. The GIS software allows the retrieval, processing of the satellite images for the estimation of the LST and the atmospheric temperature with a very coarse spatial resolution. In particular, the proposed procedure allows increasing the spatial resolution of satellite images, from 250 m (LRES) to 10 m (HRES) through the principle of "Disaggregation of thermal images". The analysis provided georeferenced maps which show the LST, as well as the atmospheric temperature within the investigated area with a very fine resolution, 10 m. Such spatial resolution reveals evident correlations between areas with different urban densities and their microclimate. An important result of this study is that significant LST differences can be observed during both day (15–17 °C) and night (2–3 °C) between green and built-up areas. The outcomes of this study highlight the effectiveness of the combined use of satellite remote sensing and GIS for analyzing the thermal response of urbanized areas with different built density. [ABSTRACT FROM AUTHOR]

The Gavkhouni wetland provides many environmental and economic benefits for the central region of Iran. In recent decades, it has completely dried up several times with substantial impacts on local ecosystems and climate. Remote sensing-based Land Surface Temperature (LST), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) in combination with in-situ data were used to investigate the trend of the Gavkhouni wetland dryness and the associated impact on the variability of local air temperature. The results indicate that the wetland has increasingly experienced drier conditions since the year 2000. The wetland was almost completely dry in 2009, 2011, 2015 and 2017. In addition, the results show that Gavkhouni wetland dryness has a significant impact on local climate, increasing the mean seasonal air temperature by ~1.6 °C and ~1 °C in spring and summer, respectively. Overall, this study shows that remote sensing imagery is a valuable source for monitoring dryness and air temperature variations in the region. Moreover, the results provide a basis for effective water allocation decisions to maintain the hydrological and ecological functionality of the Gavkhouni wetland. Considering that many factors such as latitude, cloud cover, and the direction of prevailing winds affect land surface and air temperatures, it is suggested to use a numerical climate model to improve a regional understanding of the effects of wetland dryness on the surrounding climate. [ABSTRACT FROM AUTHOR]

In the Alps, understanding how climate change is affecting evapotranspiration (ET) is relevant due to possible implications on water availability for large lowland areas of Europe. Here, changes in ET were studied based on 20 years of MODIS data. MOD16 and operational Simplified Surface Energy Balance (SSEBop) products were compared with eddy-covariance data and analyzed for trend detection. The two products showed a similar relationship with ground observations, with RMSE between 0.69 and 2 mm day−1, and a correlation coefficient between 0.6 and 0.83. A regression with the potential drivers of ET showed that, for climate variables, ground data were coherent with MOD16 at grassland sites, where r2 was 0.12 for potential ET, 0.17 for precipitation, and 0.57 for air temperature, whereas ground data agreed with SSEBop at forest sites, with an r2 of 0.46 for precipitation, no correlation with temperature, and negative correlation with potential ET. Interestingly, ground-based correlation corresponded to SSEBop for leaf area index (LAI), while it matched with MOD16 for land surface temperature (LST). Through the trend analysis, both MOD16 and SSEBop revealed positive trends in the south-west, and negative trends in the south and north-east. Moreover, in summer, positive trends prevailed at high elevations for grasslands and forests, while negative trends dominated at low elevations for croplands and grasslands. However, the Alpine area share with positive ET trends was 16.6% for MOD16 and 3.9% for SSEBop, while the share with negative trends was 1.2% for MOD16 and 15.3% for SSEBop. A regression between trends in ET and in climate variables, LST, and LAI indicated consistency, especially between ET, temperature, and LAI increase, but low correlation. Overall, the discrepancies in the trends, and the fact that none of the two products outperformed the other when compared to ground data, suggest that, in the Alps, SSEBop and MOD16 might not be accurate enough to be a robust basis to study ET changes. [ABSTRACT FROM AUTHOR]

The mesoscale circulation along the west shore of Spitsbergen is largely controlled by the difference in temperature between the glaciers and surface sea temperatures. We describe how the mesoscale effect influences the atmospheric circulation patterns. The conducted research was based on reanalysis data, model data, and atmospheric measurements; wind data from different sources and scales were compared and analysed. We discuss the situations wherein the mesoscale effect can be identified by analysing the wind direction or its velocity. This study shows the role of the mesoscale effect on the wind in the Svalbard region. Different situations according to the atmospheric patterns taken from a catalogue of 21 circulation types for each day created for Svalbard are analysed and compared with cases of land-sea breeze type circulation for the 20-year period between 1994 and 2013. It is proved that even if it is not possible to distinguish this mesoscale effect based on the difference between local and large-scale wind directions, this factor can be observed by studying the wind speeds. It is claimed that as long as there are glaciers on Spitsbergen, there will be a mesoscale land-sea breeze type circulation controlled by the difference in air temperature over land and water. [ABSTRACT FROM AUTHOR]