基于无人机热红外影像的城市地表温度研究，尤其涉及地表热通量

Urban heat island (UHI) effects are a critical concern for densely populated tropical cities, where rapid urbanization exacerbates local temperature disparities. This study investigates the micro-scale heat distribution in residential zones of Central Jakarta using UAV thermal imaging data. Land surface temperature (LST) maps were generated to analyze thermal variations and their correlation with land use types such as impervious surfaces (e.g., asphalt and concrete) and vegetation. High-density zones exhibited significantly higher daytime temperatures, reaching up to 47°C, compared to vegetated areas, which maintained cooler temperatures around 32°C. Temporal analysis revealed prolonged nighttime heat retention in impervious areas, with residual temperatures exceeding 27°C, while vegetated zones cooled rapidly to below 20°C. These findings highlight the critical role of vegetation in mitigating UHI effects and emphasize the need for targeted urban planning strategies to reduce localized heat intensity in residential zones.

This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s I, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 ∘C between grassland (20.57 ± 5.13 ∘C) and water bodies (7.10 ± 1.25 ∘C) during afternoon periods. The Moran’s I analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 ∘C) and midnight (2.25 ± 1.51 ∘C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 ∘C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 ∘C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence (R2≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices.

Abstract. Urban areas worldwide are experiencing increase in temperatures due to urbanisation and, leading to the effect of Urban Heat Islands (UHIs), which threaten urban sustainability. Global research aims to identify UHIs and develop mitigation measures. Most existing studies rely on coarse-resolution satellite imagery, limiting the detection and characterization of heterogeneous urban surfaces and localized UHI effects. Advances in drone technology with multi-payload thermal sensors now allows LST mapping at finer spatial resolutions (<1 m), enabling detailed analysis of temperature variations across urban surfaces. Assessing the accuracy of these measurements is essential and typically involves comparing UAV-derived LST with ground-based or in situ temperature observations collected simultaneously during UAV flights. Proper calibration of the TIR sensors is necessary to minimize systematic errors. Accuracy is commonly quantified using statistical quantification like Mean Absolute Error (MAE), R squared and Root Mean Square Error (RMSE). UAVs offer much finer spatial resolution (<1 m) than satellites, enabling detection of localized UHI hotspots that coarse-resolution imagery may miss. Combining UAV, ground, and satellite data enhances confidence in LST estimates and supports precise analysis of urban heat patterns, providing critical insights for mitigation strategies and urban planning. These high-resolution datasets can support machine-learning tools for urban planners to predict localized UHI impacts, adopt mitigation strategies, and advance Sustainable Development Goals.

Thermal cameras on unoccupied aerial vehicles (UAVs) are increasingly being used in environmental and ecological research, including hydrology, wildfire detection and prediction, urban heat studies, precision agriculture, ecosystem functioning, wildlife monitoring and microclimate studies. Converting raw thermal signals to quantitative land surface temperature (LST) values requires careful application of correction procedures. However, these steps are often overlooked or ignored—either due to limited expertise in thermal remote sensing or because of the technical complexity involved. Neglecting corrections for atmospheric effects and surface emissivity can lead to discrepancies of up to 5°C in the resulting LST estimate. We introduce theRmalUAV, an R package that facilitates LST processing with two workflows: an orthomosaic‐based and an image‐based approach. The orthomosaic workflow applies a single function to the entire dataset, whereas the image‐based workflow can account for variations in environmental conditions during the flight that affect surface temperature. The package corrects for atmospheric effects, background temperature, spatial emissivity and weather fluctuations, incorporating a novel method to handle rapid illumination changes. The package currently supports 11 common thermal sensors. It also includes tools for data cleaning, co‐registration and reporting. We demonstrate both the importance of the workflow and its implementation using two distinct case studies to highlight its versatility. The main text presents a detailed example using the research‐grade TeAx ThermalCapture 2.0. A complementary example, featuring the more commercially oriented DJI Mavic 3T, is provided in the Supporting Information . For comprehensive guidance and tutorials, readers are directed to the package vignette and its companion website.

ABSTRACT In this study, high-resolution thermal imagery acquired with a small unmanned aerial vehicle (UAV) is used to map evapotranspiration (ET) at a grassland site in Luxembourg. The land surface temperature (LST) information from the thermal imagery is the key input to a one-source and two-source energy balance model. While the one-source model treats the surface as a single uniform layer, the two-source model partitions the surface temperature and fluxes into soil and vegetation components. It thus explicitly accounts for the different contributions of both components to surface temperature as well as turbulent flux exchange with the atmosphere. Contrary to the two-source model, the one-source model requires an empirical adjustment parameter in order to account for the effect of the two components. Turbulent heat flux estimates of both modelling approaches are compared to eddy covariance (EC) measurements using the high-resolution input imagery UAVs provide. In this comparison, the effect of different methods for energy balance closure of the EC data on the agreement between modelled and measured fluxes is also analysed. Additionally, the sensitivity of the one-source model to the derivation of the empirical adjustment parameter is tested. Due to the very dry and hot conditions during the experiment, pronounced thermal patterns developed over the grassland site. These patterns result in spatially variable turbulent heat fluxes. The model comparison indicates that both models are able to derive ET estimates that compare well with EC measurements under these conditions. However, the two-source model, with a more complex treatment of the energy and surface temperature partitioning between the soil and vegetation, outperformed the simpler one-source model in estimating sensible and latent heat fluxes. This is consistent with findings from prior studies. For the one-source model, a time-variant expression of the adjustment parameter (to account for the difference between aerodynamic and radiometric temperature) that depends on the surface-to-air temperature gradient yielded the best agreement with EC measurements. This study showed that the applied UAV system equipped with a dual-camera set-up allows for the acquisition of thermal imagery with high spatial and temporal resolution that illustrates the small-scale heterogeneity of thermal surface properties. The UAV-based thermal imagery therefore provides the means for analysing patterns of LST and other surface properties with a high level of detail that cannot be obtained by traditional remote sensing methods.

Analysis of turbulent heat fluxes in urban forests is crucial for understanding structural variations in the urban sub-surface boundary layer. This study used data captured by an unmanned aerial vehicle (UAV) and an improved semi-empirical triangle method to estimate small-scale turbulent heat fluxes in the sub-surface of an urban forest. To improve the estimation accuracy, the surface temperature (TS) of the UAV-based remote sensing inversion was corrected using the hot and cold spot correction method, and the process of calculating ϕmax using the traditional semi-empirical triangle method was improved to simplify the calculation process and reduce the number of parameters in the model. Based on this method, latent heat fluxes (LE) and sensible heat fluxes (H) were obtained with a horizontal resolution of 0.13 m at different time points in the study area. A comparison and validation with the measured values of the eddy covariance (EC) system showed that the absolute error of the LE estimates ranged from 4.43 to 23.11 W/m2, the relative error ranged from 4.57% to 25.33%, the correlation coefficient (r) with the measured values was 0.95, and the root mean square error (RMSE) was 35.96 W/m2, while the absolute error of the H estimates ranged from 3.42 to 15.45 W/m2, the relative error ranged from 7.51% to 28.65%, r was 0.91, and RMSE was 9.77 W/m2. Compared to the traditional triangle method, the r of LE was improved by 0.04, while that of H was improved by 0.06, and the improved triangle method was more accurate in estimating the heat fluxes of urban mixed forest ecosystems in the region. Using this method, it was possible to accurately track the LE and H of individual trees at the leaf level.

Land Surface Temperature (LST) is a key variable used across various applications, including irrigation monitoring, vegetation health assessment and urban heat island studies. While satellites offer moderate-resolution LST data, unmanned aerial vehicles (UAVs) provide high-resolution thermal infrared measurements. However, the continuous and rapid variation in LST makes the production of orthomosaics from UAV-based image collections challenging. Understanding the environmental and meteorological factors that amplify this variation is necessary to select the most suitable conditions for collecting UAV-based thermal data. Here, we capture variations in LST while hovering for 15–20 min over diverse surfaces, covering sand, water, grass, and an olive tree orchard. The impact of different flying heights and times of the day was examined, with all collected thermal data evaluated against calibrated field-based Apogee SI-111 sensors. The evaluation showed a significant error in UAV-based data associated with wind speed, which increased the bias from −1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. Different surfaces, albeit under varying ambient conditions, showed temperature variations ranging from 1.4 to 6 °C during the flights. The temperature variations observed while hovering were linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. Irrigation and atmospheric conditions (i.e., thin clouds) also contributed to observed temperature variations. This research offers valuable insights into LST variations during standard 15–20 min UAV flights under diverse environmental conditions. Understanding these factors is essential for developing correction procedures and considering data inconsistencies when processing and interpreting UAV-based thermal infrared data and derived orthomosaics.

An extended UAV campaign that covered the historic city center of Heraklion, Greece has been performed during two consecutive days in July 2018. Heraklion city center is equipped with a permanent micrometeorological tower measuring net radiation and turbulent heat and CO2 fluxes. UAV cameras with RGB/NIR bands, as well as an integrated thermal (IR) band were used during the campaign. A calibrated and orthorectified RGB/NIR image mosaic (5 cm) was produced and a Support Vector Machine (SVM) algorithm was applied for the classification of the urban surface materials. The emissivity of the different materials was obtained by spectral libraries and used to calibrate the thermal maps (10 cm) by the IR camera. The first results show the pronounced effects of urban canyon orientation, building density and materials to the surface temperature. Some roofing materials present significantly lower temperature than other materials (cool materials). The surface temperature maps by the UAV were evaluated using tower-mounted net radiometer measurements. Increased differences between the two methods were found, attributed mainly to the different field of view of the two instruments, the increased thermal anisotropy of the urban environment and the uncertainty regarding the emissivity of the different materials.

Urban microclimates result from complex interactions between buildings, vegetation, and human activities, impacting energy consumption, air quality, and urban planning. Understanding and mapping these microclimates is essential for sustainable city development. Geographic Information Systems (GIS) play a crucial role in analyzing microclimate patterns by integrating spatial datasets such as land cover, building heights, and meteorological data. This study examines urban microclimates in İzmir’s Konak District using GIS and unmanned aerial vehicles (UAVs) equipped with thermal sensors. By classifying Local Climate Zones (LCZs) and analyzing their relationship with land surface temperatures (LSTs), the research highlights how urban morphology shapes microclimatic conditions. The study area was divided into 2,435 grids, with UAV-based thermal imaging providing high-resolution temperature data. Findings indicate that LCZs with high impermeable surface fractions (e.g., LCZ 7, LCZ 8, and LCZ E) exhibited elevated temperatures, while vegetated or water-rich zones (e.g., LCZ B and LCZ G) demonstrated cooling effects. The Heat Load Map identified 8.8% of the district as experiencing excessive heat, while 21.7% benefited from optimal thermal conditions due to green and blue spaces. This study underscores the importance of increasing vegetation and permeable surfaces to mitigate urban heat islands (UHIs). By integrating UAV technology with GIS, it advances LCZ-based urban climate research and provides practical tools for climate-responsive planning. Understanding microclimates in dense urban areas enables targeted strategies to reduce heat stress, improve air quality, and enhance urban livability.

Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.

In the scope of this study, a system was developed for performing essential thermal measurements with the aim of support energy efficiency in industrial places and public dwellings and determining heat energy losses. The system was established through using unmanned aerial vehicle, thermal cameras with wireless and RGB communication. Primarily calibration tests were carried out for flight time of unmanned aerial vehicle, landing after the flight, accuracy of lift-off, balance tests, images taken from colored and thermal camera. Radiometric, thermal and geometric calibrations were fulfilled at the calibration stage. After the calibration stage, thermal measurements were performed and heat losses were determined. It was selected two buildings and a parking lot, then essential measurements were done. According to heat loss calculation published by Turkish Plumbing and Engineers Association, heat losses were calculated, heat loss values taken from thermal cameras were compared and verifications and optimization processes were carried out for the system. Keywords: Colored camera, energy efficiency, heat loss, thermal camera, unmanned aerial vehicle.

Increasing solid waste volume from human activities makes solid waste stockpiles grows higher in municipal landfill site. This phenomenon has potential risk especially in form of increasing land surface temperature (LST) when compared to surrounding environment. However, detailed survey of LST in solid waste piles using conventional tools might be time-consuming. Therefore, this study offers alternative of elevation and LST measurement in waste piles using combined images from unmanned aerial vehicle (UAV) and Landsat 8. Herein, DJI Phantom 4 Pro was flown in a waste stockpile located in Cipayung landfill site, Depok Municipality, Indonesia. Digital Surface Model (DSM) was acquired from UAV images processing. LST prediction is processed from Landsat 8 thermal infrared sensors (TIRS) band 10. Resampling technique was employed to match spatial resolution between DSM and LST. Both solid waste elevation and LST were paired statistically using Pearson correlation coefficient to observe linear relationship between them. Results show that waste elevation and LST have positive correlation.

The urban heat island effect is a common phenomenon during urbanization, characterized by significantly higher temperatures in urban areas compared to surrounding rural areas. Sensible heat flux (H) and temperature are important factors affecting the urban heat island effect. By optimizing the calculation of sensible heat flux, the urban thermal environment can be assessed more accurately, thereby guiding urban planning and green space layout to mitigate the heat island effect. This study proposes a method to optimize the calculation of sensible heat flux by combining low-pass filtering and wavelet transform. Low-pass filtering removes high-frequency noise, while wavelet transform extracts multi-scale fluctuation components, thereby improving the accuracy and stability of sensible heat flux calculations. Experimental results show that this method significantly enhances the precision of sensible heat flux calculations and reduces noise interference, providing reliable data for urban thermal environment research. This paper discusses in detail the application of this method in the study of the urban thermal environment in Jiangsu Province.

The urban thermal environment significantly influences regional climate change, urban resilience, and sustainability. Although traditional remote sensing technologies are common tools for observing urban thermal environments, it has limitations, including low spatial resolution, long revisit cycles, and cloud cover. In responding to these limitations, a low-altitude dual-source remote sensing platform (LADSRSP) was researched and developed in this study through a series of tests. First, a thermal infrared sensor and a hyperspectral sensor were simultaneously equipped on a multirotor drone via a customized three-axis gimbal in LADSRSP. Furthermore, LADSRSP was optimized based on the stability test results and was then confirmed to have the ability to collect data with high spatial resolution and quality through the data acquisition tests. Finally, the performance of LADSRSP was assessed by data integrity, classification accuracy, and temperature analysis. The image classification results demonstrate high accuracy with an overall classification accuracy of 97.92% and a Kappa coefficient of 0.97 from collected data quality analysis tests. The temperature values of each urban underlying surface were then accurately extracted to facilitate further statistical analysis. Overall, the LADSRSP has been proven to be a feasible, efficient, and accurate tool for urban thermal environment research.

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design.

The purpose of this study was to determine the effect of cool pavement, which is one of the measures applied to reduce the urban heat island effect, on the actual thermal comfort of urban residents based on mean radiant temperature (MRT). To this end, the area of Mugye-dong, Jangyu-ro, Gimhae-si, Gyeongsangnam-do, where cool pavement has already been applied to roofs and roads, was selected as a study area. The distribution of MRT in the area was identified for each observation session using images taken by an unmanned aerial vehicle (UAV), commonly known as a drone, and observations from an automated weather station (AWS). To understand the characteristics of each of the spatial conditions of the survey targets, the cool pavements applied to roofs and roads were classified and analyzed, and the directions were broken down. Through the analysis, it was found that cool pavements applied to roofs and roads in open spaces showed the largest deviation in MRT values from those in general spaces. For roofs, the highest deviation was found to be 1.926 and 2.151 at 13:00 on both cloudy and sunny days, respectively; whereas for roads the highest deviation was 0.774 and 1.122 at 7:00 on both cloudy and sunny days, respectively. This suggests that the MRT of cool pavements have various distributions according to spatial and temporal conditions.

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element.

The existence of urban green open space is very important for the creation of a public space that can support various public activities of urban residents and become an ecological space for the creation of an adequate quality urban environment. Bogor City is one of the cities that is currently developing green open space in the form of an urban park to meet the needs of public space as a means of gathering and recreation. Thermal comfort is very important and must be considered in a green space. To obtain the thermal conditions of a landscape, this research uses a thermal drone. This study aims to identify the thermoscape characteristics of landscape elements and people's perceptions of it and also the correlations between them. The analysis used Agisoft Metashape, ArcMap 10.5, Google Earth Pro, Adobe Photoshop CS6, Microsoft Office 2019, and Image Color Summarizer online tool. The thermoscape results in the two urban parks are different due to differences in weather. However, the thermal conditions of each landscape elements are almost the same in both parks. Hardscapes in both parks can have a temperature of >40? C, while vegetation, especially trees, can have a temperature of <20? C. According to respondents, both parks are still considered warm based on their thermal sensation. Hot urban park areas can be modified by presenting a wider green space with good distribution and proportion to make it comfortable for the users.

Longwave radiation is a crucial factor affecting human thermal comfort and thermal stress, especially in outdoor spaces in summer, owing to the vast effect of longwave radiation emitted from high-heated asphalt roads, building walls, and automobiles. Although controlling the longwave radiation environment to improve thermal comfort in summer is crucial, the prediction of the longwave radiation environment is frequently conducted only at the assessment stage of the final proposal because of the high computational cost of radiation calculations and unsteady heat balance analysis considering multiple reflections. This is a significant constraint for the design of urban and architectural environments. A previous study proposed a method to rapidly estimate the longwave radiation environment based on a point-by-point method with longwave radiation intensity distributions of the heat sources. To use this method, 3D models of the geographical objects in urban areas, such as buildings and trees, must be accurately generated, and these models should have information on the longwave radiation emitted in each direction from each object. However, no specific examples of a 3D model and longwave radiation intensity distribution have been presented. In this study, a 3D modeling method for geographical objects in urban areas with longwave radiation information based on drones and photogrammetric techniques was utilized. Moreover, a 3D model of a small-scale building was generated. A longwave radiation intensity distribution was produced for the building. Based on the distribution data, the directional characteristics of longwave radiation were discussed, and the availability of the proposed method was assessed.

This paper presents a thermographic analysis of a street junction within an urban road network, focusing on identifying thermal load sources generated by vehicle traffic—an increasingly significant environmental concern for urban populations. The study explores the application of thermographic methods at urban intersections and the creation of thermal maps. These approaches support the advancement of intelligent transport systems, aligning with smart city initiatives aimed at optimizing traffic flow management. Additionally, the findings provide potential for assessing the conditions of both road transport infrastructure and vehicles. By adopting this comprehensive perspective for monitoring urban environments and transportation systems, cities can enhance overall quality of life and public well-being. The results emphasize the value of conducting broad-scope studies, suggesting that combining ground-based and aerial thermal imaging leads to more informed decision-making. Doi: 10.28991/CEJ-2025-011-05-02 Full Text: PDF

The built environment contributes 40% of energy demand and 37% of CO2 emissions globally. Its planning and management are vital for achieving SDGs. Rising urban temperature, driven by urbanization and population growth, worsens the urban heat island effect and reduces urban thermal comfort. Evaluating the urban environment is crucial to create thermally comfortable spaces that can also lower the energy use. Currently most urban building facades are mainly composed of materials that easily absorb solar radiation and later release the accumulated energy in the form of heat, further deteriorating the urban thermal comfort. This study aims to simulate the impact of building façade materials to the urban air temperature (AT) and the physiological equivalent temperature (PET). Materials that could potentially decrease the urban temperature can be identified. To build a detailed simulation model and collect meteorological data as the simulation input, this study utilized drone and temperature sensors. The 3D model created from the drone survey is imported to ENVI-met software for simulation. The measured temperature of the urban environment by sensor is used as the simulation input and simulation validation. Four scenarios assuming specific building façade materials (concrete, wood, glass and steel) were applied for simulation. Among the selected materials in the simulation glass has the lowest maximum AT (34.54°C) followed by concrete (34.59°C), steel (34.62°C) and wood (34.63°C). This might be caused by the albedo and thermal characteristics of the selected materials. Expanding this research to cover a greater variety of materials and climate conditions could inform the future urban and city planning aimed at mitigating extreme microclimates such as the UHI effect.

As urbanization accelerates, more and taller buildings and less greenery are closely related to changes in the urban thermal environment (UTE). Knowledge of spatial and temporal variations of UTE is becoming increasingly concerning, and this can be measured with the land surface temperature (LST). Satellite observation of LST is an important tool for monitoring; the strong thermal anisotropy limits the use of satellite thermal infrared (TIR) data. Hitherto, the poor investigation was focused on the modeling and analysis of urban thermal anisotropy (UTA), especially in mountainous urban areas with multislope environments. These areas exhibit a distinctive “roof effect,” which is defined as the radiative transfer (RT) effect between the roof and the adjacent wall due to the slope that results in different heights between the roofs; multiple scattering has also been changed. Although an analytical thermal anisotropy model for the urban canopy over sloping terrain (AU3SM) has been proposed, its inability to effectively account for roof effects and multiscattering mechanisms limits its daytime TIR observation applicability. To address these limitations, we developed an enhanced AU3SM that considers the roof effect and multiple scattering, which is labeled AU3SM-RS. The model was evaluated using measurements based on uncrewed aerial vehicles (UAV) in the mountainous city of Chongqing, China, with values of the root-mean-square error (RMSE) and coefficient of determination ( ${R}^{2}$ ) of 0.83 K and 0.93 in UTA, respectively. Comparison with a graphic processing unit-based solution for the faster 3-D RT model (GRay) further validates the model’s reliability with RMSE and ${R}^{2}$ values of 0.12 K and 0.96, respectively. Simulations in a certain scenario reveal that as the slope increases, the roof effect increases and the multiple scattering effect decreases in UTA and brightness temperature (BT), ignoring the roof effect and multiple scattering can result in maximum UTA biases of approximately 0.54, 0.48, and 0.72 K, BT biases approximately 1.02, 1.62, and 2.4 K at 5°, 15°, and 30° slopes, the biases due to neglecting the second scattering are very slight compared to the first scattering. Under certain conditions with a slope of 10°, the wider roof and narrower roadway, a related more dramatic roof effect; the narrower and deeper street canyon, a related more dramatic scattering effect. The proposed model is an efficient computational tool to assess UTA in mountainous areas quickly.

The built environment is responsible for 40% of global energy demand, and, in line with urbanisation and population growth, this demand is expected to increase steadily. Urban areas are mostly composed of materials that can absorb energy from solar radiation and dissipate the accumulated energy in the form of heat. This study integrates a UAV-based Zenmuse XT S IR camera and handheld FLIR C5 thermal camera with ENVI-met microclimate simulation, providing quantitative insights for sustainable urban planning. From the 24 h experiment results, the characteristics of building surface materials are profiled for lowering energy use for internal thermal control during the operation stage of buildings. This study shows that building surface materials with the lowest solar reflectance and highest specific heat capacity reached a peak surface temperature of 73.5 °C in Jakarta (tropical hot climate) and 44.3 °C in Xiamen (subtropical late winter climate). In contrast, materials with the highest solar reflectance and lowest specific heat only reach a peak surface temperature of 58.1 °C in Jakarta and 27.9 °C in Xiamen. The peak surface temperature occurs at 2 PM in the afternoon. Moreover, we demonstrate the capability of an infrared drone to identify the peak surface temperatures of 55.8 °C at 2 PM in the study area in Xiamen. In addition, the ENVI-met validated model shows satisfactory correlation values of R > 0.9 and R2 > 0.8. This result demonstrates UAV-IR and ENVI-met simulation integration as a scalable method for city-level UHI diagnostics and monitoring.

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The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria’s capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024.

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In the context of climate change, surface urban heat islands (SUHIs) have become critical factors affecting the quality of the urban built environment. However, low-precision satellite thermal infrared remote sensing is suitable for urban scales but is insufficient to reveal the spatiotemporal distribution roles of surface heat islands at the neighborhood scale. This research takes the Sipailou Campus of Southeast University as an example and employs UAV thermal infrared imaging to acquire high-precision surface temperature data. It then systematically investigates the relationship and association mechanism between the surface urban heat island intensity (SUHII) and campus 2D/3D landscape configuration. The results indicate that the campus has a cooling effect during the daytime, with an average SUHII of −0.90 °C. It demonstrates the SUHII characteristics for campus land use types are as follows: SUHII_BD > SUHII_IS > SUHII_GS > SUHII_WB. Furthermore, the campus landscape has a significant hierarchical driving effect on SUHII, with the configuration of campus buildings and the impervious surface driving the strong heat island (SHI) and the 3D configuration and structure of greenspace dominantly strengthening the strong cool island (SCI). The overall design strategy of “two-dimensional priority, three-dimensional optimization” enables us to effectively mitigate the campus SUHII. This study reveals the spatiotemporal distribution characteristics of campus SUHII and the key influencing factors, and it also broadens the application of UAV thermal infrared imaging technology in the meso–micro-scale urban heat island assessment, providing suggestions for constructing a climate-adaptive urban landscape.

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Evapotranspiration (ET) estimations at high spatiotemporal resolutions in urban areas are crucial for extreme weather forecasting and water management. However, urban ET estimation remains a major challenge in current urban hydrology and regional climate research due to highly heterogeneous environments, human interference, and a lack of observations. In this study, an urban ET model, called the PT-Urban model, was proposed for half-hourly ET estimations at a 10 m resolution. The PT-Urban model was validated using observations from the Hotel Torni urban flux site during the 2018 growing season. The results showed that the PT-Urban model performed satisfactorily, with an R2 and root-mean-square error of 0.59 and 14.67 W m−2, respectively. Further analysis demonstrated that urban canopy heat storage and shading effects are essential for the half-hourly urban energy balance. Ignoring the shading effects led to a 38.7% urban ET overestimation. Modeling experiments further proved that flux footprint variations were critical for the accurate estimation of urban ET. The setting source areas either as an invariant 70% historical footprint or as a circle with a 1 km radius both resulted in poor performances. This study presents a practical method for the accurate estimation of urban ET with high spatiotemporal resolution and highlights the importance of real-time footprints in urban ET estimations.

No abstract available

No abstract available

Sensible heat exchange has important consequences for urban meteorology and related applications. Directional radiometric surface temperatures of urban canopies observed by remote sensing platforms have the potential to inform estimations of urban sensible heat flux. An imaging radiometer viewing the surface from nadir cannot capture the complete urban surface temperature, which is defined as the mean surface temperature over all urban facets in three dimensions, which includes building wall surface temperatures and requires an estimation of urban sensible heat flux. In this study, a numerical microclimate model, Temperatures of Urban Facets in 3-D (TUF-3D), was used to model sensible heat flux as well as radiometric and complete surface temperatures. Model data were applied to parameterize an effective resistance for the calculation of urban sensible heat flux from the radiometric (nadir view) surface temperature. The results showed that sensible heat flux was overestimated during daytime when the radiometric surface temperature was used without the effective resistance that accounts for the impact of wall surface temperature on heat flux. Parameterization of this additional resistance enabled reasonably accurate estimates of urban sensible heat flux from the radiometric surface temperature.

Land surface temperature (LST) is a vital parameter for energy budget and land surface process models. With the development of high spatial resolution remote sensing technologies, especially the wide application of uncrewed aerial vehicle (UAV) in land surface observations, the acquisition of high-resolution thermal infrared (TIR) data has made the accurate extraction of varied LST products possible. However, the complex heterogeneous structure of land surface may cause adjacency effect, i.e., multiple scattering of radiation and geometric occlusion, which is inevitable in high-resolution UAV-based observations. The existing TIR temperature retrieval methods mainly focus on the 1-D scenarios, lacking consideration for the 3-D structure of actual ground objects, resulting in significant errors in extracting LST from high-resolution data. To address these limitations, we propose a novel retrieval method to eliminate the influence of multiple scattering caused by finer spatial resolution on the accuracy of LST. This method introduces a 3-D radiative transfer model to account for the radiative transfer in a heterogeneous scenario, based on remotely sensed imagery, corresponding to 3-D structure of the ground surface and component attributes. An optimization strategy is adopted to iteratively converge toward physically consistent LST results. The proposed retrieval method is validated using the UAV-based TIR images and in situ surface temperature measurement data obtained from the Huailai remote sensing test site in Hebei province, China. Simulated TIR image datasets of typical vegetation and urban scenes were additionally employed to validate the applicability at different values of key parameters. The factors influencing the adjacency effect were extensively analyzed. Results show the following: 1) the effects of adjacent objects on LST can result in an overestimate exceeding 0.9 K in cases of typical vegetation scene for TIR observations when the spatial resolution was finer than 1 m; 2) the proposed LST retrieval method based on a 3-D radiative transfer model can significantly reduce the influence of adjacency effect on the accuracy of LST; and 3) in addition to the sky view factor (SVF), the irradiance from the adjacent objects can also have a significant impact on the accuracy of the temperature retrieval, especially when the emissivity is relatively low. This retrieval method provides a solution for high-resolution near-surface UAV/airborne TIR data and a promising framework for enhancing the LST accuracy using multisource geographic information assistance.

Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is an important way to obtain land surface temperature (LST) with high spatial and temporal resolutions. Due to wide spectral response function (SRF) ranges of UAV thermal imagers, currently available LST retrieval methods suitable for satellite sensors may induce significant uncertainty when applied to UAV sensors. Despite that some methods have been proposed to retrieve LST from UAV remote sensing, studies considering the adverse effect caused by the SRF ranges are still rare. Here, we present a so-called Temperature Retrieval for UAV Broadband thermal imager data (TRUB) method to retrieve LST from UAV broadband thermal imager data. TRUB’s core includes two parts: 1) a simple lookup table (LUT) algorithm for reducing the uncertainty induced by the wide SRF ranges; and 2) models suitable for UAV remote sensing for estimating the atmospheric parameters. Validation from the Heihe River Basin shows that the LST retrieved by TRUB, of which the root mean square error (RMSE) and mean bias error (MBE) is 1.71 and −0.02 K, respectively, is highly consistent with the in situ LST. TRUB is helpful to reduce the uncertainty caused by the wide SRF ranges of UAV thermal imagers and quantify the influence of atmosphere, thus can obtain UAV remote-sensing LST with better accuracy in large-area operating missions.

The longwave infrared (LWIR) sensors are widely used for measuring land surface radiation in ground and uncrewed aerial vehicle (UAV) remote sensing missions. Although the land surface temperature (LST) retrieval algorithms for thermal infrared (TIR) satellite sensors with narrow spectral response ranges have achieved good results, they are generally unsuitable for LWIR sensors. At present, the LST retrieval algorithm for LWIR data needs further investigation. In this study, an improved radiative transfer (IRT) algorithm based on the segmentation of spectral response function (SRF) is proposed for retrieving LST from LWIR data. The IRT algorithm is applied to three types of commonly used LWIR sensors. The simulation results show that the root-mean-squared error (RMSE) is lower than 0.1 K when the segmentation width is $0.2~\mu $ m. The higher the height of the sensor, the more obvious the fluctuation of the accuracy increases with the segmentation width. Using the in situ data of the Heihe River basin (HRB) for validation, RMSEs are between 1.1 and 1.8 K, depending on different land cover types. The IRT algorithm can retrieve the relatively high-accuracy LSTs from LWIR data observed by a variety of LWIR sensors and promote the collaborative application of multisensor LSTs, which is of great significance in ecological environment research.

Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data. Remote Sens. 2020, 12, 1075; doi:10.3390/rs12071075 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1075 2 of 27

No abstract available

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (TC) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of TC. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (Tr-3TC), derived from TC data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with Tr-3TC. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H2O m−2 s−1 and 0.555 mmol H2O m−2 s−1 for the respective years, indicating strong interannual stability.

Evapotranspiration (ET) from tropical forests plays a significant role in regulating the climate system. Forests are diverse ecosystems, encompass heterogeneous site conditions and experience seasonal fluctuations of rainfall. Our objectives were to quantify ET from a tropical rainforest using high-resolution thermal images and a simple modeling framework. In lowland Sumatra, thermal infrared (TIR) images were taken from an uncrewed aerial vehicle (UAV) of upland and riparian sites during both dry and wet seasons. We predicted ET from land surface temperature data retrieved from the TIR images by applying the DATTUTDUT energy balance model. We further compared the ET estimates to ground-based sap flux measurements for selected trees and assessed the plot-level spatial and temporal variability of ET across sites and seasons. Average ET across sites and seasons was 0.48 mm h–1, which is comparable to ET from a nearby commercial oil palm plantation where this method has been validated against eddy covariance measurements. For given trees, a positive correlation was found between UAV-based ET and tree transpiration derived from ground-based sap flux measurements, thereby corroborating the observed spatial patterns. Evapotranspiration at upland sites was 11% higher than at riparian sites across all seasons. The heterogeneity of ET was lower at upland sites than at riparian sites, and increased from the dry season to the wet season. This seasonally enhanced ET variability can be an effect of local site conditions including partial flooding and diverse responses of tree species to moisture conditions. These results improve our understanding of forest-water interactions in tropical forests and can aid the further development of vegetation-atmosphere models. Further, we found that UAV-based thermography using a simple, energy balance modeling scheme is a promising method for ET assessments of natural (forest) ecosystems, notably in data scarce regions of the world.

Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m x 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where the x-axis is a vegetation index (VI) and the y-axis is radiometric temperature. Next, with an accurate VI threshold that separates soil and vegetation pixels from one another, the corresponding soil and vegetation temperatures can be extracted from the linear equation. Although this method is simpler than other approaches, such as TSEB with Priestly-Taylor (TSEB-PT), it could be sensitive to VIs and the parameters that affect VIs, such as shadows. Recent studies have revealed that, on average, the values of VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), that are located in sunlit areas are greater than those in shaded areas. This means that involving or compensating for shadows will affect the linear relationship parameters (slope and bias) between radiometric temperature and VI, as well as thresholds that separate soil and vegetation pixels. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques. The retrieved temperatures, using the TSEB-2T approach, both before and after shadow correction, are compared to the average temperature values for both soil and canopy in each grid. The imagery was acquired by the Utah State University AggieAir UAV system over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to retrieve vegetation and soil temperature directly.

The retrieval of land surface temperature (LST) from remote sensing techniques has been studied and validated during the past 40 years, leading to important improvements. Accurate LST values are currently obtained through measurements using medium resolution thermal infrared (TIR) sensors. However, the most recent review reports demonstrated that the future TIR LST products need to obtain reliable temperature values at a high spatial resolution (100 m or higher) to study temperature variations between different elements in a heterogeneous kilometric area. The launch of high-resolution TIR sensors in the near future requires studies of the temporal evolution and spatial heterogeneities of the elements in a mixed region. The present study analyzes the LST in a sub-kilometric highly heterogeneous area, combining the use of LST products from high-resolution TIR orbiting sensors with the LST maps created from a TIR camera onboard an unmanned aerial vehicle (UAV). The aim is to estimate the LST variability in a heterogeneous area containing different surfaces (roads, buildings, and grass), observed from different TIR sensors at different spatial resolutions, covering from the meter to the kilometer scales. Several results showed that variations in the LST up to 18 °C were identified with the UAV-TIR camera, and significant differences were also present in the LST products obtained from simultaneous overpasses of high-resolution satellite TIR sensors. A second objective of the study, due to the availability of the high-resolution LST fields, was to explore the thermal advection between different elements and determine if it correlates with the surface energy budget in the same area, thus indicating that this process is of importance for heterogeneous terrains at these scales. This paper also highlights the relevance of the UAV-TIR camera flight for future studies since it is not commonly used in TIR remote sensing but has substantial potential advantages.

The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes.

No abstract available

Abstract. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate observations on small-scale variations in debris thickness and sub-debris ice melt rates are only available for a few locations worldwide. Here we describe a customised low-cost unoccupied aerial vehicle (UAV) for high-resolution thermal imaging of mountain glaciers and present a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a radiometric orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the radiometric orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The thermal orthophoto reveals distinct spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using an empirical or inverse surface energy balance model that relates surface temperature to debris thickness and is calibrated against in situ measurements. Our results from a small-scale experiment on the Kanderfirn (also known as Kander Neve) in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy using an empirical or physical model. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼ 0 ∘C) was 0.6 ± 2.0 ∘C. The root-mean-square error of the modelled debris thickness was 1.3 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for comprehensive high-resolution supraglacial debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.

Stream temperature is a measure of water quality that reflects the balance of atmospheric heat exchange at the air-water interface and gains or losses of water along a stream reach. In urban areas, stormwater sewers deliver water with varying magnitude and temperature to streams at variable timescales. Understanding the impacts of stormwater through space and time is therefore difficult to do with conventional approaches like in situ sensors. To study the impacts of stormwater on creek water temperatures, we combined in situ water temperature observations with thermal infrared (TIR) imagery collected via unoccupied aerial vehicle (UAV). Imagery was collected in May, June, and July of 2017. As ongoing work with UAV-based TIR suggests that this imagery is prone to poor accuracy, we focused on creating several data products beyond absolute water temperatures that can be used to assess temporal and spatial water temperature variations. In particular, TIR data products were used to extract the length of the observed stormwater plume as well as the width of the creek cross-section impacted by stormwater. From these values, we conclude that relatively narrow stormwater plumes affecting a small fraction of creek width can alter creek water temperatures for considerable distances downstream. We also applied TIR data to constrain results of a deterministic stream temperature model (HFLUX 3.0) that simulates the physical processes affecting stream heat exchanges. Stormwater plume lengths obtained from TIR imagery were used to refine spatially-distributed simulations, demonstrating that relative temperature information obtained from UAV imagery can provide useful calibration targets for stream temperature models. Overall, our work demonstrates the added value of UAV datasets for understanding urban stream temperatures, calibrating water quality models, and for modeling and monitoring of the impact of spatially explicit hydrologic processes on stream temperature.

Land surface temperature (LST) is one of the crucial factors that is important in various fields, including the study of climate change and the urban heat island (UHI) phenomenon. The existing LST was acquired using satellite imagery, but with the development of unmanned aerial vehicles (UAV) and thermal infrared (TIR) cameras, it has become possible to acquire LST with a spatial resolution of cm. The accuracy evaluation of the existing TIR camera for UAV was conducted by shooting vertically. However, in the case of a TIR camera, the temperature value may change because the emissivity varies depending on the viewing angle. Therefore, it is necessary to evaluate the accuracy of the TIR camera according to each angle. In this study, images were simultaneously acquired at 2–min intervals for each of the three research sites by TIR camera angles (70°, 80°, 90°). Then, the temperature difference by land cover was evaluated with respect to the LST obtained by laser thermometer and the LST obtained using UAV and TIR. As a result, the image taken at 80° showed the smallest difference compared with the value obtained with a laser thermometer, and the 70° image showed a large difference of 1–6 °C. In addition, in the case of the impervious surface, there was a large temperature difference by angle, and in the case of the water-permeable surface, there was no temperature difference by angle. Through this, 80° is best when acquiring TIR data, and if it is impossible to take images at 80°, it is considered good to acquire TIR images between 80° and 90°. To obtain more accurate LST, correction studies considering the external environment, camera attitude, and shooting height are needed in future studies.

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. In contrast, plot-scale LST estimation at flux tower sites is commonly based on the inversion of upwelling longwave radiation captured by tower-mounted radiometers, whereas the role of the downwelling longwave radiation component is often ignored. We found that neglecting the reflected downwelling longwave radiation leads not only to substantial bias in plot-scale LST estimation, but also have important implications for the estimation of surface emissivity on which LST is co-dependent. The present study proposes a novel method for simultaneous estimation of LST and emissivity at the plot-scale and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both upwelling and downwelling longwave radiation components are 0.5–1.5 K lower than estimates using only upwelling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the upwelling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. The correspondence between plot-scale LST and landscape-scale LST depends on site-specific characteristics, such as canopy density, sensor locations and viewing angles. Here we also quantify the uncertainty in plot-scale LST estimates due to uncertainty in tower-based measurements using the different methods. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

No abstract available

Advective heat fluxes (chimney effect) in porous debris facilitate ground cooling on scree slopes, even at low altitudes, and promote the occurrence of sporadic permafrost. The spatial distribution of ground surface temperature on an overcooled, low-altitude scree slope in the Romanian Carpathians was analyzed using UAV-based infrared thermography in different seasons. The analysis revealed significant temperature gradients within the scree slope, with colder, forest-insulated lower sections contrasting with warmer, solar-exposed upper regions. Across all surveyed seasons, this pattern remained evident, with the strongest temperature contrasts in December and April. February exhibited the most stable temperatures, with thermal readings primarily corresponding to snow surfaces rather than exposed rock. Rock surfaces displayed greater temperature variation than vent holes. Vent holes were generally cooler than rock surfaces, particularly in warmer periods. The persistent presence of ice and low temperatures at the end of the warm season suggested the potential existence of isolated permafrost. The results confirm the chimney effect, where cold air infiltrates the lower talus, gradually warms as it ascends, and outflows at higher elevations. UAV-based thermal imagery proved effective in detecting microclimatic variability and elucidating thermal processes governing talus slopes. This study provides valuable insights into extrazonal permafrost behavior, particularly in the context of global climate change.

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivityand atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.