Mineral dust particles emitted from dry, uncovered soil can be transported over vast distances, thereby influencing climate and environment. Its impacts are highly size-dependent, yet large particles with diameters d(p)>10 mu m remain understudied due to their low number concentrations and instrumental limitations. Accurately characterizing the particle size distribution (PSD) at emission is crucial for understanding dust transport and climate interactions. Here we characterize the dust PSD at an emission source during the Jordan Wind Erosion and Dust Investigation (J-WADI) campaign, conducted in Wadi Rum, Jordan, in September 2022, focusing on super-coarse (10 < d(p )<= 62.5 m) and giant (d(p)>62.5 mu m) particles. This study is the first to continuously cover the full range of diameters from d(p)=0.4 to 200 mu m at an emission source by using a suite of aerosol spectrometers with overlapping size ranges. This overlap enabled a systematic intercomparison and validation across instruments, improving PSD reliability. Results show significant PSD variability over the course of the campaign. During periods with friction velocities (u(*)) above 0.22 m s(-1) (or similar to 3.3 m s(-1) threshold 4 m wind speed), the approximate threshold for local dust emission by saltation, both dust concentrations and the contributions of super-coarse and giant particles typically increased with increasing u(*), especially under neutral to unstable atmospheric stability conditions. These large particles accounted for about 90 % of the total mass concentration during the campaign. A prominent mass concentration peak was observed near d(p)=60 mu m in geometric diameter. While particle concentrations for d(p)<10 m showed good agreement among most instruments, discrepancies appeared for larger d(p) due to reduced instrument sensitivity at the size range boundaries and sampling inefficiencies. Despite these challenges, physical samples collected using a flat-plate sampler largely confirmed the PSDs derived from the aerosol spectrometers. These findings help to advance our understanding of the dust PSD and the abundance of super-coarse and giant particle at emission sources.

This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages.The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8–14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information.The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy–atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.

Abstract. Thermal infrared (TIR) satellite remote sensing is essential for monitoring land surface temperature (LST) and surface energy fluxes, supporting applications in hydrology, agriculture, climate, and urban climate. Existing TIR missions—such as LANDSAT, ASTER, ECOSTRESS, and Sentinel-3 SLSTR—offer complementary capabilities but remain constrained by trade-offs between spatial resolution, revisit frequency, and radiometric accuracy, limiting their ability to capture rapidly evolving surface processes at field to regional scales. The TRISHNA (Thermal Infrared Satellite for High-Resolution Natural Resource Assessment) mission, expected in 2027, addresses this gap by providing 60 m TIR imagery over a ~1000 km swath with sub-weekly revisit, enabling systematic monitoring of surface energy processes in natural and managed ecosystems. Its integrated design —including orbit configuration, spectral channels (VNIR, SWIR, TIR), viewing geometry, and calibration strategy— supports accurate retrievals of evapotranspiration, vegetation water stress, and surface temperature dynamics. Synergies with upcoming missions such as ESA’s Land Surface Temperature Mission (LSTM) and NASA’s EAGLE mission (Explorer for Artemis Geology Lunar and Earth) will enhance temporal coverage, cross-calibration, and long-term data continuity.

This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.

Large land parcels of photovoltaic (PV) parks are expanding rapidly worldwide to support the energy transition. Their growing footprint in rural landscapes raises concerns about the impact such a development has on local micrometeorology and surface-atmosphere exchanges. Yet, the aerodynamic behavior of PV canopies compared to existing land uses remains poorly understood. This study compared wind dynamics and turbulence under near-neutral conditions above a 140-hectare PV park and a nearby homogeneous pine forest in southwestern France. Results showed that turbulence within the PV canopy was highly anisotropic and strongly dependent on wind direction. Under winds perpendicular to the panel rows, the flow displayed characteristics typical of dense or row-structured vegetative canopies, with an inflection in the mean velocity profile, strong drag, and efficient momentum transport dominated by sweep motions. Parallel winds, however, produced weaker drag and turbulence, a logarithmic mean profile, and elongated streamwise structures typical of shear flows over weakly rough surfaces. Based on these findings, we proposed a physically based parameterization of the mean wind velocity profile for PV canopies, depending on the wind direction and key structural properties of the PV array. The parameterization combined a logarithmic law with a roughness-sublayer correction above the panels and an exponential-logarithmic formulation within. This parameterization enabled the introduction of directional aerodynamic effects of PV parks into land-atmosphere models.

Utility-scale ground-mounted photovoltaic power plants can represent a way to reduce greenhouse gas emissions and mitigate climate change. However, their development requires large land areas, creating land-use conflicts. Further, their influence on local climate remains poorly understood, particularly in temperate regions. We compare radiative and energy balances of a managed coniferous forest and a nearby photovoltaic park, and their effects on air temperature, using three years of continuous, innovative eddy covariance measurements. This rare dataset from temperate solar parks improves understanding of land-use change impacts, informing land selection and infrastructure design. Results revealed higher albedo in the photovoltaic park than in the pine canopy (0.13 vs 0.09), leading to net radiation 20% lower than in the forest. Consistent with lower available energy, heat fluxes above the park were reduced compared to the forest, particularly sensible heat flux in summer (227.6 +/- 9.3 W m- 2 vs 377.9 +/- 56.6 W m- 2). Despite these observations, daytime temperatures remained similar between sites. In the park, energy diverted to electricity production reduced the stored energy available for release, occasionally resulting in cooler nights and greater diurnal thermal variations than in the forest. The Bowen ratio, defined as the ratio of sensible to latent heat fluxes, was up to twice as high in the solar park as in the forest, indicating that sensible heat dominated energy partitioning despite evapotranspiration from the undergrowth. However, the park vegetation's higher drought resistance maintained stable evapotranspiration, highlighting its regulatory role in moderating air temperature by limiting sensible heat flux.

Leaf photosynthesis and respiration respond to leaf temperature, which differs from air temperature depending on radiation load, transpiration and heat exchange rates. However, most terrestrial biosphere models (TBMs) do not use leaf temperature to compute photosynthesis and respiration. Instead, they use directly air temperature, or an average surface temperature that incorporate soil and non-green biomass compartments. While these two approaches are computationally efficient, the absence of explicit leaf temperature simulation potentially hinders the representation of extreme events (e.g. heat stress) and their repercussion on carbon and water fluxes. To predict leaf temperature, it is necessary to explicitly account for leaf energy budget, photosynthesis and transpiration feedbacks (E-P-T).Here, we explored the merits of coupling E-P-T processes in TBMs to simulate explicit leaf temperature and its feedback on carbon assimilation. Sensitivity analysis of the coupled E-P-T processes using the big-leaf configuration of the ORCHIDEE v2.2 TBM (used in CMIP6) resulted into leaf-to-air temperature differences varying between 1 and 10 °C in natural conditions. This translated into a change in carbon assimilation ranging from -35 % to +110 % at the leaf level. A comparison of simulated leaf temperature with measured canopy surface temperature at eddy-covariance fluxes sites in various E-P-T configurations showed that adding ecological constraints on photosynthesis and transpiration through the photosynthesis coordination and the least-cost hypothesis (P-model) improved the representation of top canopy temperature compared to a classical fixed parameterization. The improvement in canopy temperature estimates was best for deciduous broadleaved forests with an average reduction of the error by 1.2 ± 0.8 °C (9 sites). More importantly, the improvement in leaf temperature estimates mainly occurs at elevated temperature (> 30°C). Our results argue for the inclusion of an explicit representation of leaf temperature in TBMs to avoid biases in the carbon balance estimates. Fully coupling leaf processes through temperature will also be essential for accurately simulating and disentangling the effects of heat and drought stresses under future conditions. However, such implementation will only be possible if accompanied with space-time concomitant observations of leaf temperature and traits that are currently lacking. The ongoing deployment of digital cameras (e.g. thermal, multispectral, SIF etc.) on existing networks (e.g. ICOS) for tracking canopy temperature and trait variability, combined with punctual field observation campaigns, future remote sensing missions (e.g. TRISHNA), as well as new hybrid modelling methods are all timely and promising ways for improving our understanding and representation of leaf temperature in TBMs.

Trees located within the urban canopy are known to mitigate the urban heat island effect and heat waves by lowering the canopy air temperature via evapotranspiration and surface shading. They are also invoked as a nature-based solution to improve air quality through the absorption and deposition of anthropogenic pollutants. However, urban trees can have the adverse effect of reducing ventilation within the canopy as well as the dispersion of pollutants. Heat and mass exchanges between the urban canopy and the overlying atmosphere play a key role in air temperature and air quality at the pedestrian level. These exchanges are driven by turbulent motions developing at different scales, within the urban canopy, in the overlying atmospheric boundary layer, and at their interface. Quantifying the role of urban trees in these exchanges and their links to ventilation or dispersion processes within the canopy is challenging due to the complexity of urban surfaces and the various processes involved in surface-atmosphere interactions.The CITRY field campaign was carried out to characterize turbulent exchanges of momentum, heat, and mass as well as fine particles concentrations in an urban environment from canopy to boundary-layer scales, under various stability conditions and seasonal tree characteristics, specifically the presence of leaves. The experimental set-up was installed in a residential area of the city of Nantes (France) over a period of 10 months (March-December 2025). A wide range of instruments was deployed at different heights from the roof of a 14m-tall building and within the urban canopy. Within the urban canopy, four ultrasonic anemometers were installed on two masts, at 6 m and 10 m above ground level (a.g.l.). Two ultrasonic anemometers and one Doppler LiDAR wind profiler were positioned on the rooftop to measure wind and turbulence at 20 m and 24 m a.g.l., and between 55 m and 400 m, respectively. Within and above the canopy, gas analyzers (LI‐COR) were also installed at 10 and 24 m a.g.l., close to the ultrasonic anemometers, to deduce water vapor and carbon dioxide turbulent fluxes. Particulate matter sensors, covering particle diameters from 0.2 to 40 micrometers, were deployed across the site, on streetlight poles and on masts at several heights within and above the urban canopy, to capture the spatial variability of particle concentration.We will present a preliminary analysis of this observational dataset, including a statistical assessment of the effects of wind sector, wind speed, atmospheric stability, and leaf state on water vapor and carbon dioxide fluxes and particulate matter concentrations. Additionally, for selected periods, the main characteristics of the turbulent structures responsible for canopy–atmosphere exchanges will be shown, as well as their size dependency to the thermal stability.

Gaining a precise understanding of the particle size distribution (PSD) of mineral dust at emission is critical to assess its climate impacts. Despite its importance, comprehensive measurements at dust sources remain scarce and usually neglect part of the super-coarse (particle diameter d between 10 and 62.5 μm) and the entire giant (d > 62.5 μm) particle size ranges. Measurements in those size ranges are particularly challenging due to expected relatively low number concentrations and low sampling efficiencies of instrument inlets. This study aims to better constrain the abundance of super-coarse and giant dust at emission as part of the Jordan Wind erosion And Dust Investigation (J-WADI, https://www.imk-tro.kit.edu/11800.php) field campaign conducted north of Wadi Rum in Jordan in September 2022. The goal of J-WADI is to improve our fundamental understanding of the emission of desert dust, in particular its full-range size distribution and mineralogical composition. To capture the dust PSD across the entire size spectrum, we deployed multiple aerosol spectrometers, including active, passive, and open-path devices, such that in combination, a size range from approximately 0.4 to 200 μm was covered. Here we investigate the variability of the PSD in the super-coarse and giant ranges from observed dust events, address instrumental uncertainties and the impact of different inlets on the resulting PSDs. Our preliminary results reveal a mass concentration peak at around 30 μm, potentially limited toward larger sizes by substantially reduced inlet efficiencies. Giant dust particles were generally detected during active dust emission starting from friction velocities larger than around 0.2 m s-1. Based on our results, we will investigate the mechanisms facilitating super-coarse and giant dust particle emission and transport. Quantifying the conditions for and the amount of super-coarse and giant dust at emission will lay the foundation to incorporate its impacts in weather and climate models.

This study evaluates the influence of the hot spot effect, i.e. when the solar and viewing angles coincide, producing a radiance peak on the diurnal reflectance and temperature cycles (DRC and DTC, respectively) observed by the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) sensor aboard the Meteosat Second Generation (MSG) satellite. Focusing on clear-sky conditions and multiple land cover types, we assess the directional impact on both spectral brightness temperature (Tb) and land surface temperature (LST). A four-parameter DTC model is coupled with a directional kernel-driven model (KDM), including a hot spot term, to create Time-Evolving KDMs. The models are applied to six diverse sites to evaluate whether optical BRDF characteristics can inform thermal BRDF (Bidirectional Reflectance Distribution Function) behavior, and to what extent directional effects distort DTC profiles. Findings indicate a clear hot spot signature in the DRC, while in the DTC, it subtly alters the bell-shaped curve, resulting in Tb deviations up to 3 K and LST differences up to 4 degrees C. The results underscore the need to correct for angular effects when comparing DTCs across sites or seasons. Moreover, visual inspections show that optical BRDF peaks align closely with cosine peaks for two satellites, whereas thermal peaks diverge-highlighting mismatches and the challenges of modeling mixed land cover. Present findings underscore the need for improved models and multi-sensor validation to support a full exploitation of thermal remote sensing.

Land Surface Temperature (LST) is a fundamental variable for determining mass (water, carbon) and energy surface fluxes. LST can be obtained from remote sensing but under varying configuration geometries that create directional effects due to the inherent anisotropy properties of most terrestrial targets. Actually, thermal infrared (TIR) measurements obtained from satellites or unmanned aerial vehicles (UAV) are seriously impacted by varying viewing and solar geometries (Cao et al., 2019). In this regard, a computationally efficient approach to handle them is using kernel-driven models (KDM), as they were shown to be an effective solution. However, in high-resolution scenes, the structural features can be very detailed and, in this case, the assumption of homogeneity in considering traditional KDM no longer holds. This is why we propose to develop a novel KDM that is able to handle typical heterogeneous scenes whose structure is dominated by rows. Rather than improving existing point-spread kernels, we propose a line-spread kernel considering the row orientation and radiative occlusion. This new KDM is validated with both airborne measurements and simulated datasets generated by three-dimensional radiative transfer models. Results indicate that: 1) This proposed Heterogenous KDM captures the directional anisotropies of temperatures in row-planted vineyard canopies, whereas the traditional pointspread KDM show limitations. In most cases, root mean squared errors (RMSE) improved up to 0.5 K. 2) A sensitivity analysis based on simulated datasets also showed a better performance of the new proposed KDM under different cases including LAI and row height/width. 3) Further simple validation using UAV and sandbox measurements has demonstrated the effectiveness of the proposed KDM in urban and mountainous areas, where stripe characteristics in thermal radiation directionality are present. In conclusion, this study proposes a novel KDM with significant practical implications for heterogeneous scenarios.

The recent development of low-cost optical particle counters (OPCs) presents new opportunities for improving spatial coverage of particle concentration in the atmosphere as they are more affordable, compact, and energy efficient than traditional OPCs. In particular, these OPCs could improve our ability to quantify dust emissions in complex environments during aeolian soil erosion. The high-frequency sampling capacity (1 Hz) of some sensors may make them suitable for estimating dust emissions using the eddy-covariance method. Here, the capability of the low-cost OPC-N3 from Alphasense to estimate size-resolved dust flux using the eddy-covariance method is evaluated. During the Jordan Wind erosion And Dust Investigation (J-WADI) experiment, we tested one OPC-N3 against two traditional reference OPCs, the Promo and Fidas, from Palas GmbH. The N3 and Promo OPCs were located in close proximity to a sonic anemometer, enabling the correlation of dust concentration and vertical velocity fluctuations for estimating dust fluxes. Despite the high-temperature and dusty wind conditions of the campaign, the N3 monitored the dynamics and magnitude of dust concentration with reasonable precision. The turbulence characteristics of the dust concentration fluctuations measured by the N3, including variance, skewness, kurtosis, and energy spectrum, were similar to those from the Promo. However, the N3 flow rate exhibited variations under these outdoor conditions that affected the concentration of fine dust particles, and certain particles around 1 µm appeared to be misclassified in the upper size bin. After correcting the N3 dust concentration to address these discrepancies and after calibrating it against a reference OPC, the N3 accurately estimated the dust emission flux, with differences of less than 30 % compared to the reference OPC. Our results confirm the potential of low-cost OPCs for dust erosion research. Nonetheless, further evaluation of low-cost OPCs is still needed across different environments and weather conditions.

Radiative transfer models (RTMs) designed to reproduce the anisotropy of surface brightness temperature (BT) are particularly useful for applications on Earth's energy budget when using remote sensing (RS) datasets. Despite the fact that several thermal infrared (TIR) RTMs have been developed, a quantitative analysis comparing the benefits and limits of these models remains necessary. Herein, three modeling frameworks (physical hybrid, analytical parameterization, and kernel driven) have been evaluated comparatively for homogeneous vegetation, a row-planted crop, and a sparse forest. Airborne measurements and the discrete anisotropy radiative transfer (DART) model simulations were retained as the benchmark. Forward modeling and inverse fitting schemes were proposed for the sake of comparison. Results reveal that: 1) in the forward modeling scheme, from airborne measurements, the hybrid model performs better with root-mean-squared errors (RMSEs) of 0.17 degrees C, 1.57 degrees C, and 0.38 degrees C for homogenous, row-planted vineyard, and sparse forest scenes, respectively; the analytical model appears similar performant (0.17 degrees C, 0.40 degrees C) for the homogeneous and sparse forest scenes, but less performant (2.39 degrees C) for the row-planted scene and 2) in the inverse fitting scheme, the uncertainties (95% of probability) of model coefficients and predicted directional anisotropies were considered. The kernel-driven model has fewer modeling constraints and statistically performs better for the homogeneous and sparse forest scenes with RMSEs of 0.07 degrees C and 0.19 degrees C, respectively, whereas it is less efficient for the row-planted scene with RMSE of 0.80 degrees C. This study highlights the differences in accuracy between models of different complexity and provides reference information for researchers to improve existing models and for users to choose their best modeling solution.

Snow plays a critical role in alpine areas, influencing the local climate and serving as a crucial water reservoir for downstream ecosystems and human activities. The surface temperature of snow provides many insights about the current state of the snowpack and helps water storage estimations. While satellites are regularly used to measure surface temperature of snow over alpine areas, accurate measurements are still difficult to retrieve from space, and calibration-validation initiatives over snow-covered areas are scarce. In this context, we produced a two-winter timeseries of approximately 130,000 maps of the radiative surface temperature of snow acquired with an uncooled Thermal Infrared camera. TIR images were acquired November 2021 to May 2022 and February to May 2023 at the Col du Lautaret, 2057 m a.sl. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration, with a rough thermal regulation (7°C - 39°C) resulted in timeseries of snow surface temperature maps with an absolute accuracy

Turbulence in canopy plays a crucial role in biosphere - atmosphere exchanges. Traditionally, canopy turbulence has been analyzed under stationary conditions based on atmospheric thermal stability, disregarding the time of the day and the atmospheric boundary layer (ABL) depth, although recent studies have suggested that daytime canopy turbulence might be in fl uenced by ABL-scale motions. The morning transition offers an intriguing period when the ABL grows and when an increasing in fl uence of large-scale motions on canopy turbulence might be anticipated. Using large-eddy simulations resolving both canopy and ABL turbulence, we investigate here how the turbulence and exchanges at the canopy top change along the morning transition according to the wind intensity. Under signi fi cant wind, simulations show that canopy turbulence and exchanges are dominated by mixing-layer-type motions whose characteristics remain constant during the morning transition even though ABL-scale motions imprint on the canopy ' s instantaneous velocity fi elds as the ABL grows. Under low wind, the canopy turbulence is dominated by plumes, whose horizontal sizes extend with the ABL, while their vertical sizes reach a limit before the morning transition ends. In the early morning, canopy-top exchanges are in fl u- enced by sources from both the canopy top and the ABL entrainment zone, explaining some of the dissimilarity in turbulent transport between scalars, apart from the differences in the location of canopy scalar sources. When reaching the residual layer, the ABL grows quicker, with intense water vapor and carbon dioxide exchanges, dominated by large-scale motions penetrating deep within the canopy, releasing into the atmosphere the nocturnal accumulated carbon dioxide.

The enhancement of the living conditions in big cities since the end of the last century is closely related to changes in the thermal environment and besides in urban microclimate, particularly for metropolitan areas. In this context, a knowledge of the spatial and temporal variability of urban heat island (UHI) became an increasing matter of concern, which can be measured from land surface temperature (LST). Actually, LST can be derived from thermal infrared (TIR) remote sensing observations to ensure the necessary spatial and time frequency coverage. But a full exploitation of satellite TIR data cannot be achieved without accounting for the strong anisotropy of urban landscape. Hitherto, a poor investigation was focused on the modeling and the analysis of the directional anisotropies of LSTs considering the complexity of urban building surfaces (e.g., heterogeneity of building morphology and temperature distribution) whereas it is fundamental to establish reliable critical indicators derived from energy balance. Herein, we propose an analytical model to simulate the angular signatures of urban temperatures, in which the geometric optical theory is considered to model the direct radiances of the main components (i.e., sunlit and shaded street, roof and wall). The built model assumes a random distribution of low/middle-rise and high-rise buildings, which depicts realistically the heterogeneity of urban architectural distribution. We evaluated the proposed model using both measured datasets from airborne and satellite sensors and a simulated dataset from a 3D ray-tracing model so-called discrete anisotropic radiative transfer (DART). Results indicate that 1) the proposed model is effective for simulating directional anisotropies of LSTs, with a root mean square error (RMSE) lower than 0.90 °C and R2 > 0.49 for comparison with measured datasets; and 2) the directional anisotropies of LSTs are significantly affected by variations in building height, with values possibly exceeding 1.5 °C. The proposed model can be perceived as a useful tool to analyze the contribution of each component and to assess the impact of urban structure. Furthermore, it can serve to improve urban radiation budget estimations in mixed pixels.

The surface temperature of snow cover is a key variable, as it provides information about the current state of the snowpack, helps predict its future evolution, and enhances estimations of the snow water equivalent. Although satellites are often used to measure the surface temperature despite the difficulty of retrieving accurate surface temperatures from space, calibration–validation datasets over snow-covered areas are scarce. We present a dataset of extensive measurements of the surface radiative temperature of snow acquired with an uncooled thermal-infrared (TIR) camera. The set accuracy goal is 0.7 K, which is the radiometric accuracy of the TIR sensor of the future CNES/ISRO TRISHNA mission. TIR images have been acquired over two winter seasons, November 2021 to May 2022 and February to May 2023, at the Col du Lautaret, 2057 m a.s.l. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration with rough thermal regulation (7–39 °C). An improved setup with a stabilized internal temperature was developed for the second campaign, and comprehensive laboratory experiments were carried out in order to characterize the physical properties of the components of the TIR camera and its calibration. Thorough processing, including radiometric processing, orthorectification, and a filter for poor-visibility conditions due to fog or snowfall, was performed. The result is two winter season time series of 130 019 maps of the surface radiative temperature of snow with meter-scale resolution over an area of 0.5 km2. The validation was performed against precision TIR radiometers. We found an absolute accuracy (mean absolute error, MAE) of 1.28 K during winter 2021–2022 and 0.67 K for spring 2023. The efforts to stabilize the internal temperature of the TIR camera therefore led to a notable improvement of the accuracy. Although some uncertainties persist, particularly the temperature overestimation during melt, this dataset represents a major advance in the capacity to monitor and map surface temperature in mountainous areas and to calibrate–validate satellite measurements over snow-covered areas of complex topography. The complete dataset is provided at https://doi.org/10.57932/8ed8f0b2-e6ae-4d64-97e5-1ae23e8b97b1 (Arioli et al., 2024a) and https://doi.org/10.57932/1e9ff61f-1f06-48ae-92d9-6e1f7df8ad8c (Arioli et al., 2024b).

TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose thermal infrared (TIR) Earth Observation (EO) mission designed to deliver images at high spatial (60 m) and temporal (3 days) resolutions. Its launch is foreseen in 2026 with a nominal mission duration of 5 years. This Indo-French polar-orbiting mission will overcome the limitations of thermal-optical observations from Landsat series and ASTER: low revisit, morning observations. TRISHNA scenes will be fully harnessed to pioneer the first cutting-edge high-resolution global maps of the land surface temperature (LST) and land surface emissivity (LSE) of natural and managed agroecosystems, man-made structures, water bodies, bare soils, rocks, snow, ice and sea. The quality of the preprocessing (radiometric calibration, atmospheric and directional corrections) is mandatory to satisfy the specifications with an expected precision of 1 K on LST in order to meet the targeted objectives. For such, it will be necessary to correct LST from directional effects with emphasis on the hot spot effect that can have an impact on LST up to several K. This is the goal of the TIRAMISU project to analyze TIR multiangular signatures over various biomes thanks to a pivoting system of cameras and a multi-model approach (1D, 3D, paramaterization). The modeling tools include 3 categories of models: SCOPE 1D, DART 3D and parametric BRDF models that are computationally efficient to perform inversion at global scale.