Thermal Infrared Observations Research Articles

Detection Limits of Thermal-infrared Observations with Adaptive Optics. I. Observational Data

Ground-based thermal infrared observations face substantial challenges in correcting the predominant background emitted as thermal radiation from the atmosphere and the telescope itself. With the upcoming 40 m class ELTs, unprecedented sensitivities from ground will be reached, underlining the need of even more sophisticated background correction strategies. This study aims to investigate the impact of thermal backgrounds on ground-based observations and identify possible limiting factors in dedicated correction strategies. We evaluate temporal and spatial characteristics of the thermal background in direct imaging data obtained with different telescopes and observation modes. In particular, three distinct data sets, acquired using VLT/NACO and KECK/NIRC2, are analyzed. Our analysis reveals that the observations are not fully photon shot noise limited, but exhibit additional sensitivity losses caused by imperfect background compensation in the different data sets. We identify correlations between background fluctuations and the activity of the adaptive optics system. We hypothesize that the pupil modulation of the adaptive optics mirrors introduces high frequency spatial and temporal fluctuations to the background, which could ultimately constrain the detection limit if they are not compensated adequately.

Non-negligible clear-sky biases of satellite thermal infrared observations for analyzing surface urban heat island intensity: A case study in China

Surface urban heat island (SUHI) intensity generally determined by satellite-derived clear-sky land surface temperature (LST) has ignored the impacts of cloud coverage and cannot reflect the real SUHI intensity. Only a few studies focus on the effects of this issue based on short-time LST datasets, which could contain non-negligible uncertainties to summarize reliable rules. To investigate the influence, the SUHI intensity (SUHII) clear-sky bias (CSB), which is defined as the SUHII difference between clear-sky and all-weather conditions, was investigated in 35 cities in China, based on clear-sky and all-weather LST datasets from 2003 to 2022. Results show that the two SUHIIs show similar spatial distribution patterns, with stronger SUHIs in southern China at daytime and weaker at nighttime. However, a non-negligible difference can be found between these two SUHIIs, with a SUHII CSB range of −1.43 to 2.27 °C at daytime and − 2.17 to 0.91 °C at nighttime. In terms of intra-annual variation, SUHII CSBs in similar climate regions exhibit similar patterns but different ranges due to their different natural properties. Generally, intra-annual variations of SUHII CSB can be divided into three groups, i.e., “Table Mountain”, single peak, and single valley, varying across climate regions and years. The main reason for SUHII CSB was analyzed, i.e., spatial gaps of the data directly caused the SUHII CSB, and the thermal properties and meteorological conditions of the missing pixels affect the magnitude of the SUHII CSB. Taking the urban system as an example, this study has provided evidence of the non-negligible SUHII clear-sky bias to emphasize the importance of using all-weather LST for relevant studies.

Spatial and temporal variability of martian water-ice cloud effective radius in EMIRS thermal infrared observations

New analyses of thermal infrared spectra obtained from the Emirates Mars Infrared Spectrometer (EMIRS) allow for retrieval of water-ice cloud effective radius, reff,ice, in the lower atmosphere of Mars across spatial, seasonal, and daytime diurnal scales. Using a one Martian-year observational dataset the mean retrieved reff,ice is 3.1 μm (standard deviation, σ = 1.5 μm) for an assumed effective variance, νeff,ice = 0.1, and with most individual values between 1 and 10 μm. Sensitivity analyses show the uncertainty associated with reff,ice is largely inversely proportional to water-ice cloud optical depth, τice. As a result, our retrieval is generally applicable to water-ice clouds with τice of at least ∼0.02 to 0.08 at 825 cm−1. Seasonal results show mean reff,ice values of 4.3 μm (σ = 1.9 μm) during aphelion season (Ls = 40°–140°), associated with larger particle sizes in the aphelion cloud belt (ACB), and 2.5 μm (σ = 0.8 μm) during perihelion season (Ls = 225°–360°). Diurnal analyses between roughly 06:00 and 20:00 local true solar time indicate the largest variability occurs in ACB clouds, with effective radii tending to be smaller during midday (zonal-mean reff,ice of 4–5 μm) as compared to the mornings and afternoons (zonal-mean reff,ice of 6–7 μm). Relatively large spatial gradients in reff,ice within the ACB are observed, including a seasonal mean reff,ice of ∼8 μm in the Tharsis region and regions in the southern mid-latitudes with seasonal mean reff,ice of ∼2 μm. Spatial variability in reff,ice tends to be smaller throughout the rest of the year, though with some similar patterns. This includes several time periods between regional dust storms when a low-latitude band of water-ice clouds formed in the afternoons near Tharsis, which grew in both optical depth and effective radius to maxima in the early evenings. Lastly, changes in reff,ice are found to be generally positively correlated with changes in τice and negatively correlated with estimated cloud height, though reff,ice distributions also show this to be a more complex relationship than simple averages might suggest.

Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign

Abstract. The high temporal resolution of thermal infrared imagery from the Geostationary Operational Environmental Satellites R-series (GOES-R) presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow- and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation), respectively. As part of the NASA SnowEx field campaign in February 2020, coincident brightness temperature observations from ground-based and airborne IR sensors were collected to compare with those from the geostationary satellites. Observations over the course of 2 cloud-free days spanned the entire study site. The brightness temperature observations from each dataset were compared to find their relative differences and how those differences may have varied over time and/or as a function of varying forest cover across the study area. GOES-16 and -17 brightness temperatures were found to match the diurnal cycle and temperature range within ∼ 1 h and ± 3 K of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun–satellite phase angle was near its daily minimum. The phase angle, the angle between the direction of incoming solar illumination and the direction from which the satellite is viewing, reached daily minimums in the morning for GOES-16 and afternoon for GOES-17. In morning observations, warm biases in GOES-16 brightness temperature were greater for pixels that contained more forest coverage. The observations suggest that a “thermal infrared shadow-hiding” effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R brightness temperatures or derived land surface temperatures (LSTs) over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect.

Short-period flow oscillation during eruptions of Onikobe geyser, NE Japan: Insights from thermal infrared observation and acoustic measurements

Geysers are episodic features with variable eruption intervals that range from minutes to years. Although many previous studies on geysers have focused on subsurface properties and processes such as plumbing geometry and recharge process, it is known that erupting fluid column exhibits short-term behaviors such as individual water jets from the vent. In the present study, we conducted observations at Onikobe geyser, NE Japan, a relatively small geyser (geysering well) erupting water up to ∼6–8 m every ∼10 min and observed the erupting fluid column using a thermal infrared camera and an acoustic sensor. We succeed in tracking the water jets by analyzing spatio-temporal temperature map obtained from the thermal infrared observation, which clearly shows the eruptions at Onikobe geyser are not completely stationary but rather a series of intermittent jets with a short period interval of <1 s. We estimate the exit velocity by fitting a ballistic model under the air drag condition of inertial resistance to the jet trajectory. The exit velocity and the averaged gas volume fraction of the erupting fluid are estimated to be ∼4–79 m/s and ∼0.93–0.96, respectively. The exit velocity is ∼30 m/s during the first bursts, then it rapidly increases to ∼50–80 m/s for ∼15 s, and then decreases to ∼20–30 m/s until ∼10 s before the eruption ends. Time series analyses of the thermal infrared and acoustic signals during an eruption indicate harmonic spectra with integer multiple peaks. The fundamental frequency showing ∼4 Hz at the beginning gradually decreases to ∼2 Hz for ∼15 s, keeps almost constant in the following 30 s, and then slightly increases near the end of the eruption. These harmonic spectra may be caused by a resonance mechanism, non-linear fluid motion, and/or subsurface two-phase flow. We discuss the case of a closed organ-pipe resonance in a subsurface crack and attribute the frequency decrease to a decrease in the water level in the crack (an increase in the length of the resonating liquid-vapor column) and/or a decrease in the gas volume fraction of the liquid-vapor mixture in the crack. Since such harmonic characteristics are observed at other geysers, further simultaneous observation of the short-period oscillations on the erupting column proposed in this study with seismic observations of harmonic tremor may provide an improved understanding of geyser subsurface phenomena.

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.

Spatio-temporal variability of small-scale leads based on helicopter maps of winter sea ice surface temperatures

Observations of sea ice surface temperature provide crucial information for studying Arctic climate, particularly during winter. We examined 1 m resolution surface temperature maps from 35 helicopter flights between October 2, 2019, and April 23, 2020, recorded during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The seasonal cycle of the average surface temperature spanned from 265.6 K on October 2, 2019, to 231.8 K on January 28, 2020. The surface temperature was affected by atmospheric changes and varied across scales. Leads in sea ice (cracks of open water) were of particular interest because they allow greater heat exchange between ocean and atmosphere than thick, snow-covered ice. Leads were classified by a temperature threshold. The lead area fraction varied between 0% and 4% with higher variability on the local (5–10 km) than regional scale (20–40 km). On the regional scale, it remained stable at 0–1% until mid-January, increasing afterward to 4%. Variability in the lead area is caused by sea ice dynamics (opening and closing of leads), as well as thermodynamics with ice growth (lead closing). We identified lead orientation distributions, which varied between different flights but mostly showed one prominent orientation peak. The lead width distribution followed a power law with a negative exponent of 2.63, which is in the range of exponents identified in other studies, demonstrating the comparability to other data sets and extending the existing power law relationship to smaller scales down to 3 m. The appearance of many more narrow leads than wide leads is important, as narrow leads are not resolved by current thermal infrared satellite observations. Such small-scale lead statistics are essential for Arctic climate investigations because the ocean–atmosphere heat exchange does not scale linearly with lead width and is larger for narrower leads.

Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible‐Near‐Infrared to Thermal Infrared

AbstractSuper‐coarse dust particles (diameters &gt;10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR.

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Supercooled Water Cloud Identification From Geostationary Satellite Thermal Infrared Observations and Its Application in the Sun Glint Region

Supercooled Water Cloud Identification From Geostationary Satellite Thermal Infrared Observations and Its Application in the Sun Glint Region

Drought Monitoring from Fengyun Satellite Series: A Comparative Analysis with Meteorological-Drought Composite Index (MCI)

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices.

An intense narrow equatorial jet in Jupiter’s lower stratosphere observed by JWST

The atmosphere of Jupiter has east–west zonal jets that alternate as a function of latitude as tracked by cloud motions at tropospheric levels. Above and below the cold tropopause at ~100 mbar, the equatorial atmosphere is covered by hazes at levels where thermal infrared observations used to characterize the dynamics of the stratosphere lose part of their sensitivity. James Webb Space Telescope observations of Jupiter in July 2022 show these hazes in higher detail than ever before and reveal the presence of an intense (140 m s−1) equatorial jet at 100–200 mbar (70 m s−1 faster than the zonal winds at the cloud level) that is confined to ±3° of the equator and is located below stratospheric thermal oscillations that extend at least from 0.1 to 40 mbar and repeat in multiyear cycles. This suggests that the new jet is a deep part of Jupiter’s Equatorial Stratospheric Oscillation and may therefore vary in strength over time.

A Spatially Self-Adaptive Multiparametric Anomaly Identification Scheme Based on Global Strong Earthquakes

Earthquake forecasting aims to determine the likelihood of a damaging earthquake occurring in a particular area within a period of days to months. This provides ample preparation time for potential seismic hazards, resulting in significant socioeconomic benefits. Surface and atmospheric parameters derived from satellite thermal infrared observations have been utilized to identify pre-earthquake anomalies that may serve as potential precursors for earthquake forecasting. However, the correlation between these anomalies and impending earthquakes remains a significant challenge due to high false alarm and missed detection rates. To address this issue, we propose a spatially self-adaptive multiparametric anomaly identification scheme based on global strong earthquakes to establish the optimal recognition criteria. Each optimal parameter exhibits significant spatial variability within the seismically active region and indicates transient and subtle anomaly signals with a limited frequency of occurrences (&lt;10 for most regions). In comparison to the fixed criterion for identifying anomalies, this new scheme significantly improves the positive Matthew’s correlation coefficient (MCC) values from ~0.03 to 0.122–0.152. Additionally, we have developed a multi-parameter anomaly synthesis method based on the best MCC value of each parameter anomaly. On average, the MCC increased from 0.143 to 0.186, and there are now more earthquake-prone regions with MCC values &gt; 0.5. Our research emphasizes the critical importance of a multiparametric system in earthquake forecasting, where each geophysical parameter can be assigned a distinct weight, and the findings specifically identify OLR, including all-sky and clear-sky ones, as the most influential parameter on a global scale, highlighting the potential significance of OLR anomalies for seismic forecasting. Encouraging results imply the effectiveness of utilizing multiparametric anomalies and provide some confidence in advancing our knowledge of operational earthquake forecasting with a more quantitative approach.

Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations

Abstract. In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 µm (DAOD10 µm) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing the dust lognormal volume particle size distribution (PSD) corresponding to retrieved Deff with the in situ-measured dust PSDs from the AERosol Properties – Dust (AER-D), Saharan Mineral Dust Experiment (SAMUM-2), and Saharan Aerosol Long-Range Transport and Aerosol–Cloud-Interaction Experiment (SALTRACE) field campaigns through case studies. The new DAOD10 µm retrievals were evaluated first through comparisons with the collocated DAOD10.6 µm retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al., 2022). The pixel-to-pixel comparison of the two DAOD retrievals indicates a good agreement (R∼0.7) and a significant reduction in (∼50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (2∘×5∘) mean DAOD10 µm retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R=0.9), Indian Ocean (IO; R=0.8) and North Pacific (NP; R=0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse-mode dust Deff over global oceans. We found that dust Deff over IO and NP is up to 20 % smaller than that over NA. Over NA in summer, we found a ∼50 % reduction in the number of retrievals with Deff&gt;5 µm from 15 to 35∘ W and a stable trend of Deff average at 4.4 µm from 35∘ W throughout the Caribbean Sea (90∘ W). Over NP in spring, only ∼5 % of retrieved pixels with Deff&gt;5 µm are found from 150 to 180∘ E, while the mean Deff remains stable at 4.0 µm throughout eastern NP. To the best of our knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust longwave radiative effects and coarse-mode dust particle size in models.

Separate retrievals of soil and vegetation temperatures using two methods

ABSTRACT Soil temperature (Ts) and vegetation temperature (Tv) provide significant information for various practical applications such as evapotranspiration estimation, water requirement analysis for vegetation, and drought monitoring during the crop growth. In this study, we estimated Ts and Tv with a land surface temperature (LST)-vegetation index (VI) trapezoidal space-based method (hereafter abbreviated as the trapezoidal method) and a dual-angle algorithm in which the estimation equations for Ts and Tv were established with thermal infrared observations from two angles and solved to using the least squares method. The Ts and Tv estimations were conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 data from the Google Earth Engine platform, together with auxiliary meteorological data from eight sites with various land cover types in the Heihe River Basin in 2019, and compared with corresponding in-situ measurements and temperatures from Landsat. The results showed that (1) both estimation methods outperformed the Ts retrievals compared to those of Tv; (2) the trapezoidal method overall had a similar but slightly better performance compared to the dual-angle algorithm, with the root mean square error for Ts retrievals of 3.7 K and 5.7 K, and for Tv retrievals of 6.5 K and 7.2 K, respectively, in comparison with temperatures from Landsat; (3) the trapezoidal method had higher accuracy over sites with grassland, desert, or meadow for Ts retrieval and sites with forests for Tv retrieval. This study provides advice about the method for Ts and Tv estimations and might be useful for rational utilization and more accurate monitoring of soil and vegetation.

Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution

Abstract. Sea ice leads play an important role in the heat exchange between the ocean and the overlying atmosphere, particularly narrow leads with widths of less than 100 m. We present a method for detecting sea ice leads in the Arctic using high-resolution infrared images from the Thermal Infrared Spectrometer (TIS) on board the Sustainable Development Science Satellite 1 (SDGSAT-1), with a resolution of 30 m in a swath of 300 km. With the spatial resolution of leads observed by infrared remote sensing increasing to tens of meters, focused on the Beaufort Sea cases in April 2022, the TIS-detected leads achieve good agreement with Sentinel-2 visible images. For the three infrared bands of the TIS, the B2 (10.3–11.3 µm) and B3 (11.5–12.5 µm) bands show similar performance in detecting leads. The B1 band (8.0–10.5 µm) can be usefully complementary to the other two bands, as a result of different temperature measurement sensitivity. Combining the detected results from the three TIS bands, the TIS is able to detect more leads with widths less than hundreds of meters compared to the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results demonstrate that SDGSAT-1 TIS data at 30 m resolution can effectively observe previously unresolvable sea ice leads, providing new insight into the contribution of narrow leads to rapid sea ice changes in the Arctic.

Assessment of a physics-based retrieval of exoplanet atmospheric temperatures from infrared emission spectra

Abstract Atmospheric temperatures are to be estimated from thermal emission spectra of Earth-like exoplanets orbiting M-stars as observed by current and future planned missions. To this end, a line-by-line radiative transfer code is used to generate synthetic thermal infrared (TIR) observations. The range of ‘observed’ intensities provides a rough hint of the atmospheric temperature range without any a priori knowledge. The equivalent brightness temperature (related to intensities by Planck’s function) at certain wavenumbers can be used to estimate the atmospheric temperature at corresponding altitudes. To exploit the full information provided by the measurement we generalize Chahine’s original approach and infer atmospheric temperatures from all spectral data using the wavenumber-to-altitude mapping defined by the weighting functions. Chahine relaxation allows an iterative refinement of this ‘first guess’. Analysis of the $4.3\rm \, \mu m$ and $15\rm \, \mu m$ carbon dioxide TIR bands enables an estimate of atmospheric temperatures for rocky exoplanets even for low signal to noise ratios of 10 and medium resolution. Inference of Trappist-1e temperatures is, however, more challenging especially for CO2 dominated atmospheres: the ‘standard’ $4.3\rm \, \mu m$ and $15\rm \, \mu m$ regions are optically thick and an extension of the spectral range towards atmospheric window regions is important. If atmospheric composition (essentially CO2 concentration) is known temperatures can be estimated remarkably well, quality measures such as the residual norm provide hints on incorrect abundances. In conclusion, temperature in the mid atmosphere of Earth-like planets orbiting cooler stars can be quickly estimated from thermal IR emission spectra with moderate resolution.

Detection of vegetation drying signals using diurnal variation of land surface temperature: Application to the 2018 East Asia heatwave

Satellite-based vegetation monitoring provides important insights regarding spatiotemporal variations in vegetation growth from a regional to continental scale. Most current vegetation monitoring methodologies rely on spectral vegetation indices (VIs) observed by polar-orbiting satellites, which provide one or a few observations per day. This study proposes a new methodology based on diurnal changes in land surface temperatures (LSTs) using Japan's geostationary satellite, Himawari-8/Advanced Himawari Imager (AHI). AHI thermal infrared observation provides LSTs at 10-min frequencies and ∼ 2 km spatial resolution. The DTC parameters that summarize the diurnal cycle waveform were obtained by fitting a diurnal temperature cycle (DTC) model to the time-series LST information for each day. To clarify the applicability of DTC parameters in detecting vegetation drying under humid climates, DTC parameters from in situ LSTs observed at vegetation sites, as well as those from Himawari-8 LSTs, were evaluated for East Asia. Utilizing the record-breaking heat wave that occurred in East Asia in 2018 as a case study, the anomalies of DTC parameters from the Himawari-8 LSTs were compared with the drying signals indicated by VIs, latent heat fluxes (LE), and surface soil moisture (SM). The results of site-based and satellite-based analyses revealed that DTR (diurnal temperature range) correlates with the evaporative fraction (EF) and SM, whereas Tmax (daily maximum LST) correlates with LE and VIs. Regarding other temperature-related parameters, T0 (LST around sunrise), Ta (temperature rise during daytime), and δT (temperature fall during nighttime) are unstable in quantification by DTC model. Moreover, time-related parameters, such as tm (time reaching Tmax), are more sensitive to topographic slope and geometric conditions than surface thermal properties at humid sites in East Asia, although they correlate with EF and SM at a semi-arid site in Australia. Additionally, the spatial distribution of the DTR anomaly during the 2018 heat wave corresponds with the drying signals indicated as negative SM anomalies. Regions with large positive anomalies in Tmax and DTR correspond to area with visible damage to vegetation, as indicated by negative VI anomalies. Hence, combined Tmax and DTR potentially detects vegetation drying indetectable by VIs, thereby providing earlier and more detailed vegetation monitoring in both humid and semi-arid climates.

Opportunities and Challenges of Spaceborne Sensors in Delineating Land Surface Temperature Trends: A Review

Understanding the land surface temperature (LST) trends is crucial for policymakers and stakeholders to develop adaptation and mitigation strategies suitable for a sustainable environment coping in the face of climate change. This article presents a systematic review of the studies related to delineating spaceborne sensor-based LST trends, including information on the instruments and constellations of satellites (missions) that provide thermal infrared (TIR) and passive microwave (PMW) observations. About 99% of the studies used TIR, where 76% were Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra/Aqua) observations. Opportunities, challenges, and research gaps for using the TIR and PMW observations were also explored, with instruments onboard either polar-orbiting or geostationary satellites. We identified that the calibrated dataset (e.g., processed, harmonized, and standardized) is extremely limited for each constellation, with multiple satellites and instruments, to make it fully useful for the entire mission period. A few problematic methodological concepts were identified, including using a few images in a longer time series. Using only a few images, acquired on different calendar months in different years, would not provide the true annual trends over the study period because they can be influenced by seasonal variations. To estimate the warming or cooling daytime, nighttime, or diurnal LST trends, the use of MODIS observations could be useful, even though it does not acquire images during the maximum or minimum temperature in a daily cycle. This article indicated further investigations into those research gaps and recommended directions to overcome most of these limitations.

Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 1011 kg (MLE method) and 1.32 ± 0.47 × 1011 kg (NN method) for Kelud; 4.48 ± 1.61 × 1010 kg (MLE method) and 4.32 ± 1.56 × 1010 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.