Thermal Infrared Research Articles

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

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.

Nighttime Thermal Infrared Image Translation Integrating Visible Images

Nighttime Thermal InfraRed (NTIR) image colorization, also known as the translation of NTIR images into Daytime Color Visible (DCV) images, can facilitate human and intelligent system perception of nighttime scenes under weak lighting conditions. End-to-end neural networks have been used to learn the mapping relationship between temperature and color domains, and translate NTIR images with one channel into DCV images with three channels. However, this mapping relationship is an ill-posed problem with multiple solutions without constraints, resulting in blurred edges, color disorder, and semantic errors. To solve this problem, an NTIR2DCV method that includes two steps is proposed: firstly, fuse Nighttime Color Visible (NCV) images with NTIR images based on an Illumination-Aware, Multilevel Decomposition Latent Low-Rank Representation (IA-MDLatLRR) method, which considers the differences in illumination conditions during image fusion and adjusts the fusion strategy of MDLatLRR accordingly to suppress the adverse effects of nighttime lights; secondly, translate the Nighttime Fused (NF) image to DCV image based on HyperDimensional Computing Generative Adversarial Network (HDC-GAN), which ensures feature-level semantic consistency between the source image (NF image) and the translated image (DCV image) without creating semantic label maps. Extensive comparative experiments and the evaluation metrics values show that the proposed algorithms perform better than other State-Of-The-Art (SOTA) image fusion and translation methods, such as FID and KID, which decreased by 14.1 and 18.9, respectively.

Lithological mapping of charnockites using spectral mixture analysis

Charnockite, an orthopyroxene-bearing granitic rock, is the most dominant rock type of the Southern Granulite Terrain (SGT) of India. This study utilises remote sensing data to map the charnockite lithology in the Kodaikanal region within the SGT. Although remote sensing-based lithological mapping dominantly uses VNIR (Visible-Near Infrared) datasets, in this study, PRISMA (hyperspectral) datasets in the VNIR region and ASTER (multispectral) datasets in the TIR (Thermal Infrared) region were utilised in deriving the mineralogical composition and spectral characteristics of charnockites. In addition, image-derived endmember spectra from PRISMA and ASTER were validated using the laboratory-acquired spectra of charnockites. Linear mixing models (LMM) and the Hapke model were applied to quantitatively study the fractional abundance of charnockite rocks in the Kodaikanal region. Three least square methods, i.e., Fully Constrained Least Squares (FCLS), Non-Negative Least Squares (NNLS), and Unconstrained Least Squares (UCLS), were used to derive the fractional abundance of endmembers. Laboratory-derived charnockite spectra in the VNIR region exhibit spectral features characteristics of pyroxenes and hydrated minerals. In the TIR region, charnockite spectra exhibit characteristic vibrational absorption features of quartz and feldspar known as reststrahlen bands. Image analysis indicates that charnockite rocks exhibit spectral characteristics resembling the mafic rock type in the VNIR region and the felsic rock type in the TIR region. Therefore, the combined analysis of VNIR and TIR datasets allows the accurate delineation of charnockite lithology. However, vegetation in the study area introduces strong nonlinear spectral mixing effects, making it challenging to map the lithological units accurately. This study addresses this challenge using spectral mixture analysis to map charnockites in the Kodaikanal region. The fractional abundance study provides valuable insights into the lithological composition, demonstrating the effectiveness of spectral mixture analysis in mapping charnockites in the Kodaikanal region. Overall, the study demonstrates that the spectral mixture analysis significantly enhances the accuracy of lithology mapping, contributing to a more comprehensive understanding of the study area.

Estimation of Land Surface Temperature of Khost Province, Afghanistan, using Satellite Remote Sensing Technologies

One particularly important metric for examining the thermal flux and heat energy balance of land surfaces is land surface temperature (LST). It can be used for developing models of urban heat transmission, managing water resources, simulating climate change, and conducting environmental studies. This investigation focuses on determining the surface temperature of Khost province in Afghanistan, with an additional goal of assessing the correlation between the Normalized Difference Vegetation Index (NDVI) and the city's ground temperature. Utilizing data from the Landsat 8 Thermal Infrared Remote Sensor (TIRS), this study employs the Top of the Atmosphere Radiance method to estimate surface temperature. The findings indicate that the surface temperature exhibits a relatively low range of 10 to 48 °C in June 2023. Notably, areas abundantly covered with forest and vegetation manifest lower temperatures, while industrial or vegetation-deprived regions demonstrate higher temperatures, reaching up to 48 °C. The study leveraged NDVI to explore the relationship between thermal behavior and the extent of vegetation cover. Employing a regression technique, the investigation establishes a negative correlation between NDVI and LST, with the regression coefficient from NDVI to LST also being negative. In conclusion, the study determines that there is a negative correlation between NDVI and LST, highlighting the cooling effect of vegetation on surface temperatures.

On‐Orbit Spatial Performance Characterization for Thermal Infrared Imagers of Landsat 7, 8, and 9, ECOSTRESS and CTI

AbstractIn this analysis of the spatial resolving power of thermal imagery products we focus on four satellite instruments that are used in research and applications, for example, to monitor land surface temperature and derive evapotranspiration. These are thermal imagers on Landsat 7, Landsat 8, and Landsat 9, as well as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). We compiled sets of close‐in‐time, day‐time images of bridges surrounded by open water bodies, captured by each of the satellite imagers during cloud‐free moments. Where possible, we also included some images captured by the Compact Thermal Imager (CTI), a technology demonstrator that was co‐located with ECOSTRESS on the International Space Station in 2019. Bridges were found to provide a sufficient thermal contrast with the water surface to quantify the line‐spread function of satellite‐based thermal products. The full‐width‐at‐half‐max of a gaussian beam model fitted to this transect quantifies the on‐orbit spatial resolution of different imagers. The results show some loss of spatial resolving power in the final product delivered to end‐users as compared to the at‐sensor characterization of spatial resolution. For Landsat 7, 8, and 9, the spatial resolution of the thermal bands is 1.5 times the ground sampling distance of 60 and 100 m respectively. For the ECOSTRESS the difference is up to twice the sampling distance of 78 by 69 m2. Since spatial resolution is a main driver for instrument design it is important to understand and communicate this discrepancy between pre‐flight design parameters and the characteristics of the surface imagery delivered to the user community. The goal of this research is to facilitate an improved fusion of current and future satellite observations into harmonized products with superior temporal and spatial characteristics.

Thermal infrared observations of a western United States biomass burning aerosol plume

Abstract. Biomass burning smoke particles, due to their submicron particle size in relation to the average thermal infrared (TIR) wavelength, theoretically have negligible signals at the TIR channels. However, nearly instantaneous longwave (LW) signatures of thick smoke plumes can be frequently observed at the TIR channels from remotely sensed data, including at 10.6 µm (IR window), as well as in water-vapor-sensitive wavelengths at 7.3, 6.8, and 6.3 µm (e.g., lower, middle, and upper troposphere). We systematically evaluated multiple hypotheses as to causal factors of these IR signatures of biomass burning smoke using a combination of data from the Aqua MODerate resolution Imaging Spectroradiometer (MODIS), Aqua Cloud and the Earth Radiant Energy System (CERES), Geostationary Operational Environmental Satellite 16/17 (GOES-16/17) Advanced Baseline Imager, and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and Cross-track Infrared Sounder (CrIS). The largely clear transmission of light through wildfire smoke in the near infrared indicates that coarse or giant ash particles are unlikely to be the dominant cause. Rather, clear signals in water vapor and TIR channels suggest that both co-transported water vapor injected to the middle to upper troposphere and surface cooling by the reduction of surface radiation by the plume are more significant, with the surface cooling effect of smoke aloft being the most dominant. Giving consideration of the smoke impacts on TIR and longwave, CERES indicates that large wildfire aerosol plumes are more radiatively neutral. Further, this smoke-induced TIR signal may be used to map very optically thick smoke plumes, where traditional aerosol retrieval methods have difficulties.

A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal–Infrared Remote Sensing

A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal–Infrared Remote Sensing

Water Deficit Diagnosis of Winter Wheat Based on Thermal Infrared Imaging

Field experiments were conducted to analyze the effectiveness of the crop stress index (CWSI) obtained by infrared thermal imaging to indicate crop water status, and to determine the appropriate CWSI threshold range for wheat at different growth stages. The results showed that the sensitivity of plant physiological parameters to soil water was different at different growth stages. The sensitivity of stomatal conductance (Gs) and transpiration rate (Tr) to soil water was higher than that of leaf relative water content (LRWC) and photosynthetic rate (Pn). The characteristics of plant physiology and biomass (yield) at each growth stage showed that the plant production would not suffer from drought stress as long as the soil water content (SWC) was maintained above 57.0% of the field water capacity (FWC) during the jointing stage, 63.0% of the FWC during the flowering stage and 60.0% of the FWC during the filling stage. Correlation analysis showed that the correlation of CWSI with Gs, Tr and Pn was lower than that with LRWC and SWC at the jointing stage. CWSI was extremely significantly negatively correlated with SWC and LRWC (p &lt; 0.01), but significantly negatively correlated with Gs, Tr and Pn (p &lt; 0.05). At the flowering stage, CWSI was extremely significantly negatively correlated with all physiological and soil parameters (p &lt; 0.01). The regression analysis showed that the CWSI of winter wheat was correlated with biomass (grain yield) in a curvilinear relationship at each growth stage. When the CWSI increased to a certain extent, the biomass and yield showed a decreasing trend with the increase in CWSI. Comprehensive analysis of all indexes showed that CWSI can be used as a decision-making index to guide the water-saving irrigation of winter wheat, as long as the CWSI threshold of plants was maintained at 0.26–0.38 during the jointing stage, 0.27–0.32 during the flowering stage and 0.30–0.36 during the filling stage, which could not only avoid the adverse effects of water stress on crop production, but also achieve the purpose of water saving.

Infrared spectroscopy of volcanoes: from laboratory to orbital scale

Understanding the composition, texture, and morphology of volcanic rocks that have erupted at the surface better constrains the eruption style and is vital to infer subsurface processes, the development of magma upon ascent, and the potential for future eruptions. The reflectance and emission spectroscopy of these rocks, collected from the near-infrared (NIR) through the thermal infrared (TIR) portion of the electromagnetic (EM) spectrum, provides the data necessary to retrieve composition, micron-scale surface roughness, and particle size. Remote imaging systems enable the analyses of active volcanoes in remote regions, where sample collection for laboratory analysis poses a significant challenge. Laboratory hyperspectral data of samples acquired at volcanic deposits are easily resampled to the spectral resolution of any infrared sensor and provide a means of estimating the composition of volcanoes and their products worldwide, as well as those on other planetary bodies such as the Moon and Mars. In this review paper, we provide an overview of the current use of infrared reflectance and emission spectroscopy as an analysis tool in volcanology, including ground-based imaging systems that acquire unprecedented detail and serve as testbeds for new orbital concepts. We also discuss the potential impact that future satellite missions will have on volcano science as spectral, spatial, and temporal resolutions improve.

Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

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).

Laser Dynamic Control of the Thermal Emissivity of a Planar Cavity Structure Based on a Phase-Change Material.

Tailoring the thermal emission of a material in the long-wave infrared (IR) range of 8-13 μm is crucial for many IR-adaptive applications, including personal thermal management, IR camouflage, and radiative cooling. Although various materials and surface structures have been proposed for these purposes, space-selective and dynamic control of their emissivity is challenging. In this study, we present a planar surface cavity structure consisting of a Ge2Sb2Te5 (GST) film on top of a thin metal reflector to modulate its emissivity by using an ultraviolet laser beam. A laser-induced phase change in GST allowed for the local control of emissivity. The average emissivity in the long-wave IR range was tunable from 0.15 to 0.77 simply by changing the laser energy deposited on the GST film. This enabled the laser printing of high-contrast emissivity patterns, which were erasable by subsequent thermal annealing. Emissivity-modulated GST cavities could be fabricated on not only rigid substrates but also flexible plastic substrates such as polyimide. The GST surface cavity was highly flexible and remained stable upon repeated bending to a curvature radius of 0.5 cm. This study provides a promising route for realizing scalable and flexible thermal emitters with tunable surface emissivity.

PENGGUNAAN SINAR INFRA MERAH UNTUK DETEKSI PANAS BUMI DAERAH SANGKANHURIP, KUNINGAN, JAWA BARAT

Geothermal resources can be explored with aerial photographs followed by surveys and analysis of geological, geochemical and geophysical data as well as shallow drilling. This research was conducted to determine the potential of geothermal resources using a camera capable of capturing infrared rays from objects and photo targets.Geothermal exploration is generally carried out with an initial survey using remote imagery and aerial photography techniques. Taking photos using a tool equipped with a Thermal Infrared Sensor (TIRS), namely a camera equipped with Forward Looking InfraRed (FLIR).The initial survey in this study was conducted at five locations of geothermal springs in the Sangkanhurip area, Kuningan, West Java. From these five locations using FLIR cameras detected geothermal resources with hot spring temperatures between 30 to 40 degrees Celsius.To find out the types of rocks that exist, a geological survey was carried out to make a geological map. The data and information obtained are then analyzed to obtain a map that displays geology with surface temperature data in the study area.This map can be utilized by companies interested in exploring and producing geothermal energy for use as a source of energy for Geothermal Power Generation (PLTP). Further analysis can be carried out with the Geochemical Method for predicting subsurface temperature. This discovery fulfills the research objective of detecting geothermal resources using infrared rays.

Investigation of the geological and hydrogeological structure of chalk cliffs with visible, thermal infrared and electrical resistivity imaging

This study is focused on assessing the surface and subsurface properties of a coastal cliff undergoing erosion. The investigation takes place along the chalk cliffs located in Sainte-Marguerite-sur-Mer, within the region of Normandy, France. To achieve this, a combination of Electrical Resistivity Imaging (ERI), visible and Thermal Infrared (TIR) imagery, and geotechnical methods (specifically, piezometers) were employed.ERI was chosen due to its high sensitivity to changes in water saturation and salinity within subsurface formations. The research involved two transverse ERI profiles originating from the plateau, crossing the cliff, and extending to the shore platform. Additionally, two longitudinal profiles were conducted on the plateau, running parallel to the coastline, all using Wenner configurations. The primary findings unveiled the distribution of subsurface resistivity, revealing insights into cracks, potential karst formations, and weathered chalky areas. A significant discovery was the identification of a conductive zone at depth, which was associated with saltwater intrusion.The integration of piezometers, intersecting the ERI profiles, allowed for the correlation of resistivity values with the measured salinity data. Areas characterised by brackish water exhibited an apparent resistivity ranging between 4 and 75 Ω.m, commencing at the front of the saline wedge.

Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data

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.

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

AbstractSatellite‐based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear‐sky conditions for estimating surface urban heat island intensity (SUHII). Clear‐sky SUHII may not accurately represent all‐sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear‐sky and all‐sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear‐sky SUHII is mostly higher than all‐sky SUHII, particularly in summer, daytime, and precipitation‐rich regions. Besides, clear‐sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all‐sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear‐sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands.

Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused.

Laboratory Emissivity Spectra of Sulphide-Bearing Samples, New Constraints for the Surface of Mercury: Oldhamite in Mafic Aggregates

Exploration of Mercury will continue in the near future with ESA/JAXA’s BepiColombo mission, which will increase the number and the type of datasets, and it will take advantage of the results from NASA’s MESSENGER (MErcury Surface, Space ENviroment, GEochemistry and Ranging) mission. One of the main discoveries from MESSENGER was the finding of a relatively high abundance of volatiles, and in particular of sulphur, on the surface. This discovery correlates well with the morphological evidence of pyroclastic activity and with features attributable to degassing processes like the hollows. BepiColombo will return compositional results from different spectral ranges and instruments, and, in particular, among them the first results from the orbit of emissivity in the thermal infrared. Here, we investigate the results from the emissivity spectra of different samples between a binary mixture of a volcanic regolith-like for Mercury and oldhamite (CaS). The acquisitions are taken at different temperatures in order to highlight potential shifts due to both mineral variation and temperature dependence on these materials that potentially could be present in hollows. Different absorption features are present for the two endmembers, making it possible to distinguish the oldhamite with respect to the regolith bulk analogue. We show how, in the mixtures, the Christiansen feature is strongly driven by the oldhamite, whereas the Reststrahlen minima are mainly dominated by mafic composition. The spectral contrast is strongly reduced in the mixtures with respect to the endmembers. The variations of spectral features are strong enough to be measured via MERTIS, and the spectral variations are stronger in relation to the mineralogy with respect to temperature dependence.