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

Quantitative thermal infrared (TIR) remote sensing with uncrewed aerial vehicles (UAVs) enables accurate measurement and analysis of ground temperature, temperature gradients, and related parameters. This technology is widely applied in areas such as agricultural monitoring, environmental assessment, and urban heat island studies. However, the field-of-view (FOV) effect arises from the use of central projection rather than orthographic projection in TIR imaging. This causes the edge pixels to represent larger ground areas than the center pixels under the same FOV, leading to temperature discrepancies. Such inconsistency can inevitably cause serious interference in subsequent quantitative analysis. To address this, this study proposes a calibration model by analyzing the three-dimensional issue in two-dimensional planes. Using the tangent theorem, a model is constructed to calculate the ground area ratios between non-orthorectified and center-orthorectified pixels, and calibrations are applied accordingly. The calibrated data agree well with ground measurements, achieving a coefficient of determination (R2) of 0.99, a root mean square error (RMSE) of 0.52 K, and a mean bias error (MBE) of -0.05 K. Additionally, the calibrated images exhibits improved brightness uniformity at the edges, aligning more closely with orthophoto characteristics. The proposed calibration model significantly enhances the temperature consistency of TIR images, facilitating the generation of angle-independent temperature maps to support informed decision-making in various applications.

Passive microwave brightness temperature (PMW BT) images acquired by PMW imagers onboard polar-orbit satellites suffer from large observations missing between adjacent orbits due to the swath width of images, i.e., PMW BT swath gaps, limiting the spatiotemporal integrity and application potential of PMW BT-generated products. Here, we propose a gap-filling method [i.e., physical indicators-guided CNN-LSTM (PICL)] for PMW BT images by physically guided selection of surface indicators with CNN-LSTM model, which is suitable for special underlying surfaces (e.g., seasonal permafrost and snow) using only BT data to generate spatially gapless PMW BT images. The core of PICL is to use the CNN-LSTM model to capture the relationship of BT time series, thereby filling the missing BT values via historical BT data. PICL is applied to 7, 10, 18.7, 36, and 89 GHz frequencies of Advanced Microwave Scanning Radiometer 2 (AMSR2) for the Tibetan Plateau (TP). Validation results show good accuracy of the PICL filled BT, with the root-mean-squared error (RMSE) ranging from 1.28 to 2.43 K (<89 GHz), and the accuracy decreases as the frequency increases. The reconstructed BT images agree well with the original AMSR2 BT images and show no obvious boundary effect. PICL also has a good ability in capturing the temporal trends and discontinuities caused by snow and seasonal permafrost. PICL only requires historical BT before the missing moment, highlighting its feasibility to be extended to other satellite PMW imagers. It enables the generation of spatially seamless products such as all-weather land surface temperature (LST) and soil moisture.

Uncrewed aerial vehicle (UAV) thermal infrared (TIR) remote sensing is playing an increasingly important role in diverse applications such as agriculture, forestry, hydrology, and ecological monitoring. Moreover, UAV-based remote sensing significantly contributes to the understanding of fundamental remote sensing science issues, such as scale variation and its impacts on multisource data collaboration. To cover extensive areas, UAVs often need to fly multiple strips to ensure complete coverage, leading to significant intervals between the start and end of missions. This can cause notable changes in brightness temperature (BT) due to the different observation times, introducing considerable uncertainty into the final BT mosaic, which, in turn, directly impacts subsequent applications, such as the calculation of land surface temperature (LST) and other temperature-related analyses. This study presents a temporal effect removal of LST (TERL) method to effectively correct for differences due to observation time and thereby enhance the temporal consistency of BT data. The core of TERL involves three key processes: 1) modeling the temporal information of TIR mosaic pixels; 2) deriving the temporal dynamics related to specific surface features by classifying image sequences; and 3) capturing the temporal variation of BT differences and temperature compensation. Validation results indicate that TERL significantly improves both the temporal comparability of pixels and the consistency between UAV temperature data and ground observations. Specifically, the root-mean-square error (RMSE) of the corrected data is 22.50%–77.14% smaller than that of the uncorrected data, with an impressive average reduction of 51.09%. Compared to the digital number probability density function fitting and radiative transfer simulation-based (DRAT) method, which primarily addresses temperature drift, TERL achieves an average RMSE reduction of 29.65%, showcasing its better performance. Moreover, the corrected data better reflect the temperature variation trends of surface features and show strong correlations with ground observation data, with most correlation coefficients exceeding 0.5. Thus, TERL facilitates more accurate comparisons and analyses of UAV TIR data, ultimately enhancing not only the reliability and effectiveness of quantitative remote sensing research with UAVs but also advancing the understanding of fundamental issues like scale variation in remote sensing science.

Unmanned aerial vehicle (UAV)-based fine-scale evapotranspiration (ET) estimation is becoming increasingly critical in precision agricultural water management. However, existing UAV-based ET estimation studies often directly transfer satellite-based ET models and parameterization schemes to fine-scale UAV data, which hampers accurate fine-scale ET estimation. Here, we use machine learning (ML)-based alternative estimation schemes to estimate key parameters of aerodynamic roughness length (z0m) and excess resistance (kB-1) in the surface energy balance system (SEBS) ET model. In addition, we use a computational fluid dynamics (CFD) model to provide downscaled meteorological data for the SEBS model. Compared to physical parameterization schemes, ML-based estimates ofz0mandkB-1show improved accuracy, reducing the mean root mean square error (RMSE) forz0mfrom 0.07 m to 0.04 m, and forkB-1from 4.58 to 2.41. Validation against eddy covariance (EC) systems with a source area of hundreds of meters shows that ML-based estimates of latent heat flux (LE) have an RMSE of 39.94 W/m2, which is superior to the RMSE of 77.44 W/m2achieved by physical parameterization schemes. ML-based LE estimates also show comparable accuracy with an RMSE of 41.94 W/m2when using CFD-based meteorological data. A comparison with an optical-microwave scintillometer (OMS) system with a source area spanning kilometers confirmed the importance of CFD-based meteorological data and reduced the mean relative error (MRE) for LE from 26.53% (using site-observed meteorological data) to 22.28%. Our proposed robust framework improves the accuracy of UAV-based ET estimates, thus helping to bridge the scale gap between satellite remote sensing and site-based observations.

Earth’s surface temperature (EST), encompassing both land surface temperature (LST) and sea surface temperature (SST), serves as a crucial indicator of climate change. This study introduces a groundbreaking framework for the daily estimation of Earth’s Surface Temperature (EST), integrating reanalysis data with thermal infrared remote sensing data merging (RTM) techniques and employing machine learning methods. The spatial distribution of the generated all-weather EST aligns effectively with MODIS EST, showcasing its capability to recover EST values in cloudy regions and estimate missing values in orbital gap areas. Validation results for all-weather LST and SST demonstrate commendable accuracy, with minimal variations observed under both clear-sky and cloudy conditions. The Root Mean Square Error (RMSE) for LST ranges from 1.69 to 2.84 K, while for SST, it spans from 0.38 °C to 0.59 °C. The framework exhibits adaptability to diverse weather conditions, maintaining consistent relative trends across different geographical locations. In summary, this innovative approach provides a robust solution for generating all-weather ESTs, effectively addressing challenges associated with conventional Thermal Infrared (TIR) data.

While numerous algorithms have been developed for retrieving land surface temperature (LST) and various LST products have been released for satellite thermal infrared (TIR) remote sensing, capturing thermal details on finer scales remains challenging due to limitations in revisit period and spatial resolution. Unmanned aerial vehicle (UAV) TIR remote sensing, on the other hand, proves capable of obtaining LST at high to super-high spatial resolutions, thereby supporting studies such as evapotranspiration estimation and precision agriculture. However, challenges arise from the operational characteristics of UAVs and the inherent defects in UAV-borne TIR imagers, which leads to issues in the obtained data. Moreover, the lack of methods to convert LST between ground, UAV, and satellite scales hampers the validation of LST products and impacts our understanding of LST variation from regional to global scales. Therefore, a MUlti-Scale Observation Experiment on land Surface temperature using UAV remote sensing (MUSOES-UAV) was designed and implemented in the middle reaches of the Heihe River basin. MUSOES-UAV provides a research basis for obtaining reliable, high-accuracy LST, offering new insights into the spatiotemporal changes of LST.

High spatial resolution land surface temperature (LST) has widespread applications in many fields. Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is a crucial means of obtaining such data. UAV thermal cameras typically need to capture numerous images to create a comprehensive TIR mosaic covering the target region, which is then converted into an LST map. However, LST can change rapidly over time, leading to temporal inconsistencies within the LST map, thereby affecting subsequent analysis and decision-making. Although reducing the UAV flight duration can minimize such inconsistencies, most practical applications cannot meet this requirement. Therefore, acquiring the time information of UAV TIR mosaic pixels is essential for developing temporal normalization methods and for assessing temperature data quality. Here, we propose a so-called equivalent acquisition time estimation for mosaics (EATEMs) method, designed to estimate the equivalent acquisition time (EAT) of UAV TIR mosaic pixels. This method integrates principles of UAV photogrammetry and image fusion. In our experiments, the estimated time map accurately reflects the UAV’s flight path and landing situation. Additionally, evaluation results based on ground-measured data indicate that the estimated time has an uncertainty of less than 5 min when there is a good linear relationship between LST and time. The more significant the linear relationship, the smaller the uncertainty. These promising results demonstrate the potential of the EATEM method in addressing issues related to temporal variations in UAV TIR remote sensing.

In-situ land surface temperature (LST) measuring plays a crucial role in quantitative remote sensing, as well as many environmental and climate studies. However, selecting representative sites for in-situ LST measuring is often challenging, as the spatial representativeness of LST measurements must be considered. Therefore, a site selection method for radiation-based in-situ LST measuring was proposed based on the ground station’s spatial representativeness evaluation method. This paper presents a case study of the site pre-selection for a meteorological research station located at Chengdu, China. The related results can provide a basis for the subsequent selection and construction of the station.

Time series analysis of land surface temperature (LST) is one of the most important topics in climate change-related research. As the third pole of the Earth and the water tower of Asia, the temperature change of the Qinghai-Tibet Plateau will inevitably affect the rapid response of the surrounding environment. Currently, many studies analyzed the spatio-temporal variation of LST in this area. However, due to cloudy weather conditions, the time series of the clear-sky LST may introduce large errors in their conclusions. Therefore, in this study, a newly released spatiotemporal seamless satellite all-weather LST product (TRIMS LST), as well as MODIS LST (MYD21), is employed to model and analyze the LST time series under all-weather conditions on the Qing-Tibet Plateau. Results show that the tendency of LST variation from clear-sky LST is weaker than that from all-weather LST. The all-weather LST indicates a warming trend on the Qing-Tibet Plateau.

It is urgently needed to obtain the hourly near-real-time all-weather land surface temperature (NRT-AW LST) for immediately monitoring the disaster and environmental changes. Nevertheless, studies on estimating hourly NRT-AW LST are in the preliminary stage. In this study, we proposed a Spatio-TEmporal Fusion (STEF) method for fusing the reanalysis dataset derived from China Land Surface Data Assimilation System (CLDAS) and thermal infrared (TIR) data derived from the Chinese Fengyun-4A (FY-4A) geostationary satellite to estimate the hourly NRT-AW LST with 0.04° resolution. STEF method can produce NRT-AW LST without relying on the data after the target moment. STEF is tested in the Tibetan Plateau. Validation results on DOY 215-366 of 2020 indicate that STEF has good accuracy: RMSEs (MBEs) under clear-sky, cloudy-sky, and all-weather conditions vary from 2.74 K (-1.06 K) to 3.77 K (0.14 K), from 3.31 K (-1.40 K) to 4.46 K (-0.22 K), and from 3.10 K (-1.11 K) to 3.87 K (-0.22 K), respectively. STEF method can improve the accuracies of FY-4A LST, and RMSEs are reduced by about 0.77 K to 1.82 K. The NRT-AW LSTs estimated by STEF have better accuracies than CLDAS LSTs under all-weather conditions. The SETF also exhibited similar results in 2021. We believe that the proposed STEF method can meet the requirements of NRT-AW LST estimation and contributes to improving the timeliness of region monitoring and related parameter estimations.

Glacial debris flows are a common geological hazard in theglacial region of the Tibetan Plateau. This study analyzed the relationship between land surface temperature (LST) and glacial debris flow in the southeastern part of the Tibetan Plateau. LST showed a year-to-year upward trend, which was more pronounced in the glacial region, throughout the study area. After analyzing the causes of eight glacial debris flows, we found that the sudden increase of LST and the long-term high LST in the early period are the main causes besides the rainfall. The results of the study show that LST can be an effective parameter for monitoring and forecasting glacial debris flows.

Pedestrian detection from UAV images is vital for many fields. Given that visible images are susceptible to bad illumination, thermal images with the ability to characterize the temperature of an object can provide auxiliary information. It is useful to fuse the visible and thermal images to improve the pedestrian detection performance. Unfortunately, studies on pedestrian detection with UAV visible-thermal image pairs are still rare. Therefore, we propose a method for Multi-source Pedestrian Detection based on Feature Fusion (MPDFF). With the registered visible and thermal image pairs as the input, MPDFF can achieve better characterization of pedestrians by concatenating the features from the two images. MPDFF performs much better than the methods that use only single-source images, which demonstrates that visible and thermal images are complementary in pedestrian detection.

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

Thermal infrared (TIR) land surface temperature (LST) products derived from geostationary satellites have a high temporal resolution in a diurnal cycle, but they have many missing values under cloudy-sky conditions. Therefore, it is pressing to obtain all-weather LST (AW LST) with a high temporal resolution by filling the gap of TIR LST. In this study, a method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly AW LST. Then, taking the Tibetan Plateau, which is a focus of climate change as a case, RTG was applied to the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation based on thein-situLST shows that the accuracy of the AW LST is better than the FY-4A LST and CLDAS LST under clear-sky, cloudy-sky, and all-weather conditions. The mean RMSEs are 3.02 K for clear-sky conditions, 3.94 K for cloudy-sky conditions, and 3.57 K for all-weather conditions. Uncertainty and coarse resolution of the original FY-4A and CLDAS data affect the accuracy of the obtained AW LST. The results of the LST time series comparison also show that the reconstructed AW LST is consistent within-situLST. The reconstructed AW LST also has good image quality and provides reliable spatial patterns. RTG is practical in obtaining high temporal resolution AW LST from the Chinese FY-4A to satisfy related applications. It can also be extended to other geostationary satellites and reanalysis datasets.