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.

Land Surface Temperature (LST) is a critical parameter for monitoring surface energy balance and evaluating climate and environmental changes. However, LST retrieval from thermal infrared satellite remote sensing often suffers from data gaps in cloud-affected regions. Existing methods for estimating cloud-covered LST do not adequately account for physical mechanisms under complex meteorological and surface conditions, nor do they address dynamic error variations during the fusion process. To address these limitations, this study integrates the numerical weather prediction model (WRF), land surface model (Noah-MP) and satellite observation data. It comprehensively evaluates the accuracy of the LST simulated by the WRF and Noah-MP. Moreover, a data assimilation and fusion method based on the Kalman filter is used to consider the changes of errors, and the dynamic fusion of these LST data is carried out to obtain the hourly LST with a resolution of 1 km. Furthermore, assimilating downward shortwave and longwave radiation into the Noah-MP model improves its simulated LST accuracy to a certain extent. The fused LST is not only spatially continuous but also exhibits improved reliability. Validation within-situmeasurements shows that the Root Mean Square Error (RMSE) under clear-sky conditions is 2.56 K, and the RMSE of the LST under all-weather conditions is 2.88 K. This method has good potential in generating spatially continuous LST with high temporal and spatial resolution, thus promoting relevant research and applications.

MODerate-resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) time-series data are among the most widely utilized remote sensing datasets. To improve the quality of MODIS VI time-series data, most prior methods have focused on correcting negatively biased VI noise by approaching the upper envelope of the VI time series. Such treatment, however, may cause overcorrections on some true local low VI values, resulting in inaccurate simulations of vegetation phenological characteristics. In addition, another challenge in reconstructing MODIS VI time series is to fill temporally continuous gaps. The earlier spatiotemporal Savitzky-Golay (STSG) method tackled this problem by utilizing multiyear VI data, but its performance heavily relies on the consistency of data across different years. In this study, we proposed an improved STSG (iSTSG) method. The new method accounts for the autocorrelation within the VI time series and fills missing values in the VI time series by leveraging spatiotemporal VI data from the current year alone. Furthermore, iSTSG incorporates an indicator to quantify potential overcorrections in the VI time series, aiming to more accurately simulate phenological characteristics. The experiments to reconstruct MODIS normalized difference VI (NDVI) time-series product (MOD13A2) at four typical sites (a million square kilometers for each site) suggest two clear advantages in iSTSG over the iterative SG (called Chen-SG) and STSG methods. First, iSTSG more accurately reconstructs the annual NDVI time series, exhibiting the smallest mean absolute differences (MADs) between the smoothed and the simulated reference NDVI time series (0.012, 0.018, and 0.020 for iSTSG, STSG, and Chen-SG, respectively). Second, iSTSG more effectively simulates phenological characteristics in the NDVI time series, including the onset dates for vegetation greenup and dormancy, as well as the crop harvest period. The advantages of iSTSG were also demonstrated when applied to the successor of MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS) VI time-series product (VNP13A1). iSTSG can be implemented on the Google Earth Engine (GEE), offering significant benefits for various applications, particularly in crop mapping and vegetation/crop phenology studies.

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.

In this study, an end-to-end semantic segmentation method (ConvSegFormer) is proposed by utilizing the multispectral imaging capability of UAVs for images containing multispectral bands, with a special focus on thermal infrared bands. Experimental results show that the use of multispectral images, especially thermal infrared bands, achieves higher segmentation accuracy through spectral information. In addition, the end-to-end deep learning semantic segmentation method can directly learn the complex mapping relationship between image pixels and semantic categories without step-by-step feature extraction and classification, which is more direct and efficient. Finally, the maximum values of Mean Pixel Accuracy (MPA) and Mean Intersection Over Union (MIOU) are 90.35% and 73.87%. In the segmentation task of the wetland area, the maximum values of PA and IOU reached 95.42% and 90.46%. This indicates that the method is effective and feasible in automatically extracting the segmentation of wetlands and other land types.

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.

Land surface temperature (LST) is a widely focused parameter between the land surface and the atmosphere. Currently, satellite remote sensing is the main approach to obtaining regional and global LST. Validation of satellite-derived LST can promote its application and provide feedback for the retrieval algorithms and parameterization schemes. The current widely used temperature-based method faces many influences, e.g., obtaining the ground truth on the pixel scale and its uncertainty. Here, a comprehensive validation scheme is proposed for validating the satellite-derived LST by combining the near-surface atmospheric correction for longwave-radiation-based in situ LST and considering the validation station’s spatial representativeness, and applying it in the validation of AVHRR-derived LST. The LST “ground truth” of three validation stations in Heihe River Basin, China was obtained, with a mean uncertainty of 0.87 K (range:$0.47\sim 2.96$K), 1.07 K ($0.49\sim 1.81$K), and 0.61 K ($0.47\sim 1.13$K) for A’rou superstation (ARS), daman superstation (DMS), and sidaoqiao superstation (SDQ), respectively. Validation of AVHRR-derived LST against the obtained “ground truth” shows that the random error is lower than 3 K, and the system error is station-dependent, with a range of$- 1.02\sim 3.93$K. Further comparison indicated significant systematic error differences (range:$- 1.5\sim 3.45$K) and inapparent random errors difference ($- 0.84\sim 0.43$K) between the proposed comprehensive scheme and the classic scheme at the selected stations. Since the main influences are considered in the proposed comprehensive validation scheme, the validation results are more objective and credible. The comprehensive validation scheme provides a reference for LST validation and could be extended to the validation of related hydrothermal parameters.

Sea surface temperature (SST) is an important parameter for assessing sea-atmosphere energy interaction and understanding climate change. One of the primary approaches for obtaining global-scale SST is retrieving from satellite thermal infrared remote sensing data. However, it is challenging to accurately retrieve large-scale SST due to the complexity of retrieving the key intermediate parameter, i.e., sea surface emissivity (SSE), using the standard theoretical model. In this study, we proposed a simplified SSE estimation model based on the satellite view zenith angle (VZA) and wind speed and compared it with three commonly used SSE estimation models. Then, the retrieved SSTs based on those SSEs were validated againstin-situSSTs. Results show that the SSE from the proposed estimation model shows the highest consistency and the lowest biases with the theoretical values compared to the other three estimation models, especially in large VZAs.In-situobservation-based SST validation results show that the SST retrieved using the proposed SSE estimation model also achieves the highest accuracy compared to the other three SSTs, with a mean bias error of 0.08 K, and a root-mean-square error of 0.30 K, which is close to the official SST products. In conclusion, the proposed SSE estimation model shows good performance both in SSE estimating and SST retrieving. Furthermore, the proposed model has the potential to estimate SSE on large scales that can serve as a reference for obtaining SST from other similar sensors to promote the development of marine 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.

With the passive microwave remote sensing of snow, the high frequency (80-100 GHz) has the advantages of high resolution and fresh shallow snow detection, however, the application of high frequency for snow observation is often limited by atmosphere influence. Until now, few studies have quantified the atmospheric influence of high frequency in snow observations. The scattering and emission of the water vapor and cloud liquid water in the atmosphere cause the increment in Brightness Temperature (TB). To explore the influence of the atmosphere on snow cover observations, we utilize Nordic Snow Radar Experiment (NoSREx) data and the Microwave Emission Model of Layered Snowpacks (MEMLS) to quantify the influence of the atmospheric downward radiance in snow observation by using 90 GHz of the ground-based radiometer. The results show that, in Sodankylä of Finland, atmospheric contribution to ground-based radiometer at 90GHz 50-degree is up to 95.76K (H-pol) and 95.02K (V-pol), with an average of 25.36K (V-pol) and 25.95K (H-pol). The further calculated root mean square errors (RMSEs) of simulated TB with Tdown and observed TB are 41.40K (V-pol)/36.85(H-pol), and of simulated TB without Tdown are 41.25K (V-pol) and 36.72K (H-pol), respectively, the MEMLS slightly increased the simulation accuracy quantitatively of the snow emission without the influence of the atmosphere.

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST Downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the Geographically Weighted Regression (GWR) and Random Forest (RF), a new downscaling method (i.e., WGWR) considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat-8 TIRS and Terra ASTER LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the RMSEs can be reduced by 0.34 K (0.26 K) in Zhangye and 0.22 K (0.1 K) in Beijing. Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based onin-situLST indicates that the downscaled LST based on WGWR has better agreement with thein-situLST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.

Lunar exploration has put further demands on high spatial resolution of the lunar surface temperature (LuST) data. However, available LuST data with coarse resolution provided by current remote sensing platforms (e.g., LRO Diviner) are unable to meet the requirement. Therefore, it is necessary to develop new methods to obtain LuST data with high spatial resolution. Inspired by the study of land surface temperature (LST) downscaling, here, we perform the exploratory study of the LuST downscaling. The core of the exploration work consists of two parts: 1) the 16 pixels per degree (ppd) Diviner Gridded Data Records (GDRs) images are downscaled to 128 ppd using a downscaling model based on geographically weighted regression (GWR), in order to explore the feasibility of downscaling LuST; and 2) two sets of experiments with scales of 4 and 8 are conducted and the growth characteristics of the errors are observed to determine whether it is feasible to obtain higher-spatial-resolution data. The evaluation metrics for the experiments in two specific craters with scale of 8 are 4.03/5.42 K for mean absolute error, 5.89/8.05 K for root mean square error and 0.037/0.048 for normalized root mean square error, respectively. The LuST downscaling study is feasible, and it provides an effective way for obtaining high-spatial-resolution LuST data, which has positive implications for lunar exploration.

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.

Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3 km MSG DSR data was spatially downscaled to 30 m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3 km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation within-situmeasurements also revealed the significant improvement in terms of the coefficient of determination (R2) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/m2). In general, the proposed downscaling method in this study show good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.

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.

Aerosol optical depth (AOD) is closely related to atmospheric pollutants. However, a large number of missing values in satellite AOD severely limits its application. We proposed a spatiotemporal random forest (RF) model to estimate the missing AHI AOD in this study. In addition to the commonly used meteorological and topographic parameters, the spatiotemporal data and MERRA-2 AOD were introduced as the model inputs. Specifically, the training data was divided into multiple subsets based on the land cover types and local times to explicitly characterize the spatiotemporal variation of AOD. The validation results indicated that the RF model achieved promising results with RMSE of 0.03 to 0.17, MBE of −0.01 to 0.02, and R of 0.87 to 0.97 at different land cover types and local times.

Land surface temperature (LST) plays a key role in various land surface processes. Limited to the balance between the spatial resolution and revisit interval, it is difficult to obtain the high spatiotemporal resolution LST via satellite remote sensing. Downscaling is an economical approach to achieve it, and it has been relatively mature associated with a large number of land surface parameters. However, it still needs to address issues such as the physical meaning of the downscaling method. This study implemented a practical LST downscaling method that combined the spatial and temporal information to obtain a higher spatial resolution LST over an oasis in the Heihe River basin. The downscaled 500-m and 250-m LST shows more details, especially the boundary between the oasis and the desert. The validation against in-situ LST also shows that the downscaled LST has similar accuracy and precision with the original MODIS LST, with a RMSE of 1.78 K and 1.70 K at daytime, 1.36 K and 1.40 K at nighttime, for 500-m and 250-m, respectively.

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.

Near surface air temperature (NSAT) products are required for environment-related researches and applications. Existing NSAT products vary in spatial-temporal resolution and data quality. Thus, it is necessary to evaluate and investigate the difference of different NSAT products to provide an overall assessment to help researchers and users to choose and use among the many NSAT products. In this study, Tibetan Plateau was selected as our study area, and six released NSAT products were collected for comparison and evaluation. The NSAT products were compared with in situ NSAT from China Meteorological Administration stations (CMA) respectively. The evaluation process was conducted from daily and monthly scale and gave out the accuracy ranking of the six NSAT products.

A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27–1.77 K. Validation based onin situLST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46–4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.

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.

Land Surface Temperature (LST) is a key factor in the land surface energy budget. The accuracy of the LST is affected by topographical fluctuations, observation time, and other factors. Thus, it is necessary to validate the retrieved LST products. In this study, the Fengyun-4A (FY-4A) LST was evaluated against the in-situ LST, which is collected from four ground sites in the Heihe River basin from August 1st, 2019 to December 31st, 2019. The results show that the root-mean-square error(RMSE) varies from 2.39 K to 4.07 K. Therefore, it is considered that FY-4A LST has good correlations with the in-situ LST. However, the FY-4A LST product has large systematic errors over some sites, e.g Jingyangling. The main reason is that the longwave radiometer has a scale mismatch between the pixels of FY-4A, and the scale mismatch can affect the representatives of measurements at pixel scales.

Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the “similarity” criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the “consistency” criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the “consistency” criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called “cross-fusion”) to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the “similarity” criterion and then takes the “consistency” criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.

Land surface temperature (LST) is an important parameter for series land surface processes, models and applications. The accuracy of LST directly influenced its application. Therefore, a reasonable validation method is meaningful to assess the accuracy of LST datasets. In this study, an in-situ observation representativeness assessment method was proposed. Based on this method, the JPSS VIIRS LST product was validated against in-situ LST at 7 ground sites over the Heihe River Basin during the HiWATER experiments period. Results show that about 70%, 28%, 43%, 68%, 42%, 35% and 25% of the FOV LST for ARS, DMS, DSL, EBO, HHL, JYL, and SDQ are able to well represent the corresponding LST of VIIRS pixels, respectively and determined the representativeness period of each site. The VIIRS LST has a high correlation with the in-situ LST with an accuracy of 2.30 K - 5.76 K at daytime and 1.26 K-2.68 K at nighttime, respectively.

Land surface temperature (LST) is a key variable at the land-atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29-1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth's surface temperature.

Land Surface Temperature (LST) is an important input parameter for many land surface models. The accuracy of satellite LST products directly affect its application; therefore, it is necessary to evaluate LST products. In this study, two satellite remotely sensed LST products, i.e. AMSR2 LST and MODIS LST, were evaluated against the in-situ LSTs at 17 ground sites in Heihe River Basin in 2014. Results show that both AMSR2 and MODIS LSTs have good correlations with the in-situ LST, with R2 from 0.80 to 0.98 except at AR2 site at daytime. However, both of these two products have large systematic errors compared with the in-situ LST. The possible main reason is the scale mismatch between the FOV of the longwave radiometer and the AMSR2 and MODIS pixels.

Land surface temperature (LST) immensely affects the energy balance and water cycle on the earth's surface. Merging thermal infrared (TIR) and passive microwave (MW) remote sensing provides the possibility to obtain all-weather LST with moderate resolutions. However, due to difficulties in downscaling MW LST, current methods merging TIR LST and MW LST into such an all-weather LST are limited over large areas with very complicated land surfaces (e.g. the Tibetan Plateau). By fully considering the influence of the topography on estimation of merged LSTs, this study revises the recently-developed physical method for generating the 1-km all-weather LST and applies it over the Tibetan Plateau to merge MODIS (1 km) and AMSR2 (10 km) observations. Results show that the merged LST has accuracy of 0.99 K-3.22 K when validated against insitu LSTs from five ground stations with various land cover types. This study would be beneficial for continuously monitoring LST and improving spatio-temporal resolutions for associated land surface process studies requiring high-quality all-weather LST over large scales.

Directional brightness temperature (DBT) plays an important role in surface energy balance and urban climate. How to determinate the components' proportion efficiently is one of the key factors to accurate simulation of DBT. Usually, determining the proportion of each component in the sensor's field of view (FOV) uses the radiosity method. This approach is more complicated and highly accurate, however, the computational complexity increases dramatically with the decreasing splitting scale of the involved facet area. To solve this problem, this research uses message passing interface (MPI) programming model to design and implement a parallel Linux cluster-based components' proportion calculation method. The experimental results show that the speedup ratio of the algorithm increases with the decrease of the facet areas' splitting scales, and that the achieved speedup reaches to 100 times or so, i.e., the acceleration effect is satisfactory and meets the application's requirements.

Land surface temperature (LST) is widely used in various applications, such as ecology, meteorology and climatology. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing has ability to overcome the influence of atmosphere and thus has potential to estimate LST under cloudy conditions. However, the coarse spatial resolution significantly limits the application of PMW remote sensing in LST estimation. In this study, a simple multiple regression approach is proposed to downscale the LST from AMSR2 (10 km) to MODIS (1 km) scale in a different way from current downscaling methods. The method is applied to northeast China and the results shows an accuracy of 2-3 K for this method. This study is meaningful for generation of all-weather LST with high spatial resolution from PMW observations at regional and global scales.

Land surface temperature (LST) is an important parameter in many research fields. Compared to the thermal infrared (TIR) remote sensing, passive microwave (MW) remote sensing can better overcome the atmospheric influences and has advantages in LST estimation. However, there are still many problems in estimating LST by MW: traditional empirical methods mainly rely on the statistic relation; therefore, their accuracies are generally limited; physical methods are not suitable for wide applications because they need to be constructed based on complicated surface cases. Based on the optimal time-window fitting the MW radiation transfer (RT) equation, this paper facilitates the semi-empirical method to estimate LST over the Chinese landmass from the Advanced Microwave Scanning Radiometer-2 (AMSR2) data. The results show that the method has higher accuracy than the traditional semi-empirical method. The study is beneficial for estimating LSTs in cloudy conditions and merging the TIR and MW LST in difference spatial scales.

The past decade has witnessed the disaggregation of remotely sensed land surface temperature (DLST), which aims for the generation of high temporal and spatial resolution land surface temperature (LST) and which has steadily evolved into a relatively independent subfield of thermal remote sensing. Limited by Tobler's first law of geography, DLST methods require a regression between LSTs and scaling factors using image pixels within a globalized or a localized regression window. Recommendations regarding the selection of the regression window have been provided, but they are mainly subjective and based on highly specific examples. In this context, 100 DLST samples with diversified land cover types and climates were employed to assess the global window strategy (GWS) and the local window strategy (LWS). To optimize disaggregation accuracy and computational complexity, the assessments show that the optimal moving-window size (MWS) for the LWS can be estimated by the resolution ratio between pre- and postdisaggregated LSTs. To identify the better strategy between the GWS and the LWS, an indirect criterion based on aggregation-disaggregation (ICAD) was formulated, which determines the better strategy from medium to high resolution according to the associated performances from low to medium resolution. Validations demonstrate that the accuracy predicted by the ICAD achieves 72%, and in cases in which predictions are incorrect, the performances of the GWS and the LWS are similar. Further evidences indicate that the use of historical high-resolution LSTs improves the LWS by using a locally varying MWS. These findings are able to guide researchers in choosing the most suitable regression window for any particular DLST.

Satellite passive microwave (MW) remote sensing has a better ability to observe land surface temperature (LST) in cloudy conditions than thermal infrared (TIR) remote sensing. Due to the much greater thermal sampling depth (TSD) of MW, currently available MW LST do not represent the thermodynamic temperature of the land surface and, therefore, yield systematic differences from TIR LST. The TSD effect is particularly prominent over barren land and sparsely vegetated surfaces. Here, we present a novel TSD correction (TSDC) method to estimate the MW LST over barren land. The core of this method is a new formulation of the passive MW radiation balance equation, which allows linking MW effective physical temperature to the soil temperature at a specific depth. The TSDC method is applied to the 6.9-GHz channel of AMSR-E in northwestern China-western Mongolia and western Namibia (WN). Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation based on in situ LSTs measured at the Gobabeb site in WN demonstrates the high accuracy of the TSDC method: it yields a root mean squared error of about 2-3 K and slight systematic error. In contrast, other methods without TSDC yield lower accuracies and significantly underestimate LST. Therefore, the TSDC method has the potential to generate MW LST with the same physical meaning and similar accuracy as TIR LST. This study provides implications for developing practical and accurate methods to estimate MW LST over other land surface types and at the global scale.

Remotely sensed land surface temperature (LST) is influenced by the viewing angles and ground structure. By selecting a sparsely vegetated surface as the study area, the effects of structural parameters of land surface on remotely sensed LST are analyzed in this paper. Results demonstrate that both the density of canopy and size of canopy have significant influences on the remote sensing observations of LST. The difference between the LSTs obtained at nadir and off-nadir views also varies according to the acquisition season. This work is expected to be beneficial for quality control of remotely sensed LST.

With the rapid development of new satellite thermal sensors and applications of land surface temperature (LST), research on finding effective algorithms to retrieve accurate LST from satellite thermal infrared (TIR) data is becoming more and more important. In this study, multiple algorithms for retrieving LST from Landsat-8 Thermal Infrared Sensor (TIRS) data are validated and intercompared in an extremely arid region, Northwest China. According to the validation and intercomparison, we find that the radiative transfer equation (RTE) based method with TIRS band 1 (10.60-11.19 μm) has the highest accuracy, while the single-channel (SC) method using TIRS band 2 (11.50-12.51 μm) yielded the lowest accuracy. The accuracies of split-window (SW) algorithms are slightly lower than the RTE based method. However, the SW algorithms have better applicability than the RTE based method. The most suitable SW algorithm for Landsat-8 TIRS data in the study area is recommended. This study will be beneficial for developing the LST product from Landsat-8 data for the study area.

Although many challenges exist, validation of the satellite land surface temperature (LST) product over heterogeneous surface can provide new and in-depth understandings of the product. Lessons learned from the validation are important to improve the satellite LST product. In order to better understand the relationship between LSTs measured through different approaches and instruments and test the possibility to upscale the ground measured LST over heterogeneous surface, a MUlti-Scale Observation Experiment on land Surface temperature (MUSOES) was designed and conducted in an extremely arid region in Northwest China. The experiment was concentrated at two typical sites (i.e. HHL - sparsely forest, and SDQ - open shrubland). It began from July 2014 and have run normally for two years. First results of this experiment have been presented here. MUSOES provides a basis to examine the upscaling of the ground measured LST to the LST at the satellite pixel scale over the heterogeneous surface.

Airborne TIR remote sensing can obtain land surface temperature (LST) with high spatial resolution. However, the swath width of airborne stripes is usually limited. Therefore, it is necessary to generate the LSTs for a large area through temporal normalization of LSTs derived from different stripes. By selecting an agricultural oasis as the study area, this study compares the diurnal temperature cycle (DTC) model and polynomial regression (PR) technique in the temporal normalization of the LSTs derived from the Thermal Airborne Spectrographic Imager (TASI) data. The results show that the DTC model has better accuracy in normalizing the LSTs. However, the PR technique is simple and requires less ancillary data. The DTC method can normalize the LST to any specific time and generate temporally continuous LSTs, while the PR method can only do relative normalization. This study is helpful to reduce the temperature differences of different airborne stripes and obtain airborne LSTs with both high spatial and temporal resolutions.

Thermal cameras are helpful instruments for measuring surface temperatures in field experiments. However, previous studies haven't detailed the method of deriving component temperatures of vegetation and soil over heterogeneous surfaces. In addition, the sources contributing to uncertainties in the derived component temperatures require further investigation. We present a study wherein the component temperatures of a dynamically developing maize field were derived from thermal images. The sources influencing the derived component temperatures have been investigated and different parameterization schemes for atmospheric downwelling radiation have been compared. The results demonstrate that the thermal cameras provide a feasible method of deriving the component temperatures. If the thermal camera is mounted at approximately 30 m above the target and then the atmospheric upwelling radiation and transmittance is ignored, a 1.0-2.0 K error for the component temperatures may occur.

The determination of the spatial heterogeneity of the regional evapotranspiration over a complex underlying surface in an oasis-desert region is crucial for water resource management in a river basin and aiding in irrigation decisions. The surface energy balance system (SEBS) model has been widely used to estimate surface energy fluxes. However, the parameterization of surface roughness length for momentum transfer (z0m) and heat transfer (z0h) did not perform well for a complex underlying surface. Moreover, it is difficult to estimate surface soil heat flux, i.e., G0, accurately at the regional scale. In this letter, the parameterization schemes of z0m, z0h, and G0were optimized. Measurements from 21 sets of eddy covariance systems were used to validate the model performance. The results show that the revised SEBS model root-mean-square errors (RMSEs) of the satellite-based sensible and latent heat fluxes (H and LE) decreased from 97.2 W · m-2to 56.9 W · m-2and from 102.9 W · m-2to 74.8 W · m-2, respectively, at the footprint scale. At the pixel scale, the RMSEs of the revised model estimates of the H and LE were 40.9 W · m-2and 57.5 W · m-2, respectively. The improved agreements between the estimates and the measurements indicate that the revised SEBS model is appropriate for estimating regional energy fluxes over heterogeneous oasis-desert surfaces. Furthermore, the spatial and temporal patterns of the LE in the middle reaches of the Heihe River were investigated.

The two-source energy balance (TSEB) model was successfully applied to estimate evaporation (E), transpiration (T), and evapotranspiration (ET) for land covered with vegetation, which has significantly important applications for the terrestrial water cycle and water resource management. However, the current composite temperature separation approaches are limited in their effectiveness in arid regions. Moreover, E and T are difficult to measure on the ground. In this letter, the ground-measured soil and canopy component temperatures were used to estimate E, T, and ET, which were better validated with observed ratios of E (E/ET%) and T (T/ET%) using the stable oxygen and hydrogen isotopes, and the ET measurements using an eddy covariance (EC) system. Our results indicated that even under the strongly advective conditions, the TSEB model produced reliable estimates of the E/ET% and T/ET% ratios and of ET. The mean bias and root-mean-square error (RMSE) of E/ET% were 1% and 2%, respectively, and the mean bias and RMSE of T/ET% were -1% and 2%, respectively. In addition, the model exhibited relatively reliable estimates in the latent heat flux, with mean bias and RMSE values of 31 and 61 W · m-2, respectively, compared with the measurements from the EC system. These results demonstrated that a robust soil and vegetation component temperature calculation was crucial for estimating E, T, and ET. Moreover, the separate validation of E/ET% and T/ET% provides a good prospect for TSEB model improvements.

The environmental monitoring of earth surfaces requires land surface temperatures (LSTs) with high temporal and spatial resolutions. The disaggregation of LST (DLST) is an effective technique to obtain high-quality LSTs by incorporating two subbranches, including thermal sharpening (TSP) and temperature unmixing (TUM). Although great progress has been made on DLST, the further practice requires an in-depth theoretical paradigm designed to generalize DLST and then to guide future research before proceeding further. We thus proposed a generalized paradigm for DLST through a conceptual framework (C-Frame) and a theoretical framework (T-Frame). This was accomplished through a Euclidean paradigm starting from three basic laws summarized from previous DLST methods: the Bayesian theorem, Tobler's first law of geography, and surface energy balance. The C-Frame included a physical explanation of DLST, and the T-Frame was created by construing a series of assumptions from the three basic laws. Two concrete examples were provided to show the advantage of this generalization. We further derived the linear instance of this paradigm based on which two classical DLST methods were analyzed. This study finally discussed the implications of this paradigm to closely related topics in remote sensing. This paradigm develops processes to improve an understanding of DLST, and it could be used for guiding the design of future DLST methods.

Changing trajectory of urban heat island (UHI) in Beijing from 2004 to 2008 was determined by Gaussian model using daily MODIS/LST data for exploring the general variation of UHI location and distribution characteristics. The results showed that the daytime UHI centroid annually moved along southwest-northeast direction during the period and seasonally varied with a northeast-southwest direction in a large range, while the nighttime UHI in April-June and October-December had a southward trend and that in other months moved along east-west direction near the city core. The spatial characteristics of the summer daytime UHI was primarily correlated with NDVI, while that of the nighttime UHI was mainly related to NIR/SW albedo in 2004-2005, but to NDVI after 2006. Finally, a considerable expanse of UHI was observed in the day of 2007, and a contrast seasonal change of UHI magnitude, extent and volume happened between the day and night.

Land surface net radiation is one of critical factors controlling variable land surface processes. Because of the contradiction between spatial resolution and temporal resolution, it is difficult to estimate the hourly or half-hourly surface net radiation with high spatial resolution (<;100m) using remote observation. A simple approach is proposed to retrieve the surface net radiation with high spatial resolution of 30m using multi-source remote sensing data (MODIS atmospheric products and ASTER surface products) under clear sky. The sinusoidal diurnal cycle function for land surface temperature is applied to the net radiation to estimate the hourly net radiation. The estimation results of instantaneous and hourly net radiation show good agreement with field meteorological datum.

Soil and vegetation temperature separation (SVTS) is a crucial process in various fields, such as the study of evapotranspiration. An operational and novel algorithm for separating soil and vegetation temperatures from sensors that feature a single thermal channel was developed to analyze high-heterogeneity croplands. The a priori knowledge on interrelationships among neighboring pixels was coupled to both the radiation transfer equation and the conceptual thermal anisotropic model to increase the solvability of forward anisotropic models through the Bayesian theorem. Model sensitivity analysis suggests that component fractions and reference temperatures are the two main factors that control the accuracies of the inversion results. Some validation options, which include the air temperature data from local weather stations, computer simulations, up-scaling techniques, and intercomparisons among different approaches, were selected as indirect techniques in verifying the inverted results. These results demonstrated that the proposed inversion technique reached an acceptable level of accuracy and stability, which highlights the practicalities of monowindow thermal sensors in the SVTS.

Land surface temperature (LST) plays an important role in many fields. However, thermal bands in prevailing sensors that are onboard satellites have limited spatial resolutions, which seriously impede their potential applications. Many approaches that aim to downscale thermal imageries to finer spatial resolution levels have been developed in recent years. This paper managed to construct a Generalized Theoretical Framework from an Assimilation Perspective for them with semiempirical regression and modulation integration techniques. Based on three hierarchical sharpening levels, which include digital number, radiance, and surface temperature, many of them can be brought into such a unified framework as derivatives. Two typical land cover patterns were chosen as case study areas to evaluate the capabilities of various kernels to represent the LST distribution. The results demonstrate that there are great discrepancies among those kernels. The single-band kernels are dependent on different land cover types, while the band-derivative kernels perform better in most circumstances when portraying the LST variations. In addition, the simulated imageries that were resampled by scaling up the original thermal bands with an aggregation technique were utilized to validate a localization approach of temperature vegetation dryness index (TVDI). The results indicate that the TVDI has satisfactory effects when depicting slight LST variations due to soil anomalies. More intercomparisons between the approach presented here and other different methods, including artificial neural network and Gram-Schmidt techniques, were made thoroughly, coupling with the Moderate Resolution Imaging Spectroradiometer and Advanced Spaceborne Thermal Emission Reflection Radiometer data. Consequently, the generalized framework opens up the foreground for sharpening thermal images with high efficiency over a solid theoretical foundation.

Land surface temperature (LST) products of Beijing, China, during 2001-2008 provided by Terra/Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) are used to characterize the relationships between urban heat island (UHI) magnitude and the rural surface temperature (RST). Two methods for calculating the UHI magnitude and the RST are applied. Results show that UHI magnitudes are significantly related to the RST, with positive correlation in the daytime and negative correlation in the nighttime. Evident lag correlations are found between the daytime UHI magnitude and the nearby rural surface temperature, because of the differences of vegetation abundance and surface moisture between the urban and rural areas. However, similar lag relationships are not found between the nighttime UHI magnitude and the corresponding rural surface temperature.