Land surface temperature (LST) products require rigorous validation before widespread application, and the spatial representativeness of ground validation sites plays a critical role in ensuring the reliability of validation results. Therefore, accurately assessing the spatial representativeness of ground sites is essential for credible validation outcomes. However, existing studies often focus on a small number of sites within confined regional observation networks, and generally evaluate representativeness at a single spatial scale. To address these limitations, this study conducts a comprehensive evaluation of 211 sites from five observation networks globally. In order to estimate representativeness across multiple spatial scales corresponding to typical LST products (i.e., 1 km, 3 km, 5 km, and 10 km), a novel spatial representativeness assessment model is proposed. This model, leveraging long-term Landsat LST data and the Random Forest method, quantifies the relationship between spatial representativeness error and spatial scale, enabling seamless spatial representativeness evaluations for each site. Based on this framework, 24 sites that demonstrate consistently high representativeness across all scales and seasons are identified as optimal validation sites. Furthermore, this study proposes two site selection strategies: one prioritizing temporal stability, which identifies 44 sites ensuring representativeness across all seasons, and the other emphasizing spatial coverage, which selects 38 sites to guarantee representativeness at different scales. These findings provide valuable guidance and references for future LST product validation efforts.

In order to correct the land surface temperature (LST) errors caused by angular effects to normalize the multisource data, various models have been proposed, especially the kernel-driven model shows good prospects for development. However, there are fewer studies on the validation of thermal radiation directionality (TRD) characteristics of features based on real measurements. In this article, a method based on unmanned aerial vehicle (UAV) observation of TRD characteristics of different surfaces is proposed to validate the TRD, which uses the circling flight mode to obtain the thermal radiation characteristics of features in multiple directions. Observations show that there is a significant hotspot effect in the three vegetation scenes, and the TRD and dispersion degree are different due to the differences in vegetation structure. For the realization of further applications of UAV-based methods for measuring TRD, the UAV-based angular observations are evaluated against the simulation results of the current kernel-driven model with high accuracy, and the results show that the correlation coefficients are all greater than 0.78, the coefficients of determination are greater than 0.60, and the root-mean-square error (RMSE) is less than 1. However, the ability of the model to extrapolate forward varies considerably in different vegetation scenes. The present study provides an effective and potential way to observe the TRD, which can lay a practical foundation for the theoretical study and model simulation of the TRD.

The accurate acquisition of land surface temperature (LST) on complex mountainous surfaces has always been a difficult problem and hot topic in thermal infrared remote sensing inversion, and the uncertainty caused by the radiation angle effect is one of the important factors hindering the accurate inversion of LST. Researchers have proposed a variety of models to simulate and eliminate the influence of the angle effect, among which, the kernel-driven model has a bright prospect of development. However, fewer studies have been conducted to observe the effects of terrain and land cover on thermal radiation directionality (TRD) properties based on measured data. This paper intends to carry out observations on a small spatial scale of a complex mountainous area using an unmanned aerial vehicle (UAV) to investigate the specific effects of different slopes, aspects, and land cover on the TRD characteristics. The measured results show that the intensity of thermal radiation anisotropy is actively correlated with the complexity of surface structure, and the influence of slope on TRD bias is in the form of a staged “S”, where its intensity increases slowly and then rapidly before slowing down again; the dispersion of thermal radiation in TRDs of different aspects is affected by the duration and intensity of solar radiation, and there is a time lag effect. Meanwhile, in order to evaluate the accuracy of different radiation directionality models, this paper further evaluates the currently more recognized kernel-drive model named LSF-Chen and the thermal equivalent slope kernel-driven (TESKD) model based on the TRD measurements from UAVs. The results show that the correlation coefficients of simulation results and measurements are all greater than 0.6, and the RMSEs are all less than 2K, and that the two methods both have a better simulation of the TRD effect, but the TESKD is better overall in terms of accuracy and methodological details. Through this study, a new method of applying UAVs to capture the thermal direction of the complex surface in mountainous areas is proposed, which provides methodological support for the extraction and accurate simulation of the TRD characteristics on the complex mountainous areas.

Land surface emissivity (LSE) is a key parameter for estimating longwave radiation of land surface, and mounts of the satellite-based LSE products have been released, generally coupled with Land surface temperature (LST) products. However, few research focus on validation of remote sensing LSE products, particularly over complex and heterogeneous mountainous surface. In this study, two-year field experiments designed for the LSE observation was implemented over typical mountainous regions of southwestern China, using a Model 102 hand-portable Fourier-transform Infrared (FTIR) spectrometer. Through an optimized sampling method, mixed pixel emissivity measurements were obtained to systematically evaluate the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 level-3 daily LSE products, including MOD/MYD11A1 and MOD/MYD21A1 and the Landsat8/9 Collection 2 level 2 LSE products. In this study, the Landsat LSE product shows slightly higher accuracy, with the mean Absolute Bias (Abs_Bias) of 0.0104, compared to MxD11A1 (Abs_Bias = 0.0118) and MxD21A1 (Abs_Bias = 0.0125). Between the two MODIS products, MxD11A1 tends to overestimate LSE with the mean Bias of 0.0115, while MxD21A1 shows a slight underestimation, with the mean Bias of –0.0022. Regarding sensor differences, MxD11A1 shows negligible discrepancies between Terra and Aqua platforms (Bias, Abs_Bias, RMSE < 0.0001) whereas MYD21A1 exhibits larger errors than MOD21A1, with the Abs_Bias and RMSE higher by 0.0029 and 0.0043, respectively, indicating that Terra products generally perform better than Aqua. For daytime and nighttime comparisons, MOD21A1 exhibits minor differences, with Abs_Bias values of 0.0112 and 0.0110, whereas MYD21A1 nighttime product performs better than daytime counterpart, with lower Abs_Bias (0.0122 vs. 0.0157) and RMSE (0.0149 vs. 0.0209). While the Landsat product achieves slightly better overall absolute accuracy, it exhibits spatial artifacts that result in underestimation in affected regions and slight overestimation in unaffected areas. These artifacts also limit its sensitivity to temporal variation. Overall, this study provides a reliable accuracy reference for MODIS and Landsat LSE products over complex and heterogeneous mountainous surfaces, supporting their application and the future improvement of product quality.

Spatiotemporally continuous Land Surface Temperature (LST) is crucial for monitoring extreme weather and providing disaster warnings. It captures abnormal temperature fluctuations, offering timely early warning and response for sudden climate events and natural disasters. However, cloud cover and satellite observation gaps often limit the spatial completeness of LST, while previous reconstruction methods seldom consider the effects of solar radiation and cloud cover on land surface temperature. To address these challenges, this study proposed the All-Weather Real Estimation (AWRE) method, which integrated thermal infrared and passive microwave data with environmental factors to estimate the LST under all-weather conditions. By incorporating deep learning and land surface energy balance models, and analyzing the impact of clouds on temperature fluctuations, the proposed method retrieves all-weather LST. Applied to the 2022 data of China, the AWRE method demonstrated high accuracy in estimating LST. The overall average RMSE and Bias were 2.90 K and 0.56 K, respectively, with daytime and nighttime RMSEs of 2.97 K and 2.83 K, respectively. Specifically, for daytime (nighttime) conditions, the RMSEs under clear sky were 2.94 K (2.58 K), partially cloudy 3.08 K (2.76 K), and fully cloudy 2.9 K (3.14 K). The estimated all-weather LST effectively captured diurnal and seasonal variations, with accuracy comparable to in-situ LST measurements, maintaining temporal continuity. This approach improves the detection of extreme heat events and addresses spatiotemporal coverage gaps, providing more accurate data for climate models, weather monitoring, and public health decisions.

Given the increasing severity of the Surface Urban Heat Island (SUHI) phenomenon, urban residents faced heightened heat stress. Consequently, mitigating the effects of SUHI became critically important to improve urban livability. However, there is a notable deficiency in research pertaining to the effects of SUHI on the health of urban populations. This study analyzed 717 cities globally to examine the interplay between SUHI and heat stress, including potential contributing factors. Our study revealed that arid cities were subjected to more intense thermal stress challenges, while equatorial cities exhibited a higher ratio of heat stress risk. Despite the absence of severe heat stress in cities with snow zone, there was a notable correlation between heat stress and SUHI, indicating a potential exacerbation of this issue in the future. This study provided key insights for precise urban climate adaptation and sustainable planning in cities.

Land cover changes (LCCs) affect surface temperatures at local scale through biophysical processes. However, previous studies on the temperature effects of LCCs, whether the potential impacts of virtual LCCs using the space-for-time assumption or the actual impacts of observed LCCs using the space-and-time scheme, have primarily concentrated on analyzing their spatial distribution patterns. Consequently, the temporal trends of temperature effects due to LCCs are less discussed. This study analyzed the temporal trends of land surface temperature (LST) effects induced by actual LCCs by using long-term European Space Agency land cover data and Advanced Very High Resolution Radiometer LST data. The results show that, from 2001 to 2016, there was a gradual reduction in the count of pixels experiencing LCCs globally in which cultivated land expansion is an important cause of LCC. The LST's response to actual LCCs presented a trend of initial increase followed by a subsequent decrease.

Land surface temperature (LST) is crucial for the energy balance between the Earth’s surface and the atmosphere. Thermal infrared (TIR) and passive microwave (PMW) remote sensing are key methods for acquiring surface temperature globally and regionally. TIR observations have certain limitations due to their inability to penetrate cloud cover. Conversely, PMW measurements partially overcome this drawback to some extent, but their lower retrieval accuracy and coarse resolution limit its wider application. This study developed an artificial intelligence (AI) framework for precise and high-resolution LST estimation from PMW measurements, comprising PMW LST retrieval and downscaling components. Within this framework, high-resolution LST products have been obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2), and the station-based validations and sensitivity analysis have also been conducted on the algorithm. The results were given as follows. First, the GeoFusionNet algorithm achieved higher LST retrieval accuracy than empirical or physical models. The mean absolute error (MAE) was 2.37 K (1.60 K) during daytime (nighttime). Second, the downscaled PMW LST retained high accuracy, with a daytime (nighttime) MAE increase of 0.28 K (0.14 K) compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km product. Station-based validations showed that the coefficient of determination$R^{2}$was above 0.9, with an average root-mean-squared error (RMSE) of 3.4 K (2.4 K) for daytime (nighttime) and an MAE of 2.80 K (1.98 K). Third, sensitivity analysis demonstrated the algorithm’s stable performance, especially in summer and autumn. Spatially, the accuracy remained within 3 K for various land types, including cropland, evergreen forests, and deciduous forests. These results indicate that PMW LST retrieved by this framework has sufficient accuracy and fine-spatial resolution for monitoring dynamic changes in large-scale hydrological, climatic, and agricultural fields.

The validation of remote sensing land surface temperature (LST) data necessitates a comparison between satellite retrieval outcomes andin situobservations. The efficiency ofin situobservations can be ameliorated via analysis and modeling, whereby the heterogeneity ofin situobservations on temporal and spatial scales is central to the analysis. A fresh algorithm has been developed to optimize deployment by relying on the standard deviation of spatial heterogeneity. The validation outcomes indicated that the coefficient of determination (R2) of the five typical surface features at three time points was 0.66, with a root mean square error (RMSE) of 1.99 °C and a mean absolute error (MAE) of 1.62 °C. Moreover, the spatiotemporal heterogeneity character of typical surface features displayed different features, and the LST variation curves of each typical surface feature displayed a similar pattern under sunny conditions. The application of the Savitzky–Golay filtering method reduced errors by 4% of the total errors caused by random errors inin situobservations. With the analysis of the spatiotemporal characteristics of in-situ observation. First, the number of required sites algorithm computed a minimum sampling number of 4. Second, the analysis of the means algorithm computed the 5 optimal points. Additionally, the multipointin situobservations were regularized by standard scores. The optimization of the selected points could be executed to improve the results by eliminating the "distance" points, which are located further away from the multipointin situobserved LST statistical mean. Our outcomes will deepen the comprehension of the spatiotemporal character ofin situobserved LST and enhance the efficiency of equipment with equivalent accuracy.

Accurate estimates of downward surface shortwave radiation (DSSR) are critical for hydrological, biogeochemical, and ecological studies and remote sensing-based estimation of DSSR is an important way to derive DSSR at different spatio-temporal ranges. However, current estimation algorithms usually somewhat rely on atmospheric parameters or in-situ measurements, further blocking the application of these methods. Inspired by the emerging DSSR reanalysis data from the model simulation, this study proposed an integrated method by initializing the estimation model with ERA5 reanalysis data and further refining the estimation through iterative training. The random forest regression method was applied in the estimation model to build the connection between DSSR with the MODIS top-of-atmosphere reflectance, cloud flag, geometry information, elevation, latitude, and coefficient of Sun-Earth distance as input features. To separately consider the impact from cloud cover, the estimation model was established for clear-sky and cloudy-sky conditions, respectively. The proposed method was applied to estimate instantaneous DSSR of MODIS daytime overpasses in the Southwest part of China in 2020. Comparison between the estimates of the initialized model and the finalized model shows that the iterative process improves the DSSR estimates on both spatial distribution and accuracy. Validated by the measurements from nine sites in the study area, the DSSR estimates of the finalized model show a 0.02 higher correlation coefficient (CC) and 7.35 W m-2lower root mean squared error (RMSE) than that of the initialized model. To better evaluate the performance of the proposed method, three popular DSSR products including ERA5, MCD18A1, and Himawari-8 were introduced to make an inter-comparison with the estimation of this study. The validation results showed that the all-sky DSSR estimated in this study had the best accuracy, with a CC of 0.90, a mean bias error of 37.80 W m-2, a RMSE of 125.30 W m-2, and a relative root mean squared error of 42.73%. Obvious improvements can be observed under cloudy-sky and clear-sky conditions, respectively. Because of the simplicity and reliable performance of the proposed method, it shows good potential for DSSR estimation.

Land surface temperature (LST) is a crucial parameter in the study of Land Surface processes. Currently, there are great progresses in LST retrieval based on thermal infrared (TIR) remote sensing. However, TIR-based LST suffers from serious spatial discontinuities due to clouds. Although there are methods developed to address this issue, the methods show high uncertainty in days with extremely clouds. Therefore, this study proposed an integrated method to reconstruct cloudy LSTs using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the China Land Data Assimilation System (CLDAS) LST. This method was separated into two parts according to the ratio of clear-sky pixels (RCP). On days with RCP more than 30%, a random forest reconstruction method was used to establish the complicated relationship between LST and its predicting variables, including solar radiation factor, vegetation index, water index, topographic information and latitude, and then applied to cloudy pixels to derive LSTs. For the rest days, the CLDAS LST was selected to assist the reconstruction via downscaling it to 1 km and then merged with clear-sky data to generate spatially continuous results. The proposed method was applied to the Southwest China and generate daily LST product in 2019. Validation with ground measurements demonstrated a high accuracy with the correlation coefficient changing from 0.73 to 0.88. Additionally, the reconstructed LST dataset exhibits similar temporal variability as existing all-weather satellite-based and reanalysis LST products. The findings reveal that this method shows good potential in generating gap-free LST dataset, especially for the mountain regions with heavy clouds.

With diversified demands for location-based services (LBS), smartphone-based indoor pedestrian positioning becomes a research hotspot in the academic and industrial society. Due to the complexity of the indoor environments and the insufficient accuracy of smartphone inertial sensors, it is still challenging to get an indoor pedestrian positioning solution with stable positioning accuracy and good environmental adaptability. Aiming at this problem, a novel iBeacon deployment scheme for indoor pedestrian is proposed in this paper. Firstly, we introduce an abstract method for complex and diverse plane structures of the indoor environments. Secondly, a mapping function between positioning accuracy and pedestrian walking distance is deduced by error analysis of pedestrian dead-reckoning (PDR) method. Finally, this paper proposes a generation algorithm of iBeacon deployment scheme which not only satisfies the positioning accuracy requirement of LBSs but also greatly reduces the number of deployed iBeacons. We have carried out experimental analysis on three different plane structures of real indoor environments. And it turns out that the proposed iBeacon deployment scheme can help PDR-based indoor pedestrian positioning solutions to achieve breakthrough in indoor environment adaptability.

The systemic validation works were carried out at a watershed scale based on the ground-based observation data of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). Three validation strategies, scaling-up, spatial representation analysis, footprint analysis were used based on different data acquirement techniques. Some studies were performed and four types of remote sensing products were validated. This paper makes a general introduction on the validation results based on these systematic validation activities, which aims to support the integrated study of the water-ecosystem-economy in the Heihe River Basin.

The ground-based sites are critical for assessing the uncertainties and evaluating the accuracy of the satellite-derived land surface temperature (LST) products by using the temperature-based validation (T-based) method. However, there is often a risk of the spatial mismatch between the observations of the satellite-sensor and the ground validation sites. Therefore the heterogeneity analysis of the ground-based sites, which can indicate the representativeness of the sites' observation, has been a basic and key stage in the whole process. The purpose of this paper is to analyses the heterogeneity of the ground sites based on the variogram function. In this study, the variograms of the eighteen sites in the Heihe River Basin (HRB) based on NDVI data were calculated to discuss the heterogeneities of these sites. Then according to the heterogeneity analysis results, these sites were leveled ground observation scale for validating satellite-derived LSTs.

The validation of remote sensing land surface temperature (LST) products is vital for their broad application. Conventional validation methods use ground-based measurements to evaluate the LSTs retrieved from remote sensing data. However, it is difficult to directly validate low or medium spatial resolution LST products because of the scale mismatch between in situ and remote sensing observations. In this letter, we compare two ground-based in situ observations with Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km-resolution LST products (MOD11A1). We focus on the differences in scale between the ground-based measurements and the MODIS observations. A comparison of the results obtained during the daytime with those obtained at night indicates that stronger heterogeneity leads to a greater scale-mismatch effect. Because the LST heterogeneity influences the mismatch effect, semivariance is employed to analyze the heterogeneity of the MODIS 1-km mixed-pixel data using the LSTs retrieved from the high-resolution Thermal Airborne Spectrographic Imager and the Advance Spaceborne Thermal Emission and Reflection Radiometer data.

An evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products is essential for their use in scientific studies. We evaluated the accuracy of MODIS NDVI data that were derived from the MOD09GQ and MYD09GQ products using ground-based measurements from nearly one complete growing season (from May 2 to September 28, 2013) for six land-use types in the upper and middle reaches of the Heihe River Basin. The spatial heterogeneity and scale effects of the NDVI were analyzed using TM8 images of the observation sites. A comparison of the field measurements showed that the MODIS NDVI data were correlated with the in situ data and had an R2of 0.60-0.98. The semivariance analysis results showed that scale effects were common for MODIS data at a pixel resolution of 250 m for corn, potato, rape, and barley crops. Upscaling the in situ NDVI data using high spatial resolution satellite images can improve the validation accuracy of pixel-level NDVI. The results from this letter provide ground-based NDVI data that are required for the calibration and validation of satellite observations and products.

Remotely sensed data have long been seen as the best way to determine spatially distributed evapotranspiration (ET) fluxes owing to their spatial and temporal continuity. A simple biophysical model by using remotely sensed leaf area index data and the Penman-Monteith (PM) equation was introduced to calculate daily ET of grassland and farmland in the middle and upper reaches of Heihe River Basin. The modelled daily ET agrees well with measurements and the R2 is over 0.80. This study confirmed that the PM equation with MODIS LAI can provide reliable estimates of ET at daily time scales and with different ecosystems in arid and cold regions.

Land surface temperature (LST) is a key parameter in climatological and environmental studies [1]. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, can provide global temperature and narrowband emissivity data on a daily basis. However, when the surface is obscured by clouds, the variable cannot be measured directly by using satellite thermal infrared channels, which leads to many invalid value pixels in the MODIS LST products. Methods for calculating LST of the MODIS cloudy pixels are important, yet few studies have been done. The objective of this paper is to estimate the LST values of the cloudy-pixels using the neighboring-pixel approach (NP) and MODIS NSSR (net surface shortwave radiation) product. In this study, the Heihe River Basin was selected as a case study area. The estimation was validated using ground-measured data of Huazhaizi (HZZ) desert station which is covered by homogeneous desert steppe. The validation shows that the reconstruction values of MODIS LSTs can agree well with the ground-measured data, and the biggest absolute error is 2.6K.