Sun Elevation Angle Research Articles

Assessment and Validation of Land Surface Temperature Retrieval Algorithms Using Landsat 8 TIRS Data in Antarctic Ice-Free Areas

This study evaluates the effectiveness of different Land Surface Temperature (LST) retrieval algorithms applied to Landsat 8 Thermal Infrared Sensor (TIRS) data in the ice-free regions of the Antarctic Peninsula. The primary objective is to determine the most accurate algorithm for LST estimation in these environments. Three algorithms, namely radiative transfer equation (RTE), single channel (SC), and mono window (MW), were utilised and compared to in-situ measurements at two locations in the northern part of James Ross Island (JRI), Antarctic Peninsula. The study considered various factors influencing LST accuracy, including land surface emissivity, atmospheric conditions, and sun elevation angles. The findings reveal that all three algorithms demonstrate significant sensitivity to emissivity. The MW algorithm emerged as the most suitable, showing the lowest root mean square error (RMSE) of 3.06 °C, followed by the SC and RTE algorithms with RMSE values of 3.68 and 3.98 °C, respectively. The study also underscores a strong positive correlation between LST retrieval accuracy and sun elevation angle, with more accurate results obtained from satellite images acquired in February, characterised by lower sun elevation angles. No significant relationship with water vapour content in the atmosphere was identified during the investigated period.

Investigating perforated egg crates as dynamic shading systems for open-plan offices in tropical climates

This study explores the use of perforated egg crates as a dynamic shading system to improve daylight quality by achieving better uniformity in open-plan offices. Glazed curtain walls, although a popular choice in modern open-plan offices, can cause problems especially in tropical climates, such as glare and heat particularly near the windows. As egg crates (EC) generally perform better as shading devices than plain horizontal or vertical systems, this study simulated three different combinations of perforated, solid, overlapping and non-overlapping egg crate models for an existing open-plan office building in Indonesia. Daylight measurements of the existing horizontal shading device without any egg crate (NO-EC) were first recorded. Subsequent simulations of non-overlapping perforated (EC-NP) egg crate models with different blade widths and blade rotation angles in accordance with the sun elevation angles on specified working hours were performed. Similar simulations were then performed for two combinations: other combinations of overlapping solid (EC-OS) and overlapping perforated (EC-OP) egg crate models. Finally, a full-scale site mock-up of the EC-NP and NO-EC were tested under real-life climatic conditions. Results show that EC-NP provides better uniformity than the other two combinations, which is within the range of 0.59–0.99. This is due to the deeper penetration of daylight achieved by the overlapping placement of the horizontal blades in front of vertical blades, while keeping the building envelope shaded.

The Use of Deep Learning Methods for Object Height Estimation in High Resolution Satellite Images.

Processing single high-resolution satellite images may provide a lot of important information about the urban landscape or other applications related to the inventory of high-altitude objects. Unfortunately, the direct extraction of specific features from single satellite scenes can be difficult. However, the appropriate use of advanced processing methods based on deep learning algorithms allows us to obtain valuable information from these images. The height of buildings, for example, may be determined based on the extraction of shadows from an image and taking into account other metadata, e.g., the sun elevation angle and satellite azimuth angle. Classic methods of processing satellite imagery based on thresholding or simple segmentation are not sufficient because, in most cases, satellite scenes are not spectrally heterogenous. Therefore, the use of classical shadow detection methods is difficult. The authors of this article explore the possibility of using high-resolution optical satellite data to develop a universal algorithm for a fully automated estimation of object heights within the land cover by calculating the length of the shadow of each founded object. Finally, a set of algorithms allowing for a fully automatic detection of objects and shadows from satellite and aerial imagery and an iterative analysis of the relationships between them to calculate the heights of typical objects (such as buildings) and atypical objects (such as wind turbines) is proposed. The city of Warsaw (Poland) was used as the test area. LiDAR data were adopted as the reference measurement. As a result of final analyses based on measurements from several hundred thousand objects, the global accuracy obtained was ±4.66 m.

Evaluation of UAV multispectral cameras for yield and biomass prediction in wheat under different sun elevation angles and phenological stages

Quantitative trait prediction using multispectral UAV imagery is gaining popularity in field trials. However, the reliability of models is influenced by the type of camera and its consistency under different light conditions. In this study, we investigate the comparability of two popular multispectral cameras, the Phantom 4 Multispectral (P4M) and the Micasense RedEdge-M (RedEdge-M), for wheat yield and biomass prediction under varying sun angles and phenological changes. Our results indicate that the P4M camera produces more stable values for wavelength bands and derived vegetation indices (VIs) than the RedEdge-M camera under different sun angles. However, the Green Normalized Difference Vegetation Index (GNDVI) showed minimal anisotropic reflectance effect and was consistently correlated with yield, regardless of the camera type. Both cameras were found suitable for yield and biomass prediction, with P4M being more robust against sun angle and providing more stable results. The maximum R2 values for yield were 0.71 and 0.68 in season 2020, and 0.66 and 0.64 in season 2021 for RedEdge-M and P4M, respectively. For biomass prediction, the maximum R2 values were 0.73 and 0.71 for dry biomass and 0.62 and 0.57 for fresh biomass for RedEdge-M and P4M, respectively. These findings have implications for researchers and practitioners using multispectral UAV imagery for quantitative trait prediction in field trials.

Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.

An Indoor Microwave Radiometer for Measurement of Tropospheric Water

This paper presents the first detailed description of the innovative measurement set up of an indoor tropospheric microwave radiometer (TROWARA) that avoids water films on radome. We discuss the performance of a commercial outdoor microwave radiometer (HATPRO) for measuring tropospheric water parameters in Bern, Switzerland. The HATPRO is less than 20 meters from the TROWARA and has different instrument characteristics. Brightness temperatures measured by HATPRO are analyzed by comparing them with coincident measurements from TROWARA and radiative transfer simulations based on the ECMWF operational analysis data (denoted as RTSE). To find the source of brightness temperature bias, a gradient boosting decision tree is used to analyze the sensitivity of eight feature factors to bias. Data processing routines of the two radiometers use different algorithms to retrieve integrated water vapour (IWV) and integrated cloud liquid water (ILW), whereas the same physical algorithms based on the radiative transfer equation are applied to obtain the opacity and rain rate. Using 62 days of data with varied weather conditions, it was found that TROWARA brightness temperatures are in good agreement with RTSE. HATPRO brightness temperatures are significantly overestimated by about 5 K at 22 GHz, compared to TROWARA and RTSE. HATPRO brightness temperatures at 31 GHz agree well with TROWARA and RTSE (within about +/-1 K). The overestimated brightness temperatures in the K-band and the HATPRO retrieval algorithm lead to an overestimation of IWV and ILW by HATPRO. The opacities at 31 GHz match very well for TROWARA and HATPRO during no rain with a verified R2 of 0.96. However, liquid water floating or remaining water films on the radome of the outdoor HATPRO radiometer induce an overestimation of the rain rate. The physical reason for the overestimated 22 GHz brightness temperatures of the HATPRO is mainly the result of the combined effect of instrument calibration, the surrounding environment of the instrument, and the sun elevation angle. This can be a problem with the Generation 2 HATPRO radiometer and this problem was resolved in the Generation 5 HATPRO radiometer.

Bias-Correction of Diffuse Solar Irradiance Modeled Through Parametric Models

Abstract In this paper, a bias-correction of clear-sky models in prediction of the diffuse solar irradiance is presented. High quality ground date from two networks is used for model input and validation: aerosol optical depth from AERONET and diffuse solar irradiance from BSRN. Four established clear-sky models are tested: REST2, Yang, PS and MAC2. Two types of bias-corrections are developed: i) a correction for multiple aerosol scattering (MSC), accounting for extinction at high turbidity, and ii) a general regression on the model errors as a function of Sun elevation angle (h) and turbidity (β) correction (MOS). While the impact of MSC proves marginal, the MOS correction shows good results. Two versions of MOS are developed, one on the global dataset and a site-specific adaptation. Both versions bring significant improvements over the original implementation of the models, while the site-specific version reduces the errors with a few additional percentage points. The aggregate nRMSE of the REST2 model is reduced from 17.8% (no correction) to 11.3% (global MOS) and 9.4% (site-specific MOS).

Mapping of Geological Lineaments from Different Sun Elevation Angles Using Digital Elevation Model (DEM): A Case Study of Nigeria Geology Map Series Sheet 135 (Duhu Sheet)

The study focuses on the mapping of geological lineaments from the following different sun elevation angles: 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° of the Digital Elevation Model (DEM). Four of the images with azimuth; 0°, 45°, 90° and 135° were combined to produce a single image with multi-illumination directions. The images with azimuth; 180°, 225°, 270°, and 315° were equally combined to produce another single shaded relief image with multi-illumination directions. The two multi-illumination direction images were used for the automatic lineament extraction using PCI Geomatica software. The extracted lineaments were overlaid, and duplicate lineaments were eliminated. The final lineaments of the study area were screened using high-resolution Google images for non-geological features. A total of 237 lineaments were extracted, with a total length of 649.77Km, a mean length of 2.742Km and standard deviation of 1.187. The dominant trending pattern in the final lineaments of the study area is NE-SW direction. The Lineament density for the study area ranges from 0 to 1.36 km/sqkm with about 69.1% of the study area being mapped as poor and low density while the high and very high densities area account for just about 30.9% of the study area.

HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model

Abstract. Snow cover plays an essential role in climate change and the hydrological cycle of the Tibetan Plateau. The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) snow products have two major issues: massive data gaps due to frequent clouds and relatively low estimate accuracy of snow cover due to complex terrain in this region. Here we generate long-term daily gap-free snow cover products over the Tibetan Plateau at 500 m resolution by applying a hidden Markov random field (HMRF) technique to the original MODIS snow products over the past two decades. The data gaps of the original MODIS snow products were fully filled by optimally integrating spectral, spatiotemporal, and environmental information within HMRF framework. The snow cover estimate accuracy was greatly increased by incorporating the spatiotemporal variations of solar radiation due to surface topography and sun elevation angle as the environmental contextual information in HMRF-based snow cover estimation. We evaluated our snow products, and the accuracy is 98.29 % in comparison with in situ observations, and 91.36 % in comparison with high-resolution snow maps derived from Landsat images. Our evaluation also suggests that the incorporation of spatiotemporal solar radiation as the environmental contextual information in HMRF modeling, instead of the simple use of surface elevation as the environmental contextual information, results in the accuracy of the snow products increases by 2.71 % and the omission error decreases by 3.59 %. The accuracy of our snow products is especially improved during snow transitional period, and over complex terrains with high elevation and sunny slopes. The new products can provide long-term and spatiotemporally continuous information of snow cover distribution, which is critical for understanding the processes of snow accumulation and melting, analyzing its impact on climate change, and facilitating water resource management in Tibetan Plateau. This dataset can be freely accessed from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272204 (Huang and Xu, 2022).

Analysis of boulders population around a young crater using very high resolution image of Orbiter High Resolution Camera (OHRC) on board Chandrayaan-2 mission

The Orbiter High Resolution Camera (OHRC) is a very high spatial resolution panchromatic camera (0.45–0.70 μm) on-board Chandrayaan-2 orbiter. Its spatial resolution of 0.25 m from 100 km altitude is highest among all lunar orbiter missions. A simple crater with substantial boulder population was observed in an OHRC image of a region near Boguslawsky E crater. Boulders are distinctly seen in this image because of high spatial resolution of 0.28 m and low sun elevation angle (6°) which enhanced the boulders' shadows. We have identified and mapped >2000 boulders around this young un-named simple crater (74.9216° S, 54.5148° E). It is observed that the OHRC is capable of extending the lower limit of size for identifiable boulders below 1 m. The distributions of mapped boulders are studied and compared with previous studies. It was found that the coefficient values estimated by fitting power laws to various distributions, such as size-frequency, size-range, etc., are well within the ranges reported in literature for craters distributed on lunar surface around the landing sites. Weibull distribution was also fit to the data, and the fitting coefficients were compared with the values obtained in similar studies. The crater age was estimated to be in the range of 50‐90 Ma using empirical relations, and comparison with areal density of other craters near lunar landing sites. This study also provides a glimpse of the low-light imaging capability of the OHRC showing inside the shadow regions, which were illuminated by reflected light from adjoining areas.

Integration of Reflector to Enhance Energy Production of PV Collector

This paper presents a theoretical study to determine the capacity of collecting solar energy, thus improving the electrical performance of a photovoltaic collector in a horizontal position mounted by a mirror. In order to validate the presented results, they have been compared with experimental results found in literature by respecting the same conditions and the same properties of the system employed in the experimental study. By combining a reflecting mirror and a photovoltaic collector, the solar radiation collected, which depends on the optimal orientation of the mirror, is improved. Therefore, the aim of this work is to find the optimal angles of the mirror, which depends on the sun elevation angle, the azimuth angle for typical days. Consequently, after a critical analysis of the sum of daily solar radiation collected by the solar collector with and without the reflector, a significant increase in solar radiation has been noticed using the reflector.

Ideal Data to Determine Accurate Fajr Time

Early fajr time research was carried out by various parties with various techniques, one of which was using a sky quality meter (SQM) photometric tool. Observational data from various regions that have varying night levels result in a varying early fajr time as well. By paying attention to the effect of sky quality represented by the night level at the observation location, this research wants to answer whether the 20 mpsas night level limit is ideal data by looking at the correlation coefficient between night level and the turning point solution. From 1068 data with varying night levels, the correlation coefficient ( ) between the night level and the turning point solution is 0,42 which means there is an effect, while for data with a minimum night level of 20mpsas the correlation coefficient is 0,07 which means there is no influence. Based on the results of the analysis, the night level of 20mpsas can be the minimum limit for conducting an ideal early fajr time research. From 241 ideal observation data from 6 LAPAN observation stations, early fajr time presents when the Sun's elevation angle is -16,51°. Early fajr time is also the beginning of subuh prayer time, with its standard used in Indonesia, which is -20° or 3,49° different from the analysis results, if it is converted there is a difference of 13 minutes 57 seconds.

Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems

Sky imaging systems use lenses to acquire images concentrating light beams in a sensor. The light beams received by the sky imager have an elevation angle with respect to the device normal. Thus, the pixels in the image contain information from different areas of the sky within the imaging system field of view. The area of the field of view contained in the pixels increases as the elevation angle of the incident light beams decreases. When the sky imager is mounted on a solar tracker, the light beam's angle of incidence in a pixel varies over time. This investigation formulates and compares two geospatial reprojections that transform the original euclidean frame of the imager plane to the geospatial atmosphere cross-section where the sky imager field of view intersects the cloud layer. One assumes that an object (i.e., cloud) moving in the troposphere is sufficiently far so the Earth's surface is approximated \emph{flat}. The other transformation takes into account the curvature of the Earth in the portion of the atmosphere (i.e., voxel) that is recorded. The results show that the differences between the dimensions calculated by both geospatial transformations are in the order of magnitude of kilometers when the Sun's elevation angle is below $30^\circ$.

Optimization of Fixed Photovoltaic Panel “Tilt” Angles for Maximal Energy Harvest Considering Year-Around Sky Coverage Conditions

Abstract The fixed setup angles of photovoltaic (PV) panels are typically optimized properly in order to maximize the electrical energy harvest. In the present work, the sunlight availability or sky coverage conditions of sufficiently small time intervals for everyday around the year are counted in the modeling for computation of solar energy on the PV panel in order to optimize the panel tilt angles. Maximal energy harvest in a year is the objective of choosing the optimal tilt angles. The analysis calculates vectors of instantaneous sunray and solar panel normal direction to consider the ‘cosine’ effect. The energy harvested in every 6 min by a PV panel of per square meter is summated for day-long period from sunrise to sunset when the sun elevation angle is above 5 deg. The general model is applied to the authors’ local city, Tucson, Arizona, USA. The results show that the annual solar energy received by a solar panel tilted with a fixed angle of equal to the local latitude could reach to 2297 kWh/m2 with the 10-year averaged sky coverage conditions of every 6 min considered. However, if a PV panel is inclined using the discovered optimal angles with two times, four-seasonal, and monthly adjustments, the gain in the yearly solar energy harvest can be 7.59%, 7.60%, and 9.19%, respectively, greater than that with the fixed angle equal to local latitude.

A Spherical Temperature Sensor Array Design for Near-Surface Atmospheric Temperature Studies

AbstractThe observation accuracy of the surface air temperature less than 0.1 K is a requirement, stated by the meteorological and climatological community. However, the accuracy of a temperature sensor inside a shield is affected by a number of factors including solar radiation, wind speed, upwelling longwave radiation, air density, sun elevation angle, sun azimuth angle, underlying surface, precipitation, moisture, structure, and coating of the radiation shield. Due to these factors, the temperature error of the temperature sensor may be much larger than 1 K under adverse conditions. To improve the observation accuracy, this paper proposed a spherical temperature sensor array. A series of analytical calculations based on a computational fluid dynamics (CFD) method is performed to verify the design principle of this sensor array. The calculation results show that the temperature error ratio can be assumed as a constant. To verify the accuracy of this sensor array, simulations and observation experiments are conducted. The simulation results show that the mean difference between the temperature provided by this sensor array and the reference air temperature is 0.072 K. The field experiment results show that a root-mean-square error (RMSE) and a mean absolute error (MAE) between the temperature provided by this sensor array and the reference air temperature are 0.173 and 0.153 K, respectively.

Diurnal and crepuscular activity during fall migration for four species of aerial foragers

Most migratory birds, particularly passerines or songbirds, migrate at night. However, diurnal migration has many benefits, including the ability to use a fly-and-forage migration strategy that allows an individual to refuel while migrating. Despite these benefits, very little research has been undertaken on diurnal migrants, including aerial foragers, who can refuel on the wing. In this study, we use the Motus Wildlife Tracking System to examine the timing of Bank Swallow (Riparia riparia), Barn Swallow (Hirundo rustica), Cliff Swallow (Petrochelidon pyrrhonota), and Tree Swallow (Tachycineta bicolor) movements during fall migration. Using these data, we confirm the “common knowledge” that these species of aerial foragers are diurnal migrants. During fall migration, 88.2% detections across all species were during the day, and the remaining detections were during civil twilight (range of sun elevation angles: –5.4° to 0°). Most of the detections during the day were consistent with migratory movement, and most detections during civil twilight were consistent with movements to and from communal roosts. Collectively, these results indicate that during fall migration, these 4 swallow species migrate during the day and, like other aerial foragers, may use a fly-and-forage migration strategy.

Estimation of antenna phase center offsets for BeiDou IGSO and MEO satellites

Based on a global GNSS tracking network, the B1I/B3I ionospheric-free phase center offsets (PCOs) are estimated for BeiDou Inclined Geosynchronous Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. Peak-to-peak variations of up to several decimeters or even meters that depend on the solar radiation pressure (SRP) model and the sun elevation angle above the orbital plane (β angle) are found in the daily x-offset estimates. Different SRP modeling has a negligible effect on the y-offset estimation, but the scatter of y-offset estimates depends on the β angle. The z-offset estimates do neither exhibit a dependence on the SRP model nor the β angle, but they have a larger scatter than x-offset and y-offset estimates. By comparing the performance of different SRP models in the estimation of BeiDou PCOs, the SRP models suitable for the estimation of BeiDou-2 and BeiDou-3 PCOs are determined to solve final PCOs. The BeiDou satellite PCO estimates are compared with the model values currently used within the International GNSS Service, the manufacturer values published by the Test and Assessment Research Center of China Satellite Navigation Office, and a set of BeiDou-3 MEO PCO estimates previously reported by the Chang’an University. Finally, it is demonstrated that the orbit quality of BeiDou IGSO/MEOs and the accuracy of station coordinates obtained from BeiDou-only precise point positioning benefit from the newly estimated BeiDou PCO model.

Global Autonomous Positioning in GNSS-Challenged Environments: A Bioinspired Strategy by Polarization Pattern

How to determine the absolute position of users, such as underwater autonomous vehicles, ships or other long-range vehicles, is a fascinating and challenging issue, especially in global navigation satellite system -denied environments. In this article, a bioinspired global autonomous positioning system based on polarized skylight is proposed. By comparing with the related literatures, the system is able to global positioning based on the degree of polarization pattern (DoP) pattern. To be more specific, in an attempt to use the DoP pattern for the geolocalization purposes, a bioinspired polarization compass is designed and in addition, an overall maximum DoP (MDoP) determination algorithm is presented. Moreover, on the basis of the MDoP value, the sun elevation angle can be ultimately measured by using the DoP pattern. The sun elevation angle difference algorithm is used to deduce the absolute position of users. Finally, the bioinspired global autonomous positioning system is developed and validated through experiments. The test results show that the location errors are in range of few kilometers, of which the latitude accuracy is 0.113° (1σ), and the longitude accuracy is 0.082° (1σ).

Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow.

Research on empirical correction models of GPS Block IIF and BDS satellite inter-frequency clock bias

Triple-frequency observations will introduce an inter-frequency clock bias (IFCB) between the new frequency and the original dual-frequency observations. It has been verified that satellite IFCB can reach dozens of centimeters and several centimeters for GPS Block IIF satellite and BDS satellite, respectively. The existence of satellite IFCB will significantly affect undifferenced triple-frequency data processing. Based on 4-year data collected from 80 globally distributed stations, the long-term characteristics of IFCB coefficients obtained by using harmonic analysis have been studied. The results demonstrate that the coefficients of IFCB periodic model cannot be well fitted only by using sun elevation angle. Also, coefficients have obvious periodic characteristics and their periods differ among different satellites. Thus, a new linear-plus-periodic model is proposed to fit the long-term coefficients. Then, IFCB empirical correction models for 12 GPS Block IIF satellites and BDS GEO and IGSO satellites are built. In order to validate the correction model, IFCB standard deviation (STD), triple-frequency precise point positioning (PPP) and undifferenced extra-wide-lane (EWL) ambiguity resolution are employed. The results based on more than 4-year observations show that, with correction model applied, the average IFCB STD decreases by about 65.5% and 45.5% for GPS and BDS satellites, respectively. Compared to triple-frequency PPP without IFCB correction, triple-frequency PPP results with IFCB correction show that Up, North and East components accuracy are improved by 12.3%, 16.0% and 13.2%, respectively. Besides, IFCB correction will greatly improve the consistence of EWL fractional cycle bias among different stations and improve the success rate of EWL ambiguity resolution.