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ICESat-2 ATL08 is an important data source for global land surface elevation monitoring, while its accuracy has not been systematically evaluated in the complex terrain areas of central and southern China. Taking high-resolution digital surface models as reference data, this study carries out systematic verification with a total of 949 valid verification points covering 18 typical geomorphological areas in central and southern China. The verification sites cover various terrain types including plains, hills, mountains and alpine canyons. The results show that the average root mean square error of all sites is 3.318 m, ranging from 1.044 m to 5.120 m. Among them, plain areas have the highest accuracy (HS, RMSE = 1.044 m), followed by hilly areas with RMSE of approximately 1.610–3.871 m, and mountainous and alpine canyon areas show relatively poorer accuracy with RMSE of approximately 2.374–5.120 m. The overall mean error (ME) is −1.032 m, with ME values ranging from −4.575 m to +2.548 m across sites. The accuracy of ICESat-2 ATL08 in central-southern China is highly terrain-dependent: RMSE is 1.044 m in the plain site and ranges from 1.610 to 3.871 m in hilly areas and from 2.374 to 5.120 m in mountainous and alpine canyon areas. Therefore, users should consider terrain complexity when applying this product, and post-processing correction incorporating topographic information is recommended for alpine canyon areas where RMSE exceeds 5 m.

The Antarctic Ice Sheet is one of the largest potential contributors to global sea level rise. As mass loss has intensified since the 21st century, there is an increasing urgency for high-precision, full-coverage, and continuous monitoring of the Antarctic. With 250-m spatial resolution and 5-min temporal resolution, the Medium-Resolution Spectral Imager II (MERSI-II) sensor aboard the Fengyun-3D (FY-3D) satellite provides a solid data foundation for Antarctic change detection. However, its geolocation performance in Antarctic has not yet been systematically evaluated. This study proposes a sub-pixel geolocation accuracy evaluation method tailored for 250-m MERSI-II data by integrating frequency-domain cross-correlation and outlier isolation techniques. Using MERSI-II data over major rock outcrop areas of Antarctica and monthly Landsat 8-OLI composites generated via Google Earth Engine (GEE), the method quantifies the Level 1 dataset’s geometric displacements in the along-track and cross-track directions. The results reveal the spatiotemporal characteristics of the FY-3D MERSI-II onboard geolocation accuracy across Antarctica. Overall, MERSI-II exhibits a consistent geolocation bias pattern characterized by the majority of geolocation displacements along the track are negative, while most displacements across the track are positive. The mean along-track error is 87.5 [Formula: see text] 19.5 m, with 96.1% of values below 125 m, while the mean cross-track error is 82.5 [Formula: see text] 15.8 m, with 99.5% below 125 m. These results are in good agreement with previous researches. This study reliably ascertains that the geolocation accuracy of MERSI-II over the Antarctic remains within 1/2 of the Instantaneous Field of View (IFOV).

Digital Elevation Models (DEMs) are fundamental for ensuring the geolocation accuracy of Synthetic Aperture Radar (SAR) imagery, as vertical errors in elevation data propagate into horizontal displacements. This study examines the impact of DEM accuracy on the geolocation performance of Sentinel-1 Ground Range Detected (GRD) products over an urban area in Ankara, Türkiye. A comparative analysis was conducted using four global DEMs (SRTM 1″, SRTM 3″, AW3D30, Copernicus 30 m), a high- resolution national DEM (YÜKPAF), and two interferometrically derived DEMs generated from Sentinel-1 Single Look Complex (SLC) pairs. Vertical accuracy was validated against ICESat-2 ATL08 reference elevations, with Copernicus (2.2 m RMSE) and YÜKPAF (3.2 m RMSE) providing the highest reliability, while the InSAR-derived DEMs showed larger errors (>12 m) due to coherence loss and phase unwrapping inconsistencies. Horizontal accuracy was evaluated using Ground Control Points (GCPs) obtained from HGM-Küre. The results demonstrated that high-quality DEMs, particularly YÜKPAF, achieved the lowest horizontal RMSE (8.2 m), whereas InSAR-based DEMs produced the largest errors, approaching 15 m. Despite these variations, all orthorectified outputs remained below 15 m geolocation error, owing to the precise orbital information of Sentinel-1 and the flat topography of the study area. Overall, the study confirms that DEM quality is a decisive factor for SAR geolocation accuracy and offers practical guidance for dataset selection in operational SAR orthorectification workflows.

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA’s Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure’s reliability.

GaoFen-3 (GF-3) is a multi-polarization C-band synthetic aperture radar (SAR) satellite in China with a resolution of up to 1 m. Up to now, the geolocation accuracy of GF-3 could be improved to 3 m. According to the current study, there still exist meter-level geolocation residuals caused by atmospheric path delay after compensating with a static tropospheric model. In this paper, we compensate the residuals with the sophisticated tropospheric model based on real meteorological data. The experimental results show that the tropospheric model has an accuracy on the millimeter level, which can increase GF-3’s geolocation accuracy to several decimeters compared with the static tropospheric model.

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km).

This paper reviews the development of space-borne laser altimetry technology over the past 40 years. Taking the ICESAT satellite as an example, a rigorous space-borne laser altimeter geolocation model is studied, and an error propagation equation is derived. The influence of the main error sources, such as the platform positioning error, attitude measurement error, pointing angle measurement error and range measurement error, on the geolocation accuracy of the laser spot are analysed by simulated experiments. The reasons for the different influences on geolocation accuracy in different directions are discussed, and to satisfy the accuracy of the laser control point, a design index for each error source is put forward.

The high resolution satellite with the longer focal length and the larger aperture has been widely used in georeferencing of the observed scene in recent years. The consistent end to end model of high resolution remote sensing satellite geometric chain is presented, which consists of the scene, the three line array camera, the platform including attitude and position information, the time system and the processing algorithm. The integrated design of the camera and the star tracker is considered and the simulation method of the geolocation accuracy is put forward by introduce the new index of the angle between the camera and the star tracker. The model is validated by the geolocation accuracy simulation according to the test method of the ZY-3 satellite imagery rigorously. The simulation results show that the geolocation accuracy is within 25m, which is highly consistent with the test results. The geolocation accuracy can be improved about 7 m by the integrated design. The model combined with the simulation method is applicable to the geolocation accuracy estimate before the satellite launching.

Electronic tags have enhanced our understanding of the movements and behavior of pelagic animals by providing position information from the Argos system satellites or by geolocation estimates using light levels and/or sea surface temperatures (SSTs). The ability to geolocate animals that remain submerged is of great value to fisheries management, but the accuracy of these geolocation estimates has to be validated on free-swimming animals. In this paper, we report double-tagging experiments on free-swimming salmon sharksLamna ditropisand blue sharksPrionace glauca, tagged with satellite telemetry and pop-up satellite tags, which provide a direct comparison between Argos positions and geolocation estimates derived from light levels and SSTs. In addition, the Argos-based pop-up satellite tag endpoints and GPS-based recapture locations of Atlantic bluefin tunasThunnus thynnuswere compared with the last geolocation estimates from pop-up satellite and archival tags. In the double-tagging experiments, the root mean square errors of the light level longitude estimates were 0.89 and 0.55°; while for SST latitude estimates, the root mean square errors were 1.47 and 1.16° for salmon sharks and blue sharks respectively. Geolocation estimates of Atlantic bluefin tuna, using archival data from surgically implanted archival tags or recovered pop-up satellite tags, had root mean square errors of 0.78 and 0.90° for light level longitude and SST latitude estimates, respectively. Using data transmitted by pop-up satellite tags deployed on Atlantic bluefin tunas, the light level longitude and SST latitude estimates had root mean square errors of 1.30 and 1.89°, respectively. In addition, a series of computer simulations were performed to examine which variables were most likely to influence the accuracy of SST latitude estimates. The simulations indicated that the difference between the SST measured by the electronic tag and the remotely sensed SST at a given location was the predominant influence on the accuracy of SST latitude estimates. These results demonstrate that tag-measured SSTs can be used in conjunction with light level data to significantly improve the geolocation estimates from electronic tags.