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20 pages, 2314 KiB

Open AccessArticle

Geometric Calibration of Parameters in the Perpendicular-Orbit Circular Scanning Satellite Camera

by Xufeng Zhang, Peng Wang, Wu Xue and Xian Liu

Remote Sens. 2025, 17(3), 472; https://doi.org/10.3390/rs17030472 - 29 Jan 2025

Viewed by 809

Abstract

Perpendicular-orbit circular scanning satellites overcome the conflict between ground resolution and width observed in traditional optical satellites by using a perpendicular-orbit circular scanning imaging method and splicing along the orbit, achieving a balance between an ultra-large width and a high resolution. However, laboratory [...] Read more.

Perpendicular-orbit circular scanning satellites overcome the conflict between ground resolution and width observed in traditional optical satellites by using a perpendicular-orbit circular scanning imaging method and splicing along the orbit, achieving a balance between an ultra-large width and a high resolution. However, laboratory calibrations of perpendicular-orbit circular scanning satellites exhibit large errors due to the influence of factors such as the thermal and mechanical environment of space during the launch and operation of satellites, and thus, they cannot be applied. In this paper, we start by analysing the in-camera azimuth element errors of perpendicular-orbit circular scanning satellites, then derive a probe element pointing angle calibration model from the physical in-camera calibration model and carry out in-camera parameter calibration based on simulated image data from an ultra-wide perpendicular-orbit circular scanning satellite. Edge and centre strips were selected for the experiment, and a certain number of control points were placed uniformly near the middle column (perpendicular orbit) of the image in each strip and covering all row directions (along orbit). Checkpoints were uniformly selected across a range of widths. The results show that in-orbit geometric calibration can significantly improve the direct-to-ground positioning accuracy of perpendicular-orbit circular scanning satellites, with the positioning accuracy error shown to be better than 30 m within a width of 300 km, 30 m within a width of 1000 km, and 50 m within a width of 2000 km. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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19 pages, 3375 KiB

Open AccessArticle

Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching

by Jennifer Leahy, Shabnam Jabari, Derek Lichti and Abbas Salehitangrizi

Remote Sens. 2025, 17(3), 357; https://doi.org/10.3390/rs17030357 - 22 Jan 2025

Cited by 1 | Viewed by 1200

Abstract

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This [...] Read more.

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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31 pages, 8626 KiB

Open AccessArticle

Calibration and Validation of NOAA-21 Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper Sensor Data Record Data

by Banghua Yan, Trevor Beck, Junye Chen, Steven Buckner, Xin Jin, Ding Liang, Sirish Uprety, Jingfeng Huang, Lawrence E. Flynn, Likun Wang, Quanhua Liu and Warren D. Porter

Remote Sens. 2024, 16(23), 4488; https://doi.org/10.3390/rs16234488 - 29 Nov 2024

Viewed by 779

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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18 pages, 8007 KiB

Open AccessArticle

Spectral Response Function Retrieval of Spaceborne Fourier Transform Spectrometers: Application to Metop-IASI

by Pierre Dussarrat, Guillaume Deschamps and Dorothee Coppens

Remote Sens. 2024, 16(23), 4449; https://doi.org/10.3390/rs16234449 - 27 Nov 2024

Viewed by 912

Abstract

In the past decades, satellite hyperspectral remote sensing instruments have been providing key measurements for environmental monitoring, such as the analysis of water and air quality, soil usage, weather forecasting, or climate change. The success of this technology, however, relies on an accurate [...] Read more.

In the past decades, satellite hyperspectral remote sensing instruments have been providing key measurements for environmental monitoring, such as the analysis of water and air quality, soil usage, weather forecasting, or climate change. The success of this technology, however, relies on an accurate knowledge of the instrument’s spectral response functions (SRFs). Usually, the SRFs are assessed on-ground and then monitored on-flight using tedious analysis of the acquired radiances coupled with instrumental models; nonetheless, the complete retrieval of the SRFs is generally out of reach. In this context, EUMETSAT has developed a novel SRF retrieval methodology, with the intention of applying it routinely to the current Metop IASI instruments and soon to Metop-SG IASI-NG, and MTG-S IRS. By making use of spatiotemporal colocations of different detectors within a single instrument or between different platforms, relative SRFs may be retrieved on-flight without any a priori knowledge. The presented methodology is suited for instruments acquiring radiances with contiguous sampling over large spectral bands as the SRFs are retrieved by analyzing the neighboring channels’ correlations. This article focuses on Fourier transform spectrometers (FTS) in the far infrared as they possess these characteristics per design, but it is believed that the method could be extended to other technology and spectral bands. The SRFs are further processed to evaluate the relative self-apodization functions (SAFs), as they represent the discrepancies between the detectors at the interferograms level, the primary measurements of FTS. The following article presents both simulations and applications of the SRF retrieval for the three IASI instruments aboard the Metop platforms of the EPS program. We analyze both IASI sensors aboard Metop-B and C as well as the evolution of Metop-A IASI over 13 years of operation. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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30 pages, 18505 KiB

Open AccessArticle

Identification of Global Extended Pseudo Invariant Calibration Sites (EPICS) and Their Validation Using Radiometric Calibration Network (RadCalNet)

by Juliana Fajardo Rueda, Larry Leigh and Cibele Teixeira Pinto

Remote Sens. 2024, 16(22), 4129; https://doi.org/10.3390/rs16224129 - 5 Nov 2024

Cited by 2 | Viewed by 920

Abstract

This study introduces a global land cover clustering using an unsupervised algorithm, incorporating the novel step of filtering data to retain only temporally stable pixels before applying K-means clustering. Unlike previous approaches that did not assess the pixel-level temporal stability, this method provides [...] Read more.

This study introduces a global land cover clustering using an unsupervised algorithm, incorporating the novel step of filtering data to retain only temporally stable pixels before applying K-means clustering. Unlike previous approaches that did not assess the pixel-level temporal stability, this method provides more reliable clustering results. The K-means identified 160 distinct clusters, with Cluster 13 Global Temporally Stable (Cluster 13-GTS) showing significant improvements in temporal stability. Compared to Cluster 13 Global (Cluster 13-G) from earlier research, Cluster 13-GTS reduced the coefficient of variation by up to 1% and increased the number of calibration locations from 23 to over 50. This study also validated these clusters using TOA reflectance from ground-truth measurements collected at the Radiometric Calibration Network (RadCalNet) Gobabeb (RCN-GONA) site, incorporating data from Landsat 8, Landsat 9, Sentinel-2A, and Sentinel-2B. The GONA Extended Pseudo Invariant Calibration Sites (EPICS) GONA-EPICS cluster used for the validation provided statistically comparable mean TOA reflectance to RCN-GONA, with a reduced chi-square test indicating minimal differences within the cluster’s uncertainty range. Notably, the difference in reflectance between RCN-GONA and GONA-EPICS was less than 0.023 units across all the bands. Although GONA-EPICS exhibited slightly higher uncertainty (6.4% to 10.3%) compared to RCN-GONA site (<5%), it offered advantages such as 80 potential calibration points per Landsat cycle and reduced temporal instability, and it provided alternatives to reduce the reliance on single sites like traditional PICS or RCN-GONA, making it a valuable tool for calibration efforts. These findings highlight the potential of the newly developed EPICS for radiometric calibration and stability monitoring of optical satellite sensors. Distributed across diverse regions, these global targets increase the number of calibration points available for any sensor in any orbital cycle, reducing the reliance on traditional PICS and offering more robust targets for radiometric calibration efforts. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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20 pages, 8679 KiB

Open AccessArticle

Estimation of Infrared Stellar Flux Based on Star Catalogs with I-GWO for Stellar Calibration

by Yang Hong, Peng Rao, Yuxing Zhou and Xin Chen

Remote Sens. 2024, 16(12), 2198; https://doi.org/10.3390/rs16122198 - 17 Jun 2024

Viewed by 1111

Abstract

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, [...] Read more.

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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21 pages, 16282 KiB

Open AccessArticle

Research on Calculation Method of On-Orbit Instrumental Line Shape Function for the Greenhouse Gases Monitoring Instrument on the GaoFen-5B Satellite

by Yunfei Han, Hailiang Shi, Haiyan Luo, Zhiwei Li, Hanhan Ye, Chao Li, Yi Ding, Shichao Wu, Xianhua Wang, Wei Xiong and Chenhui Hou

Remote Sens. 2024, 16(12), 2171; https://doi.org/10.3390/rs16122171 - 15 Jun 2024

Viewed by 894

Abstract

The Greenhouse Gases Monitoring Instrument is based on the spectroscopic principle of spatial heterodyne spectroscopy technology and has the characteristics of no moving parts, a hyperspectral resolution, and a large luminous flux. The instrumental line shape function is one of the most important [...] Read more.

The Greenhouse Gases Monitoring Instrument is based on the spectroscopic principle of spatial heterodyne spectroscopy technology and has the characteristics of no moving parts, a hyperspectral resolution, and a large luminous flux. The instrumental line shape function is one of the most important parameters characterizing the features of the instrument, and it plays a vital role in the system error analysis of the instrument’s measurements. To accurately obtain the instrumental line shape function of a spatial heterodyne spectrometer during the on-orbit period and improve the accuracy of gas concentration retrieval, this study develops a method to model and characterize the characteristics of the instrumental line shape function, including modulation loss and phase error. This study employs the solar calibration spectrum in the 1.568–1.583 μm bands to conduct iterative calculations of the instrumental line shape function error model. After the instrumental line function is updated, the average relative deviation is reduced from 1.83% to 0.756% between the theoretical and measured solar spectra. Additionally, the average relative deviation is reduced from 7.049% to 2.106% between the GMI nadir and theoretical nadir spectra. The findings demonstrate that updating the instrumental line shape function mitigates the impact of variations in the spectrometer’s instrumental line shape due to alterations in the orbital environment. This study offers a dependable reference for both the enhancement and oversight of a spectrometer’s instrumental line shape function, along with an investigation of shifts in instrument parameters. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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19 pages, 4700 KiB

Open AccessArticle

Radiometric Cross-Calibration of GF6-PMS and WFV Sensors with Sentinel 2-MSI and Landsat 9-OLI2

by Hengyang Wang, Zhaoning He, Shuang Wang, Yachao Zhang and Hongzhao Tang

Remote Sens. 2024, 16(11), 1949; https://doi.org/10.3390/rs16111949 - 29 May 2024

Cited by 2 | Viewed by 1190

Abstract

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration [...] Read more.

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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27 pages, 2083 KiB

Open AccessArticle

A Wide-Angle Hyperspectral Top-of-Atmosphere Reflectance Model for the Libyan Desert

by Fuxiang Guo, Xiaobing Zheng, Yanna Zhang, Wei Wei, Zejie Zhang, Quan Zhang and Xin Li

Remote Sens. 2024, 16(8), 1406; https://doi.org/10.3390/rs16081406 - 16 Apr 2024

Cited by 1 | Viewed by 1206

Abstract

Reference targets with stability, uniformity, and known reflectance on the Earth’s surface, such as deserts, can be used for the absolute radiometric calibration of satellite sensors. A wide-angle hyperspectral reflectance model at the top of atmosphere (TOA) over such a reference target will [...] Read more.

Reference targets with stability, uniformity, and known reflectance on the Earth’s surface, such as deserts, can be used for the absolute radiometric calibration of satellite sensors. A wide-angle hyperspectral reflectance model at the top of atmosphere (TOA) over such a reference target will expand the applicability of on-orbit calibration to different spectral bands and angles. To achieve the long-term, continuous, and high-precision absolute radiometric calibration of remote sensors, a wide-angle hyperspectral TOA reflectance model of the Libyan Desert was constructed based on spectral reflectance data, satellite overpass parameters, and atmospheric parameters from the Terra/Aqua and Earth Observation-1 (EO-1) satellites between 2003 and 2012. By means of angle fitting, viewing angle grouping, and spectral extension, the model is applicable for absolute radiometric calibration of the visible to short-wave infrared (SWIR) bands for sensors within viewing zenith angles of 65 degrees. To validate the accuracy and precision of the model, a total of 3120 long-term validations of model accuracy and 949 cross-validations with the Landsat 8 Operational Land Imager (OLI) and Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors between 2013 and 2020 were conducted. The results show that the TOA reflectance calculated by the model had a standard deviation (SD) of relative differences below 1.9% and a root-mean-square error (RMSE) below 0.8% when compared with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 OLI. The SD of the relative differences and the RMSE were within 2.7% when predicting VIIRS data. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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22 pages, 15570 KiB

Open AccessArticle

Time-Series Cross-Radiometric Calibration and Validation of GF-6/WFV Using Multi-Site

by Yingxian Wang, Yaokai Liu, Weiwei Zhao, Jian Zeng, Huixian Wang, Renfei Wang, Zhaopeng Xu and Qijin Han

Remote Sens. 2024, 16(7), 1287; https://doi.org/10.3390/rs16071287 - 5 Apr 2024

Cited by 2 | Viewed by 1831

Abstract

The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these [...] Read more.

The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these quantitative applications. Currently, the absolute radiometric calibration of GF-6/WFV relies primarily on vicarious calibration conducted by the China Center for Resources Satellite Data and Application (CRESDA). However, annual vicarious calibration may not adequately capture the radiometric performance of GF-6/WFV due to performance degradation. Therefore, increasing the frequency of on-orbit radiometric calibration throughout the lifetime of GF-6/WFV is essential. This study proposes a method for conducting long-term cross-radiometric calibrations of GF-6/WFV by taking the multispectral imager (MSI) onboard the Sentinel-2 satellite as a reliable reference sensor and the sites from RadCalNet as reference ground targets. Firstly, we conducted 62 on-orbit cross-radiometric calibrations of GF-6/WFV since its launch by tracking with the Sentinel-2/MSI sensor after correcting the discrepancy spectrum and solar zenith angle. Then, validation of cross-radiometric calibration results against RadCalNet products indicated an average absolute relative error between 3.55% and 4.64%. Cross-validation with additional reference sensors, including Landsat-8/OLI and MODIS, confirmed the reliability of calibration, demonstrating relative differences from GF-6/WFV of less than 5%. Furthermore, the overall uncertainty of the cross-radiometric calibration was estimated to be from 4.08% to 4.89%. Finally, trend analysis of the time-series radiometric performance was also conducted and revealed an annual degradation rate ranging from 0.57% to 2.31%. This degradation affects surface reflectance retrieval, introducing a bias of approximately 0.0073 to 0.0084. Our findings highlight the operational effectiveness of the proposed method in achieving long-time-series on-orbit radiometric calibration and degradation monitoring of GF-6/WFV. The study also demonstrates that the radiometric performance of GF-6/WFV is relatively stable and suitable for further quantitative applications, especially for long-term monitoring applications. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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18 pages, 3027 KiB

Open AccessArticle

Joint Panchromatic and Multispectral Geometric Calibration Method for the DS-1 Satellite

by Xiaohua Jiang, Xiaoxiao Zhang, Ming Liu and Jie Tian

Remote Sens. 2024, 16(2), 433; https://doi.org/10.3390/rs16020433 - 22 Jan 2024

Viewed by 1692

Abstract

The DS-1 satellite was launched successfully on 3 June 2021 from the Taiyuan Satellite Launch Center. The satellite is equipped with a 1 m panchromatic and a 4 m multispectral sensor, providing high-resolution and wide-field optical remote sensing imaging capabilities. For satellites equipped [...] Read more.

The DS-1 satellite was launched successfully on 3 June 2021 from the Taiyuan Satellite Launch Center. The satellite is equipped with a 1 m panchromatic and a 4 m multispectral sensor, providing high-resolution and wide-field optical remote sensing imaging capabilities. For satellites equipped with panchromatic and multispectral sensors, conventional geometric processing methods in the past involved separate calibration for the panchromatic sensor and the multispectral sensor. This method produced distinct internal and external calibration parameters in the respective bands, and also resulted in nonlinear geometric misalignments between the panchromatic and multispectral images due to satellite chattering and other factors. To better capitalize on the high spatial resolution of panchromatic imagery and the superior spectral resolution of multispectral imagery, it is necessary to perform registration on the calibrated panchromatic and multispectral images. When registering separately calibrated panchromatic and multispectral images, poor consistency between panchromatic and multispectral images leads to a small number of corresponding points, resulting in poor accuracy and registration effects. To address this issue, we propose a joint panchromatic and multispectral calibration method to register the panchromatic and multispectral images. Before geometric calibration, it is necessary to perform corresponding points matching. When matching, the small interval between the panchromatic and multispectral Charge-Coupled Devices (CCDs) results in a small intersection angle of the corresponding points between the panchromatic and multispectral images. As a result of this, the consistency between the spectral bands significantly improves, and the corresponding points match to have a more uniform distribution and a wider coverage. The technique enhances the consistent registration accuracy of both the panchromatic and multispectral bands. Experiments demonstrate that the joint calibration method yields a registration accuracy of panchromatic and multispectral bands exceeding 0.3 pixels. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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27 pages, 9855 KiB

Open AccessArticle

Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2

by Zuomin Xu, Ruijing Sun, Shuang Wu, Jiali Shao and Jie Chen

Remote Sens. 2024, 16(2), 424; https://doi.org/10.3390/rs16020424 - 22 Jan 2024

Cited by 1 | Viewed by 1618

Abstract

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational [...] Read more.

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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19 pages, 10316 KiB

Open AccessArticle

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

by Supeng Jiang, Haibo Luo and Yunpeng Liu

Remote Sens. 2024, 16(1), 161; https://doi.org/10.3390/rs16010161 - 30 Dec 2023

Cited by 1 | Viewed by 1314

Abstract

Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these [...] Read more.

Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these methods rarely consider the interrelationship between different feature indicators and use the same set of screening thresholds for different categories of images, which has poor versatility and can easily cause mis-selection and omission. To solve this problem, a suitable-matching areas’ selection method based on multi-level saliency is proposed. The matching performance score is obtained by fusing several segmentation levels’ salient feature extraction results and performing weighted calculations with the sub-image edge density. Compared with the hierarchical screening methods, the matching performance of the candidate areas selected by our algorithm is at least 22.2% higher, and it also has a better matching ability in different scene categories. In addition, the number of missed and wrong selections is significantly reduced. The average matching accuracy of the top three areas selected by our method reached 0.8549, 0.7993, and 0.7803, respectively, under the verification of multiple matching algorithms. Experimental results show this paper’s suitable-matching areas’ selection method is more robust. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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29 pages, 17743 KiB

Open AccessArticle

Cross-Radiometric Calibration and NDVI Application Comparison of FY-4A/AGRI Based on Aqua-MODIS

by Xiaohui He, Hongli Li, Guangsheng Zhou, Zhihui Tian and Lili Wu

Remote Sens. 2023, 15(23), 5454; https://doi.org/10.3390/rs15235454 - 22 Nov 2023

Viewed by 1860

Abstract

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, [...] Read more.

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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21 pages, 8095 KiB

Open AccessArticle

A General Relative Radiometric Correction Method for Vignetting Noise Drift

by Liming Fan, Shuhai Yu, Xing Zhong, Maosheng Chen, Dong Wang, Jinyan Cao and Xiyan Cai

Remote Sens. 2023, 15(21), 5129; https://doi.org/10.3390/rs15215129 - 26 Oct 2023

Cited by 1 | Viewed by 1876

Abstract

Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise [...] Read more.

Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise ratio, and poor response stability of vignetting are key factors that restrict the relative radiometric correction of optical splicing remote satellites. This paper proposes a stability analysis method and a relative radiometric correction method for vignetting. First, we analyzed the stability of the response and the noise impact of vignetting. Massive data from the Jilin-1 GF03D satellites was used to analyze the stability of the response using the vignetting stability analysis method. Secondly, the data on the deep sea during nighttime (DDSN) of Jilin-1 GF03D satellites was used to obtain the characteristics of the sensors’ noise. Thirdly, by building a noise drift model, we calculated the coefficient of the noise drift according to its characteristics. Using the coefficient to eliminate the noise drift of each pixel in vignetting can improve the response stability of vignetting. The average response stability increased by 37.64% by this method. Finally, the automatic relative radiometric correction method was completed through histogram matching. Furthermore, we proposed color aberration metrics (CAMs) to evaluate the multi-spectral images after relative radiometric correction, and massive data from the 16 satellites of Jilin-1 GF03D was used to verify the effectiveness and generality. The experimental results show that the average CAM of the images increased by 15.97% using the proposed method compared to the traditional method. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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20 pages, 6207 KiB

Open AccessArticle

Analysis of the Matchability of Reference Imagery for Aircraft Based on Regional Scene Perception

by Xin Li, Guo Zhang, Hao Cui, Jinhao Ma and Wei Wang

Remote Sens. 2023, 15(17), 4353; https://doi.org/10.3390/rs15174353 - 4 Sep 2023

Cited by 2 | Viewed by 1598

Abstract

Scene matching plays a vital role in the visual positioning of aircraft. The position and orientation of aircraft can be determined by comparing acquired real-time imagery with reference imagery. To enhance precise scene matching during flight, it is imperative to conduct a comprehensive [...] Read more.

Scene matching plays a vital role in the visual positioning of aircraft. The position and orientation of aircraft can be determined by comparing acquired real-time imagery with reference imagery. To enhance precise scene matching during flight, it is imperative to conduct a comprehensive analysis of the reference imagery’s matchability beforehand. Conventional approaches to image matchability analysis rely heavily on features that are manually designed. However, these features are inadequate in terms of comprehensiveness, efficiency, and taking into account the scene matching process, ultimately leading to unsatisfactory results. This paper innovatively proposes a core approach to quantifying matchability by utilizing scene information from imagery. The first proposal for generating image matchability samples through a simulation of the matching process has been developed. The RSPNet network architecture is designed to effectively leverage regional scene perception in order to accurately predict the matchability of reference imagery. This network comprises two core modules: saliency analysis and uniqueness analysis. The attention mechanism employed by saliency analysis module extracts features at different levels and scales, guaranteeing an accurate and meticulous quantification of image saliency. The uniqueness analysis module quantifies image uniqueness by comparing neighborhood scene features. The proposed method is compared with traditional and deep learning methods for experiments based on simulated datasets, respectively. The results demonstrate that RSPNet exhibits significant advantages in terms of accuracy and reliability. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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18 pages, 7254 KiB

Open AccessArticle

Improved On-Orbit MTF Measurement Method Based on Point Source Arrays

by Litao Li, Jiayang Cao, Shaodong Wei, Yonghua Jiang and Xin Shen

Remote Sens. 2023, 15(16), 4028; https://doi.org/10.3390/rs15164028 - 14 Aug 2023

Cited by 3 | Viewed by 3157

Abstract

The modulation transfer function (MTF) is a key characteristic used to assess the performance of optical remote sensing satellite sensors. MTF detection can directly measure a sensor’s two-dimensional (2D) point spread function (PSF); therefore, it has been applied to various high-resolution remote sensing [...] Read more.

The modulation transfer function (MTF) is a key characteristic used to assess the performance of optical remote sensing satellite sensors. MTF detection can directly measure a sensor’s two-dimensional (2D) point spread function (PSF); therefore, it has been applied to various high-resolution remote sensing satellites (e.g., Pleiades) using point sources. However, current point source methods mainly use 2D Gaussian functions to fit the discrete digital number (DN) of the point source on the image to extract the center of the point source and fit the PSF after encrypting multiple point sources; thus, noise robustness is poor and measurement accuracy varies widely. In this study, we developed a noise-resistant on-orbit MTF detection method based on the object space constraint among point source arrays. Utilizing object space constraint relationships among points in a point source array, a homography transformation model was established, enabling accurate extraction of sub-pixel coordinates for each point source response. Subsequently, aligning the luminosity distribution of all point sources concerning a reference point source, the encrypted PSF was obtained and then fitted to obtain the MTF. To validate the method, Gaofen-2 (GF-2) satellite images were used to conduct an in-orbit imaging experiment on the point source array of the Chinese Zhongwei remote sensing satellite calibration site. Compared with the Gaussian model methods, the proposed method yielded more accurate peak positions for each point source. Standard deviations of peak position constant ratios in along- and cross-track directions improved by 2.8 and 4.8 times, respectively. The root-mean-square error (RMSE) of the collinearity test results increased by 92%, and the noise resistance of the MTF curve improved by two times. Dynamic MTF values at the Nyquist frequency for the GF-2 panchromatic band in along- and cross-track directions were 0.0476 and 0.0705, respectively, and MTF values in different directions were well distinguished. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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16 pages, 4700 KiB

Open AccessTechnical Note

Precision and Characteristics of Satellite Spatial Quality Estimators’ Measurement Using an Edge Target Imaged with KOMPSAT-3A

by Donghan Lee, Daesoon Park and Daehoon Yoo

Remote Sens. 2024, 16(24), 4660; https://doi.org/10.3390/rs16244660 - 12 Dec 2024

Cited by 1 | Viewed by 880

Abstract

After the launch of a high-resolution remote sensing satellite, representative spatial quality estimators (RER, FWHM, MTF50, MTFA) are measured from images taken of ground Edge targets. In this work, the best spatial quality estimator is proposed by quantitatively comparing and analyzing the precision [...] Read more.

After the launch of a high-resolution remote sensing satellite, representative spatial quality estimators (RER, FWHM, MTF50, MTFA) are measured from images taken of ground Edge targets. In this work, the best spatial quality estimator is proposed by quantitatively comparing and analyzing the precision between the Relative Edge Response (RER), the Full Width at Half Maximum (FWHM), the MTF value at the Nyquist frequency (MTF50), and the MTF Area between 0 and the Nyquist frequency (MTFA). While the basic method for the measurement of spatial quality estimators on Edge targets is already well established, this work summarizes and explains the uncertain factors and problems in the measurement procedure that affect the accuracy and precision of spatial quality estimators. It also considers how to improve the precision of spatial quality estimators during the measurement procedure. The contents and results of this work were discussed by various satellite development organizations in the Geo-Spatial Working Group within CEOS WGCV IVOS from 2012 to 2019, and the Edge target Spatial quality Measurement Python code (ESMP) was developed in 2019 to reflect the findings of this workshop. Using 483 Edge targets from worldwide images taken by KOMPSAT-3A, which has been in operation since 2017, the results obtained via ESMP show that the precision levels of RER, FWHM, and MTFA are approximately three to four times higher than that of MTF50 when comparing the Coefficient of Variance (CV) statistics. This is the first statistical comparison of spatial quality estimators using 7 years of ground Edge target imagery from a single satellite of KOMPSAT-3A. Full article

(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)

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