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Remote Sensing Satellites Calibration and Validation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 27 November 2024 | Viewed by 7842

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Special Issue Editors

Prof. Dr. Yonghua Jiang

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: satellite calibration and validation; satellite image analysis; satellite image processing

Dr. Raad A. Saleh

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Guest Editor

Office of Systems Architecture and Advanced Planning (OSAAP), National Environmental Satellite, Data, and Information Service (NESDIS), NOAA, Silver Spring, MD 20910, USA
Interests: ground segment capabilities; enterprise architecture; satellite remote sensing systems; calibration and validation; interoperability; multi-sensor networking; planetary mapping; space weather; AI/ML/DL

Dr. Mingjun Deng

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Guest Editor

School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
Interests: geometric calibration; radiometric calibraiton; space-borne SAR; SAR geolocation

Dr. Litao Li

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Guest Editor

College of City and Environment, Hubei Normal University, Huangshi 435002, China
Interests: radiometric calibration and processing of spaceborne optical imagery; radiometric calibraiton; relative calibration; night-time remote sensing calibraiton

Special Issue Information

Dear Colleagues,

Satellite remote sensing images have the advantages of low cost, high efficiency, and rich information on fine-scale spectral and texture geometry of objects. However, the data quality of these images is affected unavoidably by all kinds of error. Remote sensing satellite calibration and validation, through advanced technological means, is dedicated to elimination of some systematic error, ensuring data quality from the source. This lays a solid foundation for the high-precision, efficient processing, analysis and prediction, and quantitative application of massive data.

This topic aims to gather high-level contributions related to Satellite Calibration and Validation in Remote Sensing. Both original research articles with innovative ideas and review articles discussing the state of the art are welcomed.

We would like to invite research papers on the following topics, such as an overview of satellite remote sensing calibration and validation: importance, methods, and challenges, the performance of traditional photogrammetry and emerging techniques in the calibration and validation of remote sensing satellite data, the challenges of calibration and validation of high spectral and high-resolution satellite data, the calibration and accuracy enhancement strategies for extra-terrestrial observation satellite data, the importance of open data policies for satellite remote sensing calibration and validation, the future of satellite remote sensing calibration and validation: new technologies and emerging research directions. We cordially invite fully prepared, unpublished research papers that cover one or more of the above topics.

Prof. Dr. Yonghua Jiang
Dr. Raad A. Saleh
Dr. Mingjun Deng
Dr. Litao Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

new calibration method
accuracy of satellite calibration
challenge of satellite calibration

Published Papers (9 papers)

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Research

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

Viewed by 262

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 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 - 05 Apr 2024

Viewed by 426

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 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 695

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 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

Viewed by 619

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 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

Viewed by 553

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 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 860

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, 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

Viewed by 917

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 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 - 04 Sep 2023

Cited by 1 | Viewed by 751

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 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 1 | Viewed by 1417

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 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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