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Accuracy and Quality Control of Remote Sensing Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 27538

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Special Issue Editors

Dr. Mohammadmehdi Saberioon

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Guest Editor

German Research Centre for Geosciences, 14473 Potsdam, Germany
Interests: applied remote sensing in different discipline of agriculture and environment studies; field and imaging spectroscopy; image and signal processing; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Daniel Spengler

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Guest Editor

Deutsches GeoForschungsZentrum (GFZ), Potsdam, Germany
Interests: hyperspectral data analysis; spectroscopy; vegetation analysis, phenology studies; applied remote sensing vegetation and soil; synergetic analysis of optical, thermal and microwave data

Special Issue Information

Dear Colleagues,

Remote sensing products enable us to have regular monitoring of our planet’s surface from local to global scale. With increasing Earth observation sensors and platforms, reliable compliance information of quantitative remote sensing products is a prerequisite for future synergetic usages of remotely sensed data. Surface reflectance retrieved from remote sensing data is frequently contaminated by noise from atmospheric corrections to convert the top of atmospheric reflectance to surface reflectance, effects of cloud contamination, as well as using insufficient multiangular measurements in BRDF modeling. Additionally, the uncertainty of the reference should ideally be smaller than that of the candidate item, and their combined uncertainty should be lower than the width of the interval defining allowable variations.

This Special Issue aims (i) to report the up-to-date advancements and trends regarding the remote sensing data uncertainty measurements (ii) to report the quality and validation approaches for quantitative earth observation products over the land and water, and (iii) to communicate new sensors, methods and algorithms for improving and validating the quality of remotely sensed data.

Dr. Mohammadmehdi Saberioon
Dr. Daniel Spengler
Guest Editors

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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

Remote sensing data validation
High-quality surface information
Atmospheric correction
Harmonization
Quality control

Published Papers (11 papers)

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24 pages, 8478 KiB

Open AccessArticle

High Accuracy Solar Diffuser BRDF Measurement for On-Board Calibration in the Solar Reflective Band

by Zhiyuan Zhang, Hongyao Chen, Wenxin Huang, Xiaobing Zheng and Liming Zhang

Remote Sens. 2023, 15(15), 3783; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15153783 - 29 Jul 2023

Viewed by 1173

Abstract

In the solar reflective band, an on-board calibration method based on a solar diffuser (SD) can realize full aperture, full field of view, and end-to-end absolute radiometric calibration of optical remote sensors. The SD’s bidirectional reflectance distribution function (BRDF) is a key parameter that affects the accuracy of the on-board calibration. High-accuracy measurement of the SD BRDF is required in the laboratory before launch. Due to the uncertainty of the goniometer system, polarization effects, and other factors, the measurement uncertainty of the SD BRDF at large incident angles is much higher than that at a 0° incident zenith angle and 45° reflection zenith angle. In this paper, an absolute BRDF measurement facility is reported. The goniometric system consists of a high-brightness integrating sphere as a radiation source, a six-axis robot arm, and a large rotation stage. The measurement wavelength range was from 350 nm to 2400 nm. An improved data processing method based on the reciprocity theorem was proposed to reduce the measurement uncertainty of the SD BRDF at large incident angles. At an incident zenith angle of 75°, the improved data processing method reduced the measurement uncertainty of the SD BRDF by 52% at 410 nm to 480 nm, by 70% at 480 nm to 1000 nm, and by 20% at other bands compared to the absolute measurement method. The influence of the radiation source, goniometer system, detection system, and other factors on the measurement uncertainty are analyzed in this paper. The results show that the measurement uncertainty (coverage factor k = 2) of the SD BRDF was better than 1.04% at 350 nm to 410 nm, 0.60% at 410 nm to 480 nm, 0.43% at 480 nm to 1000 nm, and 0.86% at 1000 nm to 2400 nm. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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32 pages, 8554 KiB

Open AccessArticle

Vicarious Radiometric Calibration of the Multispectral Imager Onboard SDGSAT-1 over the Dunhuang Calibration Site, China

by Zhenzhen Cui, Chao Ma, Hao Zhang, Yonghong Hu, Lin Yan, Changyong Dou and Xiao-Ming Li

Remote Sens. 2023, 15(10), 2578; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102578 - 15 May 2023

Cited by 7 | Viewed by 1448

Abstract

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world’s first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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26 pages, 20840 KiB

Open AccessArticle

Vicarious CAL/VAL Approach for Orbital Hyperspectral Sensors Using Multiple Sites

by Daniela Heller Pearlshtien, Stefano Pignatti and Eyal Ben-Dor

Remote Sens. 2023, 15(3), 771; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030771 - 29 Jan 2023

Cited by 4 | Viewed by 2021

Abstract

The hyperspectral (HSR) sensors Earth Surface Mineral Dust Source Investigation (EMIT) of the National Aeronautics and Space Administration (NASA) and Environmental Mapping and Analysis Program (EnMAP) of the German Aerospace Center (DLR) were recently launched. These state-of-the-art sensors have joined the already operational HSR sensors DESIS (DLR), PRISMA (Italian Space Agency), and HISUI (developed by the Japanese Ministry of Economy, Trade, and Industry METI and Japan Aerospace Exploration Agency JAXA). The launching of more HSR sensors is being planned for the near future (e.g., SBG of NASA, and CHIME of the European Space Agency), and the challenge of monitoring and maintaining their calibration accuracy is becoming more relevant. We proposed two test sites: Amiaz Plain (AP) and Makhtesh Ramon (MR) for spectral, radiometric, and geometric calibration/validation (CAL/VAL). The sites are situated in the arid environment of southern Israel and are in the same overpass coverage. Both test sites have already demonstrated favorable results in assessing an HSR sensor’s performance and were chosen to participate in the EMIT and EnMAP validation stage. We first evaluated the feasibility of using AP and MR as CAL/VAL test sites with extensive datasets and sensors, such as the multispectral sensor Landsat (Landsat5 TM and Landsat8 OLI), the airborne HSR sensor AisaFENIX 1K, and the spaceborne HSR sensors DESIS and PRISMA. Field measurements were taken over time. The suggested methodology integrates reflectance and radiometric CAL/VAL test sites into one operational protocol. The method can highlight degradation in the spectral domain early on, help maintain quantitative applications, adjust the sensor’s radiometric calibration during its mission lifetime, and minimize uncertainties of calibration parameters. A PRISMA sensor case study demonstrates the complete operational protocol, i.e., performance evaluation, quality assessment, and cross-calibration between HSR sensors. These CAL/VAL sites are ready to serve as operational sites for other HSR sensors. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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20 pages, 7524 KiB

Open AccessArticle

An Improved Vicarious Calibration Method Based on Multi-Grayscale Targets

by Shiwei Bao, Hongyao Chen, Yan Li, Liming Zhang, Wenxin Huang, Xiaolong Si, Xianhua Wang, Zhou Fang, Yuanwei Chen, Xinrong Wang and Xiaowen Zhao

Remote Sens. 2022, 14(15), 3779; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153779 - 06 Aug 2022

Viewed by 2105

Abstract

Vicarious calibration is a well-developed method for electro-optical (EO) sensor calibration that has been used since the early 1980s. The radiometric calibration of reflectance solar band is mainly applied to reflection inversion. In this paper, a radiometric calibration-reflectance inversion (RCRII) model is proposed as an improved vicarious calibration method. Taking the reflectance of grayscale targets with constant spectrum, suitable uniformity, and near-Lambertian characteristics as the known information, the grayscale target calibration is realized, and the initial value of calibration coefficient and offset are calculated. Then, the adjacency effect is evaluated and corrected by reflectance inversion, and the results are fed back to the calibration process to realize the iterative process of calibration inversion rescaling. The results indicate that the absolute difference between the reflectance calculated with the RCRII model and measured reflectance is less than 0.01. By comparing with Sentinel-2A images, it is cross-verified that the difference of radiance between them is within 4%, and the absolute reflectance difference is less than 0.01, in the range of 0.1~0.3 reflectance. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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14 pages, 1094 KiB

Open AccessArticle

Accuracy of the Copernicus High-Resolution Layer Imperviousness Density (HRL IMD) Assessed by Point Sampling within Pixels

by Geir-Harald Strand

Remote Sens. 2022, 14(15), 3589; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153589 - 27 Jul 2022

Cited by 7 | Viewed by 1930

Abstract

The Copernicus high-resolution layer imperviousness density (HRL IMD) for 2018 is a 10 m resolution raster showing the degree of soil sealing across Europe. The imperviousness gradation (0–100%) per pixel is determined by semi-automated classification of remote sensing imagery and based on calibrated NDVI. The product was assessed using a within-pixel point sample of ground truth examined on very high-resolution orthophoto for the section of the product covering Norway. The results show a high overall accuracy, due to the large tracts of natural surfaces correctly portrayed as permeable (0% imperviousness). The total sealed area in Norway is underestimated by approximately 33% by HRL IMD. Point sampling within pixels was found to be suitable for verification of remote sensing products where the measurement is a binomial proportion (e.g., soil sealing or canopy coverage) when high-resolution aerial imagery is available as ground truth. The method is, however, vulnerable to inaccuracies due to geometrical inconsistency, sampling errors and mistaken interpretation of the ground truth. Systematic sampling inside each pixel is easy to work with and is known to produce more accurate estimates than a simple random sample when spatial autocorrelation is present, but this improvement goes unnoticed unless the status and location of each sample point inside the pixel is recorded and an appropriate method is applied to estimate the within-pixel sampling accuracy. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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Graphical abstract

24 pages, 6449 KiB

Open AccessArticle

A Quadrifocal Tensor SFM Photogrammetry Positioning and Calibration Technique for HOFS Aerial Sensors

by Tao Wang, Yan Zhang, Yongsheng Zhang, Ying Yu, Lei Li, Shaocong Liu, Xiang Zhao, Zhenchao Zhang and Longhui Wang

Remote Sens. 2022, 14(15), 3521; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153521 - 22 Jul 2022

Viewed by 1416

Abstract

Nowadays, the integration between photogrammetry and structure from motion (SFM) has become much closer, and many attempts have been made to combine the two approaches to realize the positioning, calibration, and 3D reconstruction of a large number of images. For the positioning and calibration of high oblique frame sweep (HOFS) aerial cameras, a quadrifocal tensor SFM photogrammetry technique is proposed to resolve the positioning and calibration task of such cameras. It adopts the quadrifocal tensor idea into the OpenMVG SFM pipeline to solve the complexity problem caused by the small single-viewing imaging area and the high image overlapping ratio. It also integrates the photogrammetry iteration idea into the OpenMVG SFM pipeline to enhance the positioning and calibration accuracy, which includes a coarse to fine three-stage Bundle Adjustment (BA) processing approach. In this paper, the overall workflow of the proposed technique was first introduced in detail, from feature extraction and image matching, relative rotation and translation estimation, global rotation and translation estimation, and the quadrifocal tensor model construction to the three-stage BA process and calibration. Then, experiments were carried out in the Zhengzhou area, implementing four types of adjustment methods. The results suggest that the proposed quadrifocal tensor SFM photogrammetry is suitable for large tilt frame sweep camera positioning and calibration without prior information on detailed camera intrinsic parameters and structure. The modifications made to the OpenMVG SFM pipeline enhanced the precision of image positioning and calibration and provided the precision level of professional photogrammetry software. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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26 pages, 8675 KiB

Open AccessArticle

SkySat Data Quality Assessment within the EDAP Framework

by Sebastien Saunier, Gizem Karakas, Ilyas Yalcin, Fay Done, Rubinder Mannan, Clement Albinet, Philippe Goryl and Sultan Kocaman

Remote Sens. 2022, 14(7), 1646; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071646 - 29 Mar 2022

Cited by 9 | Viewed by 3667

Abstract

Cal/Val activities within the Earthnet Data Assessment Pilot (EDAP) Project of the European Space Agency (ESA) cover several Earth Observation (EO) satellite sensors, including Third-Party Missions (TPMs). As part of the validation studies of very-high-resolution (VHR) sensor data, the geometric and radiometric quality of the images and the mission compliance of the SkySat satellites owned by Planet were evaluated in this study. The SkySat constellation provides optical images with a nominal spatial resolution of 50 cm, and has the capacity for multiple visits of any place on Earth each day. The evaluations performed over several test sites for the purpose of the EDAP Maturity Matrix generation show that the high resolution requirement is fulfilled with high geometric accuracy, although various systematic and random errors could be observed. The 2D and 3D information extracted from SkySat data conform to the quality expectations for the given resolution, although improvements to the vendor-provided rational polynomial coefficients (RPCs) are essential. The results show that the SkySat constellation is compliant with the specifications and the accuracy results are within the ranges claimed by the vendor. The signal-to-noise ratio assessments revealed that the quality is high, but variations occur between the different sensors. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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18 pages, 7606 KiB

Open AccessArticle

On-Orbit Radiometric Performance of GF-7 Satellite Multispectral Imagery

by Hongzhao Tang, Junfeng Xie, Xinming Tang, Wei Chen and Qi Li

Remote Sens. 2022, 14(4), 886; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040886 - 12 Feb 2022

Cited by 8 | Viewed by 2469

Abstract

China’s first civilian, sub-meter, high-resolution stereo mapping satellite, GF-7, launched on 3 November 2019. Radiometric characterization of GF-7 multispectral imagery has been performed in this study. A relative radiometric accuracy evaluation of the GF-7 multispectral imagery was performed using several large uniform scenes, and the results showed that the accuracy is better than 2%. The absolute radiometric evaluation of the GF-7 satellite sensor was conducted at the Baotou and Dunhuang calibration sites, using the reflectance-based vicarious approach. The synchronous measurements of surface reflectance and atmospheric parameters were collected as the input for the radiative transfer model. The official radiometrically calibrated coefficient of the GF-7 multispectral imagery was evaluated with the predicted top-of-atmosphere (TOA) radiance from the radiative transfer model. The results indicated that the absolute radiometric accuracy of GF-7 multispectral imagery is better than 5%. In order to monitor the radiometric stability of the GF-7 satellite multispectral sensor, a relative and absolute radiometric accuracy assessment campaign should be performed several times a year. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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17 pages, 8487 KiB

Open AccessArticle

A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images

by Tianxin Chen and Yongxue Liu

Remote Sens. 2021, 13(17), 3351; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173351 - 24 Aug 2021

Cited by 1 | Viewed by 2897

Abstract

A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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17 pages, 67654 KiB

Open AccessFeature PaperArticle

Validation of Permafrost Active Layer Estimates from Airborne SAR Observations

by Andrew D. Parsekian, Richard H. Chen, Roger J. Michaelides, Taylor D. Sullivan, Leah K. Clayton, Lingcao Huang, Yuhuan Zhao, Elizabeth Wig, Mahta Moghaddam, Howard Zebker and Kevin Schaefer

Remote Sens. 2021, 13(15), 2876; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152876 - 22 Jul 2021

Cited by 9 | Viewed by 3320

Abstract

In permafrost regions, active layer thickness (ALT) observations measure the effects of climate change and predict hydrologic and elemental cycling. Often, ALT is measured through direct ground-based measurements. Recently, synthetic aperture radar (SAR) measurements from airborne platforms have emerged as a method for observing seasonal thaw subsidence, soil moisture, and ALT in permafrost regions. This study validates airborne SAR-derived ALT estimates in three regions of Alaska, USA using calibrated ground penetrating radar (GPR) geophysical data. The remotely sensed ALT estimates matched the field observations within uncertainty for 79% of locations. The average uncertainty for the GPR-derived ALT validation dataset was 0.14 m while the average uncertainty for the SAR-derived ALT in pixels coincident with GPR data was 0.19 m. In the region near Utqiaġvik, the remotely sensed ALT appeared slightly larger than field observations while in the Yukon-Kuskokwim Delta region, the remotely sensed ALT appeared slightly smaller than field observations. In the northern foothills of the Brooks Range, near Toolik Lake, there was minimal bias between the field data and remotely sensed estimates. These findings suggest that airborne SAR-derived ALT estimates compare well with in situ probing and GPR, making SAR an effective tool to monitor permafrost measurements. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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Other

Jump to: Research

15 pages, 2170 KiB

Open AccessTechnical Note

Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems

by Danlu Guo, Arash Parehkar, Dongryeol Ryu, Quan J. Wang and Andrew W. Western

Remote Sens. 2022, 14(5), 1286; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051286 - 05 Mar 2022

Viewed by 2293

Abstract

Missing data and low data quality are common issues in field observations of actual evapotranspiration (ET_a) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ET_a or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ET_a and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ET_a observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ET_a or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ET_a data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited. Full article

(This article belongs to the Special Issue Accuracy and Quality Control of Remote Sensing Data)

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