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Atmospheric Correction of Remote Sensing Imagery

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 26471

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Dr. Federico Santini

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Guest Editor

IMAA-CNR, Via del Fosso del Cavaliere 100, 00133 Roma, Italy
Interests: remote sensing images; atmospheric corrections; radiative transfer; multispectral and hyperspectral imagers
Special Issues, Collections and Topics in MDPI journals

Dr. Angelo Palombo

E-Mail Website
Guest Editor

IMAA-CNR, Via del Fosso del Cavaliere 100, 00133 Roma, Italy
Interests: techniques of noise reduction in the hyperspectral images; radiometric and geometric correction of the hyperspectral images; atmospheric correction of the hyperspectral images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As satellite sensor performance increases, developing accurate pre-processing procedures has become a priority to meet the need for many new and powerful remote-sensing applications.

One of the most challenging issues, particularly important in ground studies, is making the data repeatable over time and uniform with respect to the different sensors and acquisition platforms, i.e., to obtain data that represent the intrinsic characteristics of the observed target. The signal detected by the sensor depends on the geometry of the acquisition and the illumination conditions at the time of acquisition and is impacted by the presence of the air column and other factors, such as topography and BRDF. The atmospheric corrections have the role of cleaning the signal from unwanted contributions as much as possible and moving from the sensor radiance to a quantity more closely related to the properties of the target, such as ground reflectance or emissivity.

Over time, many techniques for atmospheric correction and image harmonization have been developed and, at present, a large amount of already corrected data have been distributed. The great diversity of approaches followed, and the fact that the various approaches can have different impacts depending on the specific application, mean that a discussion has become urgent.

Contributions relevant to this Special Issue may include:

Review of currently existing methods for the correction and harmonization of remote sensing images;
Comparative studies between different models and products;
Validation approaches;
Proposals for new technologies;
Analysis work on the advantages and disadvantages of the different approaches in relation to specific applications.

Dr. Federico Santini
Dr. Angelo Palombo
Guest Editors

Related References

Palombo, A.; Santini, F. ImaACor: A Physically Based Tool for Combined Atmospheric and Topographic Corrections of Remote Sensing Images. Remote Sens. 2020, 12, 2076. https://doi.org/10.3390/rs12132076
Santini, F.; Palombo, A. Physically Based Approach for Combined Atmospheric and Topographic Corrections. Remote Sens. 2019, 11, 1218. https://doi.org/10.3390/rs11101218

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Keywords

Atmospheric correction
Radiative transfer model
Harmonization
Interoperability
Physical model
Radiative transfer
Adjacency
Landsat
Sentinel
PRISMA
Topographic correction
BRDF
Hyperspectral
Imagery
Reflectance
Emissivity

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Published Papers (9 papers)

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27 pages, 6900 KiB

Open AccessArticle

Algorithm for the Reconstruction of the Ground Surface Reflectance in the Visible and Near IR Ranges from MODIS Satellite Data with Allowance for the Influence of Ground Surface Inhomogeneity on the Adjacency Effect and of Multiple Radiation Reflection

by Mikhail V. Tarasenkov, Vladimir V. Belov, Marina V. Engel, Anna V. Zimovaya, Matvei N. Zonov and Alexandra S. Bogdanova

Remote Sens. 2023, 15(10), 2655; https://doi.org/10.3390/rs15102655 - 19 May 2023

Cited by 4 | Viewed by 1545

Abstract

An atmospheric correction algorithm is proposed for the reconstruction of the ground surface reflectance from the data of satellite measurements. A distinctive feature of the algorithm is that it takes into account the influence of the ground surface inhomogeneity on the adjacency effect and additional illumination of the ground surface by reflected radiation. These factors are important for the reconstruction of the reflectance of ground surface fragments with sharp reflectance changes and high atmospheric turbidity. The algorithm is based on Monte Carlo programs developed by the authors. To reduce the computing time, we have proposed some original criteria and approaches. To estimate the capabilities of the developed algorithm, its results have been validated by comparing with the results of the MOD09 algorithm for four MODIS bands and measurements for the Portugal surface fragment with coordinates 38.829 N, 8.791 W. Good agreement of the results obtained by the proposed algorithm with the surface measurements and the data obtained by the MOD09 algorithm demonstrates the efficiency of the proposed algorithm in the reconstruction of the ground surface reflectance. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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16 pages, 17072 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

by Gian Luigi Liberti, Mattia Sabatini, David S. Wethey and Daniele Ciani

Remote Sens. 2023, 15(9), 2453; https://doi.org/10.3390/rs15092453 - 6 May 2023

Cited by 2 | Viewed by 2637

Abstract

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km² is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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25 pages, 4646 KiB

Open AccessArticle

An Operational Atmospheric Correction Framework for Multi-Source Medium-High-Resolution Remote Sensing Data of China

by Hao Zhang, Dongchuan Yan, Bing Zhang, Zhengwen Fu, Baipeng Li and Shuning Zhang

Remote Sens. 2022, 14(21), 5590; https://doi.org/10.3390/rs14215590 - 5 Nov 2022

Cited by 5 | Viewed by 2883

Abstract

Land surface reflectance (LSR) data form the basis of quantitatively remotely sensed applications. For accurate LSR retrieval, atmospheric correction has been investigated by many researchers and implemented in typical processing systems, including common atmospheric correction software for various types of datasets and automatic operating systems for application to certain individual data sources. In recent years, China has launched multiple medium–high-resolution satellites but has not provided standard LSR products partly because of the lack of an appropriate operational system. In this paper, a multi-source remote sensing LSR product system for medium- and high-resolution data is proposed, called the “Operational Atmospheric Correction Framework for multi-source Medium-high-resolution Remote Sensing data of China” (ACFrC). The AC algorithm, processing flow, and design of the multi-source LSR system were described in detail. A practical atmospheric correction algorithm was proposed specially for data in only the visible and near-infrared (VNIR) bands. The entire processing chain was divided into modules for multi-source data ingestion, apparent reflectance calculation, cloud and water identification, atmospheric correction, and standard LSR product generation. To date, most types of multi-source data have been tested using the ACFrC system, with reasonable results being obtained. From the preliminary results, the 313 scenes of LSR products from the GaoFen-2 (GF-2) satellite over China for the period from 2015 to 2018 were cross-compared with Landsat-8 LSR acquired on the same day, showing an overall uncertainty less than 0.112 × LSR + 0.0112. Further, the ACFrC data processing efficiency was found to be suitable for automatic operation. System improvement is ongoing and future refinements will include online cloud parallel computing functionality and services, more robust algorithms, and other radiometric processing functions. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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21 pages, 1902 KiB

Open AccessArticle

Assessment of Sentinel-2-MSI Atmospheric Correction Processors and In Situ Spectrometry Waters Quality Algorithms

by Xavier Sòria-Perpinyà, Jesús Delegido, Esther Patricia Urrego, Antonio Ruíz-Verdú, Juan Miguel Soria, Eduardo Vicente and José Moreno

Remote Sens. 2022, 14(19), 4794; https://doi.org/10.3390/rs14194794 - 26 Sep 2022

Cited by 11 | Viewed by 3080

Abstract

The validation of algorithms developed from in situ reflectance to estimate water quality variables has the challenge of atmospheric correction (AC) when applied to satellite images. Estimating water quality variables from satellite images requires an accurate estimation of remote sensing reflectances (Rrs) which vary according to the AC applied. Validation processes for both Rrs and water quality algorithms were carried out, relating the in situ Rrs (convoluted to Sentinel-2-MSI spectral response function) with the satellite Rrs coming from different ACs (C2RCC, C2X, C2XC, and Polymer), and also relating the in situ water quality variable data with estimated water quality variable values, applying the water quality algorithms to the Rrs obtained for each AC. Regarding the Rrs validation results, the best ACs tested in this work were C2XC and Polymer. Regarding the water quality algorithm validation, the best results were also obtained using C2XC and Polymer Rrs. The results demonstrate the usefulness of the water quality algorithms developed from in situ reflectances since they are not specific to an AC and can be used with any processor. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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16 pages, 3116 KiB

Open AccessArticle

Impact of Topographic Correction on PRISMA Sentinel 2 and Landsat 8 Images

by Federico Santini and Angelo Palombo

Remote Sens. 2022, 14(16), 3903; https://doi.org/10.3390/rs14163903 - 11 Aug 2022

Cited by 4 | Viewed by 2618

Abstract

Over the past decades, remote sensing satellite sensors have significantly increased their performance and, at the same time, differed in their characteristics. Therefore, making the data repeatable over time and uniform with respect to different platforms has become one of the most challenging issues to obtain a representation of the intrinsic characteristics of the observed target. In this context, atmospheric correction has the role of cleaning the signal from unwanted contributions and moving from the sensor radiance to a quantity more closely related to the intrinsic properties of the target, such as ground reflectance. To this end, atmospheric correction procedures must consider a number of factors, closely related to the specific scene acquired and to the characteristics of the sensor. In mountainous environments, atmospheric correction must include a topographic correction level to compensate for the topographic effects that heavily affect the remote signal. In this paper, we want to estimate the impact of topographic correction on remote sensing images based on a statistical analysis, using data acquired under different illumination conditions with different sensors. We also want to show the benefits of introducing this level of correction in second level products such as PRISMA L2C reflectance, which currently do not implement it. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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21 pages, 6529 KiB

Open AccessArticle

Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

by Kyeong-Sang Lee, Eunkyung Lee, Donghyun Jin, Noh-Hun Seong, Daeseong Jung, Suyoung Sim and Kyung-Soo Han

Remote Sens. 2022, 14(2), 360; https://doi.org/10.3390/rs14020360 - 13 Jan 2022

Cited by 2 | Viewed by 2599

Abstract

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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24 pages, 35918 KiB

Open AccessArticle

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

by Fangjia Dou, Xiaolei Lv and Huiming Chai

Remote Sens. 2021, 13(22), 4670; https://doi.org/10.3390/rs13224670 - 19 Nov 2021

Cited by 7 | Viewed by 2528

Abstract

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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17 pages, 8359 KiB

Open AccessArticle

On the Assessment GPS-Based WRFDA for InSAR Atmospheric Correction: A Case Study in Pearl River Delta Region of China

by Zhenyi Zhang, Yidong Lou, Weixing Zhang, Hua Wang, Yaozong Zhou and Jingna Bai

Remote Sens. 2021, 13(16), 3280; https://doi.org/10.3390/rs13163280 - 19 Aug 2021

Cited by 6 | Viewed by 2762

Abstract

The accuracy and applications of synthetic aperture radar interferometry (InSAR) are severely suppressed by tropospheric error. Numerical Weather Models (NWMs) and GPS-derived tropospheric delays have been widely used to correct the tropospheric error considering their complete spatial coverage or high accuracy. However, few studies focus on the fusion of both NWMs and GPS for the tropospheric error correction. In this study, we used the Weather Research and Forecasting (WRF) to obtain NWMs with a higher spatial-temporal resolution of 3 km and 20 s from both ERAI (79 km and 6 h) and ERA5 (0.25° and 1 h). After that, we utilized the WRF Data Assimilation (WRFDA) system to assimilate the GPS ZTD into these enhanced NWMs and generate merged NWMs products. The tropospheric correction effectiveness from different NWMs products was evaluated in a case in the Pearl River Delta region of China. The results showed that all the NWMs products could correct the stratified component in the interferogram but could not mitigate the turbulence well, even after improving the spatial-temporal resolution. As for the trend component, the merged NWMs products showed obvious superiority over other products. From the statistics perspective, the stdev of the interferogram decreased further over 20% by the merged NWMs products than other products when using both ERAI and ERA5, indicating the significant effectiveness of GPS ZTD assimilation. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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17 pages, 4562 KiB

Open AccessTechnical Note

Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites

by Andrea Pellegrino, Alice Fabbretto, Mariano Bresciani, Thainara Munhoz Alexandre de Lima, Federica Braga, Nima Pahlevan, Vittorio Ernesto Brando, Susanne Kratzer, Marco Gianinetto and Claudia Giardino

Remote Sens. 2023, 15(8), 2163; https://doi.org/10.3390/rs15082163 - 19 Apr 2023

Cited by 14 | Viewed by 3168

Abstract

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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