The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

Abstract. Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral water-leaving reflectance from HICO™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

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Impact of the aerosol type on HICO™ atmospheric correction in coastal waters

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-5147-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 5147-5172 ◽

Cited By ~ 2

Author(s):

C. Bassani ◽

C. Manzo ◽

F. Braga ◽

M. Bresciani ◽

C. Giardino ◽

...

Keyword(s):

Physical Properties ◽

Atmospheric Correction ◽

Solar Spectrum ◽

Hyperspectral Data ◽

Quality Analysis ◽

Urban Aerosol ◽

Physically Based ◽

Vector Version ◽

Satellite Signal ◽

Aerosol Type

Abstract. The aim of this work is to evaluate the radiative impact of the aerosol type on the results of the atmospheric correction of HICO™ (Hyperspectral Imager for the Coastal Ocean) hyperspectral data. The reflectance was obtained by using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) algorithm, a physically-based atmospheric correction algorithm developed specifically for HICO™ data by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on six HICO™ images acquired in the Northern part of the Mediterranean Basin, using the micro-physical properties measured with a CIMEL sun sky-radiometer at the Acqua Alta Oceanographic Tower (AAOT) AERONET site and the optical properties of the maritime, continental, and urban aerosol types provided by default by the 6SV. The results highlight that the aerosol type can improve the accuracy of the atmospheric correction. Indeed, the accuracy of the water reflectance retrieved from the available HICO™ data decreases in the sensor spectral domain, considering the AERONET micro-physical properties, of 30% using the urban aerosol type, of 20% using the continental type, and finally of less than 10% assuming a maritime type. Thus, the aerosol type has to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environment, if water quality analysis has to be performed, because of the influence of aerosol type on the water reflectance.

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What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

Remote Sensing ◽

10.3390/rs12244077 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4077

Author(s):

Michał Krupiński ◽

Anna Wawrzaszek ◽

Wojciech Drzewiecki ◽

Małgorzata Jenerowicz ◽

Sebastian Aleksandrowicz

Keyword(s):

Infrared Imaging ◽

Atmospheric Correction ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

High Spectral Resolution ◽

Absorption Bands ◽

Landscape Type ◽

Spectral Bands ◽

Before And After ◽

Additional Value

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

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Atmospheric Correction of Landsat ETM+ Remote Sensing Data Using 6S Code and its Validation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.2365 ◽

2010 ◽

Vol 29-32 ◽

pp. 2365-2368

Author(s):

Xiao Feng Yang ◽

Xing Ping Wen

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Atmospheric Correction ◽

Solar Spectrum ◽

Quantitative Information ◽

Visual Interpretation ◽

Quantitative Remote Sensing ◽

Before And After ◽

Ground Object

Atmospheric correction is one of the most important pre-processing steps in quantitative remote sensing. To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. Furthermore, multi-temporal images after atmospheric correction can be compared to each other quantitatively. The Second simulation of satellite signal in the solar spectrum (6S) radiative code can process many types of satellite data and provide several standard atmosphere and aerosol models for atmospheric correction. This paper demonstrates atmospheric correction of Landsat ETM+ data using 6S code. Comparing images before and after atmospheric correction, the different of image before and after correction was not obvious using visual interpretation. Therefore, different ground object spectral curves after atmospheric correction are illustrated. They were similar with the standard ground object spectra. The correlation coefficient of ETM+ band 1 to band 4 and NDVI (Normalized Difference Vegetation Index) after atmospheric correction increases. The atmospheric correction removed the atmosphere effect, so the inherent relevant increased. 6S code was an effective tool for atmospheric correction of remote senisng data.

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Aerosol models from the AERONET data base. Application to surface reflectance validation

10.5194/amt-2021-322 ◽

2021 ◽

Author(s):

Jean-Claude Roger ◽

Eric Vermote ◽

Sergii Skakun ◽

Emilie Murphy ◽

Oleg Dubovik ◽

...

Keyword(s):

Critical Role ◽

Spectral Band ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Surface Reflectance ◽

Aerosol Model ◽

Microphysical Properties ◽

The Impact ◽

Source Of Error ◽

Base Application

Abstract. Aerosols play a critical role in radiative transfer within the atmosphere, and they have a significant impact on climate change. As part of the validation of atmospheric correction of remote sensing data affected by the atmosphere, it is critical to utilize appropriate aerosol models as aerosols are a main source of error. In this paper, we propose and demonstrate a framework for building and identifying an aerosol model. For this purpose, we define the aerosol model by recalculating the aerosol microphysical properties (Cvf, Cvc, %Cvf, %Cvc, rvf, rvc, σr, σc, nr440, nr650, nr850, nr1020, ni440, ni650, ni850, ni1020, %Sph) based on the optical thickness at 440 nm τ440 and the Ångström coefficient α440–870 obtained from numerous AERosol RObotic NETwork (AERONET) sites. Using aerosol microphysical properties provided by the AERONET dataset, we were able to evaluate our own retrieved microphysical properties. The associated uncertainties are up to 23 %, except for the challenging, imaginary part of the refractive index ni (about 38 %). Uncertainties of the retrieved aerosol microphysical properties were incorporated in the framework for validating surface reflectance derived from space-borne Earth observation sensors. Results indicate that the impact of aerosol microphysical properties varies 3.5 × 10−5 to 10−3 in reflectance units. Finally, the uncertainties of the microphysical properties yielded an overall uncertainty of approximately of 1 to 3 % of the retrieved surface reflectance in the MODIS red spectral band (620–670 nm), which corresponds to the specification used for atmospheric correction.

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Multi-Year Analyses of Columnar Aerosol Optical and Microphysical Properties in Xi’an, a Megacity in Northwestern China

Remote Sensing ◽

10.3390/rs10081169 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1169 ◽

Cited By ~ 2

Author(s):

Xiaoli Su ◽

Junji Cao ◽

Zhengqiang Li ◽

Kaitao Li ◽

Hua Xu ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Single Scattering ◽

Mean Value ◽

Asymmetry Factor ◽

Microphysical Properties ◽

Sun Photometer ◽

Spatio Temporal ◽

The Impact ◽

Aerosol Types

A thorough understanding of aerosol optical properties and their spatio-temporal variability are required to accurately evaluate aerosol effects in the climate system. In this study, a multi-year study of aerosol optical and microphysical properties was firstly performed in Xi’an based on three years of sun photometer remote sensing measurements from 2012 to 2015. The multi-year average of aerosol optical depth (AOD) at 440 nm was about 0.88 ± 0.24 (mean ± SD), while the averaged Ångström Exponent (AE) between 440 and 870 nm was 1.02 ± 0.15. The mean value of single scattering albedo (SSA) was around 0.89 ± 0.03. Aerosol optical depth and AE showed different seasonal variation patterns. Aerosol optical depth was slightly higher in winter (0.99 ± 0.36) than in other seasons (~0.85 ± 0.20), while AE showed its minimum in spring (0.85 ± 0.05) due to the impact of dust episodes. The seasonal variations of volume particle size distribution, spectral refractive index, SSA, and asymmetry factor were also analyzed to characterize aerosols over this region. Based on the aerosol products derived from sun photometer measurements, the classification of aerosol types was also conducted using two different methods in this region. Results show that the dominant aerosol types are absorbers in all seasons, especially in winter, demonstrating the strong absorptivity of aerosols in Xi’an.

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Sensitivity of thermal infrared nadir instruments to the chemical and microphysical properties of UTLS secondary sulfate aerosols

Atmospheric Measurement Techniques ◽

10.5194/amt-9-115-2016 ◽

2016 ◽

Vol 9 (1) ◽

pp. 115-132 ◽

Cited By ~ 9

Author(s):

P. Sellitto ◽

B. Legras

Keyword(s):

Sulfuric Acid ◽

Radiative Transfer ◽

Thermal Infrared ◽

High Spectral Resolution ◽

Spectral Signature ◽

Transfer Model ◽

Sulfate Aerosols ◽

Absorption Bands ◽

Microphysical Properties ◽

The Impact

Abstract. Monitoring upper-tropospheric–lower-stratospheric (UTLS) secondary sulfate aerosols and their chemical and microphysical properties from satellite nadir observations is crucial to better understand their formation and evolution processes and then to estimate their impact on UTLS chemistry, and on regional and global radiative balance. Here we present a study aimed at the evaluation of the sensitivity of thermal infrared (TIR) satellite nadir observations to the chemical composition and the size distribution of idealised UTLS sulfate aerosol layers. The extinction properties of sulfuric acid/water droplets, for different sulfuric acid mixing ratios and temperatures, are systematically analysed. The extinction coefficients are derived by means of a Mie code, using refractive indices taken from the GEISA (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database and log-normal size distributions with different effective radii and number concentrations. IASI (Infrared Atmospheric Sounding Interferometer) pseudo-observations are generated using forward radiative transfer calculations performed with the 4A (Automatized Atmospheric Absorption Atlas) radiative transfer model, to estimate the impact of the extinction of idealised aerosol layers, at typical UTLS conditions, on the brightness temperature spectra observed by this satellite instrument. We found a marked and typical spectral signature of these aerosol layers between 700 and 1200 cm−1, due to the absorption bands of the sulfate and bisulfate ions and the undissociated sulfuric acid, with the main absorption peaks at 1170 and 905 cm−1. The dependence of the aerosol spectral signature to the sulfuric acid mixing ratio, and effective number concentration and radius, as well as the role of interfering parameters like the ozone, sulfur dioxide, carbon dioxide and ash absorption, and temperature and water vapour profile uncertainties, are analysed and critically discussed. The information content (degrees of freedom and retrieval uncertainties) of synthetic satellite observations is estimated for different instrumental configurations. High spectral resolution (IASI-like pseudo-observations) and broadband spectral features (Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and InfraRed Imager (SEVIRI)-like pseudo-observations) approaches are proposed and discussed.

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DSM Generation from Single and Cross-Sensor Multi-View Satellite Images Using the New Agisoft Metashape: The Case Studies of Trento and Matera (Italy)

Remote Sensing ◽

10.3390/rs13040593 ◽

2021 ◽

Vol 13 (4) ◽

pp. 593

Author(s):

Lorenzo Lastilla ◽

Valeria Belloni ◽

Roberta Ravanelli ◽

Mattia Crespi

Keyword(s):

Case Studies ◽

Accuracy Assessment ◽

Stereo Pair ◽

Commercial Software ◽

Processing Module ◽

Land Covers ◽

Image Metadata ◽

The Impact ◽

The City ◽

Reliable Software

DSM generation from satellite imagery is a long-lasting issue and it has been addressed in several ways over the years; however, expert and users are continuously searching for simpler but accurate and reliable software solutions. One of the latest ones is provided by the commercial software Agisoft Metashape (since version 1.6), previously known as Photoscan, which joins other already available open-source and commercial software tools. The present work aims to quantify the potential of the new Agisoft Metashape satellite processing module, considering that to the best knowledge of the authors, only two papers have been published, but none considering cross-sensor imagery. Here we investigated two different case studies to evaluate the accuracy of the generated DSMs. The first dataset consists of a triplet of Pléiades images acquired over the area of Trento and the Adige valley (Northern Italy), which is characterized by a great variety in terms of geomorphology, land uses and land covers. The second consists of a triplet composed of a WorldView-3 stereo pair and a GeoEye-1 image, acquired over the city of Matera (Southern Italy), one of the oldest settlements in the world, with the worldwide famous area of Sassi and a very rugged morphology in the surroundings. First, we carried out the accuracy assessment using the RPCs supplied by the satellite companies as part of the image metadata. Then, we refined the RPCs with an original independent terrain technique able to supply a new set of RPCs, using a set of GCPs adequately distributed across the regions of interest. The DSMs were generated both in a stereo and multi-view (triplet) configuration. We assessed the accuracy and completeness of these DSMs through a comparison with proper references, i.e., DSMs obtained through LiDAR technology. The impact of the RPC refinement on the DSM accuracy is high, ranging from 20 to 40% in terms of LE90. After the RPC refinement, we achieved an average overall LE90 <5.0 m (Trento) and <4.0 m (Matera) for the stereo configuration, and <5.5 m (Trento) and <4.5 m (Matera) for the multi-view (triplet) configuration, with an increase of completeness in the range 5–15% with respect to stereo pairs. Finally, we analyzed the impact of land cover on the accuracy of the generated DSMs; results for three classes (urban, agricultural, forest and semi-natural areas) are also supplied.

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A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

Remote Sensing ◽

10.3390/rs13152869 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2869

Author(s):

MohammadAli Hemati ◽

Mahdi Hasanlou ◽

Masoud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Time Series ◽

Change Detection ◽

Policy Development ◽

Atmospheric Correction ◽

Open Data ◽

Detection Methods ◽

Landsat 8 ◽

Landsat Images ◽

Detection Analysis ◽

The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Remote Sensing ◽

10.3390/rs13101927 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1927

Author(s):

Fuqin Li ◽

David Jupp ◽

Thomas Schroeder ◽

Stephen Sagar ◽

Joshua Sixsmith ◽

...

Keyword(s):

Coastal Waters ◽

Atmospheric Correction ◽

Correction Method ◽

In Situ Measurements ◽

Landsat 8 ◽

Correction Algorithm ◽

Simple Method ◽

North East ◽

The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

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Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

Remote Sensing ◽

10.3390/rs13091693 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1693

Author(s):

Anushree Badola ◽

Santosh K. Panda ◽

Dar A. Roberts ◽

Christine F. Waigl ◽

Uma S. Bhatt ◽

...

Keyword(s):

Land Cover ◽

Spectral Resolution ◽

Hyperspectral Image ◽

Spectral Response ◽

Spectral Characteristics ◽

Hyperspectral Data ◽

Spectral Unmixing ◽

High Spectral Resolution ◽

Fuel Mapping ◽

Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

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