Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss

Müller, Rupert; Bachmann, Martin; Alonso, Kevin; Carmona, E.; Cerra, Daniele; Reyes, R. de los; Gerasch, Birgit; Krawczyk, Harald; Ziel, Valentin; Heiden, Uta; Krutz, David

doi:10.1109/igarss.2018.8519572

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…The framework of the present study is applicable not only to reflectance-to-radiance conversion, but also to reflectance-to-reflectance and radiance-to-radiance conversions. For example, the German hyperspectral sensor onboard the ISS, DESIS, provides radiance products [2]. Intercalibration between HISUI and DESIS [7] requires the radiance-to-radiance conversion of the spectral band adjustment, and the uncertainty could be evaluated using the equations derived in the present study (Equations (10)- (13)).…”

Section: Discussionmentioning

confidence: 99%

“…The International Space Station (ISS) has become the platform for deploying hyperspectral sensors covering the solar reflective spectral range for earth observation [1]. Currently, the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) [2] on the ISS makes measurements over the spectral range 400-1000 nm. Hyperspectral sensors will be placed on the ISS, including the Hyperspectral Imager Suite (HISUI) [3], the Earth Surface Mineral Dust Source Investigation (EMIT) [4], and the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) [5].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI

Obata

2019

Remote Sensing

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The International Space Station has become the platform for deploying hyperspectral sensors covering the solar reflective spectral range for earth observation. Intercalibration of hyperspectral sensors plays a crucial role in evaluating/improving radiometric consistency. When intercalibrating between hyperspectral sensors, spectral band adjustment is required to mitigate the effects of differences between the relative spectral responses (RSRs) of the sensors. Errors in spectral parameters used in spectral band adjustment are propagated through to the adjustment results. The present study analytically approximated the uncertainty in the spectral band adjustment for evaluating the relative contributions of uncertainties in parameters associated with the exo-atmosphere, atmosphere, and surface to the total uncertainty. Numerical simulations using the derived equations were conducted to perform a sensitivity analysis for the case of the spectral band adjustment between the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) and the Hyperspectral Imager Suite (HISUI). The results show that the effects of errors in the solar irradiance were greater than those of other sources of error, indicating that accurate estimates of atmospheric reflectances and tranismittances are not needed for spectral band adjustment between CPF and HISUI in the atmospheric windows. The accuracy of the analytical approximation was also evaluated in the simulations. The framework of the sensitivity analysis is applicable to other pairs of hyperspectral sensors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI

Obata

2019

Remote Sensing

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“…As imaging spectroscopy technology advances and becomes more widely available via spaceborne sensors [57][58][59][60], it will become increasingly important to address challenges associated with large-scale vegetation classifications. Many of these challenges were present in this mapping effort as differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenged the accurate classification of M. polymorpha as they have for other species [1,4].…”

Section: Considerations For Future Large-scale Modeling Effortsmentioning

confidence: 99%

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Seeley,

Vaughn,

Shanks

et al. 2023

Remote Sensing

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Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai’i Island using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, the support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied Gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m × 2 m M. polymorpha presence dataset and a 30 m × 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

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“…As imaging spectroscopy technology advances and becomes more widely available via spaceborne sensors [56][57][58][59], it will become increasingly important to address challenges associated with large-scale vegetation classifications. Many of these challenges were present in this effort as differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenged accurate classification of M. polymorpha as they have for other species [1,4].…”

Section: Considerations For Future Large-scale Modeling Effortsmentioning

confidence: 99%

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Seeley¹,

Vaughn²,

Shanks³

et al. 2023

Preprint

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Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing, data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i island, using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that, while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m x 2 m M. polymorpha presence dataset and a 30 m x 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

show abstract

Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss

Cited by 5 publications

References 9 publications

Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI

Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

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