RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range

Bouvet, Marc; Thome, Kurtis J.; Berthelot, Béatrice; Białek, Agnieszka; Czapla-Myers, Jeffrey S.; Fox, Nigel; Goryl, Philippe; Henry, Patrice; Ma, Lingling; Marcq, Sébastien; Meygret, Aimé; Wenny, Brian N.; Woolliams, Emma

doi:10.3390/rs11202401

Cited by 147 publications

(135 citation statements)

References 39 publications

(49 reference statements)

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“…In the absence of a field team, network data can be used to determine surface reflectance. Data derived from the in situ instruments at RRV are accessible to the public via the on-line RadCalNet portal [4,5]. TOA radiances are available at a nadir-view angle, as well as the bottom-of-atmosphere reflectances.…”

Section: Vicarious Calibrationmentioning

confidence: 99%

Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa

et al. 2019

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Vicarious calibration is the determination of an on-orbit sensor’s radiometric response using measurements over test sites such as Railroad Valley (RRV), Nevada. It has the highest accuracy when a remote sensor’s view angle is aligned with that of the surface measurements, namely at a nadir view. For view angles greater than 10, the dominant error is the uncertainty in the off-nadir correction factor. The factor is largest in the back-scatter principal plane and can reach 20%. The Orbiting-Carbon Observatory has access to a number of datasets to determine this deviation. These include measurements from field instruments such as the Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), as well as satellite measurements from Multi-angle Imaging SpectroRadiometer (MISR) and MODerate resolution Imaging Spectroradiometer (MODIS). The correction factor derived from PARABOLA is consistent in time and space to within 2% for view angles as large as 30. Field spectrometer data show that the correction term is spectrally invariant. For this reason, a time-invariant model of RRV surface reflectance, along with empirically derived coefficients, is sufficient to use in the calibration of off-nadir sensors, provided there has been no recent rainfall. With this off-nadir correction, calibrations can be expected to have uncertainties within 5%.

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Section: Vicarious Calibrationmentioning

confidence: 99%

Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa

et al. 2019

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“…Numerous post-launch absolute calibration techniques have been developed over the past decades; they can be broadly classified as on-board and vicarious methods [33][34][35][36][37][38][39][40][41][42][43]. On-board absolute radiometric calibration technique relies on calibration devices such as calibration lamps.…”

Section: Absolute Calibrationmentioning

confidence: 99%

“…However, there is a reported limitation in using these data as they are provided only at nadir view, causing viewing an angle effect on the non-nadir viewing sensor [34]. To address this issue, Bouvet et al suggested an approach based on simulating off-nadir TOA reflectances to match the viewing angle of the sensor of interest [40].…”

Section: Radiometric Calibration Networkmentioning

confidence: 99%

Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

2020

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Over the past decade, number of optical Earth-observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lack an on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with their specifications or requirements. In cases of multiple methods, recommendations have been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that need to be assessed to evaluate remote sensing satellite data quality, and the best vicarious methodologies to evaluate identified quality parameters such as coherent noise and ground sample distance.

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“…The validation exercise in [39] compares surface reflectance with simulations over AERONET sites, using the atmospheric parameters measured by AERONET station. Since simulated data over AERONET sites are not available, the validation exercise presented in this paper uses the publicly available surface reflectance measurements on the RadCalNet sites [44] [45] as reference data. This RadCalNet reference data are compared with PACO BOA surface reflectance retrieved from Sentinel-2 satellite scenes.…”

Section: Surface Reflectance: Comparison To Radcalnet Sites Datamentioning

confidence: 99%

“…This RadCalNet reference data are compared with PACO BOA surface reflectance retrieved from Sentinel-2 satellite scenes. These sites provide measurements for a certain extended area, typically flat and approximately Lambertian [45]. Therefore, the BOA reflectance compared in this study does not include the application of any Bidirectional Reflectance Distribution at a surface.…”

Section: Surface Reflectance: Comparison To Radcalnet Sites Datamentioning

confidence: 99%

PACO: Python-Based Atmospheric Correction

Reyes

Langheinrich

Schwind

et al. 2020

Sensors

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The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.

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RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range

Cited by 147 publications

References 39 publications

Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa

Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa

Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

PACO: Python-Based Atmospheric Correction

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