HYPXIM &amp;#x2014; A hyperspectral satellite defined for science, security and defence users

Magne, Sylvain; Gamet, Philippe; Lefèvre-Fonollosa, Marie-José

doi:10.1109/whispers.2011.6080864

Cited by 50 publications

(38 citation statements)

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“…The identification of spectral regions that are important for SOC prediction could also help create new indices with high potential to be transferred to future hyperspectral sensors. Several next generation satellite VIS-NIR-SWIR hyperspectral sensors are planned to be launched, including: HISUI from Japan [90], PRISMA from Italy [91], and EnMAP from Germany in 2018 [92]; HYPXIM from France in 2020 [93]; SHALOM from Italy and Israel in 2021 [94]; and HyspIRI from USA in~2022 [95]. These hyperspectral sensors, which have spatial resolutions between~8-30 m, are expected to provide higher SNRs than current sensors, especially in the SWIR region, and could allow for an operational quantitative SOC mapping at low cost with global coverage.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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Abstract:The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R 2 = 0.60-0.62, while models for the Hyperion sensor showed R 2 = 0.49-0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600-700 nm), and the short wave infrared region betweeñ 2000-2250 nm. The use of SMLR and spectral indices based on reference channels at~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method.

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Section: Discussionmentioning

confidence: 99%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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“…EnMAP is expected to become a key system in the field of spaceborne imaging spectroscopy in potential co-existence with other comparable and complementary missions, such as the Japanese Hyperspectral Imager Suite (HISUI) [42], the Italian PRISMA (Hyperspectral Precursor of the Application Mission) [43], the USA's Hyperspectral Infrared Imager (HyspIRI) [44], the French HYPXIM [45] and the Italian-Israeli SHALOM (Spaceborne Hyperspectral Applicative Land and Ocean Mission). Even though no coordination between different missions for the optimization of acquisitions is foreseen, imaging spectroscopy users will benefit from the larger diversity of data sources.…”

Section: Introductionmentioning

confidence: 99%

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

et al. 2015

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Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

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“…The rapid development of hyperspectral sensors allows providing more comprehensive remote sensing data of water reflectance spectral properties, attributable to the full range of visible light, i.e., to more wavebands, and higher spectral resolution. The increasing quantity of hyperspectral satellite missions, from existing Hyperion (Folkman et al, 2001), CHRIS (Barnsley et al, 2004), and HICO (Corson et al, 2008(Corson et al, ) (terminated in 2014 to the expected missions such as EnMAP , PRISMA (Meini et al, 2015), HyspIRI (Lee et al, 2015), HYPXIM (Michel et al, 2011), and PACE (Gregg and Rousseaux, 2017), has and will provide much potential for applications of hyperspectral satellite data in aquatic ecosystems (Guanter et al, 2015;Xi et al, 2015). Band placement for improving PFTs retrieval from remote sensing data was investigated by analyzing dominant spectral features in the absorption spectra of the PFTs determined with different methods, with recommendations of using continuous hyperspectral data as they will provide better results (Wolanin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

Hieronymi

Krasemann

et al. 2017

Front. Mar. Sci.

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In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, R rs (λ), was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 10 5 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance), Chl, are ranging from 0 to 200 mg m −3 , concentrations of non-algal particles, NAP, from 0 to 1,500 g m −3 , and absorption coefficients of chromophoric dissolved organic matter (CDOM) at 440 nm from 0 to 20 m −1 . A second, independent, smaller dataset of simulated R rs (λ) used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ R rs (λ) dataset collected during a cyanobacterial bloom in Lake Taihu (China) is carried out and the approach predicts blue cyanobacteria to be dominant. This fits well with observation of the blue cyanobacteria Microcystis sp. in the lake. This study provides an efficient approach, which can be promisingly applied to hyperspectral sensors, for identifying dominant phytoplankton spectral groups purely based on R rs (λ) spectra.

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HYPXIM — A hyperspectral satellite defined for science, security and defence users

Cited by 50 publications

References 1 publication

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

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