HYPXIM: A second generation high spatial resolution hyperspectral satellite for dual applications

Carrère, V.; Briottet, Xavier; Jacquemoud, Stéphane; Marion, Rodolphe; Bourguignon, Anne; Chami, Malik; Dumont, Marie; Minghelli-Roman, Audrey; Weber, Christiane; Lefèvre-Fonollosa, Marie-José; Mandea, Mioara

doi:10.1109/whispers.2013.8080685

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…revisit frequency should be lower than a week, and spatial resolution should at least be decametric). While the optical domain is already considerably exploited, investigation on thermal infrared missions for water resources management (Hulley et al, 2017;Lagouarde et al, 2018) as well as hyperspectral missions for vegetation (Guanter et al, 2015) or soil degradation monitoring (Carrere et al, 2013) are currently running. New types of data sources are also becoming increasingly available and are being tested for agricultural applications.…”

Section: Remote Sensing For Agriculture: Caveats and Ways Forwardmentioning

confidence: 99%

Remote sensing for agricultural applications: A meta-review

Weiss

Jacob

Duveiller

2020

Remote Sensing of Environment

1,088

416

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Section: Remote Sensing For Agriculture: Caveats and Ways Forwardmentioning

confidence: 99%

Remote sensing for agricultural applications: A meta-review

Weiss

Jacob

Duveiller

2020

Remote Sensing of Environment

1,088

416

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“…Further work is needed to determine how well LMA and EWT can be estimated from hyperspectral images acquired with a lower spatial resolution. Indeed, the present and future satellite hyperspectral missions, such as PRISMA [66], SBG (inheritor mission of HyspIRI [67]), or Biodiversity (inheritor mission of HYPXIM [68]), will have spatial resolution ranging from 8 to 30 m. Indeed, difficulties should arise with 30 m-resolution images acquired over open canopies, as spectral mixing would occur. However, 8 mresolution images could, in general, allow the effective isolation of tree crowns quite well, and the results obtained in the present study concerning the sensitivity of crown reflectance to the modeling used within the RTM, as well as the conclusions regarding the best spectral range to use for the training of the estimator, should still be valid.…”

Section: Ewt and Lma Estimationsmentioning

confidence: 99%

Impact of Modeling Abstractions When Estimating Leaf Mass per Area and Equivalent Water Thickness over Sparse Forests Using a Hybrid Method

et al. 2021

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Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting of training a random forest regressor (RFR) over the outputs of the discrete anisotropic radiative transfer (DART) model, to yield accurate EWT and LMA estimates depending on the scene modeling within DART and the spectral interval considered. We show that canopy abstractions mostly affect crown reflectance over the 0.75–1.3 μm range. It was observed that excluding these wavelengths when training the RFR resulted in the abstraction level having no effect on the subsequent LMA estimates (RMSE of 0.0019 g/cm2 for both the detailed and abstract models), and EWT estimates were not affected by the level of abstraction. Over AVIRIS-Next Generation images, we showed that the hybrid method trained with a simplified scene obtained accuracies (RMSE of 0.0029 and 0.0028 g/cm2 for LMA and EWT) consistent with what had been obtained from the test dataset of the calibration phase (RMSE of 0.0031 and 0.0032 g/cm2 for LMA and EWT), and the result yielded spatially coherent maps. The results demonstrate that, provided an appropriate spectral domain is used, the uncertainties inherent to the abstract modeling of tree crowns within an RTM do not significantly affect EWT and LMA accuracy estimates when tree crowns can be identified in the images.

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“…Then, satellite simulated radiance is computed with the Comanche code [48]. Spatial aggregation, noise and point spread function are applied following the specifications of the future hyperspectral satellite Hypxim [51]. Finally, reflectance spectra are retrieved using COCHISE.…”

Section: Hymap Imagementioning

confidence: 99%

Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Alakian

Achard

2020

Remote Sensing

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A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile , asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

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HYPXIM: A second generation high spatial resolution hyperspectral satellite for dual applications

Cited by 10 publications

References 17 publications

Remote sensing for agricultural applications: A meta-review

Remote sensing for agricultural applications: A meta-review

Impact of Modeling Abstractions When Estimating Leaf Mass per Area and Equivalent Water Thickness over Sparse Forests Using a Hybrid Method

Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

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