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New processor and reference dataset for hyperspectral CHRIS-PROBA images over coastal and inland waters
Lavigne, H.; Vanhellemont, Q.; Ruddick, K.; Dogliotti, A. (2021). New processor and reference dataset for hyperspectral CHRIS-PROBA images over coastal and inland waters, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE International Symposium on Geoscience and Remote Sensing IGARSS, : pp. 1-4. https://dx.doi.org/10.1109/IGARSS47720.2021.9554430
In: (2021). 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE International Symposium on Geoscience and Remote Sensing IGARSS. IEEE: [s.l.]. ISBN 978-1-6654-4762-1; e-ISBN 978-1-6654-0369-6. https://dx.doi.org/10.1109/IGARSS47720.2021
In: IEEE International Symposium on Geoscience and Remote Sensing IGARSS. IEEE: New York. ISSN 2153-6996
Peer reviewed article  

Available in  Authors 
Document type: Conference paper

Author keywords
    hyperspectral, CHRIS-PROBA, water reflectance, atmospheric correction

Authors  Top 
  • Lavigne, H.
  • Vanhellemont, Q.
  • Ruddick, K.
  • Dogliotti, A.

Abstract
    Hyperspectral remote sensing is expected to facilitate aquatic applications, such as the monitoring of harmful algal blooms or the determination of suspended sediment properties. However, this requires accurate processing to retrieve water reflectance from top of atmosphere radiance. Here we present a new processor for hyperspectral (mode 1) CHRIS-PROBA images and make available to the public a sample dataset of water reflectance images. Although the CHRIS-PROBA sensor has been operating for about 20 years, only a few images with water targets were acquired with the hyperspectral mode (mode 1) and a dedicated atmospheric correction was needed. The processor presented here includes systematic noise removal, atmospheric correction and georeferencing. Validation of water reflectance products shows generally good consistency between in situ and satellite measurements although an underestimation in the 400 nm _ 470 nm range was observed. Finally, it is expected that the sample dataset of hyperspectral water reflectance images will be useful to test new algorithms for water products or to compare processing methods.

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