In a time of rising concern about climate change and pollution, the water quality of large
lakes acts as an indicator of the health of the environment. To study the water quality at a
large scale - up to several hundreds of kilometres - hyperspectral remote sensing is emerging
as the main solution. Indeed, different quantities relevant to water quality, like turbidity
or concentratrion in chlorophyll-a, can be measured using the spectral reflectance of the
water column. Additionally, airborne and spaceborne sensors can cover large areas, thus
allowing to study the water at a much larger scale than when simply taking water samples at
specific points. Airborne hyperspectral imaging, in particular, offers an acceptable ground
resolution - around a metre - which allows to map relevant quantities precisely. However,
few existing projects deliver maps that have both a sufficient ground resolution and a large
coverage. Furthermore, most existing sensors do not offer a fine spectral resolution, which is
for instance crucial when studying the presence of chlorophyll-a, which can only be detected
in a narrow range of the electromagnetic spectrum. This thesis presents our work with a
hyperspectral sensor developed and used by the Geodetic Engineering Laboratory of EPFL
in the Léman-Baïkal project, a cooperative work which aimed at studying both Lake Geneva
(Switzerland) and Lake Baikal (Russia). The project included ultralight plane flights with
an onboard pushbroom scanner, which allowed to collect data over large areas with a fine
spectral resolution. Alongside the use of this sensor came problematics which are at the
centre of this thesis: the georeferencing of the scan lines, their radiometric calibration, their
analysis and the softwaremanagement of this data. In the following, we present a new method
to georeference pushbroom scan lines that uses co-acquired frame images to perform coregistration
and to achieve a georeferencing, which RMSE is up to 20 times smaller than the
direct one. We propose an efficient radiometric self-calibration method to convert the sensor
output to water-leaving reflectance; this method makes use of the visible peaks of atmospheric
absorption to align the spectral bands with those of a reference acquisition, and uses the
near infrared properties of deep water and vegetation to performabsolute calibration. The
last part of the processing - the software management, including data compression - was
solved by developing a software called HYPerspectral Orthorectification Software (HypOS).
This software is the synthesis of our work, including the tools to performgeometric correction,
radiometric calibration and data compression of our hyperspectral data. Two examples of
applications are given: the first one deals with mapping chlorophyll-a in the Rhone Delta of
Lake Geneva; the second, at a larger scale, uses satellite data to monitor ice coverage over large
lakes like Onega or Ladoga (Russia).