-
[show abstract]
[hide abstract]
ABSTRACT: Accurate spectral calibration of airborne and spaceborne imaging spectrometers is essential for proper preprocessing and scientific exploitation of high spectral resolution measurements of the land and atmosphere. A systematic performance assessment of onboard and scene-based methods for in-flight monitoring of instrument spectral calibration is presented for the first time in this paper. Onboard and ground imaging data were collected at several flight altitudes using the Airborne Prism Experiment (APEX) imaging spectrometer. APEX is equipped with an in-flight characterization (IFC) facility allowing the evaluation of radiometric, spectral, and geometric system properties, both in-flight and on-ground for the full field of view. Atmospheric and onboard filter spectral features present in at-sensor radiances are compared with the same features in reference transmittances convolved to varying instrument spectral configurations. A spectrum-matching algorithm, taking advantage of the high sensitivity of measurements around sharp spectral features toward spectrometer spectral performance, is used to retrieve channel center wavelength and bandwidth parameters. Results showed good agreement between spectral parameters estimated using onboard IFC and ground imaging data. The average difference between estimates obtained using the O(2) and H(2)O features and those obtained using the corresponding filter features amounted to about 0.3 nm (0.05 of a spectral pixel). A deviation from the nominal laboratory instrument spectral calibration and an altitude-dependent performance was additionally identified. The relatively good agreement between estimates obtained by the two approaches in similar spectral windows suggests they can be used in a complementary fashion: while the method relying on atmospheric features can be applied without the need for dedicated calibration acquisitions, the IFC allows assessment at user-selectable wavelength positions by custom filters as well as for the system on-ground.
Applied Optics 08/2011; 50(24):4755-64. · 1.41 Impact Factor
-
IEEE Geosci. Remote Sensing Lett. 01/2011; 8:844-848.
-
IEEE T. Geoscience and Remote Sensing. 01/2011; 49:4308-4317.
-
IEEE T. Geoscience and Remote Sensing. 01/2011; 49:2601-2611.
-
[show abstract]
[hide abstract]
ABSTRACT: The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval.
Applied Optics 05/2010; 49(15):2858-71. · 1.41 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Although the GMES/Sentinel satellite series were primarily designed to
provide observations for operational services and routine applications,
there is a growing interest in the scientific community towards the
usage of Sentinel data for more advanced and innovative science. Apart
from the improved spatial and spectral capabilities, the availability of
consistent time series covering a period of over 20 years opens
possibilities never explored before, such as systematic data
assimilation approaches exploiting the time-series concept, or the
incorporation in the modelling approaches of processes covering time
scales from weeks to decades. Sentinel-3 will provide continuity to
current ENVISAT MERIS/AATSR capabilities. The results already derived
from MERIS/AATRS will be more systematically exploited by using OLCI in
synergy with SLST. Particularly innovative is the case of Sentinel-2,
which is specifically designed for land applications. Built on a
constellation of two satellites operating simultaneously to provide 5
days geometric revisit time, the Sentinel-2 system will providing global
and systematic acquisitions with high spatial resolution and with a high
revisit time tailored towards the needs of land monitoring. Apart from
providing continuity to Landsat and SPOT time series, the Sentinel-2
Multi-Spectral Instrument (MSI) incorporates new narrow bands around the
red-edge for improved retrievals of biophysical parameters. The
limitations imposed by the need of a proper cloud screening and
atmospheric corrections have represented a serious constraint in the
past for optical data. The fact that both Sentinel-2 and 3 have
dedicated bands to allow such needed corrections for optical data
represents an important step towards a proper exploitation, guarantying
consistent time series showing actual variability in land surface
conditions without the artefacts introduced by the atmosphere. Expected
operational products (such as Land Cover maps, Leaf Area Index,
Fractional Vegetation Cover, Fraction of Absorbed Photosynthetically
Active Radiation, and Leaf Chlorophyll and Water Contents), will be
enhanced with new scientific applications. Higher level products will
also be provided, by means of mosaicking, averaging, synthesising or
compositing of spatially and temporally resampled data. A key element in
the exploitation of the Sentinel series will be the adequate use of data
synergy, which will open new possibilities for improved Land Models.
This paper analyses in particular the possibilities offered by
mosaicking and compositing information derived from Sentinel-2
observations in high spatial resolution to complement dense time series
derived from Sentinel-3 data with more frequent coverage. Interpolation
of gaps in high spatial resolution time series (from Sentinel-2 data) by
using medium/low resolution data from Sentinel-3 (OLCI and SLSTR) is
also a way of making series more temporally consistent with high spatial
resolution. The primary goal of such temporal interpolation / spatial
mosaicking techniques is to derive consistent surface reflectance data
virtually for every date and geographical location, no matter the
initial spatial/temporal coverage of the original data used to produce
the composite. As a result, biophysical products can be derived in a
more consistent way from the spectral information of Sentinel-3 data by
making use of a description of surface heterogeneity derived from
Sentinel-2 data. Using data from dedicated experiments (SEN2FLEX,
CEFLES2, SEN3EXP), that include a large dataset of satellite and
airborne data and of ground-based measurements of atmospheric and
vegetation parameters, different techniques are tested, including
empirical / statistical approaches that builds nonlinear regression by
mapping spectra to a high dimensional space, up to model inversion /
data assimilation scenarios. Exploitation of the temporal domain and
spatial multi-scale domain becomes then a driver for the systematic
exploitation of GMES/Sentinels data time series. This paper review
current status, and identifies research priorities in such direction.
04/2010; 12:6805.
-
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010
-
IEEE T. Geoscience and Remote Sensing. 01/2010; 48:3046-3054.
-
[show abstract]
[hide abstract]
ABSTRACT: An accurate knowledge of the spectral calibration of imaging spectrometers is required for optimum data processing and interpretation. The scene-based spectral characterization of imaging spectrometers is frequently necessary to update or replace the pre-flight laboratory-based spectral characterization supplied by the data provider. An automatic method for the estimation of spectral calibration parameters (channel position and bandwidth) at atmospheric absorption regions from high spectral resolution imaging spectrometers (spectral sampling interval below 5 nm) is presented in this contribution. The method has been tested on two commercial instruments with spectral sampling intervals below 2.5 nm. Optical aberrations such as smile, spectrometer shift and rotation and degradation of channel bandwidth have been detected and are discussed in terms of potential error sources at the instrument level.
Optics Express 08/2009; 17(14):11594-606. · 3.59 Impact Factor
-
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings; 01/2009
-
IEEE T. Geoscience and Remote Sensing. 01/2009; 47:2340-2351.
-
[show abstract]
[hide abstract]
ABSTRACT: Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, which is a typical example of a push-broom instrument exhibiting a relatively high noise component. The proposed correction method is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. A technique to exclude the contribution of the spatial high frequencies of the surface from the destriping process is introduced. First, the performance of the proposed algorithm is tested on a set of realistic synthetic images with added modeled noise in order to quantify the noise reduction and the noise estimation accuracy. Then, algorithm robustness is tested on more than 350 real CHRIS images from different sites, several acquisition modes (different spatial and spectral resolutions), and covering the full range of possible sensor temperatures. The proposed algorithm is benchmarked against the CHRIS reference algorithm. Results show excellent rejection of the noise pattern with respect to the original CHRIS images, especially improving the removal in those scenes with a natural high contrast. However, some low-frequency components still remain. In addition, the developed correction model captures and corrects the dependency of the noise patterns on sensor temperature, which confirms the robustness of the presented approach.
Applied Optics 11/2008; 47(28):F46-60. · 1.41 Impact Factor
-
Hermann Kaufmann,
Karl Segl, Luis Guanter,
Stefan Hofer,
Klaus-Peter Förster,
Timo Stuffler,
Andreas Mueller,
Rudolf Richter,
Heike Bach,
Patrick Hostert,
Christian Chlebek
[show abstract]
[hide abstract]
ABSTRACT: The Environmental Mapping and Analysis Program (EnMAP) is a German built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure over the 420-2450 nm spectral range at a varying spectral sampling of 5-10 nm. Images will covered 30 kmtimes30 km areas at approximate pixel sizes of 30 m. The primary goal of EnMAP is the exploitation of hyperspectral data for the derivation of high-spectral resolution observations of biophysical, biochemical and geochemical variables from a range of surface covers, such as vegetation canopies, rock and soil targets and coastal waters, on a global scale. General descriptions of the EnMAP instrument, the satellite operation concept, the data processing and archiving structures and current project development activities are provided in this paper.
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2008, July 8-11, 2008, Boston, Massachusetts, USA, Proceedings; 01/2008
-
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2008, July 8-11, 2008, Boston, Massachusetts, USA, Proceedings; 01/2008
-
IEEE T. Geoscience and Remote Sensing. 01/2007; 45:4105-4118.
-
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2007, July 23-28, 2007, Barcelona, Spain, Proceedings; 01/2007
-
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2007, July 23-28, 2007, Barcelona, Spain, Proceedings; 01/2007
-
[show abstract]
[hide abstract]
ABSTRACT: One of the initial steps in the preprocessing of remote sensing data is the atmospheric correction of the at-sensor radiance images, i.e., radiances recorded at the sensor aperture. Apart from the accuracy in the estimation of the concentrations of the main atmospheric species, the retrieved surface reflectance is also influenced by the spectral calibration of the sensor, especially in those wavelengths mostly affected by gaseous absorptions. In particular, errors in the surface reflectance appear when a systematic shift in the nominal channel positions occurs. A method to assess the spectral calibration of hyperspectral imaging spectrometers from the acquired imagery is presented in this paper. The fundamental basis of the method is the calculation of the value of the spectral shift that minimizes the error in the estimates of surface reflectance. This is performed by an optimization procedure that minimizes the deviation between a surface reflectance spectrum and a smoothed one resulting from the application of a low-pass filter. A sensitivity analysis was performed using synthetic data generated with the MODTRAN4 radiative transfer code for several values of the spectral shift and the water vapor column content. The error detected in the retrieval is less than +/- 0.2 nm for spectral shifts smaller than 2 nm, and less than +/- 1.0 nm for extreme spectral shifts of 5 nm. A low sensitivity to uncertainties in the estimation of water vapor content was found, which reinforces the robustness of the algorithm. The method was successfully applied to data acquired by different hyperspectral sensors.
Applied Optics 05/2006; 45(10):2360-70. · 1.41 Impact Factor
-
IEEE T. Geoscience and Remote Sensing. 01/2005; 43:2908-2917.
-
[show abstract]
[hide abstract]
ABSTRACT: An algorithm for the derivation of atmospheric parameters and surface reflectance data from MEdium Resolution Imaging Specrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT) images has been developed. Geo-rectified aerosol optical thickness (AOT), columnar water vapor (CWV) and spectral surface reflectance maps are generated from MERIS Level-1b data over land. The algorithm has been implemented so that AOT, CWV and reflectance products are provided on an operational manner, making no use of ancillary parameters apart from those attached to MERIS products. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamental basis of the algorithm and applicable error figures are presented in the first part of this paper. In particular, errors of ± 0.03, ± 4% and ± 8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis based on a synthetic data set simulated under a usual MERIS scene configuration over land targets. The assumption of a fixed aerosol model, the coarse spatial resolution of the AOT product and the neglection of surface reflectance directional effects were also identified as limitations of SCAPE-M. Validation results are detailed in the second part of the paper. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed an average Root Mean Square Error (RMSE) of 0.05, and an average correlation coefficient R2 of about 0.7–0.8. R2 values grew up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the MERIS Level-2 ESA CWV product. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from the Compact High Resolution Imaging Spectrometer (CHRIS) on board the PRoject for On-Board Autonomy (PROBA) in the first place. Reflectance retrievals have also been compared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method. A good correlation in the red and near-infrared bands was found, although a considerably higher proportion of pixels was successfully processed by SCAPE-M.
Remote Sensing of Environment 112(6):2898-2913. · 4.57 Impact Factor
