Sindy Sterckx

Detecting the Adjacency Effect in Hyperspectral Imagery With Spectral Unmixing Techniques

The adjacency effect is an interesting phenomenon characterized by the occurrence of path interferences between the reflectances coming from different ground-cover materials. The effect is caused by atmospheric scattering, hence a typical approach to its detection has been the modeling of radiation transfer and spectral correspondence at particular wavelengths. In this paper, we investigate the detection of adjacency effects as being a general unmixing problem. This means that we opt to use spectral unmixing to separate the true signature of a pixel from the background scatter reflected from its adjacent neighborhood. To account for different types of atmospheric scattering, we consider several unmixing methods. These include the established linear- and a recently studied generalized bilinear model, as well as a more data-driven unmixing that could implicitly address nonlinearities not covered by the first mentioned approaches. We evaluate these unmixing models by comparing their results with those obtained from a specialized treatment of the adjacency effect in turbid waters surrounded by vegetated land. This comparison is demonstrated on real data acquired under varying atmospheric conditions.

Rayleigh, Deep Convective Clouds, and Cross-Sensor Desert Vicarious Calibration Validation for the PROBA-V Mission

PROBA-V is a remote sensing satellite mission for global monitoring of vegetation. It is designed to offer almost daily coverage of all land masses and to provide data continuity with the VGT2 instrument aboard SPOT-5. Accurate radiometric calibration is key to the success of the mission; therefore, a comprehensive system for in-flight radiometric calibration has been developed. Without no onboard calibration devices, this in-flight calibration will rely fully on vicarious methods. In total nine techniques for vicarious calibration have been implemented and tested in order to meet the radiometric mission requirements. Three key methods that contribute largely to the calibration performance are presented in this paper: Rayleigh, deep convective clouds, and cross-sensor calibration over stable desert sites. As the PROBA-V sensor has still to be launched, calibration algorithm verification is performed using data from the spectrally very similar SPOT-VGT1 and SPOT-VGT2 sensors.

Use of simulated reflectances over bright desert target as an absolute calibration reference

This letter presents the improvements of an absolute calibration reference system based on simulated top-of-atmosphere bidirectional reflectance factor time series over bright desert targets. The current work highlights a case study performed over Committee on Earth Observation Satellites (CEOS) calibration target Libya4, demonstrating that it is possible to achieve a mean accuracy of 3% when simulation is compared with calibrated observations acquired by polar orbiting satellites.

Retrieval of oceanic constituents from ocean color using simulated annealing

Abstract : The color of the sea is determined by the contents of the water, especially the concentrations of suspended particulate matter (SPM), phytoplankton pigments such as chlorophyll (CHL) and colored dissolved organic matter (CDOM). Reversely, optical sensors that measure the water-leaving reflectance spectra allow us to calculate the desired concentration products. In this paper, a method is introduced that is valid for both case 1 and 2 waters. To this end, model is fitted to reflectance spectra, using simulated annealing for optimizing the mean square of the reflectance over all spectra.

A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River

A seasonally robust algorithm for the retrieval of Suspended Particulate Matter (SPM) in the Scheldt River from hyperspectral images is presented. This algorithm can be applied without the need to simultaneously acquire samples (from vessels and pontoons). Especially in dynamic environments such as estuaries, this leads to a large reduction of costs, both in equipment and personnel. The algorithm was established empirically using in situ data of the water-leaving reflectance obtained over the tidal cycle during different seasons and different years. Different bands and band combinations were tested. Strong correlations were obtained for exponential relationships between band ratios and SPM concentration. The best performing relationships are validated using airborne hyperspectral data acquired in June 2005 and October 2007 at different moments in the tidal cycle. A band ratio algorithm (710 nm/596 nm) was successfully applied to a hyperspectral AHS image of the Scheldt River to obtain an SPM concentration map.

Optical Sensor CAlibration using simulated radiances over desert sites

This paper presents the improvements of an absolute calibration reference system based on simulated TOA BRF time series over bright desert targets. The current work highlights a case study performed over Libya4 demonstrating that it is possible to achieve a mean accuracy of 3% when simulation are compared with calibrated observations. This activity is part of VITO contribution to CEOS/WGCV/IVOS calibration mission.

Fractal dimension as correction factor for stand-level indirect leaf area index measurements

Rapid, reliable and objective estimation of Leaf Area Index (LAI) at various scales is of utmost importance in numerous studies on the Earths ecosystem. The Licor LAI-2000 Plant Canopy Analyzer (PCA) correlates measured gap fractions to overall LAI by means of the inversion of a radiative transfer model. The PCAs model assumes a random distribution of foliage elements in the stand canopy. However, clumping is observed at different scales in nature. The objectives of this study were, first, the quantification of the LAI measurement error of the PCA due to foliage clumping at stand-level, and second, the derivation of an easily measurable correction factor. For this, foliage elements were simulated in a virtual 3D-space. PCA LAI measurements were simulated by applying the same PCA inversion model onto virtually taken hemispherical photographs resulting in both exact reference LAI values and corresponding PCA measurements. Fractal dimension, quantifying the deviation from a complete random foliage distribution, was tested as a correction factor for PCA measurements. Correction models for PCA measurements were build, based on the measured fractal dimension. A post validation as performed on field data obtained by means of littertraps (reference). A clear relation between fractal dimension and the proportion of underestimation of LAI by the PCA with increasing clumping of foliage was found. Implementation of the regression model resulted in significantly improved LAI measurements.

Multiple vicarious calibration using combined accuracy estimation

Radiometric calibration often employs several independent vicarious calibration techniques to increase robustness and accuracy. We present a statistical methodology for combining results in a hierarchical scheme. The method, developed for the PROBA-V remote sensing mission, is based on handling and propagating of accuracies in accordance with the ISO GUM. Robust estimation is performed and outliers removed. Results over different sites are combined using weighted averaging. Weighted linear regression is used for temporal averaging. Results from different methods are combined taking into account possible bias. Finally an operational update strategy is proposed which relies on a significance criterion.

In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument

Since its launch in May 2013, the in-orbit radiometric performance of PROBA-V has been continuously monitored. Due to the absence of on-board calibration devices, in-flight performance monitoring and calibration relies fully on vicarious calibration methods. In this paper, the multiple vicarious calibration techniques used to verify radiometric accuracy and to perform calibration parameter updates are discussed. Details are given of the radiometric calibration activities during both the commissioning and operational phase. The stability of the instrument in terms of overall radiometry and dark current is analyzed. Results of an independent comparison against MERIS and SPOT VEGETATION-2 are presented. Finally, an outlook is provided of the on-going activities aimed at improving both data consistency over time and within-scene uniformity.

Upscaling of spectroradiometer data for stress detection in orchards with remote sensing

This paper studies the detection of vegetation stress in orchards via remote sensing. During previous research, it was shown that stress can be detected reliably on hyperspectral reflectances of the fresh leaves, using a generic wavelet based hyperspectral classification. In this work, we demonstrate the capability to detect stress from airborne/spaceborne hyperspectral sensors by upscaling the leaf reflectances to top of atmosphere (TOA) radiances. Several data sets are generated, measuring the foliar reflectance with a portable field spectroradiometer, covering different time periods, fruit variants and stress types. We concentrated on the Jonagold and Golden Delicious apple trees, induced with mildew and nitrogen deficiency. First, a directional homogeneous canopy reflectance model (ACRM) is applied on these data sets for simulating top of canopy (TOC) spectra. Then, the TOC level is further upscaled to TOA, using the atmospheric radiative transfer model MODTRAN4. To simulate hyperspectral imagery acquired with real airborne/spaceborne sensors, the spectrum is further filtered and subsampled to the available resolution. Using these simulated upscaled TOC and TOA spectra in classification, we will demonstrate that there is still a differentiation possible between stresses and non-stressed trees. Furthermore, results show it is possible to train a classifier with simulated TOA data, to make a classification of real hyperspectral imagery over the orchard.