This paper evaluates the potential use of nonlinear retrieval methods to derive cloud, surface and atmospheric
properties from hyperspectral MetOp-IASI and MTG-IRS spectra. The methods are compared in terms of both
accuracy and speed with the current IASI and IRS L2 PPFP implementation, which consists of a principal component
extraction, typically referred as to Empirical Orthogonal Functions (EOF), and a subsequent canonical
linear regression. This research proposes the evaluation of some other methodological advances considering 1)
other linear feature extraction methods instead of EOF, such as (orthonormalized) partial least squares, and
2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear
regression models considered in this work are artificial neural networks (NN) and kernel ridge regression (KRR)
as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models outperform
the linear retrieval both in the presence of noise and noise-free settings, and for both IASI and IRS
synthetic and real data. The combination of models makes the retrieval more robust, improves the accuracy,
and decreases the estimated bias. These results confirm the validity of the proposed approach for retrieval of
In addition to typical random noise, remote sensing hyperspectral images are generally affected by non-periodic partially deterministic disturbance patterns due to the image formation process and characterized by a high degree of spatial and spectral coherence. This paper presents a new technique that faces the problem of removing the spatial coherent noise known as vertical stripping (VS) usually found in images acquired by push-broom sensors, in particular for the Compact High Resolution Imaging Spectrometer (CHRIS). The correction is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. The proposed method introduces a way to exclude the contribution of the spatial high frequencies of the surface from the destripping process that is based on the information contained in the spectral domain. Performance of the proposed algorithm is tested on sites of different nature, several acquisition modes (different spatial and spectral resolutions) and covering the full range of possible sensor temperatures. In addition, synthetic realistic scenes have been created, adding modeled noise for validation purposes. Results show an excellent rejection of the noise pattern with respect to the original CHRIS images. The analysis shows that high frequency VS is successfully removed, although some low frequency components remain. In addition, the dependency of the noise patterns with the sensor temperature has been found to agree with the theoretical one, which confirms the robustness of the presented approach. The approach has proven to be robust, stable in VS removal, and a tool for noise modeling. The general nature of the procedure allows it to be applied for destripping images from other spectral sensors.
Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant source of error in both sea and land cover biophysical parameter retrieval. Sensors with spectral channels beyond 1 um have demonstrated good capabilities to perform cloud masking. This spectral range can not be exploited by recently developed hyperspectral sensors that work in the spectral range between 400- 1000 nm. However, one can take advantage of their high number of channels and spectral resolution to increase the cloud detection accuracy, and to describe properly the detected clouds (cloud type, height, subpixel coverage, could shadows, etc.) In this paper, we present a methodology for cloud detection that could be used by sensors working in the VNIR range. First, physically-inspired features are extracted (TOA reflectance and their spectral derivatives, atmospheric oxygen and water vapour absorptions, etc). Second, growing maps are built from cloud-like pixels to select regions which potentially could contain clouds. Then, an unsupervised clustering algorithm is applied in these regions using all extracted features. The obtained clusters are labeled into geo-physical classes taking into account the spectral signature of the cluster centers. Finally, an spectral unmixing algorithm is applied to the segmented image in order to obtain an abundance map of the cloud content in the cloud pixels. As a direct consequence of the detection scheme, the proposed system is capable to yield probabilistic outputs on cloud detected pixels in the image, rather than flags. Performance of the proposed algorithm is tested on six CHRIS/Proba Mode 1 images, which presents a spatial resolution of 32 m, 62 spectral bands with 6-20 nm bandwidth, and multiangularity.
Multiangular and hyperspectral capabilities of the last generation of remote sensing sensors require new data processing algorithms that can take advantage of this new type of information. In terms of atmospheric correction, taking into account surface directional reflectance properties leads to a coupling between surface and atmospheric radiative transfer effects that cannot be analitically decoupled in the most general case, so other strategies must be
developed. In addition to this, commonly used radiative transfer codes are based on a plane-parallel atmosphere approximation, what causes problems for large view and illumination zenith angles. The aim of this paper is to present an atmospheric correction method based on Vermote et al. scheme for MODIS atmospheric correction.
It considers BRDF effects in the surface, improving 6S code calculations in off-nadir configurations . We have simulated the top-of-the-atmosphere reflectances using nine different natural surfaces by means of MODTRAN4 radiative transfer code. The reflectance angular pattern retrieved for each surface has allowed us to validate the
model and check the improvements versus the original MODIS algorithm.