New commercial-off-the-shelf imaging spectrometers promise the combination of high spatial and spectral resolution needed to retrieve solar induced fluorescence (SIF). Imaging at multiple wavelengths for individual plants and even individual leaves from low-altitude airborne or ground-based platforms has applications in agriculture and carbon-cycle science. Data from these instruments could provide insight into the status of the photosynthetic apparatus at scales of space and time not observable with tools based on gas exchange, and could support the calibration and validation activities of current and forthcoming space missions to quantify SIF. High-spectral resolution enables SIF retrieval from regions of strong telluric absorption by molecular oxygen, and also within numerous solar Fraunhofer lines in atmospheric windows not obscured by oxygen or water absorptions. Because the SIF signal can be < 5 % of background reflectance, rigorous instrument characterization and reduction of systematic error is necessary. Here we develop a spectral stray-light correction algorithm for a commercial off-the-shelf imaging spectrometer designed to quantify SIF. We use measurements from an optical parametric oscillator laser at 44 wavelengths to generate the spectral line-spread function and develop a spectral stray-light correction matrix using a novel exposure-bracketing method. The magnitude of spectral stray light in this instrument is small, but spectral stray light is detectable at all measured wavelengths. Examination of corrected line-spread functions indicates that the correction algorithm reduced spectral stray-light by 1 to 2 orders of magnitude.
The performance analysis of a satellite mission requires specific tools that can simulate the behavior of the platform; its payload; and the acquisition of scientific data from synthetic scenes. These software tools, called End-to-End Mission Performance Simulators (E2ES), are promoted by the European Space Agency (ESA) with the goal of consolidating the instrument and mission requirements as well as optimizing the implemented data processing algorithms. Nevertheless, most developed E2ES are designed for a specific satellite mission and can hardly be adapted to other satellite missions. In the frame of ESA's FLEX mission activities, an E2ES is being developed based on a generic architecture for passive optical missions. FLEX E2ES implements a state-of-the-art synthetic scene generator that is coupled with dedicated algorithms that model the platform and instrument characteristics. This work will describe the flexibility of the FLEX E2ES to simulate complex synthetic scenes with a variety of land cover classes, topography and cloud cover that are observed separately by each instrument (FLORIS, OLCI and SLSTR). The implemented algorithms allows modelling the sensor behavior, i.e. the spectral/spatial resampling of the input scene; the geometry of acquisition; the sensor noises and non-uniformity effects (e.g. stray-light, spectral smile and radiometric noise); and the full retrieval scheme up to Level-2 products. It is expected that the design methodology implemented in FLEX E2ES can be used as baseline for other imaging spectrometer missions and will be further expanded towards a generic E2ES software tool.
The uncertainties in the knowledge of the Instrument Spectral Response Function (ISRF), barycenter of the spectral channels and bandwidth / spectral sampling (spectral resolution) are important error sources in the processing of satellite imaging spectrometers within narrow atmospheric absorption bands. The exhaustive laboratory spectral characterization is a costly engineering process that differs from the instrument configuration in-flight given the harsh space environment and harmful launching phase. The retrieval schemes at Level-2 commonly assume a Gaussian ISRF, leading to uncorrected spectral stray-light effects and wrong characterization and correction of the spectral shift and smile. These effects produce inaccurate atmospherically corrected data and are propagated to the final Level-2 mission products. Within ESA's FLEX satellite mission activities, the impact of the ISRF knowledge error and spectral calibration at Level-1 products and its propagation to Level-2 retrieved chlorophyll fluorescence has been analyzed. A spectral recalibration scheme has been implemented at Level-2 reducing the errors in Level-1 products below the 10% error in retrieved fluorescence within the oxygen absorption bands enhancing the quality of the retrieved products. The work presented here shows how the minimization of the spectral calibration errors requires an effort both for the laboratory characterization and for the implementation of specific algorithms at Level-2.
Chlorophyll fluorescence (Chf) emission allows estimating the photosynthetic activity of vegetation - a key parameter for the carbon cycle models - in a quite direct way. However, measuring Chf is difficult because it represents a small fraction of the radiance to be measured by the sensor. This paper analyzes the relationship between the solar induced Chf emission and the photosynthetically active radiation (PAR) in plants under water stress condition. The solar induced fluorescence emission is measured at leaf level by means of three different methodologies. Firstly, an active modulated light fluorometer gives the relative fluorescence yield. Secondly, a quantitative measurement of the Chf signal is derived from the leaf radiance by using the Fraunhofer Line-Discriminator (FLD) principle, which allows the measurement of Chf in the atmospheric absorption bands. Finally, the actual radiance spectrum of the leaf fluorescence emission is measured by a field spectroradiometer using a device that filters out the incident light in the Chf emission spectral range. The diurnal cycle of fluorescence emission has been measured for both healthy and stressed plants in natural and simulated conditions. The main achievements of this work have been: (1) successful radiometric spectral measurement of the solar induced fluorescence; (2) identification of fluorescence behavior under stress conditions; and (3) establishing a relationship between full spectral measurements with the signal provided by the FLD method. These results suggest the best time of the day to maximize signal levels while identifying vegetation stress status.
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.
We explicitly formulate a family of kernel-based methods for (supervised and partially supervised) multitemporal classification and change detection. The novel composite kernels developed account for the static and temporal cross-information between pixels of subsequent images simultaneously. The methodology also takes into account spectral, spatial, and temporal information, and contains the familiar difference and ratioing methods in the kernel space as a particular cases. The methodology also permits straightforward fusion of multisource information. Several scenarios are considered in which partial or complete labeled information at the prediction time is available. The developed methods are then tested under different classification frameworks: (1) inductive support vector machines (SVM), and (2) one-class support vector data description (SVDD) classifier, in which only samples of a class of interest are used for training. The proposed methods are tested in a challenging real problem for urban monitoring. The composite kernel approach is additionally used as a fusion methodology to combine synthetic aperture radar (SAR) and multispectral data, and to integrate the spatial and textural information at different scales and orientations through Gabor filters. Good results are observed in almost all scenarios; the SVDD classifier demonstrates robust multitemporal classification and adaptation capabilities when few labeled information is available, and SVMs show improved performance in the change detection approach.
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.
In this paper, we propose a new approach to the classification of hyperspectral images. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning methods that avoids these drawbacks and automates the classification process. The method is based on the general formulation of the expectation-maximization (EM) algorithm. This method is applied to crop cover recognition of six hyperspectral images from the same area acquired with HyMap spectrometer during the DAISEX99 campaign. For classification purposes, six different classes are considered in this area: corn, wheat, sugar beet, barley, alfalfa, and soil. Classification accuracy results are compared to common methods: ISODATA, Learning Vector Quantization, Gaussian Maximum Likelihood, Expectation-Maximization, and Neural Networks. The good performance confirms the validity of the proposed approach in terms of accuracy and robustness.
Direct gradient analysis, and other canonical community ordination techniques, have been most commonly used by plant ecologists and others attempting to analyse complex multivariate datasets. These multivariate statistical techniques can be applied to a variety of spectral analyses. Particularly useful is the ability to test significance of environmental variables based upon Monte Carlo permutations, allowing for a step-wise model of variance to be built. This technique has been now applied to hyperspectral remotely sensed data, within the overall context of ESA DAISEX-99 experiment. An extensive field campaign in La-Mancha (Spain) was carried out, simultaneously with the overflight of two airborne imaging spectrometers (DAIS, HYMAP) and other sensors (POLDER, LEANDRE).We use in this work data from the 128-channels HYMAP imaging spectrometer jointly with the ground truth data. Direct gradient analysis of the imagery spectra indicated an overall statistical significance when a model based upon three variables was used. Leaf moisture, LAI, and total chlorophyll were the most highly correlated variables, and all demonstrated statistically significant p-values. Hyperspectral remote sensing data requires new techniques to analyse the increasingly complex data. Application of ordination techniques, although not commonly applied within the remote sensing data processing, show good perspectives for more in depth analysis of the whole DAISEX-99 dataset.
Scaling issues are always playing a critical role in most studies based on remote sensing data. The process of getting quantitative scaling information from raw multi-resolution images is not trivial, and many aspects must be taken very carefully into consideration. To get a better picture about the role of spatial resolution, we conducted a series of flights in summer 1997, in several test sites over Spain and Portugal. In order to minimize the time of acquisition (to get minimal changes in atmospheric status and solar illumination) we used three flight altitude levels, that produced images with 1.25 m, 3 m and 12 m resolutions. The main steps in our methodology are: a) Geometrical registration of the multi-resolution dataset; b) Compensation of atmospheric effects; c) Compensation of angular view changes; d) Multi-resolution analysis. This work evaluates the importance of applying all steps thoroughly in order to achieve a fully comparable multi-resolution data set. Particularly BRDF effects have been commonly disregarded despite the big influence of these effects in apparent reflectance. Results obtained from two different test sites (La Mancha, in Spain, and Evora Natural Park, Portugal), with very different spatial characteristics, indicate the robustness of the approach, but also point out the importance of perturbing effects in getting actual multi-resolution information.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.