Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements

The use of satellites to monitor the color of the ocean requires effective removal of the atmospheric signal. This can be performed by extrapolating the aerosol optical properties in the visible from the near-infrared (NIR) spectral region assuming that the seawater is totally absorbant in this latter part of the spectrum. However, the non-negligible water-leaving radiance in the NIR which is characteristic of turbid waters may lead to an overestimate of the atmospheric radiance in the whole visible spectrum with increasing severity at shorter wavelengths. This may result in significant errors, if not complete failure, of various algorithms for the retrieval of chlorophyll-a concentration, inherent optical properties and biogeochemical parameters of surface waters.This paper presents results of an inter-comparison study of three methods that compensate for NIR water-leaving radiances and that are based on very different hypothesis: 1) the standard SeaWiFS algorithm (Stumpf et al., 2003; Bailey et al., 2010) based on a bio-optical model and an iterative process; 2) the algorithm developed by Ruddick et al. (2000) based on the spatial homogeneity of the NIR ratios of the aerosol and water-leaving radiances; and 3) the algorithm of Kuchinke et al. (2009) based on a fully coupled atmosphere-ocean spectral optimization inversion. They are compared using normalized water-leaving radiance nL_w in the visible. The reference source for comparison is ground-based measurements from three AERONET-Ocean Color sites, one in the Adriatic Sea and two in the East Coast of USA.Based on the matchup exercise, the best overall estimates of the nL_w are obtained with the latest SeaWiFS standard algorithm version with relative error varying from 14.97% to 35.27% for λ = 490 nm and λ = 670 nm respectively. The least accurate estimates are given by the algorithm of Ruddick, the relative errors being between 16.36% and 42.92% for λ = 490 nm and λ = 412 nm, respectively. The algorithm of Kuchinke appears to be the most accurate algorithm at 412 nm (30.02%), 510 (15.54%) and 670 nm (32.32%) using its default optimization and bio-optical model coefficient settings.Similar conclusions are obtained for the aerosol optical properties (aerosol optical thickness τ(865) and the Ångström exponent, α(510, 865)). Those parameters are retrieved more accurately with the SeaWiFS standard algorithm (relative error of 33% and 54.15% for τ(865) and α(510, 865)).A detailed analysis of the hypotheses of the methods is given for explaining the differences between the algorithms. The determination of the aerosol parameters is critical for the algorithm of Ruddick et al. (2000) while the bio-optical model is critical for the algorithm of Stumpf et al. (2003) utilized in the standard SeaWiFS atmospheric correction and both aerosol and bio-optical model for the coupled atmospheric-ocean algorithm of Kuchinke. The Kuchinke algorithm presents model aerosol-size distributions that differ from real aerosol-size distribution pertaining to the measurements. In conclusion, the results show that for the given atmospheric and oceanic conditions of this study, the SeaWiFS atmospheric correction algorithm is most appropriate for estimating the marine and aerosol parameters in the given turbid waters regions.     

A tele‐experiment on rover motor control via internet

Modern Internet and multimedia techniques enable experiments with remote hardware. This way engineering students can gather practical experience even in areas where their own university does not have the related equipment in place. The focus of this paper is on an experiment to develop a motor controller for a remote mobile robot. Implementation details of such an experiment are addressed with emphasis placed on the subject of tuning a PID controller for steering and speed adaptation of a rover. The complete experiment is conceived as a self‐explaining learning unit on the web. The learning unit comprises material on theory, exercises to provide feedback to the student about his learning progress, simulations to study motor control effects, and finally the experiment with remote robot hardware. This paper presents software techniques to realize a robust system implementation, the educational considerations necessary to present the experiment material properly for the tele‐education context, and experiences obtained during the use of this remote experiment in previously taught classes. © 2005 Wiley Periodicals, Inc.     

Induction=I-Axiomatization+First-Order Consistency

In the early 1980s, there was a number of papers on what should be called proofs by consistency. They describe how to perform inductive proofs, without using an explicit induction scheme, in the context of equational specifications and ground-convergent rewrite systems. The method was explicitly stated as a first-order consistency proof in the case of pure equational, constructor-based specifications. In this paper, we show how, in general, inductive proofs can be reduced to first-order consistency and hence be performed by a first-order theorem prover. Moreover, we extend previous methods, allowing nonequational specifications (even non-Horn specifications) and designing some specific strategies. Finally, we also show how to drop the ground convergence requirement (which is called Saturatedness for general clauses).     

Fast cross-validation of high-breakdown resampling methods for PCA

Cross-validation (CV) is a very popular technique for model selection and model validation. The general procedure of leave-one-out CV (LOO-CV) is to exclude one observation from the data set, to construct the fit of the remaining observations and to evaluate that fit on the item that was left out. In classical procedures such as least-squares regression or kernel density estimation, easy formulas can be derived to compute this CV fit or the residuals of the removed observations. However, when high-breakdown resampling algorithms are used, it is no longer possible to derive such closed-form expressions. High-breakdown methods are developed to obtain estimates that can withstand the effects of outlying observations. Fast algorithms are presented for LOO-CV when using a high-breakdown method based on resampling, in the context of robust covariance estimation by means of the MCD estimator and robust principal component analysis. A robust PRESS curve is introduced as an exploratory tool to select the number of principal components. Simulation results and applications on real data show the accuracy and the gain in computation time of these fast CV algorithms.     

A rank-frequency model for scientific productivity

If f(r) is the number of contributions of an author or rank r, then it is shown that f(r) is proportional to r^–, where >0. The model is dependent on the definitions of a contribution and rank of an author. Three estimation procedures are illustrated and four other scientific productivity studies and two data sets on Canadian Mathematicians are shown to adequately fit this rank-frequency relationship.Research partially supported by National Research Council Grant No. A9229.     

Representations by Contact and Intersection of Segments

A necessary and sufficient condition is given for a connected bipartite graph to be the incidence graph of a contact family of segments and points. We deduce that any four-connected three-colorable plane graph is the contact graph of a family of segments and that any four-colored planar graph without an induced C₄ using four colors is the intersection graph of a family of straight line segments.