Monthly surface thermal forcing in the tropical Pacific from 1980 to 1983

Monthly distributions of surface latent heat flux and solar irradiance in the tropical Pacific were computed from observations of the scanning multichannel microwave radiometer on Nimbus-7 and the visible infrared spin scan radiometer on GOES-W. They are the dominant variable components of the surface heat flux, the sum of which gives the approximate thermal forcing on the ocean. Monthly maps of this sum, from January 1980 to September 1983, and within 20 deg N and 20 deg S, 180 deg and 80 deg W, are presented

Bayesian Methodology for Ocean Color Remote Sensing

66 pagesThe inverse ocean color problem, i.e., the retrieval of marine reflectance from top-of-atmosphere (TOA) reflectance, is examined in a Bayesian context. The solution is expressed as a probability distribution that measures the likelihood of encountering specific values of the marine reflectance given the observed TOA reflectance. This conditional distribution, the posterior distribution, allows the construction of reliable multi-dimensional confidence domains of the retrieved marine reflectance. The expectation and covariance of the posterior distribution are computed, which gives for each pixel an estimate of the marine reflectance and a measure of its uncertainty. Situations for which forward model and observation are incompatible are also identified. Prior distributions of the forward model parameters that are suitable for use at the global scale, as well as a noise model, are determined. Partition-based models are defined and implemented for SeaWiFS, to approximate numerically the expectation and covariance. The ill-posed nature of the inverse problem is illustrated, indicating that a large set of ocean and atmospheric states, or pre-images, may correspond to very close values of the satellite signal. Theoretical performance is good globally, i.e., on average over all the geometric and geophysical situations considered, with negligible biases and standard deviation decreasing from 0.004 at 412 nm to 0.001 at 670 nm. Errors are smaller for geometries that avoid Sun glint and minimize air mass and aerosol influence, and for small aerosol optical thickness and maritime aerosols. The estimated uncertainty is consistent with the inversion error. The theoretical concepts and inverse models are applied to actual SeaWiFS imagery, and comparisons are made with estimates from the SeaDAS standard atmospheric correction algorithm and in situ measurements. The Bayesian and SeaDAS marine reflectance fields exhibit resemblance in patterns of variability, but the Bayesian imagery is less noisy and characterized by different spatial de-correlation scales, with more realistic values in the presence of absorbing aerosols. Experimental errors obtained from match-up data are similar to the theoretical errors determined from simulated data. Regionalization of the inverse models is a natural development to improve retrieval accuracy, for example by including explicit knowledge of the space and time variability of atmospheric variables

Study of atmospheric and bidirectional effects on surface reflectance and vegetation index time series: Application to NOAA AVHRR and preparation for future space missions

The objectives of the investigation, namely 'to characterize the atmospheric and directional effects on surface reflectance and vegetation index using the First International Satellite Cloud Climatology Project (ISLCSP) Field Experiment (FIFE) data set, develop new algorithms to obtain better Advanced Very High Resolution Radiometer (AVHRR) indices, and define possible improvements for future satellite missions', were addressed in three separate, yet complementary studies. First, it was shown, from theoretical calculations, that visible and near infrared reflectances combined linearly at optimum (one or two) viewing angles relate linearly to the fraction of photosynthetically available radiation absorbed by plants, f(sub par), can be used independently of the type of foliage and substrate, eliminate the effects of sub-pixel spatial heterogeneity, and improve the accuracy of the f(sub par) estimates when compared to the Normalized Difference Vegetation Index, NDVI. Second, it was demonstrated that NDVI, even though it is not a linear combination of radiances or reflectances, can be spatially integrated without significant loss of information from scales of 300 to 1000 m. Third, AVHRR visible and near-infrared reflectances over the FIFE site, separating temporal and bidirectional components and determining the model parameters through an original iterative scheme was successfully modeled. It appears that NDVI generated from the top-of-atmosphere reflectances normalized by the bidirectional effects (as determined in the scheme) is a better vegetation index than maximum NDVI. Details about the three studies are presented

Estimating Photosynthetically Available Radiation (PAR) at the Earth's surface from satellite observations

Current satellite algorithms to estimate photosynthetically available radiation (PAR) at the earth' s surface are reviewed. PAR is deduced either from an insolation estimate or obtained directly from top-of-atmosphere solar radiances. The characteristics of both approaches are contrasted and typical results are presented. The inaccuracies reported, about 10 percent and 6 percent on daily and monthly time scales, respectively, are useful to model oceanic and terrestrial primary productivity. At those time scales variability due to clouds in the ratio of PAR and insolation is reduced, making it possible to deduce PAR directly from insolation climatologies (satellite or other) that are currently available or being produced. Improvements, however, are needed in conditions of broken cloudiness and over ice/snow. If not addressed properly, calibration/validation issues may prevent quantitative use of the PAR estimates in studies of climatic change. The prospects are good for an accurate, long-term climatology of PAR over the globe

Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging

Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total Variation (TV) enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties but reasonable precision requires a small smoothing parameter, which slows down the convergence speed. To benefit from the versatility of Nesterov's smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed towards any globally desired precision. Our main contributions are: To propose an expression of the duality gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the convergence speed. We provide a convergence rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.Comment: 11 pages, 6 figures, accepted in IEEE TMI, IEEE Transactions on Medical Imaging 201

The RITS Conference: A Major Event of Biomedical Engineering in France

International audienceResearch in Imaging and HealTh TechnologieS (RITS) is a French scientific meeting dedicated to Biomedical Engineering. This biennial event has given rise to four special issues of IRBM since 2009. The present issue collects some research works presented by young researchers (first author being less than 32 years old). All of them submitted a full paper (instead of a long abstract) to the meeting in order to participate in the SFGBM competition. All the published papers followed the standard reviewing process

Comments on the "Spatial variability of coastal surface water temperature during upwelling"

International audienceComments on the article "Spatial variability of coastal surface water temperature during upwelling

Analysis of Long-Term Cloud Cover, Radiative Fluxes, and Sea Surface Temperature in the Eastern Tropical Pacific

Grant activities accomplished during this reporting period are summarized. The contributions of the principle investigator are reported under four categories: (1) AHVRR (Advanced Very High Resolution Radiometer) data; (2) GOES (Geostationary Operational Environ Satellite) data; (3) system software design; and (4) ATSR (Along Track Scanning Radiometer) data. The contributions of the associate investigator are reported for:(1) longwave irradiance at the surface; (2) methods to derive surface short-wave irradiance; and (3) estimating PAR (photo-synthetically active radiation) surface. Several papers have resulted. Abstracts for each paper are provided

Inversion Schemes to Retrieve Atmospheric and Oceanic Parameters from SeaWiFS Data

The investigation focuses on two key issues in satellite ocean color remote sensing, namely the presence of whitecaps on the sea surface and the validity of the aerosol models selected for the atmospheric correction of SeaWiFS data. Experiments were designed and conducted at the Scripps Institution of Oceanography to measure the optical properties of whitecaps and to study the aerosol optical properties in a typical mid-latitude coastal environment. CIMEL Electronique sunphotometers, now integrated in the AERONET network, were also deployed permanently in Bermuda and in Lanai, calibration/validation sites for SeaWiFS and MODIS. Original results were obtained on the spectral reflectance of whitecaps and on the choice of aerosol models for atmospheric correction schemes and the type of measurements that should be made to verify those schemes. Bio-optical algorithms to remotely sense primary productivity from space were also evaluated, as well as current algorithms to estimate PAR at the earth's surface

Inversion Schemes to Retrieve Atmospheric and Oceanic Parameters from SeaWiFS Data

Firstly, we have analyzed atmospheric transmittance and sky radiance data connected at the Scripps Institution of Oceanography pier, La Jolla during the winters of 1993 and 1994. Aerosol optical thickness at 870 nm was generally low in La Jolla, with most values below 0.1 after correction for stratospheric aerosols. For such low optical thickness, variability in aerosol scattering properties cannot be determined, and a mean background model, specified regionally under stable stratospheric component, may be sufficient for ocean color remote sensing, from space. For optical thicknesses above 0. 1, two modes of variability characterized by Angstrom exponents of 1.2 and 0.5 and corresponding, to Tropospheric and Maritime models, respectively, were identified in the measurements. The aerosol models selected for ocean color remote sensing, allowed one to fit, within measurement inaccuracies, the derived values of Angstrom exponent and 'pseudo' phase function (the product of single scattering albedo and phase function), key atmospheric correction parameters. Importantly, the 'pseudo' phase function can be derived from measurements of the Angstrom exponent. Shipborne sun photometer measurements at the time of satellite overpass are usually sufficient to verify atmospheric correction for ocean color