The atmospheric correction of remote sensing data in the reflective domain is today very well controlled under clear sky conditions. However, cirrus clouds represent 2/3 of the global terrestrial cover, making several images unusable. Gao and Li proposed an empirical method of thin cirrus correction. The method shows very good results on dark surfaces but presents bias higher than 0.02 when the surface becomes too reflective and the cirrus too thick. In addition, it only corrects for the upwelling path. Simulations show that the presence of cirrus on the sun-to-ground path has a significant influence on the received signal and must also be corrected. This paper shows that considering the transmission term of the cirrus of the upwelling and downwelling paths improves the results in the red-edge but also the SWIR, with an RMSE divided by 2.

This paper presents a kernel-based nonlinear mixing model for hyperspectral data, where the nonlinear function belongs to a Hilbert space of vector valued functions. The proposed model extends the existing ones by accounting for band-dependent and neighboring nonlinear contributions. The key idea is to work under the assumption that nonlinear contributions are dominant in some parts of the spectrum, while they are less pronounced in other parts. In addition to this, we motivate the need for taking into account nonlinear contributions originating from the ground covers of neighboring pixels by practical considerations, precisely the adjacency effect. The relevance of the proposed model is that the nonlinear function is associated with a matrix valued kernel that allows to jointly model a wide range of nonlinearities and includes prior information regarding band dependences. Furthermore, the choice of the nonlinear function input allows to incorporate neighboring effects. The optimization problem is strictly convex and the corresponding iterative algorithm is based on the alternating direction method of multipliers. Finally, experiments conducted using synthetic and real data demonstrate the effectiveness of the proposed approach.

The growing number of sensors raises questions about the image parameters required for the application, soil identification and moisture estimation. Hyperspectral images are also known to contain highly redundant information. Hence not all the spectral bands are needed for the satisfactory classification of the soil types. Hence, the work was aimed at obtaining these optimal spectral bands for identifying the soil types and to use these spectral bands to estimate the moisture content of the soils using the method proposed by Whiting et.al.

Geosynchronous satellite can measure any area with high temporal repetitivity within its coverage region because of its relative static location compared to Earth. Considering the temporal repetitivity, it can satisfy requirements for coastal zone monitoring but also has to face the influence of the varying solar angle and sensor angle (zenith and azimuth). Up to now, there is no geosynchronous sensor dedicated to ocean color monitoring (a geosynchronous sensor "Korea Geostationary Ocean Color Imager" (KGOCI) is supposed to be launched in 2009). To obtain radiances from the ocean at 36000 km of altitude, we have to use a simulation model. In this conference, we present generic model of simulation of geosynchronous optical sensor. This model is composed of different models: a water bio-optical model, an atmospheric transfer model and a sensor model. We also present our recent results, that is the influence of solar angle and sensor angle on deviation of estimation of chlorophyll concentration in open ocean (case1 water).

The objective of this letter is to find a distance metric between reflectance spectra that is not sensitive to the variations on the soil reflectance induced by the geometry of solar-view angles. This is motivated by the fact that differences between spectra measured for the same soil under different observation and illumination configurations can lead to misclassifications. Using 26 soils of different compositions simulated with Hapke's model and 92 soils of different compositions measured under 28 solar-view angle geometries in laboratory conditions, we tested three metrics, namely, root-mean-square error, spectral angle mapper, andR2(the coefficient of determination), and we compared their efficiency. The best results are obtained with the coefficient of determination with 93% of good classifications.

Ocean colour monitoring is usually based on optical remote sensing with spatial resolutions around 1 km. This spatial scale is available with such sensors as NOAA-AVHRR,POLDER or SeaWiFS, which cover one to several thousand kilometers in a single swath and providing very short revisit periods. The MeRIS sensor, launched on board ENVISAT in 2002, was designed for sea colour observation, with a 300 meter spatial resolution, 15 programmable spectral bands and a 3 day revisit period. Tree hundred meter is a high resolution for an oceanographic sensors, but it is still too rough for coastal water monitoring, where physical and biological phenomena require better spatial resolution [1]. On the opposite, multispectral Landsat ETM images offer a suitable spatial resolution, but have only 4 spectral bands in the visible and near infrared spectrum, allowing poor spectral characterization. A few years ago, in order to combine the spectral resolution of MeRIS and the spatial resolution of Landsat ETM,we had implemented a merging method proposed by Zhukov et al. [2]. Before the launch of ENVISAT, we applied this method to simulated MeRIS images [3]. MeRIS was launched in March 2002 and has been providing images since June 2002. This method is now applied to real MeRIS images. Two product levels are considered. Level 1b contains radiance measurements at the top of the atmosphere for the calibrated and geocoded fifteen (15) MeRIS bands. Level 2 contains normalized surface reflectance and several geophysical and biophysical parameters such as algal pigment index, suspended sediment,Rayleigh-corrected vegetation indices, aerosol type, cloud albedo. The method was tested for radiance (level lb) and reflectance (level 2) over a coastal area of approximately 30times30 km2located around the Thau lagoon (southern France).

The objective of this work is to find the best metric to ignore the variations of soil reflectance induced by the solar-view angles geometry. Differences between spectra measured for the same soil under different observation and illumination configurations can leads to misclassifications. Using ninety two soils of different composition measured under twenty eight solar- view angles geometries, we tested 3 metrics : RMSE, SAM, R2(the coefficient of determination) and we compared their performances. The best metric seems to be the coefficient of determination with 93 % of good classifications.

Bathymetric estimation using remote sensing images has previously been applied to high spatial resolution imagery such as CASI, Ikonos, or SPOT but not on medium spatial resolution images (i.e., MERIS). This choice can be justified when there is a need to map the bathymetry on large areas. In this letter, we present the results of the bathymetry estimation over a large known area, the Gulf of Lion (France), expanding over 270times180 km

The Medium Resolution Imaging Spectrometer (MERIS) launched in March 2002 and has been providing images since June 2002. Before its launch, we had implemented a method to improve its resolution by merging its images with Landsat Enhanced Thematic Mapper images in order to preserve the best characteristics of the two images (spatial, spectral, temporal). We now present the results of this method for real MERIS images (level 1b and 2) in a coastal area. The robustness of the method is studied as well as the influence of the delay between the acquisitions of the two images.

Deals with a form of knowledge of land use relating to mixture percentages. The authors present a method for the determination of mixture percentages in pixels of a multispectral satellite scene (Landsat TM or SPOT XS). This method is applied to images after correction for topographic and atmospheric effects. They continue with a description of the linear analysis of the satellite scene and capture of the reference spectra, pure units of the percentages calculation. This process is validated first by comparison with ground truth, and then with a comparison with classic land use classifications. An entropy coefficient has been calculated for each pixel.