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It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques. Index Terms--Image reconstruction, image restoration, wavelets.

The objective of this paper is to assess the accuracy of the Semi-Analytical CloUd Retrieval Algorithm (SACURA) that retrieves cloud-top heights (CTHs) using hyperspectral SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard Environmental Satellite measurements for overcast single-layer cloud fields. Intercomparisons with ground-based 35-GHz millimeter wave cloud radar CTHs were performed for 14 dates during 2003-2007 at the U.S. Atmospheric Radiation Measurement (ARM) program Southern Great Plains site (36.6[degrees] N, 97.5[degrees] W). In addition, for some of these dates, European Space Agency MEdium Resolution Imaging Spectrometer (MERIS) and the NASA-TERRA Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure retrievals were also collected, transformed into CTHs using nearby ARM radiosonde profiles, and compared with the SACURA SCIAMACHY and radar retrievals. The accuracy of the SACURA-SCIAMACHY CTH retrievals is better than 0.34 km for low-level clouds and 2.22 km for high-level clouds with an underestimate in CTH on average for all clouds. The average bias in SCIAMACHY CTHs was about 0.07 km for low clouds and about 0.5 km for high-level clouds. Both MODIS and MERIS slightly overestimated the CTHs of low-level clouds by 300 m, with an uncertainty better than 1 km. However, although MODIS accuracy for high-level clouds is close to SCIAMACHY, MERIS CTHs were significantly underestimated for these fairly optically thick cases. Index Terms--Clouds, radiative transfer, remote sensing.

Monitoring atmospheric water is essential for the understanding of the dynamic processes of the atmosphere and for the assessment of wave-propagation properties. Microwave radiometers, in combination with a thermal infrared channel, have the potential to fulfill these tasks. This paper is focused on the surface-based system TROWARA with microwave channels at 21.3 and 31.5 GHz. TROWARA has been used for tropospheric water measurements at Bern since 1994 together with a standard meteo station. So far, emphasis has been put on integrated water vapor (IWV) measurements, particularly for climate studies, but integrated liquid water (ILW) has been retrieved as well. We report on methodological advances with the data analysis. First, the original algorithm was replaced by a new statistical retrieval based on the simulations of TROWARA data using radiosonde profiles. Second, in a physical refinement, the cause of the variable ILW bias has been identified, and a method for its reduction to the level of 0.001 to 0.005 mm has been developed and tested. The bias is mainly a result of the variable water-vapor influence on absorption at 31 GHz. The bias correction also influences the IWV retrieval. The refined physical retrieval includes the temperature dependence of cloud absorption based on a recent dielectric model of water. The three algorithms (original, new, and refined) have been compared for two years of data. The applications of the refined algorithm are focused on physical processes, such as the development of supercooled clouds. Future advances will include precipitation measurements. Index Terms--Microwave radiometry.

Infrared channels on the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to infer cloud-top pressure (CTP), temperature, and effective cloud amount or emissivity. For low clouds, those with tops at pressures greater than 700 hPa, the infrared window 11-[micro]m channel brightness temperature is used to determine the CTP and the corresponding cloud-top temperature by comparison with the temperature profile obtained from the NCEP Global Data Assimilation System meteorological analysis. In the presence of strong inversions which are common for marine stratus and stratocumulus, this leads to the identification of an erroneously high cloud-top height (CTH). This discrepancy is illustrated by comparing MODIS CTHs with those inferred from the geometric method used by the Multiangle Imaging SpectroRadiometer on the same satellite platform, and field observations. The error in CTH is typically about 2 km and depends on the shape of the actual temperature profile. It is shown that column water vapor above cloud retrieved from the MODIS solar infrared channels in the vicinity of the 0.94-[micro]m water vapor absorption band can be used to flag the error and that the location of the true CTH could possibly be obtained using lapse rate formulations for cloud-topped boundary layers. Index Terms--Cloud-top heights (CTHs), inversion layer, marine stratus, Moderate Resolution Imaging Spectroradiometer (MODIS), remote sensing, water vapor.

Deep convective clouds have been tested to be used as stable reference for calibration purposes: the monitoring of the radiometric changes of space sensors in the spectral range from blue to short-wave infrared. After an appropriate selection, the clouds have been characterized for their brightness, spectral aspects, bidirectional signature, stability, and homogeneity. For this, radiative transfer computations using a discrete ordinate code, as well as remote sensing measurements from the PARASOL satellite, were analyzed. The first main result is a confirmation that the monthly mean reflectance over deep convective clouds is quite stable as suggested in other papers. Moreover, the excellent spectral properties of deep convective clouds are really convenient for a temporal monitoring if it can be assumed that a reference band is stable or well characterized with time. If the reference band is perfectly known, the accuracy of the temporal monitoring is about 0.2%. Experimental results are provided with PARASOL data for which the temporal drift is known with an accuracy better than 0.5 % for the three years in orbit (accuracy which includes the uncertainty of the reference band). Index Terms--Calibration, clouds, radiometry, remote sensing, scattering, spectral analysis.

A worldwide modelling of integrated liquid water and water vapor content distributions is proposed and evaluated. The knowledge of those distributions is valuable to predict attenuation for Earth-space communication systems operating at frequencies higher than 10 GHz. Index Terms--Cloud, high frequencies propagation, liquid water, lognormal distribution, tropospheric attenuation, water vapor, Weilbull distribution.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness. Index Terms--Adaptive geometry meshes, multiresolution, point clouds, self-organizing maps (SOMs), surface reconstruction.

This paper is devoted to the application of the Semi-Analytical Cloud Retrieval Algorithm (SACURA) to the cloud-top height determination using data from the Global Ozone Measurement Experiment (GOME) instrument onboard Earth Remote Sensing satellite (ERS-2). In particular, measurements of the top-of-atmosphere reflectance in the oxygen absorption A-band are used. The technique is based on the asymptotic radiative transfer theory as applied to studies of the oxygen absorption bands in reflected light. Our approach is valid for optically thick clouds with cloud optical thickness larger than approximately 5. The accuracy of the algorithm is checked against independent retrieval techniques for completely cloudy pixels. In particular, the close coincidence with data obtained from the Along Track Scanning Radiometer (ATSR-2) onboard ERS-2 is found. Some results of retrievals using these different instruments disagree (up to 2 km). This is explained by the strong horizontal inhomogeneity of clouds under investigation, which is not accounted by the SACURA or, possibly, by well-known problems of infrared techniques as applied to low-level clouds. The effective cloud geometrical thickness l is also retrieved. This parameter is defined as the geometrical thickness of a vertically homogeneous cloud, which allows for the minimization of differences between observed and calculated top-of-atmosphere reflectance spectra. For inhomogeneous clouds, the value of l differs from a real cloud geometrical thickness, but it gives us an indication of the possible existence of the multilayered cloud system in the field of view of the optical instrument. Index Terms--Clouds, radiative transfer, remote sensing, spectroscopy.

The Clouds and the Earth's Radiant Energy System (CERES) is part of NASA's Earth Observing System (EOS). CERES objectives include the following. 1) For climate change analysis, provide a continuation of the Earth Radiation Budget Experiment (ERBE) record of radiative fluxes at the top-of-the-atmosphere (TOA), analyzed using the same techniques as the existing ERBE data. 2) Double the accuracy of estimates of radiative fluxes at TOA and the earth's surface; 3) Provide the first long-term global estimates of the radiative fluxes within the earth's atmosphere. 4) Provide cloud property estimates collocated in space and time that are consistent with the radiative fluxes from surface to TOA. In order to accomplish these goals, CERES uses data from a combination of spaceborne instruments: CERES scanners, which are an improved version of the ERBE broadband radiometers, and collocated cloud spectral imager data on the same spacecraft. The CERES cloud and radiative flux data products should prove extremely useful in advancing the understanding of cloud-radiation interactions, particularly cloud feedback effects on the earth's radiation balance. For this reason, the CERES data should be fundamental to our ability to understand, detect, and predict global climate change. CERES results should also be very useful for studying regional climate changes associated with deforestation, desertification, anthropogenic aerosols, and El Nino/Southern Oscillation events. This overview summarizes the Release 2 version of the planned CERES data products and data analysis algorithms. These algorithms are a prototype for the system that will produce the scientific data required for studying the role of clouds and radiation in the earth's climate system. This release will produce a data processing system designed to analyze the first CERES data, planned for launch on Tropical Rainfall Measuring Mission (TRMM) in November 1997, followed by the EOS morning (EOS-AM1) platform in 1998. Index Terms - Algorithms, clouds, meteorology, radiation monitoring.

The Thermal Infrared Multispectral Scanner (TIMS) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were operated simultaneously from the ER2 aircraft during a March 1990 test over the Rio Bravo region, Belize. The two instruments have different scan rates and instantaneous fields of view. In addition, the undulation of the TIMS platform (i.e., the ER2 wing) induced relative motion between the normal viewing directions of the two instruments. Their scan directions also were slightly divergent. Coregistration of the imagery obtained by these two instruments is necessary to utilize the data effectively. However, coregistration is not a straightforward process, and traditional techniques based on ground control points are ineffective in this case. A technique for registering the TIMS imagery to AVIRIS imagery is presented. It takes advantage of the morphology of the fair weather cumulus (FWC) clouds present in the imagery for estimating inter-sensor distortions. It relies on an iterative process in which skew, nearest neighbor sampling, and cross-correlation (1D and 2D) are applied. Comparison between the AVIRIS three-band ratio (3BR) imagery and the coregistered TIMS imagery shows that TIMS is superior in detecting thin cloud and cloud edge pixels, especially over shadowed background. Although the seven scenes analyzed in this study were obtained within the same one-hour time period and over the same geographical region, the optimum temperature threshold for cloud detection, with respect to the background temperature, varies significantly from 2.1 to 3.3 [degrees] C. These values agree with the AVIRIS 3BR cloud fraction equivalent temperature thresholds to within 0.5 [degree] C. When applying a cloud shadow mask from the AVIRIS near infrared imagery to the coregistered TIMS background imagery, a 1 [degree] C temperature differential is found between the shadowed and nonshadowed background. This significant radiative cooling by Fair Weather Cumulus cloud shadows could introduce errors in surface emissivity retrievals by other Earth Observing System (EOS) investigators.