The performance of the cloud properties algorithm of the future Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite is compared with that of a spectrally compatible sensor, the moderate resolution image spectroradiometer (MODIS). The results obtained are evaluated against the target accuracy of the GCOM-C/SGLI satellite mission. Three direct cloud parameters: the cloud optical thickness (COT), the cloud particle effective radius (CLER), and the cloud top temperature (CTT), and an indirect parameter: the cloud liquid water path (CLWP), are the cloud properties that are evaluated. The satellite–satellite comparison shows a good alignment between the retrievals of the GCOM-C/SGLI algorithm and those of MODIS in most of the areas and agreement with the accuracy targets of the new satellite mission. However, the COT comparison shows an increasing dispersion with the increase of the cloud thickness along the GCOM-C/SGLI-MODIS 1∶1 line. The CTT is systematically overestimated by the GCOM-C/SGLI (against MODIS), particularly in mid-thermal clouds. This is found to be due to an insufficient cloud emissivity correction of the thermal radiances by the GCOM-C/SGLI algorithm. The lowest COT, CLER, and CLWP accuracies, noticed in forest areas, are found to be related to the cloud detection uncertainty and the nonabsorption channel sensitivity differences.
Linear filtering methods using convolution techniques are applied in computer vision, to detect spatial discontinuities in
the intensity of luminance of photograph images. These techniques are based on the following principal: a pixel’s
neighborhood contains information about its intensity. The variation of this intensity provides some information about
the distribution and the possible decomposition of the image in various features. This decomposition relies on the relative
position of the pixel (edge or not) on the image. These principals, integrated into remote sensing analyses, are applied in
this study to differentiate cloud morphological features representing cloud types from a thermal image product (the
Cloud top temperatures) derived from polar orbit satellites’ observations. This approach contrast with that of other
techniques commonly used in satellite cloud classification, and based on optical or thermodynamic properties of the
clouds. The interpretation of the distribution of these cloud morphological features, and their frequency is evaluated
against another cloud classification method relying on cloud optical properties. The results show a relatively good match
between the two classifications. Implications of these results, on the estimation of the impact of cloud shapes’ variations
on the recent climate are discussed.
Pixels' edges can yield useful information on physical properties of objects featured on satellite images. These properties
can be derived through the use of the imagery spatial contrast techniques. To differentiate various cloud types based on
their shapes, one of these techniques is applied on thermal products from a polar orbiting satellite, the National Oceanic
and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA-AVHRR). Edge gradients
extracted from daily global cloud temperature images of this satellite and the spatial relationship between these gradients
permit the distinction of nine major cloud shapes distributed along three cloud pressure levels (high, middle and low).
The cloud shape differentiation method utilized is a histogram-based gradient scheme describing the occurrence of
different gradients' levels (high, middle and low) in each block of pixels. A detailed analysis of the distribution of the
cloud shapes obtained is conducted, and the frequency of each cloud shape is evaluated with another cloud classification
method (based on cloud optical properties) for validation purposes. Finally, implications of the results obtained, on the
estimation of the impact of cloud shapes variations on the recent climate are discussed.
The relationship between the intensity functions of contiguous pixels of an image is used on daily global clouds satellite
data to extract local edge gradients for cloud types' classification. The images are cloud top temperatures (CTT) derived
from the National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA-AVHRR)
satellite observations. The cloud type classification method used is a histogram-based gradient scheme
described as the occurrence of low, mid or high edge gradients in a block of pixels. The distribution of these cloud types
is analyzed, then, the consistency of the monthly variations of the cloud type amount estimation is evaluated. A clear
dependence of the cloud type amount signal on the solar zenith angle is noticeable. This dependence, due to the gradual
satellite drift, is removed through a filtering process using the empirical mode decomposition (EMD) method. The EMD
component, associated with the drift or the solar zenith angle change, is filtered out. The cloud types' amount series
corrected show a substantial improvement in their trends.
As a preliminary step in the study of long-term global cloud properties variations and their contribution to the Earth
radiation budget, a classification procedure to identify various types of clouds is discussed. This classification scheme
highlighting the spatial heterogeneity of cloud structural arrangements is used to characterize and differentiate nine cloud
types. The study takes advantage of the capacity of edge gradient operators' techniques generally used to calculate the
magnitude and direction changes in the intensity function of adjacent pixels of an image, to identify the various cloud
types. The specific approach, based on variations of the edge gradient magnitude and orientation, is applied on daytime
global cloud physical features (cloud top temperatures derived from the 11-μm brightness temperature imagery) obtained
from the National Oceanic and Atmospheric Administration-Advanced Very-High-Resolution Radiometer
(NOAA-AVHRR) satellite observations. The results obtained are compared with those of the International Satellite
Cloud Climatology Project (ISCCP) cloud classification algorithm which uses cloud optical properties and pressure
levels to distinguish cloud types. Results of these two procedures show good agreement but substantial differences are
noticed at polar areas.
Meso-scale atmospheric models outputs are valuable data for cloud and aerosols retrievals. In view of the launch the
Global Change Observation Mission-Climate/Second generation Global Imager (GCOM-C/SGLI) satellite, atmospheric
models products are tested against satellite observations data in order to evaluate the degree of reliability and the
sensitivity of these models outputs to variations of atmospheric conditions. The analyses presented in this study are based
on two models outputs: the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the National Center for
Environmental Protection/Department Of Energy (NCEP/DOE) re-analysis-2 data. Terra/Moderate Resolution Imaging
Spectrometer (Terra/MODIS) satellite sensor observations of water vapor radiances are used as the verification data set
for the tests conducted. These tests are based on the comparison between upper tropospheric water vapor properties
(clear sky and above-low clouds) observed by satellite and radiative transfer forward calculations (using models'
predicted atmospheric profiles, the satellite sensor spectral response and geometrical characteristics) derived from
NICAM and NCEP/DOE. The parameters measured are the upper tropospheric brightness temperature (UTBT) and
relative humidity (UTRH). Discrepancies between simulated data and observations are analyzed in terms of atmospheric
instability, cloud convection movements and, effective emissivity. The results obtained show that both NICAM and
NCEP/DOE simulated UTBT and UTRH outputs have a relatively comparable distribution pattern. However,
simulations performed with the NCEP/DOE outputs present generally fewer discrepancies with satellite observations.
For the interpretation of these results, the stability index study shows that differences between models and observation
data tend to be high in unstable atmospheres. This atmospheric instability can be attributed to cloud convection processes affecting areas adjacent to convective clouds. As the amount of convective clouds increases, the errors in the water
vapour depiction by the models increase also. Analyses of heat movements studied through the variation of the cloud
effective emissivity suggest that the discrepancies between the observations and the models increase with the decrease of
the clouds' effective emissivity. Adjustments of some of the models parameters, notably the microphysical
parameterization of the clouds resolving scheme, are suggested in order to improve the accuracy of the models' results.
For a comprehensive vegetation monitoring and/or management, a good understanding of the distribution of the solar
radiation energy among components of this vegetation is needed. The energy received by the vegetation is measured by
spectroradiometers either at satellite elevations or near the ground (in situ measurements). In this study, in situ,
radiometric data and laser scanning techniques are combined, in order to evaluate the contribution of the vegetation
structure to the variability of canopy reflectance. Advanced processing laser techniques are not only an efficient tool for
the generation of physical models but also give information about the vertical structure of canopies (height, shape,
density) and their horizontal extension. To conduct this study, airborne multispectral radiation data and, laser pulse
returns are recorded from a low flying helicopter above the vegetation of a boreal forest. These measurements are used to
derive canopy optical and structural variables. The impact of the canopy 2-dimensional structural variability on the
distribution of the solar radiation reflected by plants of this area is discussed. The results obtained show that the laser
technology can be used for the selection of the most appropriate configuration of radiation measurements, and
optimization of canopy physical characteristics, in future airborne missions.
Geostationary satellites are well suited for radiation budget computations due to their high temporal resolution. In order to validate satellite observations and the radiative properties derived from the GMS-5/SVISSR, we compared its cloud optical depth (COD) with that from the polar orbiting satellite, TERRA/MODIS. It appears that there's a good agreement between both COD sets in thin cloud areas while, major differences (MODIS COD higher) occur in thick cloud regions. Factors affecting accurate observations of clouds by satellites range from the solar and satellites geometries to the sun-cloud scale of interaction. This study focuses on the latter effect, as the solar and satellite zenith angles are relatively low in the area and time selected. The sun-cloud interactions refer here to the three-dimensional radiative effects (e.g. asymmetry, smoothing) due to the horizontal spatial variability of clouds and their structural inhomogeneity. These are analyzed through the IR thermal gradient and small areas' standard deviation (STDEV) respectively. By combining these two parameters, it is possible to reasonably explain the differences in cloud physical and optical properties noticed between both satellites. Results show that, asymmetry and smoothing effects seem to be stronger for SVISSR data than MODIS. At the sides of the clouds SVISSR observed cloud properties are more or less comparable to MODIS data. At the top of the clouds, SVISSR data are systematically lower and do not match MODIS data. SVISSR observations fail to detect cloud inhomogeneity mostly at the top of the clouds, and therefore seem to underestimate the cloud optical properties.