1,837 research outputs found
Aerosol optical thickness retrieval from satellite observation using support vector regression
Processing of data recorded by the MODIS sensors on board the Terra and Aqua satellites has provided AOT maps that in some cases show low correlations with ground-based data recorded by the AERONET. Application of SVR techniques to MODIS data is a promising, though yet poorly explored, method of enhancing the correlations between satellite data and ground measurements. The article explains how satellite data recorded over three years on central Europe are correlated in space and time with ground based data and then shows results of the application of the SVR technique which somewhat improves previously computed correlations. Hints about future work in testing different SVR variants and methodologies are inferred from the analysis of the results thus far obtained. © 2010 Springer-Verlag
Downscaling Aerosol Optical Thickness from Satellite Observations: Physics and Machine Learning Approaches
In recent years, the satellite observation of aerosol properties has been
greatly improved. As a result, the derivation of Aerosol Optical Thickness
(AOT), one of the most popular atmospheric parameters used in
air pollution monitoring, over ocean and continents from satellite observations
shows comparable quality to ground-based measurements.
Satellite AOT products is often applied for monitoring at global scale
because of its coarse spatial resolution. However, monitoring at local
scale such as over cities requires more detailed AOT information.
The increase spatial resolution to suitable level has potential for applications
of air pollution monitoring at global-to-local scale, detecting
emission sources, deciding pollution management strategies, localizing
aerosol estimation, etc. In this thesis, we investigated, proposed, implemented
and validated algorithms to derive AOT maps with spatial
resolution increased up to 1Ă1 km2 from MODerate resolution Imaging
Spectrometer (MODIS) observations provided by National Aeronautics
and Space Administration (NASA), while MODIS standard
aerosol products provide maps at 10Ă10 km2 of spatial resolution.
The solutions are considered on two perspectives: dynamical downscaling
by improving the algorithm for remote sensing of tropospheric
aerosol from MODIS and statistical downscaling using Support Vector
Regression
A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land
Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 ”m channel. However, the CAI only has a 1.6 ”m band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 Όm band data, we attempted to analyze the relationship between reflectance at 1.6 ”m and at 2.1 ”m. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 ”m and the visible bands based on the empirical relationship between reflectances at 2.1 ”m and the visible bands. We found that the reflectance relationship between 1.6 ”m and 2.1 ”m is typically dependent on the vegetation conditions, and that reflectances at 2.1 ”m can be parameterized as a function of 1.6 ”m reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 ”m and 2.1 ”m bands was summarized. Under light aerosol loading (AOD at 0.55 ”m < 0.1), the 2.1 ”m reflectance derived by our method has an extremely high correlation with the true 2.1 ”m reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)
Suppression of local haze variations in MERIS images over turbid coastal waters for retrieval of suspended sediment concentration
Atmospheric correction over turbid waters can be problematic if atmospheric haze is spatially variable. In this case the retrieval of water quality is hampered by the fact that haze variations could be partly mistaken for variations in suspended sediment concentration (SSC). In this study we propose the suppression of local haze variations while leaving sediment variations intact. This is accomplished by a multispectral data projection (MDP) method based on a linear spectral mixing model, and applied prior to the actual standard atmospheric correction. In this linear model, the hazesediment spectral mixing was simulated by a coupled water-atmosphere radiative transfer (RT) model. As a result, local haze variations were largely suppressed and transformed into an approximately homogenous atmosphere over the MERIS top-of-atmosphere (TOA) radiance scene. The suppression of local haze variations increases the number of satellite images that are still suitable for standard atmospheric correction processing and subsequent water quality analysi
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
Technique for validating remote sensing products of water quality
Remote sensing of water quality is initiated as an additional part of the on going activities of the EAGLE2006 project.
Within this context intensive in-situ and airborne measurements campaigns were carried out over the Wolderwijd and
Veluwemeer natural waters. However, in-situ measurements and image acquisitions were not simultaneous. This poses
some constraints on validating air/space-borne remote sensing products of water quality. Nevertheless, the detailed insitu
measurements and hydro-optical model simulations provide a bench mark for validating remote sensing products.
That is realized through developing a stochastic technique to quantify the uncertainties on the retrieved aquatic inherent
optical properties (IOP).
The output of the proposed technique is applied to validate remote sensing products of water quality. In this processing
phase, simulations of the radiative transfer in the coupled atmosphere-water system are performed to generate spectra
at-sensor-level. The upper and the lower boundaries of perturbations, around each recorded spectrum, are then modelled
as function of residuals between simulated and measured spectra. The perturbations are parameterized as a function of
model approximations/inversion, sensor-noise and atmospheric residual signal. All error sources are treated as being of
stochastic nature. Three scenarios are considered: spectrally correlated (i.e. wavelength dependent) perturbations,
spectrally uncorrelated perturbations and a mixed scenario of the previous two with equal probability of occurrence.
Uncertainties on the retrieved IOP are quantified with the relative contribution of each perturbation component to the
total error budget of the IOP.
This technique can be used to validate earth observation products of water quality in remote areas where few or no inâ
situ measurements are available
A multi-sensor approach for volcanic ash cloud retrieval and eruption characterization: the 23 November 2013 Etna lava fountain
Volcanic activity is observed worldwide with a variety of ground and space-based
remote sensing instruments, each with advantages and drawbacks. No single system can give
a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This
work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary
Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI),
the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are
improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and
effective radius), the generation of new satellite products (ash concentration and particle number
density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude,
total ash mass erupted and particle number density from thermal infrared to microwave). This
approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed
within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining
event of 23 November 2013 was considered as a test case. The results of the integration show the
presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash
cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after
the integration, of about the 30%. Moreover, the percentage of the airborne âfineâ ash retrieved from
the satellite is estimated to be about 1%â2% of the total ash emitted during the eruption. Finally,
all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also
validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared
Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits
An overview of and issues with sky radiometer technology and SKYNET
This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone.
A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5â% to 2.4â% at the Tokyo and Rome sites and smaller values of around 0.3â% to 0.5â% at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL) method was also developed to correct an underestimation by the IL method in cases with large aerosol retrieval errors.
The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02 for λâ„500ânm and a larger value of about 0.03 for shorter wavelengths in city areas and smaller values of less than 0.01 in mountain comparisons. Accuracies of single-scattering albedo (SSA) and size distribution retrievals are affected by the propagation of errors in measurement, calibrations for direct solar and diffuse sky radiation, ground albedo, cloud screening, and the version of the analysis software called the Skyrad pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET, and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud contamination. Correction of these known error factors reduced the SSA difference to less than 0.03.
Retrievals of other atmospheric constituents by the sky radiometer were also reviewed. Retrieval accuracies were found to be about 0.2âcm for precipitable water vapor amount and 13âDU (Dobson Unit) for column ozone amount. Retrieved cloud optical properties still showed large deviations from validation data, suggesting a need to study the causes of the differences.
It is important that these recent studies on improvements presented in the present paper are introduced into the existing operational systems and future systems of the International SKYNET Data Center
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