Numerical methods of reconstruction of optical parameters of terrestrial surface and atmosphere using remote sensing

2004 ◽  
Vol 67 (4-5) ◽  
pp. 391-397
Author(s):  
U.M. Sultangazin ◽  
A.H. Ahmedzhanov ◽  
V.N. Glushko
Keyword(s):  
Remote Sensing ◽  
Numerical Methods ◽  
Optical Parameters ◽  
Methods Of Reconstruction

scholarly journals Measurements and significance of bio-optical parameters for remote sensing in two subalpine lakes of different trophic state

1994 ◽  
Vol 56 (3) ◽  
pp. 263-303 ◽  
Author(s):  
J. M. Jaquet ◽  
F. Schanz ◽  
P. Bossard ◽  
K. Hanselmann ◽  
F. Gendre
Keyword(s):  
Remote Sensing ◽  
Trophic State ◽  
Optical Parameters ◽  
Subalpine Lakes

scholarly journals Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

2019 ◽  
Vol 11 (2) ◽  
pp. 184 ◽  
Author(s):  
Kun Xue ◽  
Ronghua Ma ◽  
Dian Wang ◽  
Ming Shen
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Lake Taihu ◽  
Optical Parameters ◽  
Water Type ◽  
Water Types ◽  
Remote Sensing Reflectance ◽  
Inorganic Matter ◽  
Suspended Particulate ◽  
Huai River

Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters.

scholarly journals Hazardous Base Surges of Taal’s 2020 Eruption

2021 ◽  
Author(s):  
Mario Aurelio ◽  
Richard Ybanez ◽  
Audrei Anne Ybanez ◽  
Jolly Joyce Sulapas ◽  
Criselda Baldago ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Numerical Methods ◽  
Coastal Communities ◽  
Primary Data ◽  
Taal Volcano ◽  
Empirical Information ◽  
Base Surge

Abstract After 43 years of repose, Taal Volcano erupted on 12 January 2020 forming hazardous base surges. Using field, remote sensing (i.e. UAV and LiDAR), and numerical methods, we gathered primary data to generate well-constrained empirical information on dune bedform characteristics, impact dynamic pressures and velocities of base surges to advance our knowledge on this hazard to understand and evaluate its consequences and risks. The base surges traveled at 50-60 km/hr near the crater and decelerated before making impact on coastal communities with dynamic pressures of at least 1.7-2.1 kPa. The base surges killed more than a thousand livestock in the southeast of Taal Volcano Island, and then traveled another 600 meters offshore. This work is a rare document of a complete, fresh and practically undisturbed base surge deposit, important in the study of dune deposits formed by volcanic, and other processes on Earth and other planets.

scholarly journals Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data

2017 ◽  
Author(s):  
Raffaella Matarrese ◽  
Nicolas Guyennon ◽  
Diego Copetti
Keyword(s):  
Remote Sensing ◽  
Ad Hoc ◽  
High Accuracy ◽  
Optical Parameters ◽  
Surface Reflectance ◽  
Algae Bloom ◽  
Modis Data ◽  
South Italy ◽  
Spectral Behaviour ◽  
Remote Sensing Techniques

In winter 2008-2009, Lake Occhito, a strategic multiple-uses reservoir in South Italy, was affected by an extraordinary Planktothrix rubescens bloom. P. rubescens is a filamentous potentially toxic cyanobacterium which has recently colonized many environments in Europe. A number of studies is currently available on the use of remote sensing techniques to monitor different fresh water cyanobacteria species. By contrast no specific applications are available on the remote sensing monitoring of P. rubescens. In this paper we present a specific algorithm, based on Water Leaving Reflectances (WLR) from MERIS data, atmospherically corrected using the Aerosol Optical Thickness (AOT) retrieved by MODIS data, to detect P. rubescens blooms. The high accuracy in AOT data, provided by MOD09 surface reflectance product, at 1km spatial resolution, allowed obtaining a good correlation between the WLR and the P. rubescens chlorophyll-a concentrations measured in the field, through multiple stations fluorometric profiles. A modified Normalized Difference Chlorophyll index (NDCI) algorithm is presented. The performance of the proposed algorithm has been successfully compared with other specific algorithms for turbid productive waters. We demonstrated how important is to verify the spectral behaviour of bio-optical parameters in order to develop an ad hoc algorithm that better performs with respect to standard algorithms.

scholarly journals Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing

2021 ◽  
Vol 21 (23) ◽  
pp. 17969-17994
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Patric Seifert ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Gravity Wave ◽  
Detection Threshold ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Ice Formation ◽  
Absolute Difference ◽  
Optical Parameters ◽  
Mixed Phase Clouds

Abstract. Multi-year ground-based remote-sensing datasets were acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites. A highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern midlatitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets in key regions of aerosol–cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary layer coupling, and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds, our study contributes the following new aspects: (1) sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled conditions is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above -15∘C than indicated by prior lidar-only studies at all sites. A virtual lidar detection threshold of ice water content (IWC) needs to be considered in order to bring radar–lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than decoupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below -15∘C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity wave influences, Punta Arenas shows lower fractions of ice-containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and -8∘C. These differences are potentially caused by the contrast in the ice-nucleating particle (INP) reservoir between the different sites.

Improved estimates of bio-optical parameters in optically complex water using hyperspectral remote sensing data

2021 ◽  
Vol 42 (8) ◽  
pp. 3056-3073
Author(s):  
Syed Moosa Ali ◽  
Anurag Gupta ◽  
Mini Raman ◽  
Arvind Sahay ◽  
Gunjan Motwani ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Optical Parameters ◽  
Sensing Data

scholarly journals Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak

2013 ◽  
Vol 6 (6) ◽  
pp. 10955-11010
Author(s):  
M. Taylor ◽  
S. Kazadzis ◽  
A. Tsekeri ◽  
A. Gkikas ◽  
V. Amiridis
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Refractive Index ◽  
Size Distribution ◽  
Aerosol Optical Depth ◽  
Principal Components ◽  
Complex Refractive Index ◽  
Remote Sensing Data ◽  
Optical Parameters ◽  
Desert Dust

Abstract. In order to exploit the full-Earth viewing potential of satellite instruments to globally characterise aerosols, new algorithms are required to deduce key microphysical parameters like the particle size distribution and optical parameters associated with scattering and absorption from space remote sensing data. Here, a methodology based on neural networks is developed to retrieve such parameters from satellite inputs and to validate them with ground-based remote sensing data. For key combinations of input variables available from MODIS and OMI Level 3 datasets, a grid of 100 feed-forward neural network architectures is produced, each having a different number of neurons and training proportion. The networks are trained with principal components accounting for 98% of the variance of the inputs together with principal components formed from 38 AERONET Level 2.0 (Version 2) retrieved parameters as outputs. Daily-averaged, co-located and synchronous data drawn from a cluster of AERONET sites centred on the peak of dust extinction in Northern Africa is used for network training and validation, and the optimal network architecture for each input parameter combination is identified with reference to the lowest mean squared error. The trained networks are then fed with unseen data at the coastal dust site Dakar to test their simulation performance. A NN, trained with co-located and synchronous satellite inputs comprising three aerosol optical depth measurements at 470, 500 and 660 nm, plus the columnar water vapour (from MODIS) and the modelled absorption aerosol optical depth at 500 nm (from OMI), was able to simultaneously retrieve the daily-averaged size distribution, the coarse mode volume, the imaginary part of the complex refractive index, and the spectral single scattering albedo – with moderate precision: correlation coefficients in the range 0.368 ≤ R ≤ 0.514. The network failed to recover the spectral behaviour of the real part of the complex refractive index with only 39–45% of the data falling within the acceptable level of uncertainty relative to ground-truth data at the daily timescale. In the context of Saharan desert dust, this new methodological approach appears to offer some potential for moderately accurate daily retrieval of previously inaccessible aerosol parameters from space.

scholarly journals Modelling Remote Sensing Reflectance to Detect Dispersed Oil at Sea

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 863 ◽  
Author(s):  
Emilia Baszanowska ◽  
Zbigniew Otremba ◽  
Jacek Piskozub
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Spectral Index ◽  
Optical Parameters ◽  
Scattering Coefficients ◽  
Wind Speeds ◽  
Remote Sensing Reflectance ◽  
Dispersed Oil ◽  
The Monte Carlo Method ◽  
Oil In Water

This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote sensing reflectance (Rrs). Analyses were performed for the optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea). The case of seawater contaminated by dispersed oil at a concentration of 10 ppm was also discussed for different wind speeds. Two types of oils with extremely different optical properties (refraction and absorption coefficients) were taken into account for consideration. The optical properties (absorption and scattering coefficients and angular light scattering distribution) of the oil-in-water dispersion system were determined using the Mie theory. The spectral index for oil detection in seawater for different wind conditions was determined based on the results obtained for reflectance at selected wavelengths in the range 412–676 nm. The determined spectral index for seawater free of oil achieves higher values for seawater contaminated by oil. The analysis of the values of the spectral indices calculated for 28 combinations of wavelengths was used to identify the most universal spectral index of Rrs for 555 nm/440 nm for dispersed oil detection using any optical parameters.

scholarly journals Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak

2014 ◽  
Vol 7 (9) ◽  
pp. 3151-3175 ◽  
Author(s):  
M. Taylor ◽  
S. Kazadzis ◽  
A. Tsekeri ◽  
A. Gkikas ◽  
V. Amiridis
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Refractive Index ◽  
Size Distribution ◽  
Aerosol Optical Depth ◽  
Principal Components ◽  
Complex Refractive Index ◽  
Remote Sensing Data ◽  
Optical Parameters

Abstract. In order to exploit the full-earth viewing potential of satellite instruments to globally characterise aerosols, new algorithms are required to deduce key microphysical parameters like the particle size distribution and optical parameters associated with scattering and absorption from space remote sensing data. Here, a methodology based on neural networks is developed to retrieve such parameters from satellite inputs and to validate them with ground-based remote sensing data. For key combinations of input variables available from the MODerate resolution Imaging Spectro-radiometer (MODIS) and the Ozone Measuring Instrument (OMI) Level 3 data sets, a grid of 100 feed-forward neural network architectures is produced, each having a different number of neurons and training proportion. The networks are trained with principal components accounting for 98% of the variance of the inputs together with principal components formed from 38 AErosol RObotic NETwork (AERONET) Level 2.0 (Version 2) retrieved parameters as outputs. Daily averaged, co-located and synchronous data drawn from a cluster of AERONET sites centred on the peak of dust extinction in Northern Africa is used for network training and validation, and the optimal network architecture for each input parameter combination is identified with reference to the lowest mean squared error. The trained networks are then fed with unseen data at the coastal dust site Dakar to test their simulation performance. A neural network (NN), trained with co-located and synchronous satellite inputs comprising three aerosol optical depth measurements at 470, 550 and 660 nm, plus the columnar water vapour (from MODIS) and the modelled absorption aerosol optical depth at 500 nm (from OMI), was able to simultaneously retrieve the daily averaged size distribution, the coarse mode volume, the imaginary part of the complex refractive index, and the spectral single scattering albedo – with moderate precision: correlation coefficients in the range 0.368 ≤ R ≤ 0.514. The network failed to recover the spectral behaviour of the real part of the complex refractive index. This new methodological approach appears to offer some potential for moderately accurate daily retrieval of the total volume concentration of the coarse mode of aerosol at the Saharan dust peak in the area of Northern Africa.

scholarly journals Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: Disentangling the role of aerosol and dynamics with ground-based remote sensing

2021 ◽  
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Patric Seifert ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Gravity Wave ◽  
Detection Threshold ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Ice Formation ◽  
Absolute Difference ◽  
Optical Parameters ◽  
Mixed Phase Clouds

Abstract. Multi-year ground-based remote-sensing datasets acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites: a highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern mid-latitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets at key sites of aerosol-cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary-layer coupling and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds our study contributes the following new aspects: (1) Sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled temperatures is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above −15 °C than indicated by prior lidar-only studies at all sites. A virtual lidar-detection threshold of IWC needs to be considered in order to bring radar-lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than de-coupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below −15 °C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity-wave influences, Punta Arenas shows lower fractions of ice containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and −8 °C. These differences are potentially caused by the contrast in the INP reservoir between the different sites.

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