Author
Young Je Park
Other affiliations: Commonwealth Scientific and Industrial Research Organisation
Bio: Young Je Park is an academic researcher from KAIST. The author has contributed to research in topics: Lidar & Deconvolution. The author has an hindex of 6, co-authored 7 publications receiving 196 citations. Previous affiliations of Young Je Park include Commonwealth Scientific and Industrial Research Organisation.
Papers
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TL;DR: It is found that ocean color inversion with LMI is significantly sensitive to the a priori selection of the empirical parameters g0 and g1 of the equations relating the above-surface remote-sensing reflectance to the IOPs in the water column.
Abstract: To address the challenges of the parameterization of ocean color inversion algorithms in optically complex waters, we present an adaptive implementation of the linear matrix inversion method (LMI) [J. Geophys. Res.101, 16631 (1996)10.1029/96JC01414], which iterates over a limited number of model parameter sets to account for naturally occurring spatial or temporal variability in inherent optical properties (IOPs) and concentration specific IOPs (SIOPs). LMI was applied to a simulated reflectance dataset for spectral bands representing measured water properties of a macrotidal embayment characterized by a large variability in the shape and amplitude factors controlling the IOP spectra. We compare the inversion results for the single-model parameter implementation to the adaptive parameterization of LMI for the retrieval of bulk IOPs, the IOPs apportioned to the optically active constituents, and the concentrations of the optically active constituents. We found that ocean color inversion with LMI is significantly sensitive to the a priori selection of the empirical parameters g0 and g1 of the equations relating the above-surface remote-sensing reflectance to the IOPs in the water column [J. Geophys. Res.93, 10909 (1988)10.1029/JD093iD09p10909]. When assuming the values proposed for open-ocean applications for g0 and g1 [J. Geophys. Res.93, 10909 (1988)10.1029/JD093iD09p10909], the accuracy of the retrieved IOPs, and concentrations was substantially lower than that retrieved with the parameterization developed for coastal waters [Appl. Opt.38, 3831 (1999)10.1364/AO.38.003831] because the optically complex waters analyzed in this study were dominated by particulate and dissolved matter. The adaptive parameterization of LMI yielded consistently more accurate inversion results than the single fixed SIOP model parameterizations of LMI. The adaptive implementation of LMI led to an improvement in the accuracy of apportioned IOPs and concentrations, particularly for the phytoplankton-related quantities. The adaptive parameterization encompassing wider IOP ranges were more accurate for the retrieval of bulk IOPs, apportioned IOPs, and concentration of optically active constituents.
103 citations
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TL;DR: A geometric form factor for an inhomogeneous atmosphere is determined by using the polynomial regression method in a lidar equation.
Abstract: We experimentally determine a geometric form factor for an inhomogeneous atmosphere by using the polynomial regression method in a lidar equation.
48 citations
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TL;DR: A deconvolution technique for deriving more resolved signals from lidar signals with typical CO(2) laser pulses is proposed, utilizing special matrices constructed from the temporal profile of laser pulses.
Abstract: A deconvolution technique for deriving more resolved signals from lidar signals with typical CO2 laser pulses is proposed, utilizing special matrices constructed from the temporal profile of laser pulses. It is shown that near-range signals can be corrected and small-scale variations of backscattered signals can be retrieved with this technique. Deconvolution errors as a result of noise in lidar data and in the laser pulse profile are also investigated numerically by computer simulation.
29 citations
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TL;DR: In this article, the geometrical form factors in the lidar equation were determined by using a slope method in a homogeneous atmosphere, and the differential absorption lidar equations were evaluated for the dual-pulse lidar system.
Abstract: The peak position for a lidar return signal is calculated and measured for the horizontal path with variation of the laser beam divergence angle (θ), and the inclination angle (δ) between the telescope and laser axes. This work shows that θ and δ are very important parameters to use in the design or alignment of a lidar system receiving a good lidar signal. This paper describes an experimental determination of geometrical form factors in the lidar equation. We receive the signals and determine the geometrical form factors by slope method in a homogeneous atmosphere. The differential absorption lidar equation is evaluated for the dual-pulse lidar system. A method using a geometrical form factor determined by the experiment is introduced to correct the error in C2H4 measurement. This method shows good correction of measurement error in lidar dual-pulse operation, especially in the short range.
12 citations
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TL;DR: An iterative deconvolution algorithm for improving range resolution of long-pulse lidars is proposed, and can be applied to the lidar data obtained with the typical pulse of a CO2-laser which consists of a gain-switching peak and a long tail.
Abstract: An iterative deconvolution algorithm for improving range resolution of long-pulse lidars is proposed, and can be applied to the lidar data obtained with the typical pulse of a CO2-laser which consists of a gain-switching peak and a long tail. The lidar signal itself with certain temporal shift is set to be the start profile for the unknown maximally resolved profile in the proposed technique, and then is corrected in proportion to the difference between the lidar return calculated with the assumption and the real one. The same process is repeated until the correction is smaller than tolerance. Simulations are made to test the performance of the proposed algorithm. We investigate the errors in the vicinity of data boundary in the retrieved profile when a part of lidar data is absent. The sensitivity of the iteration algorithm to noise in the lidar signals and the laser pulse profile is also numerically determined.
7 citations
Cited by
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TL;DR: An overview of the state of the art in atmospheric correction algorithms is provided, recent advances are highlighted and the possible potential for hyperspectral data to address the current challenges is discussed.
Abstract: Accurate correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.
490 citations
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TL;DR: In this article, a special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms.
459 citations
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TL;DR: In this paper, a comprehensive review of multispectral ocean color sensor data is presented to understand the spatio-temporal patterns of phytoplankton blooms and their triggering factors.
414 citations
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Goddard Space Flight Center1, University of Maine2, Commonwealth Scientific and Industrial Research Organisation3, Hokkaido University4, University of Massachusetts Amherst5, University of the Littoral Opal Coast6, University of California, Santa Barbara7, University of New Hampshire8, Plymouth Marine Laboratory9, Centre national de la recherche scientifique10, Laval University11
TL;DR: The theoretical basis of GIOP is described, a preliminary default configuration for GIOP (GIOP-DC) is proposed, and its comparable performance to other popular SAAs is presented and the sensitivities of their output to their parameterization are quantified.
Abstract: Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.
312 citations
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TL;DR: A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products.
Abstract: Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue.
262 citations