The Experts below are selected from a list of 57771 Experts worldwide ranked by ideXlab platform
Carl J Legleiter - One of the best experts on this subject based on the ideXlab platform.
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removing sun glint from optical remote sensing images of shallow rivers
Earth Surface Processes and Landforms, 2017Co-Authors: B T Overstreet, Carl J LegleiterAbstract:Sun glint is the specular reflection of light from the water surface, which often causes unusually bright pixel values that can dominate fluvial remote sensing imagery and obscure the water-leaving radiance signal of interest for mapping bathymetry, bottom type, or water column optical characteristics. Although sun glint is ubiquitous in fluvial remote sensing imagery, river-specific methods for removing sun glint are not yet available. We show that existing sun glint-removal methods developed for multispectral images of marine shallow water environments over-correct shallow portions of fluvial remote sensing imagery resulting in regions of unreliable data along channel margins. We build on existing marine glint-removal methods to develop a river-specific technique that removes sun glint from shallow areas of the channel without overcorrection by accounting for non-negligible water-leaving Near-Infrared radiance. This new sun glint-removal method can improve the accuracy of spectrally-based depth retrieval in cases where sun glint dominates the at-sensor radiance. For an example image of the gravel-bed Snake River, Wyoming, USA, observed-vs.-predicted R2 values for depth retrieval improved from 0.66 to 0.76 following sun glint removal. The methodology presented here is straightforward to implement and could be incorporated into image processing workflows for multispectral images that include a Near-Infrared Band. This article is protected by copyright. All rights reserved.
Qianguo Xing - One of the best experts on this subject based on the ideXlab platform.
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an improved inherent optical properties data processing system for residual error correction in turbid natural waters
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021Co-Authors: Jun Chen, Wenting Quan, Hongtao Duan, Qianguo XingAbstract:Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the Near-Infrared Band and combined it with four “common Bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the Near-Infrared Band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with R rs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from R rs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer R rs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.
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An Improved Inherent Optical Properties Data Processing System for Residual Error Correction in Turbid Natural Waters
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Jun Chen, Wenting Quan, Hongtao Duan, Qianguo XingAbstract:Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the Near-Infrared Band and combined it with four “common Bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the Near-Infrared Band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with <28.36% uncertainty for oceanic waters and <37.83% uncertainty for inland waters, which was much more effective than what a quasi-analytical algorithm provided. Moreover, the Near-Infrared Band was better at removing the residual error and partial intermission bias in satellite remote sensing reflectance (Rrs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from Rrs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer Rrs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters
Liquan Zhang - One of the best experts on this subject based on the ideXlab platform.
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the spectral responses of a submerged plant vallisneria spiralis with varying biomass using spectroradiometer
Hydrobiologia, 2007Co-Authors: Lin Yuan, Liquan ZhangAbstract:The relationship between land features and their spectral characteristics is a key for the interpretation of remote sensing images. This study was designed to investigate the spectral responses of Vallisneria spiralis, a common submerged aquatic plant in Shanghai, with varying biomass both in the laboratory and in the Middle Lake section of a field-scale constructed wetland, using a FieldSpec™ Pro JR Field Portable Spectroradiometer. The results showed that the reflectance rate of V. spiralis increased with its increasing biomass, and this was exhibited both at the visible Band (500–650 nm) and the Near Infrared Band (700–900 nm). The water environment influenced the reflectance rate and the primary differences between the laboratory and field results mainly occurred at the Near-Infrared Band (700–900 nm). A regression analysis was carried out between the biomass of V. spiralis and the reflectance rate at the wavelengths of QuickBird™ Bands where the biomass responded most strongly. The results of this analysis showed a clear liNear relationship by which the biomass of V. spiralis could be quantitatively deduced from the reflectance rate measured in situ. The implications of this observation, in terms of the ability of hyperspectral remote sensing to estimate and monitor the distribution and dynamics of submerged aquatic vegetation on a large scale, are discussed.
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identification of the spectral characteristics of submerged plant vallisneria spiralis
Acta Ecologica Sinica, 2006Co-Authors: Lin Yuan, Liquan ZhangAbstract:Abstract The relationship between land features and their spectral characteristics is important for the interpretation of remote sensing images. In this study, the spectral characteristics of a submerged plant Vallisneria spiralis with varied coverage was measured with a ground sensor/radiometer, FieldSpec™ Pro JR Spectroradiometer in the laboratory and in the constructed wetland of “Mengqingyuan”, Shanghai, China. The results showed that the reflectance rate of Vallisneria spiralis rose with its increasing coverage, which was exhibited both at the visible Band (500–650 nm) and the Near Infrared Band (700–900 nm). Water quality influenced the reflectance rate with the primary differences between the laboratory and field experiments mainly occurring at the Near-Infrared Band (700–900 nm). A regression analysis was carried out respectively between the coverage of Vallisneria spiralis and the reflectance rate at the wavelengths of Quick Bird 4 Bands where the coverage responded to the strongest. These results of regression analyses showed a clear liNear relationship, by which the coverage of Vallisneria spiralis could be quantitatively deduced from the reflectance rate measured in situ. The implications in terms of the ability of hyperspectral remote sensing to distinguish and monitor the distribution and dynamics of submerged vegetation on a large scale are discussed.
Jun Chen - One of the best experts on this subject based on the ideXlab platform.
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an improved inherent optical properties data processing system for residual error correction in turbid natural waters
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021Co-Authors: Jun Chen, Wenting Quan, Hongtao Duan, Qianguo XingAbstract:Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the Near-Infrared Band and combined it with four “common Bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the Near-Infrared Band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with R rs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from R rs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer R rs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.
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An Improved Inherent Optical Properties Data Processing System for Residual Error Correction in Turbid Natural Waters
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Jun Chen, Wenting Quan, Hongtao Duan, Qianguo XingAbstract:Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the Near-Infrared Band and combined it with four “common Bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the Near-Infrared Band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with <28.36% uncertainty for oceanic waters and <37.83% uncertainty for inland waters, which was much more effective than what a quasi-analytical algorithm provided. Moreover, the Near-Infrared Band was better at removing the residual error and partial intermission bias in satellite remote sensing reflectance (Rrs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from Rrs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer Rrs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters
Michael S Strano - One of the best experts on this subject based on the ideXlab platform.
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detection of dna hybridization using the Near Infrared Band gap fluorescence of single walled carbon nanotubes
Nano Letters, 2006Co-Authors: Esther S Jeng, Anthonie E Moll, Joseph B Gastala, Michael S StranoAbstract:We demonstrate the optical detection of DNA hybridization on the surface of solution suspended single-walled carbon nanotubes (SWNTs) through a SWNT Band gap fluorescence modulation. Hybridization of a 24-mer oligonucleotide sequence with its complement produces a hypsochromic shift of 2 meV, with a detection sensitivity of 6 nM. The energy shift is modeled by correlating the surface coverage of DNA on SWNT to the exciton binding energy, yielding an estimated initial fractional coverage of 0.25 and a final coverage of 0.5. Hybridization on the nanotube surface is confirmed using Forster resonance energy transfer of fluorophore-labeled DNA oligonucleotides. This detection is enabled through a new technique to suspend SWNTs using adsorption of single-stranded DNA and subsequent removal of free DNA from solution. While the kinetics of free DNA hybridization are relatively fast (<10 min), the kinetics of the process on SWNTs are slower under comparable conditions, reaching steady state after 13 h at 25 °C. A ...
