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Qianguo Xing - One of the best experts on this subject based on the ideXlab platform.
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sun glint correction with an inherent optical properties Data Processing System
International Journal of Remote Sensing, 2021Co-Authors: Jun Chen, Zhongli Liu, Nan Lin, Qianguo Xing, Delu PanAbstract:Frequent and extensive sun glint is a serious obstacle to real-time monitoring of ocean colour anomalies. We semi-analytically adjust an inherent optical properties (IOPs) Data Processing System fo...
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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
Jun Chen - One of the best experts on this subject based on the ideXlab platform.
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sun glint correction with an inherent optical properties Data Processing System
International Journal of Remote Sensing, 2021Co-Authors: Jun Chen, Zhongli Liu, Nan Lin, Qianguo Xing, Delu PanAbstract:Frequent and extensive sun glint is a serious obstacle to real-time monitoring of ocean colour anomalies. We semi-analytically adjust an inherent optical properties (IOPs) Data Processing System fo...
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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
Hongtao Duan - 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
Wenting Quan - 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
Chen Jun - One of the best experts on this subject based on the ideXlab platform.
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Sun glint correction with an inherent optical properties Data Processing System
'Informa UK Limited', 2021Co-Authors: Chen Jun, He Xianqiang, Liu Zhongli, Lin Nan, Xing Qianguo, Pan DeluAbstract:Frequent and extensive sun glint is a serious obstacle to real-time monitoring of ocean colour anomalies. We semi-analytically adjust an inherent optical properties (IOPs) Data Processing System for the open ocean to correct for sun glint (called 'IDAS-SGC') and for remote sensing reflectance (R (rs)) and IOPs retrievals. Tests with synthetic Data validated the effectiveness of our algorithm in deriving ocean colour Data from severely glint-contaminated images to produce high quality images. Evaluating results from single mission images suggested that our approach provides spatially smooth and consistent ocean colour products from both severe glint and glint-free regions for Visible Infrared Imaging Radiometer Suite and for Medium Resolution Spectral Imager II instruments. Specifically, complete coverage of circulation-caused ocean colour anomalies can be recovered from a single severe sun glint image. Comparing multi-mission images found that the inter-mission consistency for IDAS-SGC R (rs) in sun glint regions is comparable with the inter-mission consistency in glint-free regions. Furthermore, we evaluate the performance of the Cox-Munk algorithm for sun glint estimation, and we find that our IDAS-SGC algorithm is more effective than the Cox-Munk algorithm in deriving R (rs) products from severe sun glint regions due to the absence of accurate real-time wind Data. Our results suggest that the IDAS-SGC algorithm obtains meaningful ocean colour products from sun glint-contaminated images of the open oceans
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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: Chen Jun, Quan Wenting, Duan Hongtao, Xing QianguoAbstract: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, IDAS(v2). We applied the IDAS(v2) algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDAS(v2) 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 IDAS(v2) algorithm provided IOPs Data with
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An Inherent Optical Properties Data Processing System for Achieving Consistent Ocean Color Products From Different Ocean Color Satellites
'American Geophysical Union (AGU)', 2020Co-Authors: Chen Jun, He Xianqiang, Liu Zhongli, Xing Qianguo, Xing Xiaogang, Pan DeluAbstract:We used field measurements and multimission satellite Data to evaluate how well an inherent optical properties (IOPs) Data Processing System performed at correcting the residual error of the atmospheric correction in satellite remote sensing reflectance (R-rs) and how well the System simultaneously minimized intermission biases between different remote sensing Systems. We developed the IOPs Data Processing System as a semianalytical algorithm called IDAS. Our results show that IDAS generates accurate and consistent IOPs products from two ocean color missions: Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer Aqua (MODISA). Specifically, with "high-quality" SeaWiFS and MODISA R-rs Data, IDAS provided temporally consistent IOPs products for the oligotrophic open ocean resulting in an annual mean intermission difference of less than 3%, which is significantly lower than what a quasi-analytical algorithm (QAA) provided. We used IDAS to generate a long time series of b(b)(555) from the Northwest Atlantic Subtropical Gyre using SeaWiFS (1998 to 2002) and MODISA (2003 to 2017) images. Our results show that the IDAS-derived annual b(b)(555) decreased monotonically by 2.81% per decade from 1998 to 2017. Comparing the IDAS-generated annual trend for b(b)(555) to the same Data processed with the QAA algorithm, we found that the QAA results differed because of impacts of the residual errors of the atmospheric correction and intermission biases. The differences in the annual trends existed despite the same temporal changing patterns of in situ particulate organic carbon existing in the Sargasso Sea and in the satellite chlorophyll-a concentration in the Northwest Atlantic Subtropical Gyre
