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| 论文题目: |
An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters (vol 239, 111648, 2020) |
| 英文论文题目: |
An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters (vol 239, 111648, 2020) |
| 第一作者: |
刘阁 |
| 英文第一作者: |
G. Liu |
| 联系作者: |
Li, Yunmei |
| 英文联系作者: |
Li, Yunmei |
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| 发表年度: |
2020 |
| 卷: |
240 |
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| 摘要: |
Accurate remote assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid case-2 waters is a challenge, owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not co-vary with phytoplankton. Here, we propose an improved Quasi-Analytical Algorithm (QAA) (denoted as TC2) for retrieving Chla concentrations from remote sensing reflectance (R-rs(lambda)) which can be applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in turbid case-2 waters. TC2 has two main extensions when compared with QAA. First, TC2 makes an additional assumption to separate the total non-water absorption at 665 nm (a(nw)(665)) into phytoplankton absorption (a(ph)(665)) and yellow matter (a(ym)(665)), which is the sum of colored dissolved matter (CDOM) and detritus. Second, for selecting the position of the near-infrared (NIR) band which is used to estimate the signal of total backscattering coefficient (b(b)(lambda(0))) at QAA reference band (lambda(0)), we take into account the assumption that the absorption of pure water should be dominant at this band, as well as the impact of the signal-to-noise ratio (SNR) in the NIR band on the Chla concentration estimating model. When applied to in situ R-rs(lambda) and OLCI match-up R-rs(lambda) data in this study, TC2 provided more accurate Chla estimation than previous Cha concentration retrieval algorithms for turbid case-2 waters. TC2 has the potential for use as a simple and effective algorithm for monitoring Chla concentrations in the turbid case-2 waters at a global scale from space. |
| 英文摘要: |
Accurate remote assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid case-2 waters is a challenge, owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not co-vary with phytoplankton. Here, we propose an improved Quasi-Analytical Algorithm (QAA) (denoted as TC2) for retrieving Chla concentrations from remote sensing reflectance (R-rs(lambda)) which can be applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in turbid case-2 waters. TC2 has two main extensions when compared with QAA. First, TC2 makes an additional assumption to separate the total non-water absorption at 665 nm (a(nw)(665)) into phytoplankton absorption (a(ph)(665)) and yellow matter (a(ym)(665)), which is the sum of colored dissolved matter (CDOM) and detritus. Second, for selecting the position of the near-infrared (NIR) band which is used to estimate the signal of total backscattering coefficient (b(b)(lambda(0))) at QAA reference band (lambda(0)), we take into account the assumption that the absorption of pure water should be dominant at this band, as well as the impact of the signal-to-noise ratio (SNR) in the NIR band on the Chla concentration estimating model. When applied to in situ R-rs(lambda) and OLCI match-up R-rs(lambda) data in this study, TC2 provided more accurate Chla estimation than previous Cha concentration retrieval algorithms for turbid case-2 waters. TC2 has the potential for use as a simple and effective algorithm for monitoring Chla concentrations in the turbid case-2 waters at a global scale from space. |
| 刊物名称: |
Remote Sensing of Environment |
| 英文刊物名称: |
Remote Sensing of Environment |
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| 参与作者: |
G. Liu, L. Li, K. S. Song, Y. M. Li, H. Lyu, Z. D. Wen, C. Fang, S. Bi, X. P. Sun, Z. M. Wang, Z. G. Cao, Y. X. Shang, G. L. Yu, Z. B. Zheng, C. C. Huang, Y. F. Xu and K. Shi |
| 英文参与作者: |
G. Liu, L. Li, K. S. Song, Y. M. Li, H. Lyu, Z. D. Wen, C. Fang, S. Bi, X. P. Sun, Z. M. Wang, Z. G. Cao, Y. X. Shang, G. L. Yu, Z. B. Zheng, C. C. Huang, Y. F. Xu and K. Shi |
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