Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme
Water clarity, commonly measured as the Secchi disk depth (<inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> <...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/11/1849 |
| _version_ | 1797565852938141696 |
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| author | Yao Liu Chenchao Xiao Junsheng Li Fangfang Zhang Shenglei Wang |
| author_facet | Yao Liu Chenchao Xiao Junsheng Li Fangfang Zhang Shenglei Wang |
| author_sort | Yao Liu |
| collection | DOAJ |
| description | Water clarity, commonly measured as the Secchi disk depth (<inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula>), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>L</mi> <mn>09</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>M</mi> <mn>14</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, for the AHSI images with concurrent in situ measurements in four inland water bodies with a <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> range of 0.3–4.5 m. The semi-analytical method with <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> can yield the most accurate <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> were 0.22, 0.46, and 0.24 m for <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring. |
| first_indexed | 2024-03-10T19:18:50Z |
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| id | doaj.art-5c2b4209ae0c412b8973f82b1666f335 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:18:50Z |
| publishDate | 2020-06-01 |
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| series | Remote Sensing |
| spelling | doaj.art-5c2b4209ae0c412b8973f82b1666f3352023-11-20T03:08:49ZengMDPI AGRemote Sensing2072-42922020-06-011211184910.3390/rs12111849Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical SchemeYao Liu0Chenchao Xiao1Junsheng Li2Fangfang Zhang3Shenglei Wang4Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaWater clarity, commonly measured as the Secchi disk depth (<inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula>), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>L</mi> <mn>09</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>M</mi> <mn>14</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>, for the AHSI images with concurrent in situ measurements in four inland water bodies with a <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> range of 0.3–4.5 m. The semi-analytical method with <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mi>A</mi> <msub> <mi>A</mi> <mrow> <mi>V</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> can yield the most accurate <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> were 0.22, 0.46, and 0.24 m for <inline-formula> <math display="inline"> <semantics> <msub> <mi>Z</mi> <mrow> <mi>s</mi> <mi>d</mi> </mrow> </msub> </semantics> </math> </inline-formula> of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring.https://www.mdpi.com/2072-4292/12/11/1849Secchi-disk depthhyperspectral imageryGF-5 satellitesemi-analytical modelQuasi-Analytical Algorithm |
| spellingShingle | Yao Liu Chenchao Xiao Junsheng Li Fangfang Zhang Shenglei Wang Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme Remote Sensing Secchi-disk depth hyperspectral imagery GF-5 satellite semi-analytical model Quasi-Analytical Algorithm |
| title | Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme |
| title_full | Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme |
| title_fullStr | Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme |
| title_full_unstemmed | Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme |
| title_short | Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme |
| title_sort | secchi disk depth estimation from china s new generation of gf 5 hyperspectral observations using a semi analytical scheme |
| topic | Secchi-disk depth hyperspectral imagery GF-5 satellite semi-analytical model Quasi-Analytical Algorithm |
| url | https://www.mdpi.com/2072-4292/12/11/1849 |
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