Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis

As a major indicator of lake eutrophication that is harmful to human health, the chlorophyll-a concentration (Chl-a) is often estimated using remote sensing, and one method often used is the spectral derivative algorithm. Direct derivative processing may magnify the noise, thus making spectral smoot...
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Autor*in:	Yu Zhou [verfasserIn] Xiaopeng Sun [verfasserIn] Yuchun Wei [verfasserIn] Chunmei Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	chlorophyll-a first-order derivative hyperspectral reflectance remote sensing Taihu Lake

Übergeordnetes Werk:	In: International Journal of Environmental Research and Public Health - MDPI AG, 2005, 10(2013), 7, Seite 2979-2994
Übergeordnetes Werk:	volume:10 ; year:2013 ; number:7 ; pages:2979-2994

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijerph10072979

Katalog-ID:	DOAJ049434640

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author	Yu Zhou
spellingShingle	Yu Zhou misc chlorophyll-a misc first-order derivative misc hyperspectral reflectance misc remote sensing misc Taihu Lake misc Medicine misc R Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis
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topic_title	Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis chlorophyll-a first-order derivative hyperspectral reflectance remote sensing Taihu Lake
topic	misc chlorophyll-a misc first-order derivative misc hyperspectral reflectance misc remote sensing misc Taihu Lake misc Medicine misc R
topic_unstemmed	misc chlorophyll-a misc first-order derivative misc hyperspectral reflectance misc remote sensing misc Taihu Lake misc Medicine misc R
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title	Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis
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title_full	Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis
author_sort	Yu Zhou
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title_sort	estimation of chlorophyll-a concentration in turbid lake using spectral smoothing and derivative analysis
title_auth	Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis
abstract	As a major indicator of lake eutrophication that is harmful to human health, the chlorophyll-a concentration (Chl-a) is often estimated using remote sensing, and one method often used is the spectral derivative algorithm. Direct derivative processing may magnify the noise, thus making spectral smoothing necessary. This study aims to use spectral smoothing as a pretreatment and to test the applicability of the spectral derivative algorithm for Chl-a estimation in Taihu Lake, China, based on the in situ hyperspectral reflectance. Data from July–August of 2004 were used to build the model, and data from July–August of 2005 and March of 2011 were used to validate the model, with Chl-a ranges of 5.0–156.0 mg/m3, 4.0–98.0 mg/m3 and 11.4–35.8 mg/m3, respectively. The derivative model was first used and then compared with the band ratio, three-band and four-band models. The results show that the first-order derivative model at 699 nm had satisfactory accuracy (R2 = 0.75) after kernel regression smoothing and had smaller validation root mean square errors of 15.21 mg/m3 in 2005 and 5.85 mg/m3 in 2011. The distribution map of Chl-a in Taihu Lake based on the HJ1/HSI image showed the actual distribution trend, indicating that the first-order derivative model after spectral smoothing can be used for Chl-a estimation in turbid lake.
abstractGer	As a major indicator of lake eutrophication that is harmful to human health, the chlorophyll-a concentration (Chl-a) is often estimated using remote sensing, and one method often used is the spectral derivative algorithm. Direct derivative processing may magnify the noise, thus making spectral smoothing necessary. This study aims to use spectral smoothing as a pretreatment and to test the applicability of the spectral derivative algorithm for Chl-a estimation in Taihu Lake, China, based on the in situ hyperspectral reflectance. Data from July–August of 2004 were used to build the model, and data from July–August of 2005 and March of 2011 were used to validate the model, with Chl-a ranges of 5.0–156.0 mg/m3, 4.0–98.0 mg/m3 and 11.4–35.8 mg/m3, respectively. The derivative model was first used and then compared with the band ratio, three-band and four-band models. The results show that the first-order derivative model at 699 nm had satisfactory accuracy (R2 = 0.75) after kernel regression smoothing and had smaller validation root mean square errors of 15.21 mg/m3 in 2005 and 5.85 mg/m3 in 2011. The distribution map of Chl-a in Taihu Lake based on the HJ1/HSI image showed the actual distribution trend, indicating that the first-order derivative model after spectral smoothing can be used for Chl-a estimation in turbid lake.
abstract_unstemmed	As a major indicator of lake eutrophication that is harmful to human health, the chlorophyll-a concentration (Chl-a) is often estimated using remote sensing, and one method often used is the spectral derivative algorithm. Direct derivative processing may magnify the noise, thus making spectral smoothing necessary. This study aims to use spectral smoothing as a pretreatment and to test the applicability of the spectral derivative algorithm for Chl-a estimation in Taihu Lake, China, based on the in situ hyperspectral reflectance. Data from July–August of 2004 were used to build the model, and data from July–August of 2005 and March of 2011 were used to validate the model, with Chl-a ranges of 5.0–156.0 mg/m3, 4.0–98.0 mg/m3 and 11.4–35.8 mg/m3, respectively. The derivative model was first used and then compared with the band ratio, three-band and four-band models. The results show that the first-order derivative model at 699 nm had satisfactory accuracy (R2 = 0.75) after kernel regression smoothing and had smaller validation root mean square errors of 15.21 mg/m3 in 2005 and 5.85 mg/m3 in 2011. The distribution map of Chl-a in Taihu Lake based on the HJ1/HSI image showed the actual distribution trend, indicating that the first-order derivative model after spectral smoothing can be used for Chl-a estimation in turbid lake.
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title_short	Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis
url	https://doi.org/10.3390/ijerph10072979 https://doaj.org/article/2bcdf58c6b124d91b5fb6d733d2e4d00 http://www.mdpi.com/1660-4601/10/7/2979 https://doaj.org/toc/1660-4601
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up_date	2024-07-03T23:19:02.234Z
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Estimation of Chlorophyll-a Concentration in Turbid Lake Using Spectral Smoothing and Derivative Analysis

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