Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance

Monitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detect...

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Main Authors: Igor Ogashawara, Lin Li
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/15/1764
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spelling doaj-ca306c27898d4925ac3f3d9e3a8eab7a2020-11-25T02:20:17ZengMDPI AGRemote Sensing2072-42922019-07-011115176410.3390/rs11151764rs11151764Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing ReflectanceIgor Ogashawara0Lin Li1Department of Earth Sciences, Indiana University—Purdue University at Indianapolis, Indianapolis, IN 46202, USADepartment of Earth Sciences, Indiana University—Purdue University at Indianapolis, Indianapolis, IN 46202, USAMonitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-<i>a</i> (chl-<i>a</i>) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-<i>a</i> contribution to the absorption at 620 nm based on empirical relationships established using chl-<i>a</i> and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 &#181;g/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.https://www.mdpi.com/2072-4292/11/15/1764phycocyanincyanobacteriabio-optical modelingwater quality
collection DOAJ
language English
format Article
sources DOAJ
author Igor Ogashawara
Lin Li
spellingShingle Igor Ogashawara
Lin Li
Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
Remote Sensing
phycocyanin
cyanobacteria
bio-optical modeling
water quality
author_facet Igor Ogashawara
Lin Li
author_sort Igor Ogashawara
title Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
title_short Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
title_full Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
title_fullStr Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
title_full_unstemmed Removal of Chlorophyll-<i>a</i> Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
title_sort removal of chlorophyll-<i>a</i> spectral interference for improved phycocyanin estimation from remote sensing reflectance
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-07-01
description Monitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-<i>a</i> (chl-<i>a</i>) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-<i>a</i> contribution to the absorption at 620 nm based on empirical relationships established using chl-<i>a</i> and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 &#181;g/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.
topic phycocyanin
cyanobacteria
bio-optical modeling
water quality
url https://www.mdpi.com/2072-4292/11/15/1764
work_keys_str_mv AT igorogashawara removalofchlorophylliaispectralinterferenceforimprovedphycocyaninestimationfromremotesensingreflectance
AT linli removalofchlorophylliaispectralinterferenceforimprovedphycocyaninestimationfromremotesensingreflectance
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