Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study

This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Leon Ćatipović [verfasserIn] Shubha Sathyendranath [verfasserIn] Frano Matić [verfasserIn] Žarko Kovač [verfasserIn] Luka Kovačić [verfasserIn] Živana Ninčević Gladan [verfasserIn] Sanda Skejić [verfasserIn] Hrvoje Kalinić [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Satellite Chlorophyll-a concentration Uncertainties Uncertainty source

Übergeordnetes Werk:	In: International Journal of Applied Earth Observations and Geoinformation - Elsevier, 2022, 128(2024), Seite 103727-
Übergeordnetes Werk:	volume:128 ; year:2024 ; pages:103727-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jag.2024.103727

Katalog-ID:	DOAJ098699245

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allfields	10.1016/j.jag.2024.103727 doi (DE-627)DOAJ098699245 (DE-599)DOAJ7f7388583229480ea7b67b4347d3f9f5 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Leon Ćatipović verfasserin aut Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation. Satellite Chlorophyll-a concentration Uncertainties Uncertainty source Physical geography Environmental sciences Shubha Sathyendranath verfasserin aut Frano Matić verfasserin aut Žarko Kovač verfasserin aut Luka Kovačić verfasserin aut Živana Ninčević Gladan verfasserin aut Sanda Skejić verfasserin aut Hrvoje Kalinić verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103727- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103727- https://doi.org/10.1016/j.jag.2024.103727 kostenfrei https://doaj.org/article/7f7388583229480ea7b67b4347d3f9f5 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000815 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 128 2024 103727-
spelling	10.1016/j.jag.2024.103727 doi (DE-627)DOAJ098699245 (DE-599)DOAJ7f7388583229480ea7b67b4347d3f9f5 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Leon Ćatipović verfasserin aut Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation. Satellite Chlorophyll-a concentration Uncertainties Uncertainty source Physical geography Environmental sciences Shubha Sathyendranath verfasserin aut Frano Matić verfasserin aut Žarko Kovač verfasserin aut Luka Kovačić verfasserin aut Živana Ninčević Gladan verfasserin aut Sanda Skejić verfasserin aut Hrvoje Kalinić verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103727- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103727- https://doi.org/10.1016/j.jag.2024.103727 kostenfrei https://doaj.org/article/7f7388583229480ea7b67b4347d3f9f5 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000815 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 128 2024 103727-
allfields_unstemmed	10.1016/j.jag.2024.103727 doi (DE-627)DOAJ098699245 (DE-599)DOAJ7f7388583229480ea7b67b4347d3f9f5 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Leon Ćatipović verfasserin aut Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation. Satellite Chlorophyll-a concentration Uncertainties Uncertainty source Physical geography Environmental sciences Shubha Sathyendranath verfasserin aut Frano Matić verfasserin aut Žarko Kovač verfasserin aut Luka Kovačić verfasserin aut Živana Ninčević Gladan verfasserin aut Sanda Skejić verfasserin aut Hrvoje Kalinić verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103727- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103727- https://doi.org/10.1016/j.jag.2024.103727 kostenfrei https://doaj.org/article/7f7388583229480ea7b67b4347d3f9f5 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000815 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 128 2024 103727-
allfieldsGer	10.1016/j.jag.2024.103727 doi (DE-627)DOAJ098699245 (DE-599)DOAJ7f7388583229480ea7b67b4347d3f9f5 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Leon Ćatipović verfasserin aut Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation. Satellite Chlorophyll-a concentration Uncertainties Uncertainty source Physical geography Environmental sciences Shubha Sathyendranath verfasserin aut Frano Matić verfasserin aut Žarko Kovač verfasserin aut Luka Kovačić verfasserin aut Živana Ninčević Gladan verfasserin aut Sanda Skejić verfasserin aut Hrvoje Kalinić verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103727- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103727- https://doi.org/10.1016/j.jag.2024.103727 kostenfrei https://doaj.org/article/7f7388583229480ea7b67b4347d3f9f5 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000815 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 128 2024 103727-
allfieldsSound	10.1016/j.jag.2024.103727 doi (DE-627)DOAJ098699245 (DE-599)DOAJ7f7388583229480ea7b67b4347d3f9f5 DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Leon Ćatipović verfasserin aut Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation. Satellite Chlorophyll-a concentration Uncertainties Uncertainty source Physical geography Environmental sciences Shubha Sathyendranath verfasserin aut Frano Matić verfasserin aut Žarko Kovač verfasserin aut Luka Kovačić verfasserin aut Živana Ninčević Gladan verfasserin aut Sanda Skejić verfasserin aut Hrvoje Kalinić verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103727- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103727- https://doi.org/10.1016/j.jag.2024.103727 kostenfrei https://doaj.org/article/7f7388583229480ea7b67b4347d3f9f5 kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000815 kostenfrei https://doaj.org/toc/1569-8432 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 128 2024 103727-
language	English
source	In International Journal of Applied Earth Observations and Geoinformation 128(2024), Seite 103727- volume:128 year:2024 pages:103727-
sourceStr	In International Journal of Applied Earth Observations and Geoinformation 128(2024), Seite 103727- volume:128 year:2024 pages:103727-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Satellite Chlorophyll-a concentration Uncertainties Uncertainty source Physical geography Environmental sciences
isfreeaccess_bool	true
container_title	International Journal of Applied Earth Observations and Geoinformation
authorswithroles_txt_mv	Leon Ćatipović @@aut@@ Shubha Sathyendranath @@aut@@ Frano Matić @@aut@@ Žarko Kovač @@aut@@ Luka Kovačić @@aut@@ Živana Ninčević Gladan @@aut@@ Sanda Skejić @@aut@@ Hrvoje Kalinić @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	359784119
id	DOAJ098699245
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Leon Ćatipović
spellingShingle	Leon Ćatipović misc GB3-5030 misc GE1-350 misc Satellite misc Chlorophyll-a concentration misc Uncertainties misc Uncertainty source misc Physical geography misc Environmental sciences Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study
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topic_title	GB3-5030 GE1-350 Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study Satellite Chlorophyll-a concentration Uncertainties Uncertainty source
topic	misc GB3-5030 misc GE1-350 misc Satellite misc Chlorophyll-a concentration misc Uncertainties misc Uncertainty source misc Physical geography misc Environmental sciences
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title	Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study
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title_full	Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study
author_sort	Leon Ćatipović
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author_browse	Leon Ćatipović Shubha Sathyendranath Frano Matić Žarko Kovač Luka Kovačić Živana Ninčević Gladan Sanda Skejić Hrvoje Kalinić
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title_sort	sources of uncertainty in satellite-derived chlorophyll-a concentration—an adriatic sea case study
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title_auth	Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study
abstract	This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.
abstractGer	This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.
abstract_unstemmed	This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.
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title_short	Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study
url	https://doi.org/10.1016/j.jag.2024.103727 https://doaj.org/article/7f7388583229480ea7b67b4347d3f9f5 http://www.sciencedirect.com/science/article/pii/S1569843224000815 https://doaj.org/toc/1569-8432
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Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study

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