scholarly journals Does chlorophyll <i>a</i> provide the best index of phytoplankton biomass for primary productivity studies?

2007 ◽  
Vol 4 (2) ◽  
pp. 707-745 ◽  
Author(s):  
Y. Huot ◽  
M. Babin ◽  
F. Bruyant ◽  
C. Grob ◽  
M. S. Twardowski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Chlorophyll A ◽  
Primary Productivity ◽  
Phytoplankton Biomass ◽  
Initial Slope ◽  
South Pacific Ocean ◽  
Error Propagation Analysis ◽  
Best Estimator

Abstract. Probably because it is a readily available ocean color product, almost all models of primary productivity use chlorophyll as their index of phytoplankton biomass. As other variables become more readily available, both from remote sensing and in situ autonomous platforms, we should ask if other indices of biomass might be preferable. Herein, we compare the accuracy of different proxies of phytoplankton biomass for estimating the maximum photosynthetic rate (Pmax) and the initial slope of the production versus irradiance (P vs. E) curve (α). The proxies compared are: the total chlorophyll a concentration (Tchla, the sum of chlorophyll a and divinyl chlorophyll), the phytoplankton absorption coefficient, the phytoplankton photosynthetic absorption coefficient, the active fluorescence in situ, the particulate scattering coefficient at 650 nm (bp (650)), and the particulate backscattering coefficient at 650 nm (bbp (650)). All of the data (about 170 P vs. E curves) were collected in the South Pacific Ocean. We find that when only the phytoplanktonic biomass proxies are available, bp (650) and Tchla are respectively the best estimators of Pmax and alpha. When additional variables are available, such as the depth of sampling, the irradiance at depth, or the temperature, Tchla becomes the best estimator of both Pmax and α. From a remote sensing perspective, error propagation analysis shows that, given the current algorithms errors for estimating bbp(650), Tchla remains the best estimator of Pmax.

scholarly journals Relationship between photosynthetic parameters and different proxies of phytoplankton biomass in the subtropical ocean

2007 ◽  
Vol 4 (5) ◽  
pp. 853-868 ◽  
Author(s):  
Y. Huot ◽  
M. Babin ◽  
F. Bruyant ◽  
C. Grob ◽  
M. S. Twardowski ◽  
...  
Keyword(s):  
Absorption Coefficient ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Initial Slope ◽  
Photosynthetic Parameters ◽  
Backscattering Coefficient ◽  
South Pacific Ocean ◽  
Almost All ◽  
Best Estimator

Abstract. Probably because it is a readily available ocean color product, almost all models of primary productivity use chlorophyll as their index of phytoplankton biomass. As other variables become more readily available, both from remote sensing and in situ autonomous platforms, we should ask if other indices of biomass might be preferable. Herein, we compare the accuracy of different proxies of phytoplankton biomass for estimating the maximum photosynthetic rate (Pmax) and the initial slope of the production versus irradiance (P vs. E) curve (α). The proxies compared are: the total chlorophyll a concentration (Tchla, the sum of chlorophyll a and divinyl chlorophyll), the phytoplankton absorption coefficient, the phytoplankton photosynthetic absorption coefficient, the active fluorescence in situ, the particulate scattering coefficient at 650 nm (bp(650)), and the particulate backscattering coefficient at 650 nm (bbp(650)). All of the data (about 170 P vs. E curves) were collected in the South Pacific Ocean. We find that when only the phytoplanktonic biomass proxies are available, bp(650) and Tchla are respectively the best estimators of Pmax and α. When additional variables are available, such as the depth of sampling, the irradiance at depth, or the temperature, Tchla is the best estimator of both Pmax and α.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Estimation of the Particulate Organic Carbon to Chlorophyll-a Ratio Using MODIS-Aqua in the East/Japan Sea, South Korea

2020 ◽  
Vol 12 (5) ◽  
pp. 840 ◽  
Author(s):  
Dabin Lee ◽  
SeungHyun Son ◽  
HuiTae Joo ◽  
Kwanwoo Kim ◽  
Myung Joon Kim ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Particulate Organic Carbon ◽  
Chlorophyll A ◽  
Primary Productivity ◽  
Seasonal Variability ◽  
Japan Sea ◽  
Global Ocean ◽  
Phytoplankton Communities

In recent years, the change of marine environment due to climate change and declining primary productivity have been big concerns in the East/Japan Sea, Korea. However, the main causes for the recent changes are still not revealed clearly. The particulate organic carbon (POC) to chlorophyll-a (chl-a) ratio (POC:chl-a) could be a useful indicator for ecological and physiological conditions of phytoplankton communities and thus help us to understand the recent reduction of primary productivity in the East/Japan Sea. To derive the POC in the East/Japan Sea from a satellite dataset, the new regional POC algorithm was empirically derived with in-situ measured POC concentrations. A strong positive linear relationship (R2 = 0.6579) was observed between the estimated and in-situ measured POC concentrations. Our new POC algorithm proved a better performance in the East/Japan Sea compared to the previous one for the global ocean. Based on the new algorithm, long-term POC:chl-a ratios were obtained in the entire East/Japan Sea from 2003 to 2018. The POC:chl-a showed a strong seasonal variability in the East/Japan Sea. The spring and fall blooms of phytoplankton mainly driven by the growth of large diatoms seem to be a major factor for the seasonal variability in the POC:chl-a. Our new regional POC algorithm modified for the East/Japan Sea could potentially contribute to long-term monitoring for the climate-associated ecosystem changes in the East/Japan Sea. Although the new regional POC algorithm shows a good correspondence with in-situ observed POC concentrations, the algorithm should be further improved with continuous field surveys.

scholarly journals A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

2019 ◽  
Vol 11 (19) ◽  
pp. 2257
Author(s):  
Ji-Yeon Baek ◽  
Young-Heon Jo ◽  
Wonkook Kim ◽  
Jong-Seok Lee ◽  
Dawoon Jung ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Sea Water ◽  
Two Dimensions ◽  
Sea Surface ◽  
Measurement Unit ◽  
Observation System ◽  
Low Altitude ◽  
Multispectral Camera

In this study, a low-altitude remote sensing (LARS) observation system was employed to observe a rapidly changing coastal environment-owed to the regular opening of the sluice gate of the Saemangeum seawall-off the west coast of South Korea. The LARS system uses an unmanned aerial vehicle (UAV), a multispectral camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU) module to acquire geometry information. The UAV system can observe the coastal sea surface in two dimensions with high temporal (1 s−1) and spatial (20 cm) resolutions, which can compensate for the coarse spatial resolution of in-situ measurements and the low temporal resolution of satellite observations. Sky radiance, sea surface radiance, and irradiance were obtained using a multispectral camera attached to the LARS system, and the remote sensing reflectance (Rrs) was accordingly calculated. In addition, the hyperspectral radiometer and in-situ chlorophyll-a concentration (CHL) measurements were obtained from a research vessel to validate the Rrs observed using the multispectral camera. Multi-linear regression (MLR) was then applied to derive the relationship between Rrs of each wavelength observed using the multispectral sensor on the UAV and the in-situ CHL. As a result of applying MLR, the correlation and root mean square error (RMSE) between the remotely sensed and in-situ CHLs were 0.94 and ~0.8 μg L−1, respectively; these results show a higher correlation coefficient and lower RMSE than those of other, previous studies. The newly derived algorithm for the CHL estimation enables us to survey 2D CHL images at high temporal and spatial resolutions in extremely turbid coastal oceans.

scholarly journals Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

2020 ◽  
Vol 32 ◽  
pp. 53-63
Author(s):  
Stefan Kazakov ◽  
Valko Biserkov ◽  
Luchezar Pehlivanov ◽  
Stoyan Nedkov
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Suspended Matter ◽  
Trophic Status ◽  
Remote Sensing Data ◽  
Water Quality Monitoring ◽  
Floodplain Lakes ◽  
In Situ Data

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

Regional empirical algorithm for an improved retrieval of chlorophyll a concentrations in the Black Sea using Sentinel 3 ocean color data

2021 ◽  
Author(s):  
Violeta Slabakova ◽  
Snejana Moncheva ◽  
Nataliya Slabakova ◽  
Nina Dzembekova
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Black Sea ◽  
Ocean Color ◽  
The Black Sea ◽  
Ocean Color Remote Sensing ◽  
Color Data ◽  
Cubic Polynomial ◽  
Polynomial Formulation

&lt;p&gt;The Black Sea is an extraordinarily complex water body for ocean color remote sensing, as it belong to Case 2 waters, which are characterized by relatively high absorption by Colored Dissolved Organic Matter (CDOM) while the concentration of non-pigmented particulate matter does not co-vary in a predictable manner with chlorophyll &lt;em&gt;a&lt;/em&gt; . The optical complexity of the Black Sea is the reason why the standard bio-optical algorithms developed for Case 1 waters, are the source of large uncertainties (of the order of hundreds of percent) of chlorophyll &lt;em&gt;a&lt;/em&gt; concentration in the coastal and shelf regions. In the framework of ESA contract &amp;#8220;BIO-OPTICS FOR OCEAN COLOR REMOTE SENSING OF THE BLACK SEA - Black Sea Color&amp;#8221; we developed empirical ocean color algorithm for chlorophyll&lt;em&gt; a &lt;/em&gt;retrieval from Sentinel 3A/OLCI primary ocean color products using the &lt;em&gt;in situ &lt;/em&gt;reference bio-optical datasets collected in the Black Sea in the period 2012-2019. Results obtained from the assessment of operational S3A/OLCI chlorophyll products, highlighted and confirmed that the specific regional algorithm is essential for the Black Sea. The coefficients of the regional algorithm were derived from the regression of log-transformed pigment concentrations and remote sensing reflectance ratio at 490nm and 560 nm with determination coefficient R&lt;sup&gt;2&lt;/sup&gt; =0.88 and number of samples N=186. The algorithm predicts chlorophyll a values using a cubic polynomial formulation. The result of assessment of the regional chlorophyll &lt;em&gt;a&lt;/em&gt; product against independent in situ measurements from the data utilized for algorithm development, showed relatively high accuracy (31.7%), fewer underestimations (MPD=-9.2%) and a good agreement (R&lt;sup&gt;2&lt;/sup&gt;=0.66) between datasets indicating that the regional algorithm is more effective in reproducing the&amp;#160; pigment concentration in the Black Sea waters in comparison to the standard Sentinel 3A/OLCI algorithms. Our analysis revealed the importance of providing regional algorithms strictly required to suit the peculiar bio-optical properties featuring this basin. However, this requires collection of accurate&lt;em&gt; in situ &lt;/em&gt;measurements in the different parts of the Black Sea. The validity of the reported empirical algorithm obviously depends on the size of the dataset used for its development. The Black Sea waters vary at a basin level due to the sub-regional features, environmental factors and seasonal variability, consequently the presented regional algorithm might have a limited generalization capability. Clearly, more&lt;em&gt; in situ&lt;/em&gt; data with improved spatial and temporal coverage are critically needed for further calibration and validation of the ocean color products in the Black Sea.&lt;/p&gt;

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