scholarly journals Correction: Zhu, Q., et al. Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning. Remote Sensing 2019, 11, 2001

2020 ◽  
Vol 12 (3) ◽  
pp. 364
Author(s):  
Qing Zhu ◽  
Fang Shen ◽  
Pei Shang ◽  
Yanqun Pan ◽  
Mengyu Li
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Transfer Learning ◽  
Hyperspectral Remote Sensing ◽  
Phytoplankton Species ◽  
Phytoplankton Species Composition

The authors wish to make the following corrections to this paper[...]

scholarly journals Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

2019 ◽  
Vol 11 (17) ◽  
pp. 2001 ◽  
Author(s):  
Qing Zhu ◽  
Fang Shen ◽  
Pei Shang ◽  
Yanqun Pan ◽  
Mengyu Li
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Transfer Learning ◽  
Natural Waters ◽  
Hyperspectral Remote Sensing ◽  
Sensor Technology ◽  
Percentage Error ◽  
Phytoplankton Species ◽  
Phytoplankton Species Composition

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.

scholarly journals The Combined Effects of Increased pCO2 and Warming on a Coastal Phytoplankton Assemblage: From Species Composition to Sinking Rate

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuanyuan Feng ◽  
Fei Chai ◽  
Mark L. Wells ◽  
Yan Liao ◽  
Pengfei Li ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Species Composition ◽  
Interactive Effects ◽  
Combined Effects ◽  
Phytoplankton Species ◽  
Phytoplankton Assemblage ◽  
Pennate Diatom ◽  
Phytoplankton Species Composition ◽  
Diatom Abundance ◽  
Coastal Phytoplankton

In addition to ocean acidification, a significant recent warming trend in Chinese coastal waters has received much attention. However, studies of the combined effects of warming and acidification on natural coastal phytoplankton assemblages here are scarce. We conducted a continuous incubation experiment with a natural spring phytoplankton assemblage collected from the Bohai Sea near Tianjin. Experimental treatments used a full factorial combination of temperature (7 and 11°C) and pCO2 (400 and 800 ppm) treatments. Results suggest that changes in pCO2 and temperature had both individual and interactive effects on phytoplankton species composition and elemental stoichiometry. Warming mainly favored the accumulation of picoplankton and dinoflagellate biomass. Increased pCO2 significantly increased particulate organic carbon to particulate organic phosphorus (C:P) and particulate organic carbon to biogenic silica (C:BSi) ratios, and decreased total diatom abundance; in the meanwhile, higher pCO2 significantly increased the ratio of centric to pennate diatom abundance. Warming and increased pCO2 both greatly decreased the proportion of diatoms to dinoflagellates. The highest chlorophyll a biomass was observed in the high pCO2, high temperature phytoplankton assemblage, which also had the slowest sinking rate of all treatments. Overall, there were significant interactive effects of increased pCO2 and warming on dinoflagellate abundance, pennate diatom abundance, diatom vs. dinoflagellates ratio and the centric vs. pennate ratio. These findings suggest that future ocean acidification and warming trends may individually and cumulatively affect coastal biogeochemistry and carbon fluxes through shifts in phytoplankton species composition and sinking rates.

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