Dear all
I am a user of OC-CCI data and also Chl-a data from MODIS in here
https://oceancolor.gsfc.nasa.gov/cgi/browse.pl?sen=amod
I have a question regarding these data;
I think satellite imagery can capture the surface and subsurface blooms.
So is it possible in these data also, to know how much was the surface and subsurface bloom? Also the data here (OC-CCI data and also Chl-a data from MODIS) is of surface bloom or subsurface?
I would be thankful that you answer me.
Thanks
Surface and subsurface blooms in MODIS data
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Surface and subsurface blooms in MODIS data
The quick answer is that the contribution from deep chlorophyll is reduced because the length of the underwater light path is doubled, so satellite chlorophyll is generally assumed to represent the surface value.
For primary production calculations the deep chlorophyll contribution can be significant. It is possible to combine other data sets such as depth profiles from ships and gliders with additional remotely sensed data to impute the depth profile for chlorophyll. For Ecological Geography of the Sea (2nd Edition) the ocean was divided into provinces. Parameters for a simple depth-profile model were generated from in situ data and summarized in tables by province and season. Note that the boundaries of the provinces were adjusted using remotely sensed data. Operational estimation of primary production at large geographical scales used nearest-neighbour methods to select in situ records that best match each pixel or bin in remotely sensed data.
For primary production calculations the deep chlorophyll contribution can be significant. It is possible to combine other data sets such as depth profiles from ships and gliders with additional remotely sensed data to impute the depth profile for chlorophyll. For Ecological Geography of the Sea (2nd Edition) the ocean was divided into provinces. Parameters for a simple depth-profile model were generated from in situ data and summarized in tables by province and season. Note that the boundaries of the provinces were adjusted using remotely sensed data. Operational estimation of primary production at large geographical scales used nearest-neighbour methods to select in situ records that best match each pixel or bin in remotely sensed data.
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Surface and subsurface blooms in MODIS data
Perhaps in the future we may be able to vertically resolve phytoplankton using
LIDAR from space.
See advance number 1 under the Satellite section of this paper.
https://doi.org/10.3389/fmars.2019.00251
Norman
LIDAR from space.
See advance number 1 under the Satellite section of this paper.
https://doi.org/10.3389/fmars.2019.00251
Norman