Estimating primary production at depth from remote sensing.

Area of Science:

• Oceanography
• Remote Sensing
• Phytoplankton Ecology

Background:

• Accurate estimation of oceanic primary production (P) and light quanta (Q) is crucial for understanding marine ecosystems.
• Current remote sensing methods often rely on pigment biomass (B) and empirical relationships, introducing uncertainties.
• High-latitude North Atlantic waters present unique challenges for primary production modeling due to varying optical properties.

Purpose of the Study:

• To compare four methods for calculating quanta (Q) and primary production (P) at depth in high-latitude North Atlantic waters.
• To evaluate the impact of using pigment biomass (B) versus phytoplankton absorption coefficient (a(ph)) as input parameters.
• To introduce and validate methods for deriving a(ph) from remotely sensed data.

Main Methods:

• Four methods were employed using a common primary production model with identical photosynthetic parameters.
• Methods 1 and 2 utilized pigment biomass (B) and empirical relationships between diffuse attenuation coefficient (K(d)) and B.
• Methods 3 and 4 used the phytoplankton absorption coefficient (a(ph)) as input, with Method 4 employing analytically derived a(ph)(440) and total absorption coefficient (a) from remote measurements.

Main Results:

• Method 4, which used analytically derived a(ph) and total absorption, yielded the closest agreement with measured Q(z) and P(z) (r(2) = 0.92 for Q(z), r(2) = 0.95 for P(z)).
• Method 1, relying on measured pigment biomass, produced the least accurate results (r(2) = 0.81 for Q(z), r(2) = 0.56 for P(z)).
• The study highlights that uncertainties in remote primary production estimation stem from mismatches in pigment-specific absorption coefficients, not necessarily pigment biomass accuracy.

Conclusions:

• Estimating the phytoplankton absorption coefficient (a(ph)) accurately is more critical for remote primary production (P) and quanta (Q) calculations than estimating pigment biomass (B).
• Models and algorithms should be designed to utilize a(ph) directly to improve accuracy.
• Analytical derivation of a(ph) from remote sensing data offers a promising approach for accurate primary production estimation in oceanic waters.