Vertically- resolved phytoplankton carbon and net primary production from a high spectral resolution lidar.

Passive ocean observing sensors are unable to detect subsurface structure in ocean properties, resulting in errors in water column integrated phytoplankton biomass and net primary production (NPP) estimates. Active lidar (light detection and ranging) sensors make quantitative measurements of depth-resolved backscatter (bbp) and diffuse light attenuation (Kd) coefficients in the ocean and can provide critical measurements for biogeochemical models. Sub-surface phytoplankton biomass, light, chlorophyll, and NPP fields were characterized using both in situ measurements and coincident airborne high spectral resolution lidar (HSRL-1) measurements collected as part of the SABOR (Ship-Aircraft Bio-Optical Research) field campaign. We found that depth-resolved data are critical for calculating phytoplankton stocks and NPP, with improvements in NPP estimates up to 54%. We observed strong correlations between coincident HSRL-1 and in situ IOP measurements of both bbp (r = 0.94) and Kd (r = 0.90).

[1]  Daniele Iudicone,et al.  Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology , 2004 .

[2]  James H. Churnside,et al.  Review of profiling oceanographic lidar , 2013 .

[3]  David A. Siegel,et al.  Carbon‐based ocean productivity and phytoplankton physiology from space , 2005 .

[4]  C. Hostetler,et al.  Spatial scales of optical variability in the coastal ocean: Implications for remote sensing and in situ sampling , 2016 .

[5]  Emmanuel Boss,et al.  Theoretical derivation of the depth average of remotely sensed optical parameters. , 2005, Optics express.

[6]  G. Gilbert,et al.  Airborne lidar detection of subsurface oceanic scattering layers. , 1988, Applied optics.

[7]  Mati Kahru,et al.  The potential for improving remote primary productivity estimates through subsurface chlorophyll and irradiance measurement , 2015 .

[8]  P. J. Werdell,et al.  An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation , 2005 .

[9]  H. Claustre,et al.  Optical properties of the “clearest” natural waters , 2007 .

[10]  R. Bidigare,et al.  Light driven seasonal patterns of chlorophyll and nitrate in the lower euphotic zone of the North Pacific Subtropical Gyre , 2004 .

[11]  David A. Siegel,et al.  Carbon‐based primary productivity modeling with vertically resolved photoacclimation , 2008 .

[12]  Xiaomei Lu,et al.  Subsurface Ocean Signals from an Orbiting Polarization Lidar , 2013, Remote. Sens..

[13]  James H. Churnside,et al.  Bio-optical model to describe remote sensing signals from a stratified ocean , 2015 .

[14]  Michael J. Behrenfeld,et al.  Analytical phytoplankton carbon measurements spanning diverse ecosystems , 2015 .

[15]  Janet W. Campbell,et al.  Role of satellites in estimating primary productivity on the northwest Atlantic continental shelf , 1988 .

[16]  Yongxiang Hu,et al.  Space‐based lidar measurements of global ocean carbon stocks , 2013 .

[17]  D. Stramski,et al.  Effects of a nonuniform vertical profile of chlorophyll concentration on remote-sensing reflectance of the ocean. , 2005, Applied optics.

[18]  James W. Brown,et al.  A semianalytic radiance model of ocean color , 1988 .

[19]  Wayne C. Welch,et al.  Airborne high spectral resolution lidar for profiling aerosol optical properties. , 2008, Applied optics.

[20]  T. Platt,et al.  Oceanic Primary Production: Estimation by Remote Sensing at Local and Regional Scales , 1988, Science.

[21]  Michael S Twardowski,et al.  Use of optical scattering to discriminate particle types in coastal waters. , 2005, Applied optics.

[22]  Michael S. Twardowski,et al.  Microscale Quantification of the Absorption by Dissolved and Particulate Material in Coastal Waters with an ac-9 , 1999 .