Airborne Lidar Observations of a Spring Phytoplankton Bloom in the Western Arctic Ocean

Abstract

One of the most notable effects of climate change is the decrease in sea ice in the Arctic Ocean. This is expected to affect the distribution of phytoplankton as the ice retreats earlier. We were interested in the vertical and horizontal distribution of phytoplankton in the Chukchi Sea in May. Measurements were made with an airborne profiling lidar that allowed us to cover large areas. The lidar profiles showed a uniform distribution of attenuation and scattering from the surface to the limit of lidar penetration at a depth of about 30 m. Both parameters were greater in open water than under the ice. Depolarization of the lidar decreased as attenuation and scattering increased. A cluster analysis of the 2019 data revealed four distinct clusters based on depolarization and lidar ratio. One cluster was associated with open water, one with pack ice, one with the waters along the land-fast ice, and one that appeared to be scattered throughout the region. The first three were likely the result of different assemblages of phytoplankton, while the last may have been an artifact of thin fog in the atmosphere.

Airborne Bioaerosol Observations Imply a Strong Terrestrial Source in the Summertime Arctic

“…Lidar results from this study have been published in Churnside et al (2020) and the detection methodology is discussed in more detail in Churnside and Marchbanks (2015) and Churnside et al (2021). Briefly, the instrument transmits 12 ns pulses of linearly polarized 532 nm light at 30 Hz and detects copolarized and cross polarized backscatter from the ocean with a sample rate of 1 GHz and a depth resolution of ∼1 m. For this work, we used the lidar attenuation coefficient, α, from the cross-polarized return when the flight altitude was 300 m. This was calculated from the slope of the logarithm of the signal versus depth over the depth range where the log signal was linear.…”

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

“…Their study identified the phytoplankton layer in the Chukchi and Beaufort Seas and examined the effect of ice floes on the depth and thickness of the phytoplankton layer. A follow-up study conducted three years later in the same area showed that the average depth of the phytoplankton layer in open waters was greater than that of the layer under ice floes [25]. Zavalas et al [26] applied an automatic classification technique of LiDAR data to the habitat classification of benthic macroalgae communities, and the result of the study proved its effectiveness.…”

Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp

“…In addition, the lidar enables to obtain bio-optical and biogeochemical parameters over the vertical in the first tens of meters, contrary to passive observations. The potential of airborne lidar has been demonstrated for accurately estimating scattering layers and phytoplankton biomass (Chen et al, 2021;Churnside, 2014;Churnside et al, 2021;Yuan et al, 2022) or for fishery surveys (Roddewig et al, 2018). Recent developments of shipborne lidar confirmed the potential of lidar to monitor the scattering and phytoplankton layers over the first tens of meters (Collister et al, 2018;Zimmerman et al, 2020;Shen et al, 2022;Zhang et al, 2022).…”