Observations of the seasonal light field in the upper Arctic Ocean are critical to understanding the impacts of changing Arctic ice conditions on phytoplankton growth in the water column. Here we discuss data from a new sensor system, deployed in seasonal ice cover north‐east of Utqiaġvik, Alaska in March 2014. The system was designed to provide observations of light and phytoplankton biomass in the water column during the formation of surface melt ponds and the transition from ice to open water. Hourly observations of downwelling irradiance beneath the ice (at 2.9, 6.9, and 17.9 m depths) and phytoplankton biomass (at 2.9 m depth) were transmitted via Iridium satellite from 9 March to 10 November 2014. Evidence of an under‐ice phytoplankton bloom (Chl a ∼8 mg m⁻³) was seen in June and July. Increases in light intensity observed by the buoy likely resulted from the loss of snow cover and development of surface melt ponds. A bio‐optical model of phytoplankton production supported this probable trigger for the rapid onset of under‐ice phytoplankton growth. Once under‐ice light was no longer a limiting factor for photosynthesis, open water exposure almost marginally increased daily phytoplankton production compared to populations that remained under the adjacent ice. As strong effects of climate change continue to be documented in the Arctic, the insight derived from autonomous buoys will play an increasing role in understanding the dynamics of primary productivity where ice and cloud cover limit the utility of ocean color satellite observations.

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.