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Abstract We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha’apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems. 

In construction applications, a robot is commonly seen a semi-automated tool or a piece of equipment that assists with specialized work tasks. However, as robots become more technically capable and widely available, they may be seen more as a teammate or co-worker that collaborates with human crews. Using a survey questionnaire, 63 project managers from two national construction management firms in the US were shown videos of three different applications of robotic systems, each exhibiting different characteristics, and were asked to share their perceptions of the robot. Through a between and across group comparison of their responses, we found that a robot was more likely to be seen as a teammate when its movement was less unpredictable, it was seen as more productive than human workers, it was considered durable, it remained constantly active, it took its surroundings into account before moving, it worked well alongside human workers, it was not unreliable, and it made the task more predictable. These findings identify clear challenges for human-robot teaming and the design of robotic systems for construction applications. 

Construction, the last major analog and craft manufacturing industry, is showing early signs of industrialization through the emergence of new robotic and automated systems that can perform construction tasks in situ. While much is understood about the technical and economic challenges to be overcome for widespread adoption of robotics, less is known about the human barriers to adoption, and much less is summarized. Considering the amount of human cooperation required by existing robotic applications, a comprehensive review of barriers that are cognitive or perceptual in nature using a systematic literature assessment methodology is warranted. However, such a review is not straightforward to design. While matters of cognition and perception as pertinent to construction and automation may be queried directly from the literature, there is no certainty that a review based on directly querying abstract phenomena (i.e., perception) could be comprehensive. Thus, systematically reviewing this topic calls for a robust methodology for the design of database queries. In this paper, we perform text analysis with the quanteda package for R in order to (1) understand the language composition of an initial review corpus, and (2) with that understanding design further queries to capture additional articles otherwise not possible through standard query design. Findings indicate that performing text analysis on a systematic review design can produce valuable insight into a review corpus and inform queries that capture additional unique literature relevant to the review. 

Abstract. Ocean color remote sensing is a challenging task over coastal watersdue to the complex optical properties of aerosols and hydrosols. Inorder to conduct accurate atmospheric correction, we previously implementeda joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm,to obtain the aerosol and water-leavingsignal simultaneously.The MAPOL algorithm has been validated with syntheticdata generated by a vector radiative transfer model, and good retrievalperformance has been demonstrated in terms of both aerosol and oceanwater optical properties (Gao et al., 2018).In this work we applied the algorithm to airborne polarimetricmeasurements from the Research Scanning Polarimeter (RSP) over bothopen and coastal ocean waters acquired in twofield campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in2014 and the North Atlantic Aerosols and Marine Ecosystems Study(NAAMES) in 2015 and 2016. Two different yet related bio-opticalmodels are designed for ocean water properties. One model aligns withtraditional open ocean water bio-optical models that parameterize theocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal watersthat includes seven free parameters to describe the absorption andscattering by phytoplankton, colored dissolved organic matter, andnonalgal particles. The retrieval errors of both aerosol opticaldepth and the water-leaving radiance are evaluated. Through thecomparisons with ocean color data products from both in situmeasurements and the Moderate Resolution Imaging Spectroradiometer(MODIS), and the aerosol product from both the High SpectralResolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrievingaerosol and water-leaving radiance properties under various aerosoland ocean water conditions. 

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.