Synergistic exploitation of hyper- and multispectral Sentinel measurements to determine Phytoplankton Functional Types at best spatial and temporal resolution (SynSenPFT)

We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) – diatoms, coccolithophores and cyanobacteria – by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted

Corrigendum: Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT) [Front. Mar. Sci,(203),4] DOI: 10.3389/fmars.2017.00203

This is the final version. Available on open access from Frontiers Media via the DOI in this recordThe article to which this is the corrigendum is in ORE at http://hdl.handle.net/10871/38250In the original article, we neglected, but would like to acknowledge the North-German Supercomputing Alliance (HLRN) for providing HPC resources that have contributed to the research results reported in this paper. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way

Synergistic exploitation of hyper- and multi-spectral precursor sentinel measurements to determine phytoplankton functional types (SynSenPFT)

This is the final version. Available from Frontiers Media via the DOI in this record.The corrigendum to this article is in ORE at http://hdl.handle.net/10871/38256We derive the chlorophyll a concentration (Chla) for three main phytoplankton functional types (PFTs) - diatoms, coccolithophores and cyanobacteria - by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 March 2012 and evaluated against PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.ESA SEOM SY-4Sci Synergy projectSFB/TR 172 (AC)3 “Arctic Amplification” subproject C03DFG-Priority Program SPP 1158 “Antarktis” PhySyn BU2913/3-1Helmholtz Climate Initiative REKLIMHelmholtz Association of German Research Centres (HGF

Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis

High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR&thinsp;≦&thinsp;GPP &lt; SIF &lt; VIs∕VOD. SIF as an indicator of photosynthesis is maximised around the time of highest annual temperatures. The modelled GPP peaks at a similar time to APAR. The time lag of the annual peak between APAR and instantaneous SIF fluxes indicates that the SIF data do contain information on light-use efficiency of tundra vegetation, but further detailed studies are necessary to verify this. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. A particularly late peak of the normalised difference vegetation index (NDVI) in regions with very small seasonality in greenness and a high amount of lakes probably originates from artefacts. Given the very short growing season in circumpolar areas, the average time difference in maximum annual photosynthetic activity and greenness or growth of 3 to 25 days (depending on the data sets chosen) is important and needs to be considered when using satellite observations as drivers in vegetation models.</p

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development

This is the final version. Available from Frontiers Media via the DOI in this record.To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.ESA SEOM SY-4Sci Synergy projectNAS

Role of the PAS sensor domains in the Bacillus subtilis sporulation kinase KinA

Histidine kinases are sophisticated molecular sensors that are used by bacteria to detect and respond to a multitude of environmental signals. KinA is the major histidine kinase required for initiation of sporulation upon nutrient deprivation in Bacillus subtilis. KinA has a large N-terminal region (residues 1 to 382) that is uniquely composed of three tandem Per-ARNT-Sim (PAS) domains that have been proposed to constitute a sensor module. To further enhance our understanding of this "sensor" region, we defined the boundaries that give rise to the minimal autonomously folded PAS domains and analyzed their homo- and heteroassociation properties using analytical ultracentrifugation, nuclear magnetic resonance (NMR) spectroscopy, and multiangle laser light scattering. We show that PAS(A) self-associates very weakly, while PAS(C) is primarily a monomer. In contrast, PAS(B) forms a stable dimer (K-d [dissociation constant] o

Evidence for a Minimal Eukaryotic Phosphoproteome?

BACKGROUND: Reversible phosphorylation catalysed by kinases is probably the most important regulatory mechanism in eukaryotes. METHODOLOGY/PRINCIPAL FINDINGS: We studied the in vitro phosphorylation of peptide arrays exhibiting the majority of PhosphoBase-deposited protein sequences, by factors in cell lysates from representatives of various branches of the eukaryotic species. We derived a set of substrates from the PhosphoBase whose phosphorylation by cellular extracts is common to the divergent members of different kingdoms and thus may be considered a minimal eukaryotic phosphoproteome. The protein kinases (or kinome) responsible for phosphorylation of these substrates are involved in a variety of processes such as transcription, translation, and cytoskeletal reorganisation. CONCLUSIONS/SIGNIFICANCE: These results indicate that the divergence in eukaryotic kinases is not reflected at the level of substrate phosphorylation, revealing the presence of a limited common substrate space for kinases in eukaryotes and suggests the presence of a set of kinase substrates and regulatory mechanisms in an ancestral eukaryote that has since remained constant in eukaryotic life

Adaptation of extremely halotolerant black yeast Hortaea werneckii to increased osmolarity: a molecular perspective at a glance

Halophilic adaptations have been studied almost exclusively on prokaryotic microorganisms. Discovery of the black yeast Hortaea werneckii as the dominant fungal species in hypersaline waters enabled the introduction of a new model organism to study the mechanisms of salt tolerance in eukaryotes. Its strategies of cellular osmotic adaptations on the physiological and molecular level revealed novel, intricate mechanisms to combat fluctuating salinity. H. werneckii is an extremely halotolerant eukaryotic microorganism and thus a promising source of transgenes for osmotolerance improvement of industrially important yeasts, as well as in crops