Description: This is a global data set on 0.5 degreee latitude and 0.5 degree longitude monthly resolution of oceanic sun-induced chlorophyll a fluorescence (SIF) at its red peak from January 2003 to December 2011. It was retrieved from top of atmosphere radiance measurements from the hyperspectral instruments SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) at its red peak (~685 nm) based on the Differential Optical Absorption Spectroscopy (DOAS) technique. This technique was used to exploit narrow spectral structures resulting from the filling-in of the Fraunhofer Fe I line, which originates from fluorescence. The reference spectra for chlorophyll fluorescence were calculated by the coupled ocean-atmosphere radiative transfer model SCIATRAN. We compared our results on marine chlorophyll fluorescence line height (FLH) observations with the MODIS Terra normalized Fluorescence Line Height (nFLH) product for the average of years 2003 to 2011 showing good agreement between the two data sets. All the details on the retrieval method and its evaluation can be found in Wolanin et al. (2015).

Description: 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 measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm (Hirata et al. 2011) applied to the Ocean Colour Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm (Bracher et al. 2009, Sadeghi et al. 2012) 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 in situ HPLC pigment data and satellite information on phytoplankton size classes (PSC) (Brewin et al. 2010, Brewin et al. 2015) and the size fraction (Sf) by Ciotti and Bricaud (2006. 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.

Description: The SynSenPFT product is presented as chlorophyll "a" concentrations (Chla) for diatoms, coccolithophores and cyanobacteria (some of the phytoplankton functional types, PFT) obtained globally over the World Ocean on a 4 km sinusoidal grid on a daily basis over the period of August 2002 - March 2012. The SynSenPFT is a synergistic combination of the PFT products of initial-input OC-PFT (Hirata et al., 2011, Soppa et al., 2014) applied to total chlorophyll "a" (TChla) data of Ocean Colour Climate Change Initiative (OC-CCI, Version 2, ESA) and PhytoDOAS (Bracher et al., 2009, Sadeghi et al., 2012) version 3.3 available at doi:10.1594/PANGAEA.870486 with an optimal interpolation (OI). The OI method is applied to OC-PFT and PhytoDOAS Chla products of diatoms, cyanobacteria (called prokarytoes by the OC-PFT method) and haptophytes (for OC-PFT) and coccolithophores (for PhytoDOAS). Note that OC-PFT retrieves haptophytes while PhytoDOAS retrieves coccolithophores, a (often dominating) sub-group of haptophytes. Algorithmically, the SynSenPFT is an update of OC-PFT Chla with PhytoDOAS Chla values weighted in accordance to our degree of belief to both initial-input data products. Within the current version of SynSenPFT algorithm the update is done for every sub-pixel of OC-PFT within a PhytoDOAS pixel. Thus, SynSenPFT in every OC-PFT sub-pixel on average is nudged towards PhytoDOAS values as close as allowed by the prescribed PhytoDOAS and OC-PFT error statistics.

Description: This phytoplankton group (PFT) concentration a (Chl a) data are output from the algorithm PhytoDOAS version 3.3 applied to SCIAMACHY data from 2 Aug 2002 to 8 Apr 2012. Data have been gridded monthly on 0.5° latitude to 0.5°. For cyanobacteria (includes all prokaryotic phytoplankton) and diatoms the PhytoDOAS PFT retrieval algorithm by Bracher et al. (2009) and for coccolithophores the algorithm by Sadeghi et al. (2012) have been used. However, these methods have slightly been improved which includes: - Data during SCIAMACHY instrument decontamination are excluded in the analysis. - SCIAMACHY level-1b input data for PhytoDOAS are now version 7.04 data (instead of version 6.0). - The wavelength window for all three phytoplankton groups (PFTs) fit factor starts at 427.5 nm (instead of 429 nm). - Coccolithophores fit factors are retrieved in a retrieval fitting simultaneously diatoms and coccolithophores (instead of a triple fit with also fitting dinoflagellates as in Sadeghi et al. 2012). - Vibrational Raman Scattering (VRS) is now fitted directly in the blue spectrum (450 to 495 nm), following Dinter et al. (2015), (instead of in the UV—A region as in Vountas et al. 2007) except that here the daily solar background spectrum measured by SCIAMACHY and the VRS pseudo absorption spectrum calculated based on a SCIAMACHY solar spectrum following Vountas et al. (2007) was used in order to correct for the variation of instrumental effects over time (this is not achieved when using the RTM simulated background spectrum as done in Dinter et al. 2015). - The PFT Chl a are derived from the ratio of the PFT fit factor to the VRS fit factor multiplied by a LUT (Look Up Table). The LUT is based on radiative transfer model (RTM) SCIATRAN simulations (see Rozanov et al. 2014) accounting also for changing solar zenith angle (SZA).

Description: Quantitative distributions of major functional PFTs of the world ocean improve the understanding of the role of marine phytoplankton in the global marine ecosystem and biogeochemical cycles. Chl-a fluorescence gives insight on the health of phytoplankton and is related to phytoplankton biomass. In this study, global ocean color satellite products of different dominant phytoplankton functional types' (PFTs') biomass and chlorophyll fluorescence retrieved from hyperspectral satellite data using Differential Optical Absorption Spectroscopy applied to phytoplankton (PhytoDOAS) are presented (see also Bracher et al. 2009, Sadeghi et al. 2012a). Data are compared to ocean color products from multispectral sensors and application of the hyperspectral data set in studying phytoplankton dynamics are presented (Sadeghi et al. 2012b, Ying et al. 2012). Although current hyperspectral sensors have poor spatial resolution (〉30kmx30km), they are useful for the verification and improvement of the high spatially resolved multi-spectral ocean color products. Future applications of PhytoDOAS retrieval to other hyperspectral sensors and its synergistic use with information gained from multispectral ocean color sensors are proposed.

Description: 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 ≦ GPP〈SIF〈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.

Description: Studying phytoplankton functional types (PFTs) from space is possible due to recent advances in remote sensing. Though a variety of products are available, the limited number of wavelengths available compared to the number of model parameters needed to be retrieved is still a major problem in using ocean-color data for PFT retrievals. Here, we investigated which band placement could improve retrievals of three particular PFTs (diatoms, coccolithophores and cyanobacteria). In addition to analyzing dominant spectral features in the absorption spectra of the target PFTs, two previously-developed methods using measured spectra were applied to simulated data. Such a synthetic dataset allowed for significantly increasing the number of scenarios and enabled a full control over parameters causing spectral changes. We evaluated the chosen band placement by applying an adapted ocean reflectance inversion, as utilized in the generalized inherent optical properties (GIOP) retrieval. Results show that the optimal band settings depend on the method applied to determine the bands placement, as well as on the internal variability of the dataset investigated. Therefore, continuous hyperspectral instruments would be most beneficial for discriminating multiple PFTs, though a small improvement in spectral sampling and resolution does not significantly modify the results. Bands, which could be added to future instruments (e.g., Ocean and Land Colour Instrument (OLCI) instrument on the upcoming Sentinel-3B,-3C,-3D, etc., and further satellites) in order to enhance PFT retrieval capabilities, were also determined.

Description: 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.