Description: SCIATRAN is a comprehensive software package which is designed to model radiative transfer processes in the terrestrial atmosphere and ocean in the spectral range from the ultraviolet to the thermal infrared (0.18–40 μm). It accounts for multiple scattering processes, polarization, thermal emission and ocean–atmosphere coupling. The main goal of this paper is to present a recently developed version of SCIATRAN which takes into account accurately inelastic radiative processes in both the atmosphere and the ocean. In the scalar version of the coupled ocean–atmosphere radiative transfer solver presented by Rozanov et al. [61] we have implemented the simulation of the rotational Raman scattering, vibrational Raman scattering, chlorophyll and colored dissolved organic matter fluorescence. In this paper we discuss and explain the numerical methods used in SCIATRAN to solve the scalar radiative transfer equation including trans-spectral processes, and demonstrate how some selected radiative transfer problems are solved using the SCIATRAN package. In addition we present selected comparisons of SCIATRAN simulations with those published benchmark results, independent radiative transfer models, and various measurements from satellite, ground-based, and ship-borne instruments. The extended SCIATRAN software package along with a detailed User's Guide is made available for scientists and students, who are undertaking their own research typically at universities, via the web page of the Institute of Environmental Physics (IUP), University of Bremen: http://www.iup.physik.uni-bremen.de.

Description: 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.

Description: The scope of the Science Plan is to describe the scientific background, applications, and activities of the Environmental Mapping and Analysis Program (EnMAP) imaging spectroscopy mission. Primarily, this document addresses scientists and funding institutions, but it may also be of interest to environmental stakeholders and governmental agencies. It is designed to be a living document that will be updated throughout the entire mission lifetime. Chapter 1 provides a brief overview of the principles and current state of imaging spectroscopy. This is followed by an introduction to the EnMAP mission, including its objectives and impact on international programs as well as major environmental and societal challenges. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation, and synergies with other missions. Chapter 3 gives an overview of the major fields of application such as vegetation and forests, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards. Finally, Chapter 4 outlines the scientific exploitation strategy, which includes the strategy for community building and training, preparatory flight campaigns and software developments. A list of abbreviations is provided in the annex to this document and an extended glossary of terms and abbreviations is available on the EnMAP website.

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.

Description: In preparation of the German spaceborne imaging spectroscopy mission EnMAP (The Environmental Mapping and Analysis Program) and its upcoming launch in early 2022, the data product validation activities have been intensified. As part of the science preparation and mission support project led by the German Research Center (GFZ) Potsdam, the overall quality of the official EnMAP products has to be accessed and evaluated independently from the data quality control activities performed by the Ground Segment at DLR EOC. Therefore, the radiometric, spectral, reflective, geometric and general quality of the three official EnMAP products (L1B, L1C and L2A) has to be validated during the commissioning and nominal phase.This paper presents an update of the data product validation activities, an in-depth insight into the overall approach and into specifically designed methods described in the EnMAP Product Validation Plan.

Description: Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions

Description: The hyperspectral EnMAP (Environmental Mapping and Analysis Program) satellite was successfully launched in April 2022, passed its commissioning phase, and entered the nominal phase of operational data acquisition in November 2022. Since then, users may submit data acquisition proposals and download the data in three processing levels: Level-1B (radiometrically-corrected and spectrallycharacterized top-of-atmosphere (TOA) radiance), Level-1C (geometrically-corrected L1B data), and Level-2A (atmospherically-corrected Level-1C data, i.e., bottom-ofatmosphere (BOA) reflectance). The official product generation is usually done by the ground segment processing chain. Alternatively, the EnMAP processing tool (EnPT) provides a highly customizable free and open-source pre-processing chain enabling additional functionalities and options to fulfill individual user requirements and quality expectations. Here, we provide an overview of the implemented pre-processing chain and its modular design with a specific focus on the additional functionalities of EnPT to obtain highly accurate and customizable hyperspectral EnMAP Level-2A data.