Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (a_ph(λ)) and corresponding simulated remote sensing reflectance spectra (R_rs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely a_ph(λ), R_rs(λ), and the absorption of water compounds obtained from inversion of the R_rs(λ)) spectra using a quasi-analytical algorithm (QAA). R_rs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on R_rs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured a_ph(λ) spectra performed best with only a few misclassified cultures. Based on R_rs(λ) spectra, the accuracy of this differentiation decreased but the performance was partly restored if wavelengths of strong water absorption were excluded and chlorophyll concentrations were higher than 1 mg∙m⁻³. When based on QAA-inverted absorption spectra, the differentiation was less precise due to loss of information at longer wavelengths. This analysis showed that, compared to inverted absorption spectra from restricted inversion models, hyperspectral R_rs(λ) is potentially suitable input data for the differentiation of phytoplankton taxonomic groups in prospective EnMAP applications, though still a challenge at low algal concentrations.

First, we retune an algorithm based on empirical orthogonal functions (EOFs) for globally retrieving the chlorophyll a concentration (Chl‐a) of phytoplankton functional types (PFTs) from multisensor merged ocean color (OC) products. The retuned algorithm, referred to as EOF‐SST hybrid algorithm, is improved by: (i) using 23% more matchups between the updated global in situ pigment database and satellite remote sensing reflectance (R_rs) products, and (ii) including sea surface temperature (SST) as an additional input parameter. In addition to the Chl‐a of the six PFTs (diatoms, haptophytes, dinoflagellates, green algae, prokaryotes, and Prochlorococcus), the fractions of prokaryote and Prochlorococcus Chl‐a to total Chl‐a (TChl‐a), are also retrieved by the EOF‐SST hybrid algorithm. Matchup data are separated for low and high‐temperature regimes based on different PFT dependences on SST, to establish SST‐separated hybrid algorithms which demonstrate further improvements in performance as compared to the EOF‐SST hybrid algorithm. The per‐pixel uncertainty of the retrieved TChl‐a and PFT products is estimated by taking into account the uncertainties from both input data and model parameters through Monte Carlo simulations and analytical error propagation. The algorithm and its method to determine uncertainties can be transferred to similar OC products until today, enabling long‐term continuous satellite observations of global PFT products. Satellite PFT uncertainty is essential to evaluate and improve coupled ecosystem‐ocean models which simulate PFTs, and furthermore can be used to directly improve these models via data assimilation.

Phytoplankton in the ocean are extremely diverse. The abundance of various intracellular pigments are often used to study phytoplankton physiology and ecology, and identify and quantify different phytoplankton groups. In this study, phytoplankton absorption spectra ( $a_{ph}\left(\lambda \right)$ ) derived from underway flow-through AC-S measurements in the Fram Strait are combined with phytoplankton pigment measurements analyzed by high-performance liquid chromatography (HPLC) to evaluate the retrieval of various pigment concentrations at high spatial resolution. The performances of two approaches, Gaussian decomposition and the matrix inversion technique are investigated and compared. Our study is the first to apply the matrix inversion technique to underway spectrophotometry data. We find that Gaussian decomposition provides good estimates (median absolute percentage error, MPE 21–34%) of total chlorophyll-a (TChl-a), total chlorophyll-b (TChl-b), the combination of chlorophyll-c1 and -c2 (Chl-c1/2), photoprotective (PPC) and photosynthetic carotenoids (PSC). This method outperformed one of the matrix inversion algorithms, i.e., singular value decomposition combined with non-negative least squares (SVD-NNLS), in retrieving TChl-b, Chl-c1/2, PSC, and PPC. However, SVD-NNLS enables robust retrievals of specific carotenoids (MPE 37–65%), i.e., fucoxanthin, diadinoxanthin and 19 ${}^{\prime }$ -hexanoyloxyfucoxanthin, which is currently not accomplished by Gaussian decomposition. More robust predictions are obtained using the Gaussian decomposition method when the observed $a_{ph}\left(\lambda \right)$ is normalized by the package effect index at 675 nm. The latter is determined as a function of “packaged” $a_{ph}\left(675\right)$ and TChl-a concentration, which shows potential for improving pigment retrieval accuracy by the combined use of $a_{ph}\left(\lambda \right)$ and TChl-a concentration data. To generate robust estimation statistics for the matrix inversion technique, we combine leave-one-out cross-validation with data perturbations. We find that both approaches provide useful information on pigment distributions, and hence, phytoplankton community composition indicators, at a spatial resolution much finer than that can be achieved with discrete samples.

In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, Rrs(λ), was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 105 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance), Chl, are ranging from 0 to 200 mg m-3, concentrations of non-algal particles, NAP, from 0 to 1500 g m-3, and absorption coefficients of chromophoric dissolved organic matter (CDOM) at 440 nm from 0 to 20 m-1. A second, independent, smaller dataset of simulated Rrs(λ) used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ Rrs(λ) dataset collected during a cyanobacterial bloom in Lake Taihu (China) is carried out and the approach predicts blue cyanobacteria to be dominant. This fits well with observation of the blue cyanobacteria Microcystis sp. in the lake. This study provides an efficient approach, which can be promisingly applied to hyperspectral sensors, for identifying dominant phytoplankton spectral groups purely based on Rrs(λ) spectra.