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: A field intercomparison was conducted at the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea, from 9 to 19 July 2018 to assess differences in the accuracy of in- and above-water radiometer measurements used for the validation of ocean colour products. Ten measurement systems were compared. Prior to the intercomparison, the absolute radiometric calibration of all sensors was carried out using the same standards and methods at the same reference laboratory. Measurements were performed under clear sky conditions, relatively low sun zenith angles, moderately low sea state and on the same deployment platform and frame (except in-water systems). The weighted average of five above-water measurements was used as baseline reference for comparisons. For downwelling irradiance ( Ed ), there was generally good agreement between sensors with differences of 〈6% for most of the sensors over the spectral range 400 nm–665 nm. One sensor exhibited a systematic bias, of up to 11%, due to poor cosine response. For sky radiance ( Lsky ) the spectrally averaged difference between optical systems was 〈2.5% with a root mean square error (RMS) 〈0.01 mWm−2 nm−1 sr−1. For total above-water upwelling radiance ( Lt ), the difference was 〈3.5% with an RMS 〈0.009 mWm−2 nm−1 sr−1. For remote-sensing reflectance ( Rrs ), the differences between above-water TriOS RAMSES were 〈3.5% and 〈2.5% at 443 and 560 nm, respectively, and were 〈7.5% for some systems at 665 nm. Seabird-Hyperspectral Surface Acquisition System (HyperSAS) sensors were on average within 3.5% at 443 nm, 1% at 560 nm, and 3% at 665 nm. The differences between the weighted mean of the above-water and in-water systems was 〈15.8% across visible bands. A sensitivity analysis showed that Ed accounted for the largest fraction of the variance in Rrs , which suggests that minimizing the errors arising from this measurement is the most important variable in reducing the inter-group differences in Rrs . The differences may also be due, in part, to using five of the above-water systems as a reference. To avoid this, in situ normalized water-leaving radiance ( Lwn ) was therefore compared to AERONET-OC SeaPRiSM Lwn as an alternative reference measurement. For the TriOS-RAMSES and Seabird-HyperSAS sensors the differences were similar across the visible spectra with 4.7% and 4.9%, respectively. The difference between SeaPRiSM Lwn and two in-water systems at blue, green and red bands was 11.8%. This was partly due to temporal and spatial differences in sampling between the in-water and above-water systems and possibly due to uncertainties in instrument self-shading for one of the in-water measurements.

Description: Phytoplankton functional-type (PFT) data are assimilated into the global coupled ocean-ecosystem model MITgcm-REcoM2 for two years using a local ensemble Kalman filter. The ecosystem model has two PFTs: small phytoplankton (SP) and diatoms. Three different sets of satellite PFT data are assimilated: Ocean-Color-Phytoplankton Functional Type (OC-PFT), Phytoplankton Differential Optical Absorption Spectroscopy (PhytoDOAS), and SYNergistic exploitation of hyper- and multi-spectral precursor SENtinel measurements to determine Phytoplankton Functional Types (SynSenPFT), which is a synergistic product combining the independent PFT products OC-PFT and PhytoDOAS. The effect of assimilating PFT data is compared with the assimilation of total chlorophyll data (TChla), which constrains both PFTs through multivariate assimilation. While the assimilation of TChla already improves both PFTs, the assimilation of PFT data further improves the representation of the phytoplankton community. The effect is particularly large for diatoms where, compared to the assimilation of TChla, the SynSenPFT assimilation results in 57% and 67% reduction of root-mean-square error and bias, respectively, while the correlation is increased from 0.45 to 0.54. For SP the assimilation of SynSenPFT data reduces the root-mean-square error and bias by 14% each and increases the correlation by 30%. The separate assimilation of the PFT data products OC-PFT, SynSenPFT, and joint assimilation of OC-PFT and PhytoDOAS data leads to similar results while the assimilation of PhytoDOAS data alone leads to deteriorated SP but improved diatoms. When both OC-PFT and PhytoDOAS data are jointly assimilated, the representation of diatoms is improved compared to the assimilation of only OC-PFT. The results show slightly lower errors than when the synergistic SynSenPFT data are assimilated, which shows that the assimilation successfully combines the separate data sources.