OLCI-A/B tandem phase: evaluation of FLuorescence EXplorer (FLEX)-like radiances and estimation of systematic differences between OLCI-A and OLCI-FLEX

During the tandem phase of Sentinel-3A and Sentinel-3B in summer 2018 the Ocean and Land Colour Imager (OLCI) mounted on the Sentinel-3B satellite was reprogrammed to mimics ESA's eighth Earth Explorer, the FLuorescence EXplorer (FLEX). The OLCI in FLEX configuration (OLCI-FLEX) had 45 spectral bands between 500 and 792 nm. The new data set with high-spectral-resolution measurements (bandwidth: 1.7–3.7 nm) serves as preparation for the FLEX mission. Spatially co-registered measurements of both instruments are used for the atmospheric correction and the retrieval of surface parameters, e.g. the fluorescence or the leaf area index. For such combined products, it is essential that both instruments are radiometrically consistent. We developed a transfer function to compare radiance measurements from different optical sensors and to monitor their consistency. In the presented study, the transfer function shifts information gained from high-resolution “FLEX-mode” settings to information convolved with the spectral response of the normal (lower) spectral resolution of the OLCI sensor. The resulting reconstructed low-resolution radiance is representative of the high-resolution data (OLCI-FLEX), and it can be compared with the measured low-resolution radiance (OLCI-A measurements). This difference is used to quantify systematic differences between the instruments. Applying the transfer function, we could show that OLCI-A is about 2 % brighter than OLCI-FLEX for most bands of the OLCI-FLEX spectral domain. At the longer wavelengths (> 770 nm) OLCI-A is about 5 % darker. Sensitivity studies showed that the parameters affecting the quality of the comparison of OLCI-A and OLCI-FLEX with the transfer function are mainly the surface reflectance and secondarily the aerosol composition. However, the aerosol composition can be simplified as long as it is treated consistently in all steps in the transfer function. Generally, the transfer function enables direct comparison of instruments with different spectral responses even with different observation geometries or different levels of observation. The method is sensitive to measurement biases and errors resulting from the processing. One application could be the quality control of the FLEX mission; presently it is also useful for the quality control of the OLCI-FLEX data

The impact of SMOS soil moisture data assimilation within the Operational Global Flood Awareness System (GloFAS)

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff

Validation of SMOS sea ice thickness retrieval in the northern Baltic Sea

The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree well (mean deviations below 3.5 K). In a second comparison, we use high-resolution measurements of total ice thickness (sum of ice and snow thickness) from an electromagnetic (EM) sounding system to simulate brightness temperatures for 12 circular areas. While the SMOS observations and the simulations that use the EM modal ice thickness are highly correlated (r2=0.95), the simulated brightness temperatures are on average 12 K higher than observed by SMOS. This would correspond to an 8-cm overestimation of the modal ice thickness by the SMOS retrieval. In contrast, if the simulations take into account the shape of the EM ice thickness distributions (r2=0.87), the mean deviation between simulated and observed brightness temperatures is below 0.1 K

The fluorescence explorer (FLEX) mission:imaging spectroscopy in very high spectral resolution

The FLuorescence EXplorer (FLEX) mission was selected in 2015, by the European Space Agency, as the 8th ESA Earth Explorer, to be launched in 2025. The key scientific objective of the mission is the quantitative mapping of actual photosynthetic activity of terrestrial ecosystems, at a global scale and with a spatial resolution adequate to resolve land processes associated to vegetation dynamics. To accomplish such objective, the FLEX satellite carries the Fluorescence Imaging Spectrometer (FLORIS). FLEX will fly in tandem with Copernicus Sentinel-3 (same orbit at 815 km, 27 days repeat cycle). Together with FLORIS, the OLCI and SLSTR instruments on Sentinel-3 provide all the necessary information to retrieve the emitted vegetation fluorescence, including compensation for atmospheric effects and the derivation of the additional biophysical information needed to map the spatial and temporal dynamics of vegetation photosynthesis from such global measurements

TriHex: combining formation flying, general circular orbits and alias-free imaging, for high resolution L-band aperture synthesis

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA’s Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging.Peer ReviewedPostprint (author's final draft

Clarifications on the "Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S."

In a recent paper, Leroux et al. compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent "an improvement [in RMSE] by a factor of 2-3 compared with the other products" and that the ASCAT soil moisture data are "very noisy and unstable." In this clarification, the analysis of Leroux et al. is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux et al. is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

Zusammenhang zwischen optischer Dicke aus dem ISCCP C1 Datensatz und Flüssigwassergehalt aus SSM/I Daten

Der Zusammenhang zwischen optischer Dicke und Flüssigwassergehalt ist für die Parametrisierung der Strahlungseigenschaften von Wolken von grundlegender Bedeutung. Das bislang am häufigsten verwendete Verfahren von Stephens (1978) wird in dieser Arbeit an Hand zweier unabhängiger Datensätze - den ISCCP Cl und SSM/I Daten - für den Oktober 1987 überprüft. Dabei enthält der Cl Datensatz die optischen Dicken während, aus den SSM/I Messungen der Flüssigwassergehalt bestimmt wird. Nach der räumlichen Anpassung der Datensätze auf ein 2.5° X 2.5° Gitter ergibt sich bei einem Vergleich, in den die Stephens Parametrisierung eingeht, global eine Überschätzung der Monatsmittelwerte der optischen Dicke aus den SSM/I Messungen um einen Faktor 1.4. In den Subtropen und Tropen ist dieser Effekt noch stärker, in den mittleren Breiten dagegen nicht vorhanden. Ein Vergleich ausgewählter Einzelwerte mit denen aus der Stephens Parametrisierung weist auf zu niedrige effektive Radien in der letzten hin. Ein konstanter Radius von 10 μm liefert in den Subtropen und Tropen bei gleichbleibender Streuung ein wesentlich besseres Ergebnis. Für den globalen Datensatz wird mit einer Kombination aus dieser linearen - und der Stephens Parametrisierung eine Korrelation von 0.74 erziehlt. Dieser Wert liegt im Bereich des maximal Möglichen, da Kollokationsfehler und Mittelbildung zu beträchtlichen Ungenauigkeiten führen. Um noch genauere Aussagen machen zu können, werden Datensätze benötigt, die zeitlich und räumlich besser übereinstimmen