Remote Sensing Advances for Earth System Science

Since their accidental discovery in the 1990s, lightning-related sprites, other transient luminous events (TLEs), and terrestrial gamma-ray flashes have shown us how the impact of thunderstorms extends from the troposphere up to the upper atmosphere and ionosphere. Thunderstorms are a key player for the climate system, in particular through lightning-produced NO_x and troposphere–stratosphere exchange. The CHemical Impact of Thunderstorms on Earth’s Atmosphere (CHIMTEA) project focused on TLE-producing thunderstorms and their possible impact on stratospheric NO_x and ozone. The distribution and seasonal cycle of thunderstorm activity were studied through global lightning data and TLE observations over Europe. Michelson Interferometer for Passive Atmosphere Sounding (MIPAS)/Environmental Satellite (ENVISAT) measurements of NO_x, ozone, and other related constituents from the upper troposphere to the mesosphere were analyzed with a 2D tomographic approach to quantify thunderstorm-induced changes and explore how to improve their detectability. The study included observations from Global Ozone Monitoring by Occultation of Stars (GOMOS)/ENVISAT, other satellites, and in situ measurements. The sensitivity of the measurements to sprite-NO_x was investigated through ad hoc radiative transfer simulations quantifying reference thresholds. Global and regional observations showed sprite-NO_x to be at the edge of current detectability, with no detectable impact on ozone. Model simulations were performed including for the first time a sprite-NO_x parameterization in the Whole Atmosphere Community Climate Model (WACCM): it was shown that sprites may contribute significantly to tropical NO_x in the middle mesosphere and reach detectable levels above particularly active thunderstorms. Extension of the adopted strategy to study lightning-NO_x was recommended, whereas the modeling and multi-satellite approach was shown to be suitable in support to the upcoming space missions.

In the framework of the TIBAGS project, spatial and temporal variations of iodine monoxide, IO, in the Earth’s atmosphere were analysed, and relations between IO and further variables of the biosphere and atmosphere were investigated. The abundances and variations of IO are not well known on a global scale, partly because IO amounts are comparably low. However, due to strong reactivity, also small amounts of IO may have a substantial impact on tropospheric composition. In the present study, satellite data from the SCIAMACHY (Scanning Imaging Absorption spectrometer for Atmospheric CHartographY) sensor on board the ENVISAT satellite is used and a more global view on the subject is obtained. IO amounts are retrieved from measurements of scattered sunlight by using an absorption spectroscopy technique. Two consistent IO data sets are retrieved, one based on near real-time data (2004–2011) and one based on reprocessed consolidated data (2003–2010). Largest amounts of IO are found in the Polar Regions of Antarctica, for example in the Weddell Sea area in spring time. In addition, enhanced IO amounts are detected above some but not all biologically active ocean areas which show high Chlorophyll-a (Chl-a) signals. Correlations between IO and diatom distributions are in some areas stronger than between IO and Chl-a in general, indicating the importance of the specific phytoplankton species present in the ocean water.

The Greenland and Antarctic Ice Sheet (GIS and AIS) are currently losing mass. The GreenSAR project aims at improving the knowledge of essential climate variables by exploiting Earth Observation (EO) datasets from past, present and future European Space Agency (ESA) satellite missions. The project is built around the measure of grounding line migration at the ice–ocean interface. Here, we show that the grounding line of the Pine Island Glacier (PIG) has retreated continuously for the past 20 years due to sustained thinning. The Petermann Gletscher glacier grounding line shows little change in the past 20 years despite the two recent large calving events reducing the area of the floating tongue by 40%.

Ocean fronts are often visible at the ocean surface as well-marked choppy rough water or, contrarily, as anomalously smooth sea surface. As such, high-resolution satellite images—e.g., obtained by synthetic aperture radars (SARs) or by radiometers viewing areas in and around the sun glitter—at times can provide clear observations of mesoscale and submesoscale oceanic fronts. These observations have thus a powerful potential to monitor the upper ocean dynamics, by providing essential information on oceanic fronts. In that perspective, we review recent advances in the qualitative and quantitative interpretation of satellite surface roughness anomalies.

The European Space Agency recently launched their Soil Moisture and Ocean Salinity (SMOS) mission, providing measurements of sea surface salinity on a global scale for the first time. However, SMOS is only able to sense the upper 1 cm of the ocean, and there are questions as to how representative this point measurement is of the upper several metres of the water column. Here we present results from investigations into near-surface salinity structure. These observations were made using a novel, upwardly rising, microstructure profiler and the data came from the tropical North Atlantic Ocean. Analysis is presented whereby the in situ data was used to quantify the strength of near-surface salinity gradients in this region. A comparison was also conducted between the in situ observations and the co-located SMOS Level 3 data. This showed that the difference between these two datasets was normally larger than the salinity gradients seen in the upper 5 m of the ocean. Effectively, this implies that near-surface salinity gradients cannot explain the discrepancy between the two datasets. Future research is required to repeat this analysis using higher temporal resolution Level 2 data.

The validation of the SMOS Level 2 soil moisture product in the Rur and Erft catchments, Germany, showed that two main directions for enhancement should be followed: (i) improving radio frequency interference (RFI) mitigation strategies, and (ii) improving the parameterization of the radiative transfer model (RTM). Therefore, in this chapter two methods are developed to investigate the characteristics of RTM parameters, with a strong focus on soil surface roughness and vegetation opacity. One approach uses a dual state-parameter estimation technique in a data assimilation environment to select adequate parameters. It is a one-dimensional synthetic experiment neglecting spatial pattern of soil moisture as well as parameters. The spatial scale comes into play by investigating the feasibility of parameter estimation from synthetic disaggregated SMOS brightness temperature time series for the Rur and Erft catchments. A new partial grid search approach to parameter estimation (PAGSAPE) is developed in order to reduce computational cost compared to ensemble methods, which is important for future global applications.

Accurately estimating the spatio-temporal distribution of energy, mass and momentum at the surface–atmosphere interface can help develop a better understanding of the complex interactions of the Earth system. By linking deterministic land surface process model, such as SimSphere, to the spatialised information provided by Earth observation (EO) data, a more powerful synergistic avenue can be developed to take advantage of the temporal and spatial benefits of both modelling and EO-based approaches. The “triangle” utilises the distribution of land surface temperature (LST) and vegetation index (VI) formed by a satellite-derived scatterplot, linked with SimSphere under a full range of vegetation cover and soil moisture, to derive spatial estimates of energy fluxes and soil moisture content (SMC). To this end, the objective of this study was to implement the “triangle” technique using Advanced Along-Track Scanning Radiometer (AATSR) satellite data products to derive and subsequently validate spatially explicit maps of land surface heat fluxes and SM for different ecosystems in Europe. The “triangle”-derived estimations of soil moisture exhibited a minor overestimation of the in-situ observations, and an average error of 0.097 vol vol⁻¹. In overall, results were comparable to those of previous validation studies of the “triangle” implementation. Results for the LE and H fluxes were within the accuracy range of 50 Wm⁻², with root mean square difference (RMSD) of 41.15 Wm⁻² and 44.37 Wm⁻², respectively. Furthermore, there was a good agreement between the “triangle”-derived and in-situ observed instantaneous LE and H fluxes, exhibited by high R values (0.88 and 0.69, respectively). Our study is one of the few studies validating the “triangle” over different ecosystems in Europe. It is a significant step forward in supporting the operational development of this method using remote sensing data in deriving key land surface parameters on a global scale.

GOCE observations are an extremely innovative and useful product for the study of the Earth crust at regional and global scales: on the one hand, they can be considered as a constraint to verify crustal models, on the other hand combining GOCE gravity observations with seismic data and taking into account additional information it is possible to retrieve important information on the Earth crust structure. After one year only of GOCE observations, thanks to the GOCE Exploitation for Moho Modelling and Applications (GEMMA) project, it has been possible to globally estimate the depth of the boundary between the Earth’s crust and mantle, usually called Moho, with unprecedented resolution. The knowledge of the Moho is a key topic in Solid Earth sciences: the new GOCE Moho has been used, for instance, as background information to improve our ability to understand and model earthquakes or for the study of the Earth’s heat flux and heat production which in turn constitutes a basic knowledge to understand the plate tectonics and the thermal evolution of our planet.