The National Academies’ 2018 decadal survey for Earth science, Thriving on Our Changing Planet, includes an imaging spectrometer as one of five "designated" system priorities. The spectrometer specifically addresses the measurement of snow reflectivity. One of the four “Most Important” objectives for hydrology—"Quantify rates of snow accumulation, snowmelt, ice melt, and sublimation from snow and ice worldwide at scales driven by topographic variability"—requires the ability to measure snow albedo and understand how and why it varies.

Our approach treats the snow properties as a continuum, and we invert the radiative transfer models to solve for a set of unknowns that include fractional snow cover, specific surface area of the snow, and the concentration and size of light absorbing particles. We account for topographic variability by incorporating a digital elevation model to account for the illumination angle on a pixel and solve a weighted least-squares problem, or alternatively we solve for a minimum spectral angle that uses only spectral shape rather than magnitude. We validate those methods with field measurements of those properties, and we also use simulation to explore the issues related to inversion of the governing equations. Applied to a spaceborne imaging spectrometer, the approach would support validation and modification of algorithms for multispectral sensors, which in turn provide wide and frequent coverage at a variety of spatial resolutions.

The material composition of urban areas is important for urban climate modeling and urban canopy models. Since 2018, a few spaceborne imaging spectrometers have been launched such as the DESIS instrument on the ISS and the Italian PRISMA. The spatial resolution of these systems is around 30 m, which is in many cases not sufficient for urban applications. However, the spectral resolution ranges between few nanometers and thus, offers the potential to detect surface materials on a sub-pixel level. Common approaches for sub-pixel analyses such as spectral mixture analyses (SMA) mostly fail given the high spatial and spectral complexity of urban areas. A possible new way to interpret complex spectral mixtures in urban areas is to use the gradient concept, which so far has been applied to map species compositions in natural ecosystems. This study analyses whether the gradient concept offers a feasible solution for the analysis of urban data sets with a high amount of spectral mixtures. The presented strategy presumes that the composition of material mixtures changes gradually across urban spaces. This study focuses on 1) the adaptation of gradient analysis from vegetation ecology, 2) the mapping of these material gradients using simulated EnMAP data of Munich, Germany, to identify characteristic material compositions and 3) an initial test to transfer the gained results to areas without knowledge about material compositions. The achieved insights of this study build an important fundament for future analyses focusing on the transferability of the gradient concept to unknown urban areas.

There is an increasing need for regular monitoring of inland and coastal waters to support national and international directives and conventions such as the European Water Framework Directive. Information on the state and development of water bodies is also a prerequisite to meet the United Nations Sustainable Development Goal No. 6 to “ensure availability and sustainable management of water and sanitation for all” by 2020.
Earth Observation may be used for acquiring timely, frequent synoptic information from local to global scales, of inland and coastal waters. Satellite data have been successfully applied for mapping inland and coastal waters for about 50 years. Nevertheless, the current satellite radiometers are designed for observing the global ocean or land surface and thus not specifically suited for observing coastal and inland waters, so that deriving coastal and inland aquatic applications from existing multispectral sensors remains challenging.
We will review the science and applications of hyperspectral remote sensing of inland and coastal waters. A focus will be given on derived accurate information products including estimates of turbidity and transparency measures, chlorophyll, suspended matter and coloured dissolved organic matter concentration, and sophisticated products such as particle size distributions, phytoplankton functional types or composition of suspended and coloured dissolved matter, estimating water depth and mapping types of heterogeneous substrates.
As the space-borne, but also airborne and in situ optical hyperspectral sensors, become increasingly available and appropriate for aquatic ecosystem detection, monitoring and assessment, the science-based applications will need to be further developed to an operational level.

Degassing from volcanoes is a key pathway for volatile transfer from Earth’s interior to its exterior. Volcanic emissions are dominated by water vapour, carbon dioxide, sulfur dioxide and halogens, and comprise a mix of volatiles from the mantle, subducting slab and crustal sediments. Globally the volcanic emissions of carbon dioxide are estimated of 34x106 tons/day. The measurements of this contribution to the atmosphere defines the natural source respect to the anthropic one. Space mission as OCO-2 and GOSAT can contribute to this issue, as well as new hyperspectral sensor. The high atmospheric background of CO2 makes difficulties in the extraction of volcanic contribution. In this work the hyperspectral technique already developed in the SWIR spectral range is presented and applied to airborne data acquired over Mt. Etna, Stromboli and Kilauea degassing volcanoes. This works has contribute to the mission requirements of the recent space mission ASI-PRISMA.