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Imaging spectroscopy is a powerful tool used to support diverse Earth science and applications objectives, ranging from understanding and mitigating widespread impacts of climate change to management of water at farm‐scale. Community studies, such as those deployed by NASA's Surface Biology and Geology and ESA's Copernicus Hyperspectral Imaging Mission for the Environment, have offered new and tangible insights into user needs that are then incorporated into overall mission planning and design. These technologies and tools will be key to develop and consolidate downstream services for users and resource management, given the current pressures on the environment posed by climate change and population growth. This process has highlighted the degree to which planned mission capabilities are responsive to community needs. In this study, we analyze user requirements belonging to the Italian Copernicus User Forum and to the user pool of NASA's Surface Biology and Geology community for the synergic use of hyperspectral imaging technology, providing a reference for the development of earth observation services and the consolidation of existing ones. In addition, potential cross‐mission coordination is analyzed to highlight key benefits—(a) addressing shared community needs around products requiring more frequent temporal revisit and (b) shared resources and community expertise around algorithm development. This paper discusses the critical role of early engagement with users to establish a community of practice ready to work with high spatial resolution imaging spectroscopy data sets. The main outcome is a guide for the synergetic use of hyperspectral mission and data together with the identification of the main gaps between user needs and satellite capabilities influencing the development of key national and trans‐national downstream services. Plain Language Summary: In this research we have analyzed needs of users to understand how to develop and improve earth observation downstream services. Hyperspectral imaging provides information across the electromagnetic spectrum in more detail than multispectral imaging, allowing more specific analysis and accurate identification of materials and substances. The users in this study belong to the Italian Copernicus User Forum and to the NASA Surface Biology and Geology community which provided requirements respectively for ESA's Copernicus Hyperspectral Imaging Mission for the Environment and NASA's Surface Biology and Geology. This work identifies common users and examines the advantages of potential synergy between these hyperspectral satellites for responding to their needs. This analysis shows that the synergy would bring significant advantages in terms of improved revisit time and represents an opportunity to address shared community needs around product and algorithm development. Key Points: Current users' needs for hyperspectral earth observation data suggest future investment in improving spatial and temporal resolutionCoordination between missions can fill this gap especially improving temporal revisitShared community and resources can facilitate the improvement of products from these missions and their use across different sectors [ABSTRACT FROM AUTHOR]

This study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 > 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes. Plain Language Summary: In the current context led by the constant availability, almost in near‐real time, of remote sensing data with high spatial, spectral and temporal resolutions, the monitoring of soil properties should no longer be solely limited to field data observations. There is a growing need of knowledge for preserving and exploiting soils and the upcoming generation of hyperspectral satellite remote sensing has popped up new opportunities for developing and adapting algorithms that break up the image pixels into several spectral behaviors that correspond to different soil properties. This paper contributes to the application of these image analysis techniques to soil texture classes retrieval considering the United Stated Agriculture Department model. The paper uses the principles of the spectral mixture concept to explore the opportunity of detecting soil textures in two farmland units and provides some highlights on the observational requirements needed to integrate the future satellite missions' architecture within the agriculture and food security application domain. Key Points: Hyperspectral imaging is used to retrieve United Stated Agriculture Department soil texture classification in two farmland units applying linear spectral mixture analysisCopernicus Hyperspectral Imaging Mission for Environment‐like and PRecursore IperSpettrale della Missione Applicativa‐derived fractional abundance intervals are found to spatially overlap the clay‐loam class obtained with field dataFractional abundances categories having the highest overlap percentage are strongly correlated with the Short‐Wave InfraRed > 1,500 nm [ABSTRACT FROM AUTHOR]

Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high‐resolution vegetation map (derived from aerial photography) and lower‐resolution satellite imagery processed with linear spectral mixture analysis. The patch‐size distribution in all vegetation types corresponded to a power law, suggesting the presence of self‐organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development. Key Points: The imprint of biogeomorphic processes on salt marsh drainage network sinuosity during the pioneer stages is inherited by later successional vegetation typesVegetation patch‐size distributions in salt marsh vegetations from the pioneer to late successional stages correspond to power laws, suggesting the presence of self‐organizational processesVegetation patch size in salt marshes shows an inverse logarithmic relationship with tidal channel sinuosity, irrespective of vegetation type and elevation [ABSTRACT FROM AUTHOR]

Comprehensive modelling of dust events requires a full physical representation of small-scale emission mechanisms and description of long-range transport dynamic. In this paper we propose a simulation system that integrates three different models in order to represent the whole dust cycle. The RAMS atmospheric model configuration has two nested grids, at 50 km and 10 km horizontal resolution, and is used to force both the dust emission model DUSTEM, and the transport model CAMx. The performance of the three-model simulation system was evaluated using a major dust storm that occurred in March 2002 in the desert of the Alashan Prefecture (Inner Mongolia, China) and which had a significant impact over a large area in northern China. In order to identify potential active dust sources, a specific remote sensed analysis, calibrated through a field campaign in the Alashan Prefecture region, has been assimilated in the modelling system. Simulated dust storm features, from the higher resolution grid, are in good agreement with observed data: surface wind values discrepancies are less than 2 m/s in the Alashan area and less than 5 m/s along the dust storm track. In comparison to ground observations, the modelled dust surface concentration peaks in Beijing differ by only 2 mg/m³ although the timing of dust peaks is delayed in the model. As a consequence this integrated numerical model, along with the remote sensed land surface characterization, is suggested to be a practical and flexible tool for simulating and analysing the whole dust storm dynamics. [ABSTRACT FROM AUTHOR]

One characteristic of a Geographic Information System (GIS) is that it addresses the necessity to handle a large amount of data at multiple scales. Lands span over an area greater than 15 million km2 all over the globe and information types are highly variable. In addition, multi-scale analyses involve both spatial and temporal integration of datasets deriving from different sources. The currently worldwide used system of latitude and longitude coordinates could avoid limitations in data use due to biases and approximations. In this article a fast and reliable algorithm implemented in Arc Macro Language (AML) is presented to provide an automatic computation of the surface area of the cells in a regularly spaced longitude-latitude (geographic) grid at different resolutions. The approach is based on the well-known approximation of the spheroidal Earth's surface to the authalic (i.e. equal-area) sphere. After verifying the algorithm's strength by comparison with a numerical solution for the reference spheroidal model, specific case studies are introduced to evaluate the differences when switching from geographic to projected coordinate systems. This is done at different resolutions and using different formulations to calculate cell areas. Even if the percentage differences are low, they become relevant when reported in absolute terms (hectares). [ABSTRACT FROM AUTHOR]