Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

Ground subsidence monitoring by Synthetic Aperture Radar interferometry (InSAR) over Arctic permafrost areas is largely limited by long revisit intervals, which can lead to signal decorrelation. Recent satellite missions such as COSMO-Skymed (X-band) and Sentinel-1 (C-band) have comparably short time intervals of a few days. We analyze dense records of COSMO-Skymed from 2013 and 2016 and of Sentinel-1 from 2016, 2017, and 2018 for the unfrozen period over central Yamal (Russia). These years were distinct in environmental conditions and 2016 in particular was unusually warm. We evaluate the InSAR-derived displacement with in situ subsidence records, active-layer thickness measurements, borehole temperature records, meteorological data, C-band scatterometer records, and a land-cover classification based on Sentinel-1 and -2 data. Our results indicate that a comparison of seasonal thaw evolution between years is feasible after accounting for the early thaw data gap in InSAR time series (as a result of snow cover) through an assessment with respect to degree-days of thawing. Average rates of subsidence agree between in situ and Sentinel-1 (corrected for viewing geometry), with 3.9 mm and 4.3 mm per 100 degree-days of thaw at the test site. X-band and C-band records agree well with each other, including seasonal evolution of subsidence. The average displacement is more than twice in magnitude at the active-layer monitoring test site in 2016 compared to the other years. We further demonstrate that InSAR displacement can not only provide information on the magnitude of ground thaw but also on soil properties through analyses of seasonal evolution in extreme years.

Land cover heterogeneity information considering soil wetness across the entire Arctic tundra is of interest for a wide range of applications targeting climate change impacts and ecological research questions. Patterns are potentially linked to permafrost degradation and affect carbon fluxes. First, a land cover unit retrieval scheme which provides unprecedented detail by fusion of satellite data using Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral) was adapted. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are interpreted at 10 m nominal resolution. Units with similar patterns were identified with a k-means approach and documented through statistics derived from comprehensive in situ records for soils and vegetation (more than 3500 samples). The result goes beyond the capability of existing land cover maps which have deficiencies in spatial resolution, thematic content and accuracy, although landscape heterogeneity related to moisture gradients cannot be fully resolved at 10 m. Wetness gradients were assessed, and measures for landscape heterogeneity were derived north of the treeline. About 40 % of the area north of the treeline falls into three units of dry types with limited shrub growth. Wetter regions have higher land cover diversity than drier regions. An area of 66 % of the analysed Arctic landscape is highly heterogeneous with respect to wetness at a 1 km scale (representative scale of frequently used regional land cover and permafrost modelling products). Wetland areas cover 9 % and moist tundra types 32 %, which is of relevance for methane flux upscaling.

Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snowpack properties are changing, and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku-band radar (scatterometer) has been applied so far across the entire Arctic. Data availability at this frequency is limited, however. The utility of other frequencies from passive and active systems needs to be explored to develop a concept for long-term monitoring. The latter are of specific interest as they can be potentially provided at higher spatial resolution. Radar records have been shown to capture the associated snow structure change based on time-series analyses. This approach is also applicable when data gaps exist and has capabilities to evaluate the impact severity of events. Active as well as passive microwave sensors can also detect wet snow at the timing of an ROS event if an acquisition is available. The wet snow retrieval methodology is, however, rather mature compared to the identification of snow structure change since ambiguous scattering behaviour needs consideration. C-band radar is of special interest due to good data availability including a range of nominal spatial resolutions (10 m–12.5 km). Scatterometer and SAR (synthetic aperture radar) data have therefore been investigated. The temperature dependence of C-band backscatter at VV (V – vertical) polarization observable down to −40 ^∘C is identified as a major issue for ROS retrieval but can be addressed by a combination with a passive microwave wet snow indicator (demonstrated for Metop ASCAT – Advanced Scatterometer – and SMOS – Soil Moisture and Ocean Salinity). Results were compared to in situ observations (snowpit records, caribou migration data) and Ku-band products. Ice crusts were found in the snowpack after detected events (overall accuracy 82 %). The more crusts (events) there are, the higher the winter season backscatter increase at C-band will be. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to ice crust formation. SAR specifically from Sentinel-1 is promising regarding ice layer identification at better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record, but the consideration of performance differences due to spatial and temporal cover, as well as microwave frequency, is crucial. Retrieval is most robust in the tundra biome, where results are comparable between sensors. Records can be used to identify extremes and to apply the results for impact studies at regional scale.

Microwave remote sensing has found numerous applications in areas affected by permafrost and seasonally frozen ground. In this study, we focused on data obtained by the Advanced Scatterometer (ASCAT, C-band) during winter periods when the ground is assumed to be frozen. This paper discusses the relationships of ASCAT backscatter with snow depth, air and ground temperature through correlations and the analysis of covariance (ANCOVA) to quantify influences on backscatter values during situations of frozen ground. We studied sites in Alaska, Northern Canada, Scandinavia and Siberia. Air temperature and snow depth data were obtained from 19 World Meteorological Organization (WMO) and 4 Snow Telemetry (SNOTEL) stations. Ground temperature data were obtained from 36 boreholes through the Global Terrestrial Network for Permafrost Database (GTN-P) and additional records from central Yamal. Results suggest distinct differences between sites with and without underlying continuous permafrost. Sites characterized by high freezing indices (>4000 degree-days) have consistently stronger median correlations of ASCAT backscatter with ground temperature for all measurement depths. We show that the dynamics in winter-time backscatter cannot be solely explained through snow processes, but are also highly correlated with ground temperature up to a considerable depth (60 cm). These findings have important implications for both freeze/thaw and snow water equivalent retrieval algorithms based on C-band radar measurements.