A New Data Processing System for Generating Sea Ice Surface Roughness and Cloud Mask Data Products from the Multi-Angle Imaging SpectroRadiometer (MISR)

This study describes two novel data products derived from Multi-angle Imaging SpectroRadiometer (MISR) imagery: Arctic-wide maps of sea ice roughness and a binary cloud detection algorithm. The sea ice roughness maps were generated using a data processing system that matched MISR pixels with co-located and concurrent lidar-derived roughness measurements from Airborne Topographic Mapper (ATM), calibrated the multi- angle data to values of surface roughness using a K-Nearest Neighbor (KNN) algorithm, and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April and July 2016. The resulting maps show good agreement with independent ATM roughness data and enable characterization of the roughness of different ice types. The binary cloud detection algorithm was developed using a neural network approach and a training dataset constructed from Top-of-Atmosphere red band values from all MISR’s nine different viewing cameras for the same two months in various regions of the Arctic. The algorithm showed good performance in classifying pixels into cloudy and clear categories in MISR images, with better performance for clear pixels in April 2016 and better performance for cloudy pixels in July 2016. The algorithm also provides a significant advantage over existing MISR cloud mask products SDCM and ASCM in terms of accuracy and spatial resolution, with a resolution of 275 meters. The data products presented here can be used to gain insights into the seasonal and interannual changes in sea ice roughness and cloud cover over the Arctic and to develop and improve more accurate classification algorithms in the field of remote sensing

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, rs = 0.66, p < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas

Gazing at the Solar System: Capturing the Evolution of Dunes, Faults, Volcanoes, and Ice from Space

Gazing imaging holds promise for improved understanding of surface characteristics and processes of Earth and solar system bodies. Evolution of earthquake fault zones, migration of sand dunes, and retreat of ice masses can be understood by observing changing features over time. To gaze or stare means to look steadily, intently, and with fixed attention, offering the ability to probe the characteristics of a target deeply, allowing retrieval of 3D structure and changes on fine and coarse scales. Observing surface reflectance and 3D structure from multiple perspectives allows for a more complete view of a surface than conventional remote imaging. A gaze from low Earth orbit (LEO) could last several minutes allowing for video capture of dynamic processes. Repeat passes enable monitoring time scales of days to years. Numerous vantage points are available during a gaze (Figure 1). Features in the scene are projected into each image frame enabling the recovery of dense 3D structure. The recovery is robust to errors in the spacecraft position and attitude knowledge, because features are from different perspectives. The combination of a varying look angle and the solar illumination allows recovering texture and reflectance properties and permits the separation of atmospheric effects. Applications are numerous and diverse, including, for example, glacier and ice sheet flux, sand dune migration, geohazards from earthquakes, volcanoes, landslides, rivers and floods, animal migrations, ecosystem changes, geysers on Enceladus, or ice structure on Europa. The Keck Institute for Space Studies (KISS) hosted a workshop in June of 2014 to explore opportunities and challenges of gazing imaging. The goals of the workshop were to develop and discuss the broad scientific questions that can be addressed using spaceborne gazing, specific types of targets and applications, the resolution and spectral bands needed to achieve the science objectives, and possible instrument configurations for future missions. The workshop participants found that gazing imaging offers the ability to measure morphology, composition, and reflectance simultaneously and to measure their variability over time. Gazing imaging can be applied to better understand the consequences of climate change and natural hazards processes, through the study of continuous and episodic processes in both domains

Hemispherical-Directional Reflectance (HDRF) of Windblown Snow-Covered Arctic Tundra at Large Solar Zenith Angles

Ground-based measurements of the hemispherical-directional reflectance factor (HDRF) of windblown snowcovered Arctic tundra were measured at large solar zenith angles (79◦–85◦) for six sites near the international research base in Ny-Ålesund, Svalbard. Measurements were made with the Gonio RAdiometric Spectrometer System over the viewing angles 0◦–50◦ and the azimuth angles 0◦–360◦, for the wavelength range 400–1700 nm. The HDRF measurements showed good consistency between sites for near-nadir and backward viewing angles, with a relative standard deviation of less than 10% between sites where the snowpack was smooth and the snow depth was greater than 40 cm. The averaged HDRF showed good symmetry with respect to the solar principal plane and exhibited a forward scattering peak that was strongly wavelength dependent, with greater than a factor of 2 increase in the ratio of maximum to minimum HDRF values for all viewing angles over the wavelength range 400– 1300 nm. The angular effects on the HDRF had minimal influence for viewing angles less than 15◦ in the backward viewing direction for the averaged sites and agreed well with another study of snow HDRF for infrared wavelengths, but showed differences of up to 0.24 in the HDRF for visible wavelengths owing to light-absorbing impurities measured in the snowpack. The site that had the largest roughness elements showed the strongest anisotropy in the HDRF, a large reduction in forward scattering, and a strong asymmetry with respect to the solar principal plane

A method to derive satellite PAR albedo time series over first-year sea ice in the Arctic Ocean

Deriving sea ice albedo from spaceborne platforms is of interest to model the propagation of the photosynthetically available radiation (PAR) through Arctic sea ice. We show here that use of the Moderate Resolution Imaging Spectroradiometer (MODIS) operational surface reflectance satellite product to derive albedo in the PAR spectral range is possible. To retrieve PAR albedo from the remote sensing surface reflectance, we trained a predictive model based on a principal component analysis with in situ and simulated data. The predictive model can be applied to first-year sea ice surfaces such as dry snow, melting snow, bare ice and melt ponds. Based on in situ measurements and the prescribed atmospheric correction uncertainty, the estimated PAR albedo had a mean absolute error of 0.057, a root mean square error of 0.074 and an R2 value of 0.91. As a demonstration, we retrieved PAR albedo on a 9-km2 area over late spring and early summer 2015 and 2016 at a coastal location in Baffin Bay, Canada. On-site measurements of PAR albedo, melt pond fraction and types of precipitation were used to examine the estimated PAR albedo time series. The results show a dynamic and realistic PAR albedo time series, although clouds remained the major obstacle to the method. This easy-to-implement model may be used for the partitioning of PAR in the Arctic Ocean and ultimately to better understand the dynamics of marine primary producers.publishedVersio

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Influence of snow properties on directional surface reflectance in Antarctica

The significance of the polar regions for the Earth’s climate system and their observed amplified response to climate change indicate the necessity for high temporal and spatial coverage for the monitoring of the reflective properties of snow surfaces and their influencing factors. Therefore, the specific surface area (SSA, as a proxy for snow grain size) and the hemispherical directional reflectance factor (HDRF) of snow were measured for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The SSA data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 29 and 96 m2 kg-1. Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data underestimated the ground-based results. The spatial variability of SSA in Dronning Maud Land ranged in the same order of magnitude as the temporal variability revealing differences between coastal areas and regions in interior Antarctica. The validation presented in this study provided an unique test bed for retrievals of SSA under Antarctic conditions where in situ data are scarce and can be used for testing prognostic snowpack models in Antarctic conditions. The HDRF of snow was derived from airborne measurements of a digital 180° fish-eye camera for a variety of conditions with different surface roughness, snow grain size, and solar zenith angle. The camera provides radiance measurements with high angular resolution utilizing detailed radiometric and geometric calibrations. The comparison between smooth and rough surfaces (sastrugi) showed significant differences in the HDRF of snow, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven model for the bidirectional reflectance distribution function (BRDF), the snow HDRF was parameterized with respect to surface roughness, snow grain size, and solar zenith angle. This allows a direct comparison of the HDRF measurements with BRDF products from satellite remote sensing

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Gazing at the Solar System: Capturing the Evolution of Dunes, Faults, Volcanoes, and Ice from Space

Gazing imaging holds promise for improved understanding of surface characteristics and processes of Earth and solar system bodies. Evolution of earthquake fault zones, migration of sand dunes, and retreat of ice masses can be understood by observing changing features over time. To gaze or stare means to look steadily, intently, and with fixed attention, offering the ability to probe the characteristics of a target deeply, allowing retrieval of 3D structure and changes on fine and coarse scales. Observing surface reflectance and 3D structure from multiple perspectives allows for a more complete view of a surface than conventional remote imaging. A gaze from low Earth orbit (LEO) could last several minutes allowing for video capture of dynamic processes. Repeat passes enable monitoring time scales of days to years. Numerous vantage points are available during a gaze (Figure 1). Features in the scene are projected into each image frame enabling the recovery of dense 3D structure. The recovery is robust to errors in the spacecraft position and attitude knowledge, because features are from different perspectives. The combination of a varying look angle and the solar illumination allows recovering texture and reflectance properties and permits the separation of atmospheric effects. Applications are numerous and diverse, including, for example, glacier and ice sheet flux, sand dune migration, geohazards from earthquakes, volcanoes, landslides, rivers and floods, animal migrations, ecosystem changes, geysers on Enceladus, or ice structure on Europa. The Keck Institute for Space Studies (KISS) hosted a workshop in June of 2014 to explore opportunities and challenges of gazing imaging. The goals of the workshop were to develop and discuss the broad scientific questions that can be addressed using spaceborne gazing, specific types of targets and applications, the resolution and spectral bands needed to achieve the science objectives, and possible instrument configurations for future missions. The workshop participants found that gazing imaging offers the ability to measure morphology, composition, and reflectance simultaneously and to measure their variability over time. Gazing imaging can be applied to better understand the consequences of climate change and natural hazards processes, through the study of continuous and episodic processes in both domains