ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE

This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.

Two optical sensing elements for H2O and NO2 gas sensing based on the single plasmonic – photonic crystal slab

Two optical sensing elements based on the surface plasmon waves at the plasmonic-photonic-crystal/air interface, excited in the Kretschmann configuration, are proposed. The sensing elements are designed to detect air humidity and NO2 concentration in air. The angular reflectance spectra of the sensing elements are theoretically analyzed as the function of the analyte concentration. The proposed NO2-sensing element has no cross-sensitivity to humidity. The two sensing elements are based on the same multilayer metal-dielectric structure with the only exception on different gas-sensitive material layers. When combined, the sensing elements can be used to measure humidity and NO2 concentration in humid air.

Comparison of the Optical Planar Waveguide Sensors’ Characteristics Based on Guided-Mode Resonance

A comparison of optical sensors’ characteristics based on guided-mode resonance has been carried out. It was considered a prism structure with a metal film, a metal grating on a metal substrate and a dielectric grating on a dielectric substrate. It is shown that the main characteristics are determined by the sensitivity of the constant propagation of the respective waveguides on a change in wavelength and a change in the refractive index of the tested medium. In addition, they depend on the full width at half maximum of the spectral or angular reflectance dependence. The corresponding analytical relationships obtained for the three types of sensors are almost the same. It is demonstrated that the ratio of the sensor spectral sensitivity on the resonance curve spectral width is equal to the ratio of the angular sensitivity on the angular width of the corresponding resonance curve for all three types of sensors.

Arctic Sea Ice Surface Roughness Estimated from Multi-Angular Reflectance Satellite Imagery

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values were nearly identical for areas with a roughness < 20 cm, but for rougher regions, the MISR-estimated roughness had a narrower range of values than the ATM data. The algorithm was able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-estimated roughness data have a variance about half that of ATM roughness data.

Arctic Sea Ice Surface Roughness Derived from Multi-Angular Reflectance Satellite Imagery

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness &lt;20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.