hyDRaCAT Spectral Reflectance Library: Hyperspectral Field Spectroscopy and Field Spectro-Goniometry of Siberian and Alaskan Tundra

hyDRaCAT Spectral Reflectance Library for tundra provides the surface reflectance data and the bidirectional reflectance distribution function (BRDF) of important Arctic tundra vegetation communities at representative Siberian and Alaskan tundra sites. The aim of this dataset is the hyperspectral and spectro-directional reflectance characterization as basis for the extraction of vegetation parameters, and the normalization of BRDF effects in off-nadir and multi-temporal remote sensing data. The spectroscopic and field spectro-goniometric measurements were undertaken on the YAMAL2011 expedition of representative Siberian vegetation fields and on the North American Arctic Transect NAAT2012 expedition of Alaskan vegetation fields both belonging to the Greening-of-the-Arctic (GOA) program. For the field spectroscopy each 100 m2 vegetation study grid was divided into quadrats of 1 × 1 m. The averaged reflectance of all quadrats represents the spectral reflectance at the scale of the whole grid at the 10 × 10 m scale. For the surface radiometric measurements two GER1500 portable field spectroradiometers (Spectra Vista Corporation, Poughkeepsie, NY, USA) were used. The GER1500 measures radiance across the wavelength range of 350-1,050 nm, with sampling intervals of 1.5 nm and a radiance accuracy of 1.2 × 10**-1 W/cm**2/nm/sr. In order to increase the signal-to-noise ratio, 32 individual measurements were averaged per one target scan. To minimize variations in the target reflectance due to sun zenith angle changes, all measurements at one study location have been performed under similar sun zenith angles and during clear-sky conditions. The field spectrometer measurements were carried out with a GER1500 UV-VIS spectrometer The spectrogoniometer measurements were carried out with a self-designed spectro-goniometer: the Manual Transportable Instrument platform for ground-based Spectro-directional observations (ManTIS, patent publication number: DE 10 2011 117 713.A1). The ManTIS was equipped with the GER1500 spectrometer allowing spectro-directional measurements with up to 30° viewing zenith angle by full 360° viewing azimuth angles. Measurements in central Yamal (Siberia) at the research site 'Vaskiny Dachi' were carried out in the late summer phenological state from August 12 2011 to August 28 2011. All measurements in Alaska along the North South transect on the North Slope were taken between 29 June and 11 July 2012, ensuring that the vegetation was in the same phenological state near peak growing season.

Time-series of snow spectral albedo and superficial snow specific surface area at Dome C in Antarctica, 2012-2015

Spectral albedo has been measured at Dome C since December 2012 in the visible and near infrared (400 - 1050 nm) at sub-hourly resolution using a home-made spectral radiometer. Superficial specific surface area (SSA) has been estimated by fitting the observed albedo spectra to the analytical Asymptotic Approximation Radiative Transfer theory (AART). The dataset includes fully-calibrated albedo and SSA that pass several quality checks as described in the companion article. Only data for solar zenith angles less than 75° have been included, which theoretically spans the period October-March. In addition, to correct for residual errors still affecting data after the calibration, especially at the solar zenith angles higher than 60°, we produced a higher quality albedo time-series as follows: In the SSA estimation process described in the companion paper, a scaling coefficient A between the observed albedo and the theoretical model predictions was introduced to cope with these errors. This coefficient thus provides a first order estimate of the residual error. By dividing the albedo by this coefficient, we produced the "scaled fully-calibrated albedo". We strongly recommend to use the latter for most applications because it generally remains in the physical range 0-1. The former albedo is provided for reference to the companion paper and because it does not depend on the SSA estimation process and its underlying assumptions.