Dielectric database of organic Arctic soils (DDOAS)

• Published in: Earth system science data 2020-12, Vol.12 (4), p.3481-3487
• Main Authors: Savin, Igor, Mironov, Valery, Muzalevskiy, Konstantin, Fomin, Sergey, Karavayskiy, Andrey, Ruzicka, Zdenek, Lukin, Yuriy
• Format: Article
• Language: English
• Subjects: Air temperature; Algorithms; Arctic soils; Artificial intelligence; Artificial satellites; Attenuation coefficients; Carbon; Coaxial waveguides; Decomposition; Dielectrics; Extinction coefficient; Field capacity; Frequency ranges; Laboratories; Loam soils; Moisture content; Mosses; Neural networks; Organic matter; Organic soils; Permafrost; Radiometers; Refractive index; Refractivity; Remote sensing; Satellites; Soil; Soil moisture; Soils; Taiga & tundra; Temperature; Training; Tundra; Tundras; Vegetation; Water content; Wave attenuation
• ISSN: 1866-3516; 1866-3508; 1866-3516
• DOI: 10.5194/essd-12-3481-2020

This article presents a dielectric database of organic Arctic soils (DDOAS). The DDOAS was created based on the dielectric measurements of seven samples of organic-rich soils collected in various parts of the Arctic tundra: Yamal Peninsula, Taimyr Peninsula, Samoylov Island (all in the Russian Federation) and the northern slope of Alaska (US). The organic matter content (by weight) of the presented soil samples varied from 35 % to 90 %. The refractive index (RI) and normalised attenuation coefficient (NAC) were measured under laboratory conditions by the coaxial-waveguide method in the frequency range from ∼ 10 MHz to ∼ 16 GHz, while the moisture content changed from air-dry to field capacity, and the temperature changed from −40 to +25 ∘C. The total number of measured values of the RI and NAC contained in the database is more than 1.5 million. The created database can serve not only as a source of experimental data for the development of new soil dielectric models for the Arctic tundra but also as a source of training data for artificial intelligence satellite algorithms of soil moisture retrievals based on neural networks. The DDOAS is presented as Excel files. The files of the DDOAS are available on https://doi.org/10.5281/zenodo.3819912 (Savin and Mironov, 2020).