Estimation and extrapolation of soil properties in the Siberian tundra, using field spectroscopy

The Siberian tundra is a complex and sensitive ecosystem. Predicted global warming will be highest in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to the carbon fluxes in the Arctic will have significant...

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Bibliographic Details
Main Authors: Bartholomeus, H., Schaepman-Strub, G., Blok, D., Udaltsov, S., Sofronov, R.
Format: Article in Journal/Newspaper
Language:English
Published: 2010
Subjects:
Online Access:https://research.wur.nl/en/publications/estimation-and-extrapolation-of-soil-properties-in-the-siberian-t
Description
Summary:The Siberian tundra is a complex and sensitive ecosystem. Predicted global warming will be highest in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to the carbon fluxes in the Arctic will have significant impact on the global carbon cycle. Increased soil temperature, active layer thickness and primary production will cause changes in carbon dioxide and methane fluxes, as well as particulate and dissolved carbon in the rivers discharging into the Arctic Ocean, but this is highly related to changes in permafrost, vegetation development and hydrological conditions. The present soil properties (e.g., organic and inorganic carbon, nutrients, and mineral composition) are an important factor for potential medium-term vegetation development. Because of the difficult access to the Arctic area and the high costs for chemical analysis of soil samples, we investigated the possibilities to use field spectroscopy for a fast assessment of the major soil properties. During a summer 2008 field campaign, soil samples at different levels within the soil core, including frozen parts, vegetation species and cover descriptions were collected, and spectral reflectance measurements (ASD Fieldspec) were made. First, soil properties as derived from a subset of the samples in the laboratory were related to the spectral reflectance properties using partial least squares regression. Reliable soil model calibrations are found for C and K, while moderately accurate models could be constructed for pH and N. Using these models, the soil properties are estimated for a large number of samples, resulting in a dataset that was used to analyze the relation of top soil properties with organic carbon decomposition and vegetation species composition. The established functions of vegetation species composition with top soil properties might be used to extrapolate top soil properties across a larger area. This extrapolation method based on vegetation proxies ...