Abstract
This paper presents the second part of the mapping of topsoil properties based on the Land Use and Cover Area frame Survey (LUCAS). The first part described the physical properties (Ballabio et al., 2016) while this second part includes the following chemical properties: pH, Cation Exchange Capacity (CEC), calcium carbonates (CaCO3), C:N ratio, nitrogen (N), phosphorus (P) and potassium (K). The LUCAS survey collected harmonised data on changes in land cover and the state of land use for the European Union (EU). Among the 270,000 land use and cover observations selected for field visit, approximately 20,000 soil samples were collected in 24 EU Member States in 2009 together with more than 2000 samples from Bulgaria and Romania in 2012. The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. GPR was selected for its capacity to assess model uncertainty and the possibility of adding prior knowledge in the form of covariance functions to the model.The derived maps will establish baselines that will help monitor soil quality and provide guidance to agro-environmental research and policy developments in the European Union.
Highlights
Soil and environmental challenges are increasing dramatically (IPBES, 2019)
We developed datasets for soil pH, Cation Exchange Capacity (CEC), calcium carbonates (CaCO3), and total phosphorus, potassium and nitrogen, plus derived products based on soil organic carbon and nitrogen (e.g. C:N ratio) covering the 26 European Union (EU) Member States
This study provides a new set of maps of baseline topsoil chemical properties at 250 m resolution for twenty-six countries of the EU, covering an area of more than 4.5 million km2
Summary
Soil and environmental challenges (climate change, pollution, water scarcity, biodiversity decline) are increasing dramatically (IPBES, 2019). Organizations such as the United Nations Convention to Combat Desertification (UNCCD), the Food Agriculture Organization (FAO) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have recognized that soil is under threat globally (Montanarella, 2015). The increased use of digital soil mapping (McBratney et al, 2003) approaches became a solution to increased requests for spatial soil data coming from research organizations, policy makers and the private sector. Among others, Grundy et al (2015) mapped 11 soil properties in Australia using a geostatistical approach, Padarian et al (2017) contributed to the development of a Global Soil Map by modelling eight properties for Chile, Mansuy et al (2014) generated national maps of soil properties for managed forests in Canada, Adhikari et al (2014) developed soil organic carbon content maps for Denmark while Poggio and Gimona (2014) modelled soil organic carbon stocks in Scotland
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