This study addressed a critical resource––soil––through the prism of processing big data at the continental scale. Here, we present the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, we highlight the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using advanced programming languages.
Challenges to this study included the reproducibility of the results, their presentation in a communicative way, and the harmonization of complex heterogeneous data in space and time based on high standards of accuracy. The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units, river catchments, to the global scale.
We used advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) to convey comprehensive and substantial information that would be of use to policy-makers. More specifically, we employed a variety of cartographic practices, including vector and raster visualization and hexagon grid maps at global or European scales and in several cartographic projections.
The study was also interdisciplinary in nature, requiring large-scale datasets to be integrated from different scientific domains, such as soil science, geography, hydrology, chemistry, climate change, and agriculture.
LIAKOS Leonidas;
PANAGOS Panagiotis;
2023-01-04
MDPI
JRC131148
2073-445X (online),
https://www.mdpi.com/2073-445X/11/12/2287,
https://publications.jrc.ec.europa.eu/repository/handle/JRC131148,
10.3390/land11122287 (online),
