Challenges in the Geo-Processing of Big Soil Spatial Data

Abstract

This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include 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. Accuracy was especially important as the results needed to be identical at all spatial scales (from point counts to aggregated countrywide data). The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units (national or regional), and river catchments, to the global scale. Advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) were used to convey comprehensive and substantial information that would be of use to policymakers. More specifically, a variety of cartographic practices were employed, including vector and raster visualization and hexagon grid maps at the global or European scale and in several cartographic projections. The information was rendered in both grid format and as aggregated statistics per polygon (zonal statistics), combined with diagrams and an advanced graphical interface. The uncertainty was estimated and the results were validated in order to present the outputs in the most robust way. 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.

An Independent Validation of SoilGrids Accuracy for Soil Texture Components in Croatia

“…SoilGrids has often been used in previous studies as an important soil covariate for total carbon stock estimates [52], soil mapping [25], and cropland suitability prediction [26], but its reliability has not been considered. Soil properties can vary significantly at different spatial scales, from small-scale variations within fields to large-scale variations across continents [53][54][55]. Therefore, the accuracy of SoilGrids predictions should be assessed at multiple spatial scales to ensure that it is suitable for various applications.…”

Digital soil characteristics mapping for aiding site-specific management practices in the West Nile Delta, Egypt

“…Soil science functions a critical role in the development of spatial data by continually creating local, regional, and global land databases [1]. Lithology and geology-hydrology-morphology are in the field of soil science, closely interconnected.…”

“…Descriptive statistics of soil nutrients1 Coefficient of variation. < 15 = low variation, 15-35 = moderate variation, > 35 = high variation[82] …”