Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.

Understanding spatio-temporal changes in soil thickness and their natural and anthropogenic driving factors are essential for earth system modeling and natural resource conservation. It remains challenging to accurately quantify the spatial pattern of soil thickness, and there is no assessment of its temporal changes at the national scale across eco-climatic zones. Here we compiled a long-term (1950–2018), large-scale (conterminous United States of America) topsoil (A horizon,n = 37,712) and solum (22,409) thickness data to quantify their spatial and temporal variations using generalized additive models and selected chronosequences in land resource regions. Climate was found associated with the spatial distribution of soil thickness, and land use and erosion associated with its temporal variation. The A horizon and solum thickness displayed strong longitudinal patterns, correlated with soil moisture and temperature, respectively. Temporal changes in the thickness varied across land resource regions, affected by topography, land use, and erosion. Severe A horizon loss primarily occurred in Mollisols of the Central Great Plains, Alfisols on steep slopes, and soils under cropping. These findings enhanced our fundamental understanding of soil formation and biogeochemical cycles during the Anthropocene across scales and identified regions for conservation practices to reduce further topsoil loss.

Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived soil classes. Three digital maps of terrain variables - slope, landforms, and topographic wetness index - were correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional soil-landscape relationships were formulated based on the resulting combinations. These rules were used to select typical areas of occurrence of each soil class and to train a decision tree model to predict the occurrence of individualized soil classes. Validation of the soil map predictions was conducted by comparison with available soil profiles. The soil map produced showed high agreement (80.5 % accuracy) with the soil classes observed in the soil profiles; Ultisols and Lithic Udorthents were predicted with greater accuracy. The soil variables selected in this study were suitable to represent the soil-landscape relationships, suggesting potential use in future studies. This approach developed a more detailed soil map relevant to current demands for soil information and has potential to be replicated in other areas in which data availability is similar.