Profound shifts have occurred during the last three centuries in which human actions have become the main driver to global environmental change. In this new epoch, the Anthropocene, human‐driven changes such as population growth, climate, and land use change are pushing the Earth system well outside of its normal operating range, causing severe and abrupt environmental change. In the Anthropocene, soil change and soil formation or degradation have also accelerated, jeopardizing soil quality and health. Thus, the need for up‐to‐date, high‐quality, high‐resolution, spatiotemporal, and continuous soil and environmental data that characterize the physicochemical, biological, and hydrologic conditions of ecosystems across continents has intensified. These needs are in sharp contrast to available digital soil data representing continental and global soil systems, which only provide coarse‐scale (1:1,000,000 or coarser) vector polygon maps with highly aggregated soil classes represented in the form of crisp map units derived from historic observations, lacking site‐specific pedogenic process knowledge, and only indirectly relating to pressing issues of the Anthropocene. Furthermore, most available global soil data are snapshots in time, lacking the information necessary to document the evolution of soil properties and processes. Recently, major advancements in digital soil mapping and modeling through geographic information technologies, incorporation of soil and remote sensing products, and advanced quantitative methods have produced domain‐specific soil property prediction models constrained to specific geographic regions, which have culminated in the vision for a global pixel‐based soil map. To respond to the challenges soil scientists face in the Anthropocene, we propose a space–time modeling framework called STEP‐AWBH (“step‐up”), explicitly incorporating anthropogenic forcings to optimize the soil pixel of the futurevv.

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350–2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.