Grand Challenges in Pedometrics-AI Research

Sabine Grunwald
2021 Frontiers in Soil Science  
The discipline of pedometrics combines pedology (i.e., understanding of the physical, chemical, and biological soil properties, patterns, and their genesis) and quantitative modeling of soils. Pedometrics research has focused extensively to model soil properties and associated uncertainties from field to large landscape scale (1, 2). Artificial intelligence (AI), specifically machine learning (ML), and deep learning (DL) algorithms, have advanced a profound transformation of the discipline with
more » ... new challenges. The conceptual frameworks underlying pedometrics and digital soil mapping (DSM) have been rooted in factorial models that relate soils and factors that influence soil formation, socalled soil-environmental covariates (3, 4). These soil factorial frameworks have moved from the conceptual CLORPT soil formation model 1 (5, 6), the spatially and temporally explicit SCORPAN framework 2 (7) toward the spatially and temporally explicit STEP-AWBH model frame 3 (8-11). The general approach of soil-factorial modeling using STEP-AWBH input variables to predict a soil property or class is showcased in Figure 1 . This latter mental frame accounts for soil-landscape conditions (STEP factors iii ) and the dynamics of the atmosphere/climate (A), water/hydrosphere (W), biosphere (B), as well as human activities (H) in the social, cultural, economic, and political domains (e.g., land management, carbon credit markets, economic incentives and programs, human resource capital). For example, the STEP-AWBH frame facilitates the incorporation of short-term temporally varying AWBH factors such as short-duration climatic variables (e.g., rainfall-runoff events preceding soil observations) and also long-term climatic patterns and variations (e.g., 40-year average annual precipitation and the 40-year amplitude of temperature variation preceding soil observations) that have impacted pedogenesis in a study region. The STEP-AWBH frame is anchored in system theory that views the totality of an ecosystem integrating a multiplicity of domains. Thus, STEP-AWBH has moved factorial models closer to mechanistic Earth simulation models through the incorporation of pedological, biogeochemical, socio-cultural, economic, and political factors in the modeling process of soils. 1
doi:10.3389/fsoil.2021.714323 fatcat:esp4js65lbfetciby3ltd7o3zm