Modeling and Mapping Spatiotemporal Patterns in Soil Health and Biogeochemical Cycling

Dear Colleagues,

Biogeochemical (BGC) cycles are pathways by which soil chemical elements, such as carbon, hydrogen, mercury, nitrogen, oxygen, phosphorus, selenium, iron, and sulfur cycle through the biotic and abiotic cubicle of the Earth. Soils play a crucial role in major global BGC cycles (i.e., carbon, nutrient, and water) while accommodating the largest diversity of organisms on land and providing basic ecosystem services. BGC cycling processes are driven by a variety of microbes, including soil fungi and bacteria, which can affect soil organic carbon pools and their decomposition. In turn, they affect nutrient and water cycling and energy flows that regulate primary production (photosynthesis) and environmental ecosystem services. Therefore, soil health and its functions are highly dependent on BGC cycles and vary largely over space and time. Understanding the underlying factors and their influences on soil health is crucial for designing management practices to promote the continued delivery of ecosystem services from soils. Advances in proximal and remote sensing technologies, geostatistical tools, geographical information systems, and analytics using machine and deep learning are enabling the modeling and mapping of spatiotemporal patterns of soils. As of yet, these technologies have not been explored to their full potential. These state-of-the-art technologies should therefore be further developed and extensively utilized in the field of soil BGC cycles to support management decisions, allowing soils to deliver ecosystem services continuously while maintaining health.

This Special Issue focuses on advances in modeling and mapping the spatiotemporal patterns in soil health and BGC cycles over time. Research related to soil health, soil quality, ecosystem services, nutrient and water cycling, carbon sequestration, and any relevant work in the field of BGC cycles is welcome. We also invite papers dedicated to sensing and modeling techniques that can better quantify and map the spatiotemporal patterns in soil BGC cycles at various spatial and temporal scales.

Dr. Muhammad Abdul Munnaf
Guest Editor

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• biogeochemical cycling
• soil microorganisms
• soil health
• digital soil mapping
• proximal and remote sensing
• spatial analytics
• intelligent learning
• carbon sequestration
• nutrient allocation and cycling