Abstract
The assessment of soil health has evolved from focusing primary on
agricultural productivity to an integrated evaluation of soil biota and
biotic processes that impact soil properties. Consequently, soil health
assessment has shifted from a predominantly physico-chemical approach to
incorporating ecological, biological and molecular microbiology methods.
These methods enable a comprehensive exploration of soil microbial
community properties and their responses to environmental changes
arising from climate change and anthropogenic disturbances. Despite the
increasing availability of soil health indicators (physical, chemical,
and biological), a holistic mechanistic linkage between indicators and
soil functions across multiple spatiotemporal scales has not yet been
fully established. This article reviews the state-of-the-art of soil
health monitoring, focusing on understanding how soil-microbiome-plant
processes contribute to feedback mechanisms and causes of changes in
soil properties, as well as the impact these changes have on soil
functions. Furthermore, we survey the opportunities afforded by the
soil-plant digital twin approach, an integrative framework that
amalgamates process-based models, Earth Observation data, data
assimilation, and physics-informed machine learning, to achieve a
nuanced comprehension of soil health. This review delineates the
prospective trajectory for monitoring soil health by embracing a digital
twin approach to systematically observe and model the soil-plant system.
We further identify gaps and opportunities, and provide perspectives for
future research for an enhanced understanding of the intricate interplay
between soil properties, soil hydrological processes, soil-plant
hydraulics, soil microbiomes, and landscape genomics.