Why a mechanistic theory of soils is crucially important: Another line of supportive argument exists, seldom invoked in soil science

In the last few decades, the effort that has been devoted to the mechanistic, quantitative description of soil processes has been justified on the grounds that theories and models help us understand how soils function, and also predict how, e.g., they are likely to adjust in the future to environmental change. The argument, familiar to physicists, that theories uniquely determine what should be measured, has rarely if ever been invoked in the soil science literature. On the contrary, to enable the classification and mapping of soils, enormous amounts of “theory-free” data have been and continue to be amassed by soil scientists. In this general context, the key objective of the present Forum article is to argue that the accumulation of more “theory-free” data, in particular to allow the application of artificial intelligence methods, does not make practical sense at this stage, and that the development of improved theories of soil processes is crucial to provide guidance about the type of measurements that should be collected. Hopefully, this Forum article will stimulate a debate on this issue, and will lead to a much-needed intensification of theoretical research and modelling in soil science. More information can be found here.

Featured Soil Modeler (Nedal Aqel)

Beyond static soil parameters: data-driven modelling of dynamic soil moisture behaviour

My name is Nedal Aqel and I am a PhD student at ETH Zurich in the Physics of Soils and Terrestrial Ecosystems group within the Department of Environmental Systems Science. My research focuses on modelling soil moisture dynamics using machine learning, remote sensing, and physically informed approaches. I work with satellite data such as SMAP and Sentinel, combined with in-situ observations, to better understand soil–water–climate interactions and how soil behaviour evolves over time.

- Please tell us briefly about yourself and your research interest.

I am currently a PhD student at ETH Zurich working at the interface of soil physics and data science. My research focuses on predicting soil moisture dynamics using data-driven approaches while maintaining a strong link to physical processes. I am particularly interested in understanding how soil–water interactions change over time under different climatic conditions, and how satellite observations can be used to capture these dynamics at larger spatial scales.

-  How did you first become interested in soil modelling and learn about ISMC?

My interest in soil modelling started during my master studies in water resources engineering, where I worked with physically based models to study groundwater and soil–water processes. Over time, I became increasingly aware of the limitations of these approaches, especially at larger scales and under changing environmental conditions. This motivated me to explore data-driven methods as a complementary approach. I learned about ISMC as a platform that connects different modelling communities, which aligns well with my interest in combining physical understanding with data-driven techniques.

-Can you share with us your current research focus?

My current research focuses on predicting soil moisture dynamics using minimal inputs such as climate forcing and satellite-derived signals, particularly microwave brightness temperature. A key aspect of my work is to reduce dependence on uncertain static soil parameters and instead learn soil behaviour directly from observations. I also investigate how extreme events, such as droughts, affect soil processes over time, especially in terms of changes in the soil water retention curve and soil structure.

- Please tell us briefly how your research could contribute to ISMC Science Panel’s activities? Or the other way around, how do you wish ISMC science panels help/support your research activities?

My research can contribute to ISMC by offering a data-driven perspective on soil modelling, particularly in representing temporal variability in soil behaviour. I am interested in contributing to discussions on integrating remote sensing data into soil modelling frameworks. At the same time, ISMC provides a valuable platform for collaboration, where I can benefit from expertise in physically based modelling and help bridge the gap between data-driven and process-based approaches.

- What resources or skills would you recommend that early career members of ISMC should acquire? And how can ISMC help and support early career members in this regard?

I would recommend that early career researchers develop strong skills in programming, data analysis, and machine learning, while maintaining a solid foundation in soil physics. The ability to work with remote sensing data is becoming increasingly important. At the same time, it is essential to interpret results in a physically meaningful way. ISMC can support early career members by providing training opportunities, workshops, and collaborative platforms that encourage interdisciplinary learning and exchange.