Announcements

Soil pollutant transport and fate modelling

Rooted in the EU-funded SOILPROM project, this working group will meet periodically to identify and highlight key developments in the ongoing work on upgrading and integrating models for pollutant transport and fate that assess the impact of soil pollution on soil functions and related ecosystem services. The pollutants under consideration pose a high risk to the environment and humans, across Europe and beyond. Models to be considered are pollutant transport models in soils, groundwater, coupled soil-groundwater but also transport models for pollutant transport via water and wind erosion. Uptake by PFAS via the root system is another focus in the model developments. 

If you are interested to join in the working group please fill in the form here.

A cooperation between SOILPROM and ISMC 
 

International Symposium on Organic Farming and Production (ISOP) organized by OrganoRice 

The OrganoRice project organized an International Symposium on Organic Farming and Production (ISOP) which was held at Can Tho University in Vietnam from the 3rd to the 6th of November 2025. The Symposium addressed different disciplines dealing with organic farming covering the topics of organic fertilization, organic pest control, irrigation and pollution, variety testing, marketing of organic products, certification process and traceability of the market chain, policies encouraging organic farming, stakeholder involvement, economy of organic production, and biodiversity and ecosystem services. The first day was reserved to present the major outcome of the OrganoRice project followed by discussion with stakeholders, farmers and delegates from the regional ministries of agriculture and environment in Vietnam. The second and third day was reserved for scientific presentations, whereby plenty time was reserved for discussion and exchange. In total more than 150 person attended the symposium over the 3 days. To get an impression a short video has been produced and can be be found on the ISMC Youtube channel. The full program and book of abstracts can be downloaded from the OrganoRice  homepage.The organizers thank ISMC for the support of the symposium. 

Impression of the ISOP conference 

EGU Sessions

Advances in Critical Zone Understanding through Integration of Novel Measurement Techniques and Modelling Approaches"
by Tobias K.D. Weber, Yijian Zeng, Anne Verhoef, Efstathios Diamantopoulos

The Critical Zone (CZ) regulates many natural processes. Within the CZ, physical, chemical, and biological processes act at different spatial and temporal scales and it is a highly complex system with strong non-linear interactions. Recent advances highlight the opportunities of integrating novel measurement techniques with sophisticated modelling approaches to enhance process understanding and representation of CZ dynamics. This inter-disciplinary session brings together scientists to explore how cutting-edge measurement techniques (remote sensing, isotopic tracing, and high-resolution sensor networks, …) can be combined with advances modelling approaches (machine learning, data assimilation, coupled process-based models, constraint-based modelling, …) with the aim to foster comprehensive understanding of CZ processes. Topics may include: advances in measurements and modelling, processes in land-atmosphere interactions, upscaling of soil processes, interactions between soil hydrology and biogeochemical cycles, processes in the soil-plant system, parameterisation of soil processes across scales, and model-data integration.
 

Measurement and Modeling of Soil Processes Across Scales
by Mahyar Naseri, Nima Shokri, Lutz Weihermueller, and Yan Jin

This session co-organized by ISMC focuses on the measurement and modeling of soil properties and processes across landscapes, from the pore scale to the field or watershed scale. Organized in collaboration with the International Soil Modeling Consortium (ISMC), the session invites contributions that:

•    Measure soil physical and chemical properties in the lab, field, or watershed using tools such as micro-scale imaging, in-situ soil sensors, drones, geophysical methods, radars, and remote sensing platforms.

•    Model soil processes using analytical, empirical, statistical, or numerical approaches that link processes across scales, including upscaling and downscaling strategies to address heterogeneity in infiltration, evaporation, salinity dynamics, gas transport, and subsurface mass and energy fluxes.

•    Investigate spatiotemporal changes in vadose zone properties at different scales through measurement or modeling campaigns, focusing on natural variability or human-driven changes such as climate variability, sea level rise and salinity intrusion, droughts, freeze-thaw cycles, heavy agricultural machinery impacts, and land management practices in forests, agricultural fields, wetlands, coastal zones, grasslands, deserts, urban soils, and mountainous regions.

Irrigation database

As you know, irrigation is the primary source of anthropogenic water use, but data on irrigation volumes is generally scarce, or not well-organized/easily available. Satellite retrievals and/or modeling platforms are valuable tools for estimating Irrigation Water Use (IWU), but reference data is needed for calibration and validation procedures. For this reason, I am happy to launch  an initiative aimed at creating the first database of IWU records.  

If you have access to such information for any area worlwide and you want to contribute, please:

1. Download (do not edit online) the official data template found here 

2. Fill it with your irrigation data + metadata as explained in the Read me section

3. Return your data and the shapefile of the reference area to me at jacopo.dari@unipg.it 

The database will be a unique scientific resource for the community. In addition, it will be described in a dedicated data paper and all the contributors will be listed as co-authors. 

The deadline for data collection is May 2026. 

On our own account

The newsletter team and ISMC wish you a pleasant holiday and all the best for the next year. ISMC wants to thank those people responsible for the newsletter: Yijian Zeng, Attila Nemes, and Lutz Weihermüller.

Featured Paper

Do you want your paper featured?

Please share your recent paper if you want to be featured in the ISMC newsletter. With your contributions, we will select one paper to be featured in every newsletter. Submission can be done here. 

Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders

Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification becomes particularly challenging when the number of sensors per field or measurement zone (MZ) is limited. When direct calculation of errors from sensor data in a certain MZ is not feasible, we propose to pool systematic and random errors of soil moisture measurements for a specific measurement setup and derive a pooled error covariance matrix that applies to this setup across different fields and soil types. In this study, a pooled error covariance matrix was derived using soil moisture sensor measurements from three TEROS 10 (Meter Group, Inc., USA) sensors per MZ and soil moisture sampling campaigns conducted over three growing seasons, covering 93 cropping cycles in agricultural fields with diverse soil textures in Belgium. The MZ soil moisture estimated from a composite of nine soil samples with a small standard error (0.0038 m³ m⁻³) was considered the “true” MZ soil moisture. Based on these measurement data, we established a pooled linear recalibration of the TEROS 10 manufacturer's sensor calibration function. Then, for each individual sensor as well as for each MZ, we identified systematic offsets and temporally varying residual deviations between the calibrated sensor data and sampling data. Sensor deviations from the “true” MZ soil moisture were defined as observational errors and lump both measurement errors and representational errors. Since a systematic offset persists over time, it contributes to the temporal covariance of sensor observational errors. Therefore, we estimated the temporal covariance of observational errors of the individual and the MZ-averaged sensor measurements from the variance of the systematic offsets across all sensors and MZ averages, while the random error variance was derived from the variance of the pooled residual deviations. The total error variance was then obtained as the sum of these two components. Due to spatial soil moisture correlation, the variance and temporal covariance of MZ-averaged sensor observational errors could not be derived accurately from the individual sensor error variances and temporal covariances, assuming that the individual observational errors of the three sensors in a MZ were not correlated with each other. The pooled error covariance matrix of the MZ-averaged soil moisture measurements indicated a significant autocorrelation of sensor observational errors of 0.518, as the systematic error standard deviation ( 0.033 m³ m⁻³) was similar to the random error standard deviation ( 0.032 m³ m⁻³). To illustrate the impact of error covariance in probabilistic soil hydrological modeling, a case study was presented incorporating the pooled error covariance matrix in a Bayesian inverse modeling framework. These results demonstrate that the common assumption of uncorrelated random errors to determine parameter and model prediction uncertainty is not valid when measurements from sparse in situ soil moisture sensors are used to parameterize soil hydrological models. Further research is required to assess to what extent the error covariances found in this study can be transferred to other areas and how they impact parameter estimation in soil hydrological modeling. More information can be found here.

Memory Matters: Integrating Soil History into Predictive Models of Water and Carbon Cycling

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

I am Mehdi Rahmati, a senior researcher at Forschungszentrum Jülich (Germany), with a research profile in soil physics, remote sensing, data science and ecohydrology. My current research focuses on understanding how soils “remember” past environmental conditions — including droughts, land-use changes, erosion and management actions — and how such memory influences present- and future-day patterns of water and carbon cycling.

From ISMC 2024 conference in Tianjin, China — a reminder that soils also keep their own memories of past events but without cameras.

One of the research venues that I am following these days is to formalize the concept of soil memory — which has been treated as descriptive-only phenomena so far — within predictive modelling by constructing scale-aware, reduced-order models (ROMs) that can leverage multi-source information ranging from in-situ observations to remote sensing. This connects soil process dynamics to vegetation responses, and climate forcing, thus combining the best of both ‘process-based’ and ‘data-driven’ modelling. Ultimately, my goal is to improve our ability to predict and manage ecosystem resilience in a changing world.

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

In fact, I have entered the world of soil modelling at the very beginning of my research career, through my PhD, where our aim was to predict soil infiltration from readily available soil properties and remote sensing proxies then use it to predict water erosion at the watershed scale. Actually, that experience formed my scientific identity — modelling was not just a tool, but an instrument to link data and process understanding. From this foundation, I moved towards my current research on soil memory which is also a tool to understand how past soil–water-plant-atmosphere interactions can determine our present and future ecosystem response.

I first became aware of ISMC when I visited Prof. Dr. Harry Vereecken at Forschungszentrum Jülich in 2017, as the institute plays a significant role in driving and supporting the consortium. At that time, we were developing the Soil Water Infiltration Global (SWIG) database for the first time, and the ISMC helped us to collect data from all around the world by connecting us with soil scientists and data providers. ISMC is helping again with the next generation of the SWIG database, which is currently under development, making it possible for us to reach out to more people. Later, when I joined Forschungszentrum Jülich as a senior researcher in 2021, I learned more about ISMC with the help of colleagues and became part of its executive board. It has been exciting to engage with this community and relate my work to larger activities aimed at enhancing the predictive modelling of soil and ecosystem processes.

-  Can you share with us your current research focus?

My current research focuses on expanding the concept of 'soil memory' (see our commentary on this topic here), whereby soils are said to 'remember' past climatic events, as well as changes induced by land use and management practices, both of which affect their physical, chemical, and biological behaviour. My aim is to formalise this concept by describing how these 'memories' are encoded in soil properties, such as moisture dynamics (read more about it here), structure, and carbon content, and how these properties influence the feedback processes in water and carbon cycling. To achieve this, I am adopting formalisms from statistical physics and climate science to incorporate memory effects into predictive models. By combining these frameworks with process-based, data-driven, and Earth observation approaches, I am working to make soil models more aware of history, thereby improving our ability to predict the resilience of ecosystems and the responses of the critical zone in the context of global change. Some of our research outputs on this topic are under development or review and will hopefully be published soon.  

- 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?

As an ISMC Executive Board member and Soil Intelligence Working Group (co-)founder, I believe in the significant synergies between my research direction and that of ISMC’s scientific panels. Given that my past research on soil memory and data-driven modelling would contribute directly to ISMC in the context of better process representation, benchmarking and cross-scale integration among various soil models. As part of the Soil Intelligence working group, I hope to contribute to the progress around AI-enhanced model–data fusion, harmonized soil databases, and interoperability between models and Earth observation systems. In turn, I trust that science panels in ISMC will continue to promote cooperation, establish the framework for memory-aware modelling, and develop common experimental–modelling platforms that also favour this new path.

- 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 urge early-career soil modellers to invest in hybrid fluency — the capacity to flow effortlessly between process-based understanding and data-driven approaches. Neither the classical physics-based modelling nor pure data science alone is enough: The real novelty comes from their combination. On my own journey — from process-based infiltration and erosion modelling to remote sensing and machine learning — I found that the most beneficial skill is not mastering one tool but connecting scientific disciplines, scales and data sources.

ISMC can play a transformative role here by offering young scientists’ opportunities to engage with model–data integration and open data sets, as well as benchmarking exercises. It is equally important to nurture a culture of open collaboration, where codes, data, and ideas are shared freely. Throughout my career, I have endeavoured to embody this principle by embracing an attitude of openness. Whenever colleagues or students request data, code, or research ideas, I provide these freely to foster community and transparency within our discipline. This is precisely what we aim to achieve as part of the Soil Intelligence Working Group: enabling the next generation to combine mechanistic understanding with computational intelligence to reveal the hidden dynamics of soil systems.