Dr. Yijian Zeng is an Associate Professor at the Department of Water Resources (WRS), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente in Enschede, the Netherlands.

Key Qualifications:

• Scientist and Educator embracing ‘Challenged-based’ and ‘Open Science’ principles;
• Expertise in monitoring and predicting Soil-Water-Plant-Energy interactions, and linking physically-based processes above and below ground to the Earth Observation Observables in the visible, infrared, thermal and microwave domains.

He is the co-chair of ISMC (International Soil Modeling Consortium), which aims to integrate and advance soil system modeling, data gathering, and observational capabilities to address key global issues and stimulate the development of transdisciplinary and translational research activities.

He serves as a member of the GLASS Panel (Global Land/Atmosphere System Study) of the GEWEX (The Global Energy and Water Exchanges) Project, which is a part of the World Climate Research Programme (WCRP) and dedicated to understanding Earth’s water cycle and energy fluxes on and below the surface and in the atmosphere. The GLASS panel focuses on the development and evaluation of models, with a particular emphasis on the new generation of land surface models.

He is the co-lead of GEWEX-SoilWat (Soil and Water) Initiative, a joint project between GEWEX and ISMC, which aims to improve the representation of soil and subsurface processes in climate models. The initiative brings together two research communities to identify benchmarking philosophy, critical datasets, challenges and unresolved issues related to this effort.

Towards a Digital Twin of Soil-Plant System

Please check out here the Geoversity Interview on Soil-Plant Digital Twin

Climate projections strongly suggest that the sweltering summer of 2022 may be a harbinger of Europe's future climate. Climate extremes, such as droughts and heatwaves, jeopardize terrestrial ecosystem carbon sequestration, impacting the EU's targets and policies to become the first climate-neutral continent by 2050. Therefore, it is pivotal to understand the climate resilience of agriculture and natural ecosystems. To address this challenge, Yijian Zeng, with colleagues of same vision, has been developing an "Open Digital Twin of Soil-Plant System," which includes three core components (see Figure1): i) The soil-plant model for a digital representation of the soil-plant system; ii) Physics-aware machine learning algorithms to approximate the soil-plant model; and iii) Data assimilation framework to digest Earth Observation data to update the states of soil-plant system.

Figure 1 Three Core Components for a Digital Twin of Soil-Plant System

i) Digital Representation of the Soil-Plant System

STEMMUS: Physically-based Modelling of Soil and Subsurface Processes

A two-phase heat and mass transport model (STEMMUS – Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) was developed to understand the coupling mechanism between liquid water, water vapor, dry air and heat transport in the soil, using field observations and numerical simulations. It shows that the STEMMUS outperforms the traditional theory in estimating surface evaporation over arid and semi-arid regions [Zeng et al. 2011a WRR; Zeng et al. 2011b JGR].

Figure 2  Diagram of STEMMUS Physics (http://blogs.itc.nl/stemmus/)

STEMMUS-SCOPE: Understanding Water, Energy and Carbon Fluxes from Leaf to Ecosystem Scales

The coupled STEMMUS-SCOPE model integrates the SIF remote sensing with the plant-hydraulics-based SPAC model to advance our mechanism understanding of the complex soil-water-plant-energy interaction. The SIF remote sensing can acquire explicit information about photosynthetic light responses and steady-state behaviors in vegetation to evaluate photosynthesis and water-stress effects, across a range of biological, spatial and temporal scales. The plant-hydraulics-based SPAC model links mechanistically tissue-level stress to ecosystem-level water and carbon fluxes, via a resistor-based manner with the tissue-level hydraulic traits (of roots, stems and leaves) and stomatal optimality theory (i.e., photosynthetic gain vs. hydraulic risk).

Figure 3 The coupling scheme of STEMMUS-SCOPE (Wang et al. 2020 GMD)

STEMMUS-FT: Liquid-Vapor-Air Flow in the Frozen Soil

Accurate representing freeze-thaw (FT) process is of great importance in cold region hydrology and climate studies. With the STEMMUS-FT model (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil), we are able to depict the simultaneous movement of soil moisture and heat flow in frozen soil. The analysis of water/vapor fluxes indicated that both the liquid water and vapor fluxes move upward to the freezing front and highlighted the crucial role of vapor flow during soil FT cycles as it connects the water/vapor transfer beneath the freezing front and above the evaporation front.

Figure 4 Simulated vertical profiles of the thermal (q_LT, q_VT) and isothermal (q_Lh, q_Vh) liquid water and vapor fluxes, as well as soil moisture liquid content θ_L , ice content θ_i , and temperature T. (Yu et al. 2018 JGR)

STEMMUS-TeC: Ecohydrological Responses to Freeze-Thaw Cycles

The vadose zone is a zone sensitive to environmental changes and exerts a crucial control in ecosystem functioning and even more so in cold regions considering the rapid change in seasonally frozen ground under climate warming. The STEMMUS(-FT)-TeC model was used to investigate the role of vadose zone physics in the ecohydrological response of an Alpine meadow to freeze–thaw cycles.

Figure 5 STEMMUS-FT model was coupled to T&C model (Yu et al. 2021 TC)

STEMMUS-UEB: Integrated Modeling of Snowpack and Soil Water and Energy Transfer

A snowpack has a profound effect on the hydrology and surface energy conditions of an area through its effects on surface albedo and roughness and its insulating properties. STEMMUS-UEB (Utah energy Balance Snowpack Model, UEB) was developed to couple the snowpack and underlying soil layer to investigate the coupled liquid–vapor–air flow mechanisms considering the snowpack effect.

Figure 6 Evaporation using the Models with and without the Snow Module (Yu et al. 2021 GMD).

CLAP: Linking Land (Sub)Surface Processes to Earth Observation Observables

Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) aims to simulate the emission and backscattering signals of land surfaces at different frequencies, which have distinctive responses to soil and vegetation physical states. The use of multi-frequency combined active and passive microwave signals provides complementary information to better understand and interpret the observed signals in relation to surface states and the underlying physical processes. CLAP can improve our ability to retrieve surface parameters and states such as soil moisture, freeze-thaw dynamics and vegetation biomass and vegetation water content (VWC) for ecosystem monitoring.

Figure 7 Flowchart illustrating the procedure for the forward backscatter coefficient and brightness temperature simulations by CLAP (the coupled ATS-AIEM-TVG model) (Zhao et al. 2023 HESS).

ii) Physics-Aware Machine Learning

The physically-based process model like STEMMUS-SCOPE can serve perfectly as the virtual laboratory to study responses of ecosystem functioning to various climate stressors (e.g., rising CO2, temperature, and increasingly frequent extreme drought events). However, a major bottleneck using such advanced model, in routine processing at global scale, is its very expensive computational cost (with a large number and variety of input variables and a long processing time). A physics-aware machine learning approach can be adopted for accelerating STEMMUS-SCOPE’s running. The core idea is to approximate the original model by a surrogate machine learning model (i.e., emulator). Based on a limited number of STEMMUS-SCOPE runs, the input-output pairs (corresponding to training samples) are used to establish the emulator, which is then used to infer the model output given a yet-unseen input configuration. Currently, the Random Forest model was adopted for this purpose with the guide of physical principles (see Figure 8).

This physics-aware machine learning algorithm has been applied to produce global soil moisture products (Han et al. 2023 Sci. Data; Zhang et al. 2021 Remote Sens.), and will be applied to develop emulators for radiative transfer models (e.g., for CLAP and STEMMUS-SCOPE).

Figure 8 Conceptual Workflow for Developing Emulators with Physics-Informed Machine Learning.

iii) Data Assimilation

When we want to estimate states and fluxes of the Earth system (e.g., water, energy, and carbon cycles), at any arbitrary past, present, and future time, we always encountered two complementary, but both incomplete and inaccurate, sources of information: the observations and the model. Data Assimilation (DA) provides the tool to tackle the problem by extracting synergies between model and observations, and by exploiting their respective information content. Here the observation is referred as Earth Observations, including satellite-, airborne-, drone-, and in-situ-based measurements. To optimize the synergy between the model and observations, it is necessary to translate physical-based process modelling into observables, which needs a forward observation simulator. Coupling the STEMMUS-SCOPE with CMEM (Community Microwave Emission Model) and TorVergata model (an emission-scattering model) can form such a forward observation simulator for optical-thermal-microwave signals (see Figure 9). The developed forward observation simulator can be integrated into a consistent DA framework to assimilate EO data (see Figure 8) for sustainable water resources management (Zhao et al. 2023 Sensor; Mwangi et al., JGR, 2020).

Figure 9 The development of a forward observation simulator by coupling physically-based soil process model with an emission-scattering radiative transfer model.

Selected Ongoing Projects:

CO-I: WUNDER: Water Use and Drought Ecohydrological Responses of Agricultural and Nature Ecosystems in the Netherlands: Towards Climate-Robust Production Systems and Water Management (2022-2028, €1,516,238)

As a result of climate change, extreme droughts are expected to occur more often in the Netherlands, potentially causing social distress and huge economic damages. The WUNDER project will develop an integrated modeling system for understanding the behaviour of soil and vegetation during prolonged drought events. The system will enable to explore scenarios and evaluate strategies for managing, planning and adapting agriculture and nature systems to extreme droughts. The project will actively engage with farmers, water managers and other decision makers and develop practical use cases for daily drought monitoring and prediction, thereby supporting climate-robust production systems and water management.

Figure 10 Coupling the Integrated Surface-Groundwater Modelling with Carbon/Nutrient Dynamics

PI: EcoExtreML: Accelerating Process Understanding for Ecosystem Functioning under Extreme Climates with Physics-Aware Machine Learning (2021-2024, €253,000 + 3 Person-Year (PYR) Research Software Engineers (RSE) from NLeScience Center, 1.0 PYR represents 1,536 hours of RSE time)

Remote sensing of fluorescence and plant-hydraulics- based vegetation models are state-of-the-art approaches to monitor and predict drought responses of ecosystem functioning. However, the disciplinary disconnect between the two approaches has hindered the full potential of synergizing them. This project aims to couple the vegetation photosynthesis model (SCOPE) with the soil moisture model (STEMMUS, considering dynamic root growth), and synergize it with Earth-Observation data, to understand how the water-carbon dynamics of ecosystems vary with variable environmental and climate stress. The bottleneck in applying STEMMUS-SCOPE globally is its expensive computational cost. As a first step, the coupled STEMMUS-SCOPE model will be exposed to Basic-Model-Interface. Second, a physics-aware machine learning emulator based on a limited number of STEMMUS-SCOPE runs, will be developed. Furthermore, to address the ‘data-gap’ issue of satellite reflectance products (i.e., revisit-time (5–27days) and cloudy condition), OpenDA will be deployed to assimilate multiscale/multi-sensor data to generate spatiotemporally continuous information on ecosystem functioning. This project will open up a variety of new opportunities for Earth-Observation, including retrieving higher-level products such as root-zone-soil-moisture and belowground-carbon-allocation, besides land-atmosphere gas exchanges.

Figure 11 EcoExtreML-Envisioned Digital Infrastructure for Soil-Plant Digital Twin

CO-I: iAqueduct: An Integrative Information Aqueduct to Close the Gaps between Global Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources (2019-2023, €262,755)

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. iAqueduct project aims to deploy multiscale (from plant scale to plot, regional and global scale) Earth Observation (EO) data for understanding ecohydrological dynamics of various biomes across the Europe and Mediterranean region, via combining medium-resolution (10m – 1km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physically-based models, as well as big-data analytics with machine learning algorithms.

Figure 12 iAqueduct - an integrative information flow to close the gaps between satellite observation of water cycle and local sustainable management of water resources (Su et al. Water 2020; Zhuang et al. RS 2020; Zeng et al. RS 2016)

Water-JPI project iAqueduct:    https://www.costharmonious.eu/iaqueduct-conceptual-framework/

COST-ACTION HARMONIOUS:   https://www.costharmonious.eu/

CO-I: MINERVA: MIcrowaves for a New Era of Remote sensing of Vegetation for Agricultural monitoring. NWO PIPP (Partnerships for space Instruments & Applications Preparatory Programme) (2020-2023, €155,373, plus bench fee)

MINERVA project (Dutch network on Microwaves for a New Era of Remote sensing of Vegetation for Agricultural monitoring) is to couple the STEMMUS-SCOPE model with a discrete scattering model (TVG) to realize a multi-wavelength forward observation simulator (from optical, thermal-infrared, to microwave), as such, linking physical processes to EO observables (e.g., reflectance, fluorescence, brightness temperature, and backscattering coefficients, see Figure 7 and Figure 8).

Selected Past Projects:

CO-I: HARMONIOUS: On the Use of Unmanned Aerial Systems for Environmental Monitoring (2018-2022)

Environmental monitoring plays a central role for the management of natural and agricultural systems. On this context, Unmanned Aerial Systems (UAS) are radically evolving offering an extraordinary opportunity to bridge the existing gap between field observations and traditional air - and space - borne remote sensing. A network of scientists is currently cooperating within the framework of a COST (European Cooperation in Science and Technology) Action named “Harmonious”. Our intention is to promote monitoring strategies, establish harmonized monitoring practices, and transfer most recent advances on UAS methodologies to others within a global network.

Yijian Zeng served as the Working Group (WG) leader for ‘WG3 soil water content monitoring’ within the EU COST ACTION HARMONIOUS (Harmonization of UAS techniques for agricultural and natural ecosystems monitoring) project.

Publications: Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments

Figure 13 Soil moisture downscaling workflow based on random forest regression (Su et al. Water 2020; Han et al. 2023 Sci. Data; Zhang et al. 2021 Remote Sens.)

FAO – WaPOR (FRAME project):  Remote Sensing for Water Productivity (2016-2019, CO-WP Lead)

WaPOR, FAO’s portal to monitor Water Productivity through Open access of Remotely sensed derived data, monitors and reports on agriculture water productivity over Africa and the Near East. It provides open access to the water productivity database and its thousands of underlying map layers. It allows for direct data queries, time series analyses, area statistics and data download of key variables associated to water and land productivity assessments. Yijian Zeng’s validation strategy (Zeng et al. 2015, JAG) was applied in this project.

Publications: (Blatchford et al., 2021, JAG; Blatchford et al., 2020, Hydrol Process; Blatchford et al., 2020, Remote Sens.; Blatchford et al., 2019, RSE).

Figure 14 Left panel: Relative error associated with in-situ methods of crop yield estimation. All methods provide estimates for at field scale for cropping season; Right panel: Relative error associated with in-situ methods of ETa estimation used for irrigation performance.

CO-I: NWO-GO SMAP-FT: Quantifying Freeze-Thaw Processes using NASA’s SMAP Satellite (2014-2018)

Current land surface models cannot adequately capture the onset of freeze-thaw cycles that marks the shift from the cold to the warm season and vice versa. Simulating such freeze-thaw processes is inherently difficult because both heat and mass exchanges need to be fully captured. Better understanding and modeling of such processes becomes imperative because important sources of water and heat are associated with freeze-thaw transitions that are expected to be impacted by climatic changes at high altitude regions. On the other hand, the difference between a frozen and thawed land surface can also be detected from space with active and passive microwave sensors. This project aims to advance our knowledge on liquid soil moisture under frozen conditions through observing and modeling the physical processes of freeze-thaw cycles from the following perspectives: a) ground measurement, b) earth observation from SMAP, and c) land surface process modelling.

Publications: (Yu et al. 2018 JGR; Lv et al. 2023 IEEE J Sel Top Appl Earth Obs Remote Sens; Lv et al. 2022 RS; Lv et al. 2022 JR;  Lv et al. et al. 2022 Cold Reg. Sci. Technol.)

Figure 15 The forward observation simulator considering freeze-thaw processes.

CORE-CLIMAX: COordinating Earth observation data validation for RE-analysis for CLIMAte ServiceS (2013-2015, WP Lead)

Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, including their value to users, to establish the relation of trust between providers of climate data and information and various downstream users. The climate data and information products (i.e., from satellite, in-situ and reanalysis) shall be fully traceable, adequately documented and uncertainty quantified and can provide sufficient guidance for users to address their specific needs and feedbacks. To achieve such aims, the quality assurance (QA) framework was developed to deliver timely assessments of the quality and usability of Essential Climate Variable (ECV) products. Such a QA framework will support a traceable climate service, in terms of understanding how the uncertainty propagates into the resulting benefit (utility) for the users of the climate service, other than rigorously evaluating the technical and scientific quality of ECV products that represent the upstream of climate services.

Publications: (Su et al. 2018 BAMS; Zeng et al. 2023; Zeng et al. 2019 Remote Sens.; Zeng et al. 2015 JAG)

Figure 16 Overarching structure for the assessment of quality and usability of ECV products (RECOMMs- Recommendations; single product (i.e., consists of only one single variable); multi-product (i.e., more than one single parameter); thematic (i.e., water, energy and carbon cycles))

CEOP-AEGIS: Coordinated Asia-European Long-Term Observing System of Qinghai- Tibet Plateau Hydro-Meteorological Processes and the Asian-Monsoon System with Ground Satellite Image Data and Numerical Simulations (2008-2012, WP Lead)

Publications: (Zhao et al. 2018 ESSD; Zhuang et al. RS 2020; Zeng et al. RS 2016)

Figure 17 Tibet-Obs for Land Surface Process Understanding and Cal/Val of Satellite Products

PhD Thesis (STEMMUS Model): Coupled Dynamics in Soil (2012)

Dr. Zeng received his PhD (Cum Laude) in vadose zone hydrology in 2012 from the University of Twente, Netherlands, in cooperation with the China University of Geosciences (Beijing) and Cold and Arid Regions Environmental and Engineering Research Institute (CAREERI, now as NIEER, North Institute of Eco-Environment and Resources), Chinese Academy of Sciences (CAS). His PhD focused on “Coupled dynamics in soil : understanding the transport mechanism of liquid water, water vapor, dry air and heat by field experiments and numerical simulation”. Dr. Zeng’s PhD work has been awarded:

• • “WMO Research Award for Young Scientists” (UT News, ITC News);
  • “Springer Theses Project for Recognizing Outstanding PhD Research”.