Remote Sensing & Natural Resources Modelling

Following proof that climate change has induced a radical shift in the Earth’s water cycle, there has been a rise in awareness and in the realization of the need for action among stakeholders. Profound changes in the global distribution of water, and the frequency, intensity and duration of extreme precipitation events, storms and floods, droughts and wildfires, and air temperature increase, as well as in the pollution of soil, air, surface- and (potentially fossil) groundwater bodies, pose complex challenges as to how eco-hydro-systems and the (global) economy will adapt to this massive turmoil.

Earth observation (EO) is expected to provide valuable contributions to addressing global change, as it has the capability to capture environmental and socio-economic data over a broad range of spatial and temporal resolutions. The Group on Earth Observation (GEO), a partnership of more than 100 national governments, advocates the value and use of EO, together with other data (e.g. crop monitoring), to counter food insecurity. This commitment is upheld by all states under the UN’s Sustainable Development Goal 2, which aims to end hunger. GEO also promotes the key role of EO in decision-making for biodiversity and ecosystem sustainability, disaster resilience, food security, sustainable agriculture and water resource management.

In the coming years, the most significant opportunities for environmental research are likely to come from the rapid development of computational capabilities, as well as in situ and remote-sensing multi-sensor platforms operating at unprecedented spatial and temporal scales. By offering new ways of observing key variables in the hydrological and nutrient cycles (e.g. evaporation, soil moisture, DOC and nitrate) alongside many other environmental parameters (e.g. vegetation structure & dynamics, plant diseases), field-deployable multi-sensors and remote sensing platforms provide an increasing amount of data that will revolutionize our environmental modelling approaches. This will broaden their application range to poorly or non-monitored areas.

These new datasets will trigger new analytical solutions, lead to new research questions and ultimately generate new theories [e.g. Montanari et al., 2013](1). Earth sciences and related disciplines are on the verge of an era with entirely new opportunities and challenges:

• Overcoming current technological limitations.
• Initiating the development of new measurement techniques.
• Reducing uncertainties in observations.
• Obtaining maximum efficiency in using new data sets and generating new knowledge.
• Adapting to changing boundary conditions (i.e. global change).

In this context, digital twins, as dynamic digital representations of physical systems [e.g. Madni et al., 2019] (2)  – alongside emerging technologies in artificial intelligence (AI), machine learning and deep learning – are among the most promising technology trends to overcome the stalling of innovation.

Objectives

The Remote Sensing and Natural Resources Modelling group is dedicated to generating actionable insights into the state and dynamics of natural resources by integrating advanced technologies and interdisciplinary expertise. By leveraging satellite and airborne remote sensing data, in-situ measurements and sophisticated modelling tools, the group provides critical information that supports evidence-based decision-making across a wide range of thematic domains.

Scope of expertise to overcome these challenges

To meet the growing complexity of environmental threats, the group applies its diverse and advanced expertise to several research and application domains:

• Interdisciplinary expertise:
  The group's strength lies in its interdisciplinary foundation, combining knowledge from remote sensing, hydrology, climatology, plant physiology and other environmental sciences. This diverse expertise enables the high-resolution monitoring of Earth’s biotic and abiotic systems, capturing changes across various temporal and spatial scales.
• Data integration and real-time decision support
  The integration of remote sensing data with land surface models, satellite and terrestrial communication systems, and in-situ observations facilitates near real-time responses to environmental challenges. This capability informs decision-making in areas such as disaster risk reduction, sustainable agriculture, ecosystem resilience, civil security and defence.
• Environmental risk assessment and early warning
  The group’s research places a strong emphasis on understanding land surface processes, ecohydrological extremes and biosphere-atmosphere interactions. Special attention is given to how global change affects natural resource systems. The group develops advanced tools, such as early warning systems, predictive models and hazard monitoring platforms, to improve environmental risk assessment and resource management. Examples include AI-based models for forecasting forced displacements resulting from natural disasters and satellite-derived indicators for tracking vegetation water stress.
• Agroecosystem monitoring
  Agroecosystem monitoring supports precision agriculture, viticulture and forestry. Through continuous observation and analysis, the group enables adaptive land management practices that respond effectively to climate variability and change. These studies enhance our understanding of vegetation dynamics, soil moisture fluctuations and water use efficiency, ultimately leading to more resilient agricultural systems.
• Disaster response and hazard mapping
  The group contributes significantly to improving disaster response and risk assessment for natural hazards, such as floods, droughts, wildfires and earthquakes. Technological innovations include thermal remote sensing for water stress detection, multi-temporal SAR data for flood mapping, and SAR/InSAR applications for urban and maritime surveillance. These advancements are critical for enhancing the operational capabilities of emergency response systems and environmental monitoring infrastructures.

An essential activity of the group is the development of Digital Twin (DT) technologies for all these application domains. These digital replicas integrate data from space-based and terrestrial sources, including Satellite Communications (SatCOM), Satellite Earth Observation (SatEO), Internet of Things (IoT), and Artificial Intelligence (AI). The DT platform offers a comprehensive view of different environmental systems, enabling accurate simulations, predictive analytics, and real-time decision support.

The group is also focused on developing science-based algorithms capable of extracting essential environmental variables from large-scale remote sensing datasets. These variables include land surface temperature, flood extent, water depth, plant water stress, soil moisture, river discharge, evaporation, and transpiration. The long-term goal is to process historical satellite imagery to produce globally consistent time series datasets. These datasets help analyze the multidecadal variability of environmental conditions and their impacts on ecosystem resilience and vulnerability.

By integrating Earth Observation datasets with numerical models, the group generates robust indicators of ecosystem health and resilience. These indicators support the assessment of long-term climate impacts on water resources, vegetation, and biodiversity, while also offering insights into short-term responses to extreme weather events and land use changes. The consolidated analysis aids in the formulation of sustainable environmental policies and climate adaptation strategies.

(1) Montanari A. et al. 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022, Hydrological Sciences Journal 58: 1256-1275

(2) Madni A.M. et al. 2019. Leveraging digital twin technology in model-based systems engineering. Systems 7, 7. doi:10.3390/systems7010007