Multimodal Data-Driven Multi-Scale Sustainability: Climate, Land Use, and Environment

Topic Information

Dear Colleagues,

The pursuit of sustainability in the Anthropocene demands a paradigm shift from reductionist approaches to a holistic understanding of the intricate nexus between Climate, Land Use, and Environment. Traditional single-source or single-discipline studies often fail to capture the full complexity of real-world coupled socio-ecological systems. A critical gap persists: biophysical observations of the natural environment are frequently analysed in isolation from unstructured, human-centric data that reflect social behaviors, policy dynamics, and public perceptions.

To bridge this divide, “Multimodal Data-Driven Multi-Scale Sustainability: Climate, Land Use, and Environment”​ emerges as a transformative paradigm. It shifts the focus from fragmented, siloed observation to integrated, systemic synthesis​ across both natural and human dimensions.

This Topic emphasizes applied sustainability science​ powered by the synergy of diverse data streams. We define "multimodal"​ in its most comprehensive sense, encompassing two synergistic pillars:

• Geospatial Earth Observation:​ Fusion of multispectral/hyperspectral optical imagery, Synthetic Aperture Radar (SAR), LiDAR, thermal infrared, Digital Elevation Models (DEMs), vector geospatial data, and Nighttime Light (NTL) imagery.
• Human-Centric Modalities:​ Integration of unstructured text (policy documents, social media, field records), audio/speech data (community interviews, public appeals), and ground-based imagery (street views, UAV photos, ecological surveys).

Methodologically, "data-driven"​ embraces the full spectrum—from robust classical statistics​ and spatial econometrics​ to deep learning, multimodal foundation models, and explainable AI (XAI).

A key feature of this issue is the integration of Digital Twin (DT) technology. We encourage submissions that utilize DT frameworks to create dynamic virtual replicas of watersheds (Digital Twin Basins), cities (Digital Twin Cities), or ecosystems. These models are crucial for "what-if" scenario modelling, allowing researchers to simulate policy interventions, predict disaster resilience, and optimize resource management before implementation in the physical world.

We strongly advocate for multi-scale research. While global and regional assessments are welcome, we place a high priority on region-specific small-scale studies. Submissions focusing on local areas (e.g., small watersheds, ecologically fragile zones, urban agglomerations) must highlight distinct regional characteristics and provide transferable insights. The ultimate scope extends beyond mere mapping to comprehensive environmental evaluation, aiming to support the UN Sustainable Development Goals (SDGs) through actionable science.

Topics of interest include, but are not limited to, the following:

1. Multimodal Fusion for Comprehensive Environmental Evaluation

• Synergistic use of optical, SAR, LiDAR, and hyperspectral data for ecosystem health and integrity assessment.
• Vector–Raster Integration:​ Combining OSM, cadastral data, and POI with imagery for fine-grained functional zoning.
• Social Sensing Fusion:​ Integrating NTL, social media sentiment, and field survey data with physical variables for composite environmental indices (habitat quality, ecological resilience, environmental carrying capacity).

2. Digital Twins for Sustainable Systems

• Digital Twin Watersheds:​ Integrating hydrological models, soil erosion data, and water quality sensors for basin-scale governance.
• Digital Twin Cities:​ Coupling urban morphology (LiDAR), energy consumption (NTL), and socio-economic data for urban metabolism simulation.
• Scenario modeling for disaster resilience (floods/droughts) and climate adaptation using multi-source calibrated virtual replicas.

3. Climate, Carbon Neutrality, and Atmospheric Environment

• Dual-Carbon Goals:​ Multimodal estimation of carbon stocks, fluxes, and sinks across terrestrial and aquatic systems.
• Atmospheric Environment:​ Remote sensing of air pollutants (PM2.5, O3, NOx), greenhouse gases (CO2, CH4), and aerosol optical depth.
• Climate dynamics, extreme event monitoring, and attribution analysis across scales.

4. Water, Soil, Food Security, and Land Sustainability

• Water Environment:​ Water quality inversion (turbidity, chlorophyll-a, nutrients) and watershed governance.
• Soil Conservation and Pollution:​ Soil erosion monitoring, heavy metal mapping, and land degradation assessment.
• Food Security:​ Crop type mapping, yield prediction, agricultural drought monitoring, and precision agriculture.
• Land System Science:​ Rural–urban–natural continuum dynamics and sustainable land management.

5. Urban Liveability, Nighttime Light, and Environmental Health

• Nighttime Light (NTL) Remote Sensing:​ Applications in socioeconomic activity mapping, energy consumption, and light pollution.
• Environmental Health Exposure:​ Linking multi-source environmental data (air, water, soil) to public health outcomes and urban livability.
• Urban heat island mitigation and local climate zone (LCZ) mapping.

6. Regional Case Studies with Distinct Characteristics

• Applied research in data-scarce regions, mountainous areas, coastal zones, and small watersheds.
• Comparative studies highlighting scale-dependent sustainability drivers and regional governance.

Prof. Dr. Chang Li
Prof. Dr. Xi Li
Prof. Dr. Wenjun Xie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Climate climate	3.2	5.7	2013	20.8 Days	CHF 1800	Submit
Land land	3.2	5.9	2012	17.5 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400	Submit

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